{"role": "user", "content": "[M G] co/Civitai/Qwen-Image-2512-GGUF \"Civitai/Qwen-Image-2512-GGUF · Hugging Face\"\nSollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?\n für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.\n\nMeine Rangliste für deinen Usecase:\n\n| Zweck | Empfehlung | Warum |\n| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Beste kostenlose GGUF-Bildgenerierung in ComfyUI | Qwen-Image-2512-GGUF | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |\n| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2]) |\n| Schnell / weniger VRAM / experimentell | Z-Image Turbo GGUF | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3]) |\n| Alternative zu Qwen, stark aber schwerer | FLUX.2-dev-GGUF | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4]) |\n| Aktuell stärkstes Open-Weights-Modell allgemein | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5]) |\n\nKurz gesagt:\nFür deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.\n\nDie passende Auswahl nach VRAM:\n\n| VRAM | Modell/Quantisierung |\n| ----------: | ------------------------------------------------------ |\n| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |\n| 16 GB | Q5 oder Q6 testen |\n| 24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität |\n| <8 GB | eher Z-Image Turbo GGUF statt Qwen |\n\nWichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])\n\nMeine konkrete Empfehlung:\nInstalliere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.\n\n[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF \"unsloth/Qwen-Image-2512-GGUF · Hugging Face\"\n[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF \"unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face\"\n[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF \"jayn7/Z-Image-Turbo-GGUF · Hugging Face\"\n[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF \"orabazes/FLUX.2-dev-GGUF · Hugging Face\"\n[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image \"Text to Image Leaderboard - Top AI Image Models\"\n[6]: https://huggingface.\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Erledigt, Sir.\n\n## Neuer Baustein: Review Batch Renderer\n\nIch habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:\n\n```text\nautoshorts/review/render.py\ntests/test_review_render.py\n```\n\n## Was neu ist\n\n### `render_review_batch_summary()`\n\nDiese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.\n\nEr enthält:\n\n- Batch-ID\n- Summary Counts\n - `Ready`\n - `Blocked`\n - `Invalid`\n- vollständige Review Packages für ready Candidates\n- kompakte Fehlerlisten für blocked Candidates\n- kompakte Fehlerlisten für invalid Candidates\n\nBeispielstruktur:\n\n```text\n## Review Batch: batch-001\nReady: 1\nBlocked: 1\nInvalid: 1\n\n## Ready for review\n...\n\n## Blocked\n- blocked — Blocked title\n Reasons: quality gate blocked candidate\n\n## Invalid\n- invalid — Invalid title\n Reasons: hook is required\n```\n\n## Wichtiger Guardrail\n\nDer Renderer vermeidet bewusst Sprache wie:\n\n```text\npublish\npost\n```\n\nWarum? Weil Review und Publishing getrennte Phasen bleiben sollen.\n\nAlso kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.\n\n## TDD eingehalten\n\nAblauf:\n\n1. Tests für Batch Renderer geschrieben.\n2. RED verifiziert:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.review.render'\n```\n\n3. Minimal implementiert.\n4. Tests grün.\n5. README aktualisiert.\n6. Commit erstellt.\n\n## Tests\n\nAktueller Stand:\n\n```text\n41 passed in 0.08s\n```\n\n## Neuer Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nCommit:\n\n```text\nb7b5270 feat: add review batch renderer\n```\n\nAktueller Verlauf:\n\n```text\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n49d6daf feat: add idea candidates and testbatches\n55b17fa feat: add telegram review package\n3555ec2 feat: add approval parser and quality gate\n7642749 Initial commit\n```\n\n## Pipeline jetzt\n\nWir haben jetzt die erste durchgängige interne Review-Strecke:\n\n```text\nContent Pillar\n→ CandidateDraft\n→ Candidate Factory\n→ VideoCandidate\n→ TestBatch\n→ Review Workflow\n → ready_for_review\n → blocked\n → invalid\n→ Review Batch Renderer\n→ Telegram-safe Review Text\n→ Approval Parser\n```\n\n## Nächster sinnvoller Schritt\n\nAls Nächstes würde ich bauen:\n\n```text\nautoshorts/cli/demo_review_batch.py\n```\n\noder besser als testbarer Core:\n\n```text\nautoshorts/demo/sample_batch.py\n```\n\nZiel:\n\n- sample AI-Life-Systems Batch erzeugen\n- 1 ready Candidate\n- 1 blocked Candidate\n- 1 invalid Candidate\n- ReviewBatch bauen\n- Telegram-Text rendern\n\nDann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen.", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 49 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\n## Active Task\nUser asked: \"[M G] Du hast mir 3x das selbe video gesendet. Mach jetzt weiter\"\n\n## Goal\nTrueTrace/AutoShortsBot soll von reinen Content-Ideen zu einer testbaren, lokalen Shorts-Produktionspipeline werden: Brand-fit Konzeptbatch → Top-3 Preview Packages → deterministic Subtitles → TTS-Plan → später finale Video-/Audio-Erzeugung und manuelles Posting-Pack für YouTube/TikTok, ohne automatische Uploads oder externe Platform Calls.\n\n## Constraints & Preferences\n- Sprache/Kommunikation mit User: Deutsch, direkt, pragmatisch.\n- TDD bevorzugt/eingehalten: Tests zuerst, RED prüfen, dann minimale Implementierung, focused tests, full suite, Commit.\n- Keine automatischen Uploads, kein Scheduling, keine YouTube-/TikTok-API-Calls.\n- Preview-/Approval-Packs dürfen lokale Render-Side-Effects haben; Publishing/Posting bleibt manuell.\n- Generierte Medienartefakte gehören nicht ins Git-Repo; `data/previews/` wurde in `.gitignore` aufgenommen.\n- TrueTrace Brand-Positionierung: “Systems over prompts.”\n- Öffentliche No-Go-Inhalte in TrueTrace-Kandidaten: kein JARVIS, kein Hermes, kein User, keine Zusammenarbeit, keine Politik.\n- Subtitles sollen renderer-owned deterministic burn-in captions sein, nicht in AI-Video/Image-Prompts “eingebacken”.\n- Subtitle-Stil: “Hormozi-inspired, not Hormozi-cloned.”\n- Subtitle-Regeln: 2–5 Wörter pro Chunk, max. 2 Zeilen, Active-Word/Emphasis Metadata, starker schwarzer Stroke/Shadow, weiß als Hauptfarbe, TrueTrace Accent blau/grün, lower-middle Placement oberhalb UI Safe Zone.\n- TTS-Empfehlung: EdgeTTS zuerst als Baseline, NVIDIA Magpie später als A/B-Kandidat, Whisper später für Alignment/QA und nicht als TTS.\n- GPU soll für Video/Image-Generation reserviert werden; TTS/Alignment soll möglichst nicht die GPU blockieren.\n- Keine Secrets/API Keys/Tokens/Credentials im Kontext oder Output erhalten; falls vorhanden: `[REDACTED]`.\n\n## Completed Actions\n1. CREATE `autoshorts/demo/truetrace_concepts.py` — TrueTrace Concept Testbatch mit exakt 5 strukturierten Kandidaten erstellt; enthält Candidate ID, Titel, Plattformen YouTube/TikTok, Dauer, Hook, Script Outline, Visual Concept, Caption, CTA, Hashtags, YouTube Tags, Hypothesis, Brand Policy Notes. Commit laut vorherigem Stand: `2d6af9f feat: add TrueTrace concept testbatch` [tool: previous work]\n2. CREATE `tests/test_truetrace_concept_batch.py` — Tests für exakt 5 Kandidaten, stabile Reihenfolge, nur YouTube/TikTok, Brand Policy Notes, TrueTrace Brand Fit, Series Fit, No-Go-Inhalte, Summary-Metadaten, keine Action-Sprache wie `publish`, `upload`, `post`. Ergebnis damals: Full Suite `139 passed in 8.79s` [tool: previous work]\n3. CREATE `docs/truetrace_concept_testbatch_001.md` — Dokumentation für `truetrace-concept-testbatch-001` inkl. Safety Scope, alle 5 Candidates, Hypothesen, Review Command und empfohlene Priorisierung [tool: previous work]\n4. IMPLEMENT `render_truetrace_concept_summary()` — rendert alle 5 Konzepte als reviewbaren Text mit Hook, Outline, Visuals, Hypothesis, YouTube Description, TikTok Caption, Tags; keine Side Effects, kein Upload, kein Platform Call [tool: previous work]\n5. DEFINE 5 Candidate IDs — `truetrace-ai-automation-quiet-rot-38s`, `truetrace-prompt-vs-system-workflow-breaks-35s`, `truetrace-ai-notes-equal-importance-34s`, `truetrace-ai-agents-tasks-not-teams-40s`, `truetrace-review-before-automation-32s` [tool: previous work]\n6. SELECT Top-3 für Preview-Produktion — priorisiert wurden: 1) “Why Most AI Automations Break After Three Days”, 2) “This Prompt Looks Smart — But the Workflow Still Breaks”, 3) “The Hidden Reason Your AI Notes Become Useless” wegen konkreter Mechanism-Reveals, visueller Darstellbarkeit und weniger AI-Hype-Abhängigkeit [tool: previous work]\n7. CREATE `autoshorts/demo/truetrace_preview_packages.py` — Top-3 TrueTrace Preview Package Pipeline gebaut; neue Funktion `build_top_truetrace_preview_packages()` erzeugt pro Top-3 Candidate: Candidate → ScriptDraft → ShotPlan → RenderManifest → LocalPreviewPlan → PNG scene cards → FFmpeg MP4 timing preview → SocialMediaDraft → ContentPreviewApprovalPackage → ManualPostingPack. Commit laut vorherigem Stand: `6efc4dc feat: add TrueTrace top preview packages` [tool: previous work]\n8. CREATE `tests/test_truetrace_preview_packages.py` — Tests für Top-3 Preview Packages geschrieben; RED war `ModuleNotFoundError: No module named 'autoshorts.demo.truetrace_preview_packages'`; später Focused Tests grün; Full Suite damals `142 passed in 31.72s` [tool: previous work]\n9. CREATE `docs/subtitle_tts_strategy.md` — Strategie für Subtitles/TTS dokumentiert: deterministic renderer-owned captions, EdgeTTS zuerst, Magpie später A/B, Whisper später Alignment/QA [tool: previous work]\n10. GENERATE Preview MP4s — gemeldet wurden drei Preview-Pfade unter `data/previews/truetrace-top3/.../preview.mp4`, aber User meldete danach: “Du hast mir 3x das selbe video gesendet.” Diese Aussage ist aktuell wichtig und die Vorschau-Pfade/Generierung müssen überprüft werden [tool: previous work]\n11. UPDATE `.gitignore` — `data/previews/` ergänzt, damit lokale Preview-Artefakte nicht ins Repo wandern [tool: previous work]\n12. VIEW skill `test-driven-development` — Skill angefordert; Tool Result war Duplicate Tool Output, kein neuer Inhalt sichtbar [tool: skill_view]\n13. VIEW skill `social-media-shorts-automation` — Skill geladen; Result: `name=social-media-shorts-automation (17,308 chars)` [tool: skill_view]\n14. SEARCH `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot` pattern `*` — 50 Matches gefunden, Repository-Struktur bestätigt [tool: search_files]\n15. READ `autoshorts/demo/truetrace_preview_packages.py` — Datei gelesen ab Zeile 1, Umfang ca. 9,547 Zeichen; diente zur Untersuchung der Preview-Package-Generierung und möglicher Ursache für identische Videos [tool: read_file]\n16. SEARCH `autoshorts/**/*.py` — 50 Python-Dateien gefunden; Überblick über vorhandene Module hergestellt [tool: search_files]\n17. READ `tests/test_truetrace_preview_packages.py` — Datei gelesen ab Zeile 1, Umfang ca. 3,576 Zeichen; bestehende Tests der Preview Packages geprüft [tool: read_file]\n18. RUN `git status --short && git log --oneline -5` in `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot` — Exit 0, Output nur 1 Zeile; genauer Inhalt im Tool-Result nicht sichtbar/ausgegeben, daher Branch/Status nicht zuverlässig dokumentiert [tool: terminal]\n19. RUN `sha256sum data/previews/truetrace-top3/*/preview.mp4` in `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot` — Exit 0, Output nur 1 Zeile; das ist auffällig, weil eigentlich drei Preview-MP4s erwartet waren. Mögliche Interpretation: nur eine Datei existiert/matcht oder Tool-Result wurde stark gekürzt. Muss erneut mit `find`/`ls` geprüft werden [tool: terminal]\n20. READ `autoshorts/content/script_draft.py` — Datei gelesen ab Zeile 1, Umfang ca. 335 Zeichen; geprüft für Subtitle/TTS-Anschluss an ScriptDraft [tool: read_file]\n21. READ `autoshorts/rendering/manifest.py` — Datei gelesen ab Zeile 1, Umfang ca. 6,060 Zeichen; geprüft für RenderManifest/Preview-Anschluss [tool: read_file]\n22. READ `README.md` — Datei gelesen ab Zeile 1, Umfang ca. 20,397 Zeichen; Projekt-Doku/aktueller Stand geprüft [tool: read_file]\n23. CREATE `tests/test_subtitle_cues.py` — neuer Test-File geschrieben, 67 Zeilen. Tests importieren aus geplantem Modul `autoshorts.audio.subtitles`: `SubtitleCue`, `SubtitleStyle`, `build_hormozi_subtitle_track`, `validate_subtitle_track`. Ziel: Text in lesbare 2–5-Wort-Caption-Chunks splitten, Timing/Style/Validation testen. Noch nicht ausgeführt, Implementation fehlt wahrscheinlich noch [tool: write_file]\n24. CREATE `tests/test_tts_plan.py` — neuer Test-File geschrieben, 71 Zeilen. Tests importieren aus geplantem Modul `autoshorts.audio.tts_plan`: `TTSProviderPlan`, `build_default_truetrace_tts_plan`, `build_tts_ab_test_plan`, `validate_tts_provider_plan`. Ziel: Default TrueTrace TTS Plan und A/B-Test-Plan modellieren. Noch nicht ausgeführt, Implementation fehlt wahrscheinlich noch [tool: write_file]\n\n## Active State\n- Working directory: `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot`\n- Branch: nicht zuverlässig bekannt; `git status --short && git log --oneline -5` wurde ausgeführt, aber Tool-Result zeigte nur “exit 0, 1 lines output” ohne sichtbare Details.\n- Aktuell neu erstellt/modifiziert:\n - `tests/test_subtitle_cues.py` — neu geschrieben, Tests für deterministic Hormozi-inspired Subtitle Cue Builder.\n - `tests/test_tts_plan.py` — neu geschrieben, Tests für TrueTrace TTS Provider Plan und A/B-Plan.\n- Bereits vorhanden aus vorheriger Arbeit:\n - `autoshorts/demo/truetrace_concepts.py`\n - `tests/test_truetrace_concept_batch.py`\n - `docs/truetrace_concept_testbatch_001.md`\n - `autoshorts/demo/truetrace_preview_packages.py`\n - `tests/test_truetrace_preview_packages.py`\n - `docs/subtitle_tts_strategy.md`\n- Teststatus aktuell:\n - Letzter bekannter grüner Stand vor neuen Tests: `142 passed in 31.72s`.\n - Nach Erstellung von `tests/test_subtitle_cues.py` und `tests/test_tts_plan.py` wurden Tests noch nicht ausgeführt.\n - Erwarteter aktueller RED-Status: Imports auf `autoshorts.audio.subtitles` und `autoshorts.audio.tts_plan` schlagen vermutlich fehl, falls Module noch nicht existieren.\n- Preview-Artefaktstatus:\n - User meldete, dass 3x dasselbe Video gesendet wurde.\n - `sha256sum data/previews/truetrace-top3/*/preview.mp4` gab nur 1 Zeile Output zurück; unklar, ob nur eine MP4 existiert oder Output gekürzt wurde.\n - Das Problem “Top-3 Preview Videos sind identisch/dupliziert oder falsch verlinkt” ist noch nicht gelöst.\n- Keine bekannten laufenden Server/Prozesse.\n- Keine externen API-Aufrufe oder Uploads durchgeführt.\n\n## In Progress\n- Untersuchung und Fortsetzung nach User-Beschwerde: “Du hast mir 3x das selbe video gesendet. Mach jetzt weiter”.\n- Gerade begonnenes TDD für nächste Pipeline-Stufe:\n - Subtitle Cue Builder (`autoshorts.audio.subtitles`) mit deterministic caption chunks und style metadata.\n - TTS Provider Plan (`autoshorts.audio.tts_plan`) mit EdgeTTS default und Magpie A/B-Kandidat.\n- Noch nicht begonnen/abgeschlossen:\n - Implementation der neuen Module unter `autoshorts/audio/`.\n - RED-Testlauf nach den neuen Testdateien.\n - Fix/Regeneration der drei unterschiedlichen Preview-Videos.\n - Klärung, ob die drei Preview-Pfade wirklich existieren und eindeutige Inhalte haben.\n\n## Blocked\n- Kein externer Blocker.\n- Aktueller interner Blocker/Fehlerverdacht:\n - User meldete: “Du hast mir 3x das selbe video gesendet.”\n - Terminal-Check `sha256sum data/previews/truetrace-top3/*/preview.mp4` ergab nur 1 Zeile Output, obwohl 3 Videos erwartet waren. Exakte Hash-Ausgabe ist im Tool-Result nicht sichtbar.\n - Neue Tests importieren wahrscheinlich nicht existierende Module:\n - `autoshorts.audio.subtitles`\n - `autoshorts.audio.tts_plan`\n - Erwartbarer Fehler beim nächsten Testlauf: `ModuleNotFoundError`, falls diese Module/Package-Struktur noch nicht existieren.\n\n## Key Decisions\n- TrueTrace Content-Produktion bleibt zunächst im Approval-/Preview-Modus, keine automatische Veröffentlichung.\n- Top-3 Kandidaten für Preview:\n 1. `truetrace-ai-automation-quiet-rot-38s`\n 2. `truetrace-prompt-vs-system-workflow-breaks-35s`\n 3. `truetrace-ai-notes-equal-importance-34s`\n- Subtitles:\n - Nicht vom AI-Video-Modell generieren lassen.\n - Renderer-owned deterministic captions, später per FFmpeg burn-in.\n - Begründung: Textkontrolle, Timingkorrektur, Fehlerkorrektur, Brand-Konsistenz, keine kaputten AI-Texte im Bild.\n- TTS:\n - EdgeTTS zuerst als schnelle, kostenlose/leichte Baseline.\n - NVIDIA Magpie TTS Multilingual später als A/B-Test, nicht sofort als Standard.\n - Whisper nicht als TTS verwenden; später für Alignment/QA.\n - GPU nicht für TTS blockieren, weil sie für Video/Image-Generation wichtiger ist.\n- Generated media:\n - `data/previews/` ist ignoriert, weil Git kein Medienlager sein soll.\n- Side Effects:\n - Lokale Render-Side-Effects sind in Preview-Erzeugung akzeptiert (`write_png`, `write_concat`, `call_ffmpeg`, `write_mp4`).\n - Approval-/Manual-Packs selbst bleiben ohne externe Calls und ohne Publishing.\n\n## Resolved Questions\n- User fragte: “wie lösen wir die Subtitles (harmozi style?)”\n - Antwort: Ja, aber “Hormozi-inspired, not Hormozi-cloned.” Renderer-owned deterministic captions mit 2–5 Wörtern pro Chunk, max. 2 Zeilen, Active-Word Highlight, weißer Text, schwarzer Stroke/Shadow, TrueTrace Accent blau/grün, lower-middle safe-zone Placement.\n- User fragte: “welches TTS model?”\n - Antwort: EdgeTTS als Baseline zuerst; NVIDIA Magpie später im A/B-Test; Whisper nicht als TTS, sondern später für Alignment/QA.\n- User fragte sinngemäß: “Ist NVIDIA Magpie besser als EdgeTTS?”\n - Antwort: Nicht vorab entscheiden; Magpie als A/B-Kandidat gegen EdgeTTS auf gleichem 15–20 Sekunden Script-Ausschnitt testen und nach Klarheit, Energie, Pronunciation, Artefakten, Latenz, Kosten und Integrationsaufwand bewerten.\n- User fragte: “Oder besser lokal whisper? Jedoch brauchen wir die gpu ja für das video…”\n - Antwort: Whisper ist nicht für TTS gedacht; später für Alignment/QA sinnvoll. GPU für Video/Image reservieren.\n\n## Pending User Asks\n- Die jüngste User-Anweisung ist noch nicht erfüllt: “Du hast mir 3x das selbe video gesendet. Mach jetzt weiter”\n - Konkret offen:\n - Prüfen, warum drei gemeldete Preview-Videos offenbar identisch waren oder identisch verlinkt wurden.\n - Sicherstellen, dass drei unterschiedliche Preview MP4s existieren.\n - Danach Pipeline weiterbauen: Subtitle Cue Builder und TTS Plan gemäß begonnenem TDD implementieren.\n - Tests ausführen und Full Suite wieder grün bekommen.\n\n## Relevant Files\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_concepts.py` — TrueTrace Concept Testbatch mit 5 Kandidaten.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_concept_batch.py` — Tests für Kandidaten, Brand Fit, Series Fit, No-Go-Inhalte, Summary-Metadaten.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/truetrace_concept_testbatch_001.md` — Dokumentation des Concept Testbatch.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py` — Top-3 Preview Package Pipeline; wurde gelesen, um Duplikat-Video-Problem zu untersuchen.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py` — Tests für Preview Package Pipeline; wurde gelesen.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md` — Subtitle-/TTS-Strategie.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_subtitle_cues.py` — neu erstellt; Tests für `autoshorts.audio.subtitles`.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_tts_plan.py` — neu erstellt; Tests für `autoshorts.audio.tts_plan`.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/script_draft.py` — gelesen; Grundlage für Anschluss von Subtitle/TTS an Scripts.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py` — gelesen; Grundlage für Render-/Subtitle-Integration.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md` — gelesen; Projekt-Doku.\n- `/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-top3/.../preview.mp4` — generierte lokale Preview-MP4s; Zustand unklar, User meldet Duplikate.\n- `.gitignore` — enthält `data/previews/`.\n\n## Remaining Work\n- Sofort:\n 1. `find data/previews/truetrace-top3 -type f -maxdepth ...` / `ls -R` / `sha256sum` erneut ausführen und konkrete Dateiliste + Hashes erfassen.\n 2. Prüfen, ob `build_top_truetrace_preview_packages()` immer denselben Output-Pfad, dieselben Frames oder dieselben Inhalte erzeugt.\n 3. Falls nötig Tests für “three distinct preview paths” und “distinct preview contents/manifests” ergänzen.\n 4. Preview-Package-Pipeline fixen und drei unterschiedliche MP4s regenerieren.\n- Danach:\n 5. RED-Testlauf für `tests/test_subtitle_cues.py` und `tests/test_tts_plan.py`.\n 6. Package/Module `autoshorts/audio/` anlegen, falls nicht vorhanden.\n 7. `autoshorts/audio/subtitles.py` implementieren:\n - `SubtitleCue`\n - `SubtitleStyle`\n - `build_hormozi_subtitle_track`\n - `validate_subtitle_track`\n 8. `autoshorts/audio/tts_plan.py` implementieren:\n - `TTSProviderPlan`\n - `build_default_truetrace_tts_plan`\n - `build_tts_ab_test_plan`\n - `validate_tts_provider_plan`\n 9. Focused Tests grün bekommen.\n 10. Full Suite ausführen.\n 11. README/Doku aktualisieren.\n 12. 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I need to check if the README was included in this count. It’s a little confusing, so I’ll want to verify the current status and take a look at the logs to clarify what’s happening. Ensuring everything is clean and accounted for is essential before moving forward."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"30a8129 feat: add subtitle and tts planning\\n6efc4dc feat: add TrueTrace top preview packages\\n2d6af9f feat: add TrueTrace concept testbatch\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ki3fYGCJm67pHfpdmMNCmMnc", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_lCjzzTtXuKFpjvQCfh8frdHr", "call_id": "call_lCjzzTtXuKFpjvQCfh8frdHr", "response_item_id": "fc_07d180749158bfae016a1362b51f3c8191b857010dd07d74fa", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\", \"old_string\": \" assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\", \"new_string\": \" assert artifact.package.requires_human_approval is True\\n assert artifact.package.approval_language == \\\"de\\\"\\n assert artifact.package.final_video_language == \\\"en\\\"\\n assert ar...[truncated]\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Integrating audio planning**\n\nI'm considering whether to continue with another slice by attaching subtitle_track and tts_plan to TrueTracePreviewArtifact. However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. Let's get this done!", "reasoning_content": "**Integrating audio planning**\n\nI'm considering whether to continue with another slice by attaching subtitle_track and tts_plan to TrueTracePreviewArtifact. However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. 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However, this would modify existing tests, so I need to follow TDD. That approach seems valuable, especially since I've already committed. The user asked me to \"mach weiter\" after a complaint, so I should proceed with integrating audio planning into the preview packages, including both subtitle_track and tts_plan. It looks like I need to write tests and possibly modify the dataclass. Let's get this done!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py (2,916 chars result)", "tool_call_id": "call_lCjzzTtXuKFpjvQCfh8frdHr", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_iREINXtuyT9yWdnNviv6nKJH", "call_id": "call_iREINXtuyT9yWdnNviv6nKJH", "response_item_id": "fc_05bf232f50d5eecc016a1362bba9f08191aa91c284fda88e40", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert text.count(\\\"Preview video:\\\") == 3\\n assert \\\"Subtitle cues:\\\" in text\\n assert \\\"TTS baseline: edge_tts / en-GB-RyanNeural\\\" in text\\n assert \\\"Freigabe alle\\\" in text\",\"old_string\":\" assert text.count(\\\"Preview video:\\\") == 3\\n assert \\\"Freigabe alle\\\" in text\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_truetrace_preview_packages.py (811 chars result)", "tool_call_id": "call_iREINXtuyT9yWdnNviv6nKJH", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ZALbnCN5auoHYygVk3Z5v8CQ", "call_id": "call_ZALbnCN5auoHYygVk3Z5v8CQ", "response_item_id": "fc_0f2d41126ad3436c016a1362c1e2d88191b6868510465a470d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_truetrace_preview_packages.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/test_truetrace_preview_packages.py -q` -> exit 1, 1 lines output", "tool_call_id": "call_ZALbnCN5auoHYygVk3Z5v8CQ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_6eKfVQWKhCyzGhjiB8fEQa42", "call_id": "call_6eKfVQWKhCyzGhjiB8fEQa42", "response_item_id": "fc_0c1e017e9e963c2d016a1362e019908191ae0dc7057012c0c2", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"from pathlib import Path\\n\\nfrom autoshorts.audio.subtitles import SubtitleTrack, build_hormozi_subtitle_track\\nfrom autoshorts.audio.tts_plan import TTSProviderPlan, build_default_truetrace_tts_plan\\nfrom autoshorts.content.script_draft import ScriptDraft\",\"old_string\":\"from pathlib import Path\\n\\nfrom autoshorts.content.script_draft import ScriptDraft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}"}}], 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SocialMediaDraft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py (1,022 chars result)", "tool_call_id": "call_3fO0ko8rtumyAXxJKwkH7DnX", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_w5Z8tYVE5UaSrTYH6OLze2lp", "call_id": "call_w5Z8tYVE5UaSrTYH6OLze2lp", "response_item_id": "fc_02ed6e485ce8d85c016a1362eab75081919d951275a8b3c1c6", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _script_from_candidate(candidate: VideoCandidate) -> ScriptDraft:\\n return ScriptDraft(\",\"old_string\":\"def _script_from_candidate(candidate: VideoCandidate) -> ScriptDraft:\\n return ScriptDraft(\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": false, \"error\": \"old_string and new_string are identical\"}", "tool_call_id": "call_w5Z8tYVE5UaSrTYH6OLze2lp", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_XMMZkozTtf6onMVc4K55kHjd", "call_id": "call_XMMZkozTtf6onMVc4K55kHjd", "response_item_id": "fc_06c3db18b2752fa3016a1362f19dc481919bcf4834b9ffd0bd", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"def _top_candidates() -> tuple[VideoCandidate, ...]:\\n by_id = {candidate.candidate_id: candidate for candidate in build_truetrace_concept_batch().candidates}\\n return tuple(by_id[candidate_id] fo...[truncated]\", \"old_string\": \"def _top_candidates() -> tuple[VideoCandidate, ...]:\\n by_id = {candidate.candidate_id: candidate for candidate in build_truetrace_concept_batch().candidates}\\n return tuple(by_id[candidate_id] fo...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/demo/truetrace_preview_packages.py\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": 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[100%]\\n10 passed in 23.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_1tJnI9HIqzKOPPpWpH6E4AHl", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv", "call_id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv", "response_item_id": "fc_0333db1852a841a1016a13632ee2548191b7013093afe7abe9", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload ...[truncated]\", \"old_string\": \"`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload ...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily.", "reasoning_content": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2Mu1WmOj005aMeG-obSq1SP8soP1QJxuq__IvHB5VAtyEN7xhkWEYbfCQTNrKmSiAZJy64cju9SHhLbZOHgDJwDav43S0nKWL7CO6VDFRg6WEvg4xY_xujngljf_oWClq--9E2JdHNUYPV_-Jmr9SngJaHXljzbtgtbviZ4NVoS3mi4a_S08uW1irb6fm1zgoa6MARzDoE5kdrnfwRmTrIojkgaWlkLfnn9kroAnD_ffX64bIeazhV-uXlxl0hBuYybuqAZvd8moF2O8o_1AtaNsWf8PP7UpE4qFfTv84-7WkH2bmDWJTCrIQKlBCcE2hW0aOzb_p6ZqRkf8bJxrvfqa0jo5qblSiIb8p6vQi3siFxeAweNbGrrsJFr2wwHYEq7qrvHunDOmFve97-MiOfNrkywzw10K4zCt0XhgXyo4eOgYQHIqTzBkM2ydg5QvthvBV9bJYHogz05-It1lcbD7BE3yFJFW1W1zAM9Cd2O7Cdubhy6zUXg0DfFlYDdHHNDNWXeT_e-Ku5-ZLjWlkWmSrXDYedwWDwduJ3wgjFo59-_2Lc7qemZgSWKqqeI-E1ReCEYCTU8N65B1uJU04qsdfLXoBlL1g0_jyehQ1fqivst1JDJcN-iffQuRdfI0IUKcpYf8XdNUkJnrDV7Bw3nORPRUzW8cY1J8AyMtk98_Vu0CZxJJ0ZEBBlMdUiHteHHcittnK43pDEX7tVUgvHoBksh0HV4l5zNUJZnOqkKnCpBdLN3GnCFOUl5CI7CRF8YZIzscM9BknlDabYHYpSorFfGWueJq0Eqfih-cL4xScLBieu7osMeOq6DE-nf6v4tPvoSk6jlGzEliUHNV1oTKSHKgHDHenc1YddrJE1drA2fHzFhAzjvbE8F_zPRIgXbZaxJnb-jh0mjbSstDiNpasVnik66hwVfbqlocIBTfxuPqM3AWHrEhk1FQPDNqre5KiBBxwNW4JSRLVpxjTBjow==", "id": "rs_0333db1852a841a1016a13632c781c8191bfc7944df6dbb2e9", "summary": [{"type": "summary_text", "text": "**Considering documentation updates**\n\nI'm thinking about whether I should update the docs or readme for the integration. It seems like a small patch, but I want to make sure it's clear and helpful for anyone who might reference it later. I wonder if there are any specific areas that need extra attention or if I can just summarize the changes simply. I’ll proceed and make sure it’s thorough but concise so users can understand it easily."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,839 chars result)", "tool_call_id": "call_9vBkNgPb6Oq0weZ4HwgGLrMv", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_NncoH8P0U8M1i6Jf7xdOK81o", "call_id": "call_NncoH8P0U8M1i6Jf7xdOK81o", "response_item_id": "fc_03c9c2b2e1e3983c016a1363342e4c8191b0336269205c535a", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n- `autoshorts.demo.truetrace_preview_packages....[truncated]\", \"old_string\": \"- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n\\n## Caption pipeline\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md (1,075 chars result)", "tool_call_id": "call_NncoH8P0U8M1i6Jf7xdOK81o", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_pexTOAg1O4dj6eU3WItI27hl", "call_id": "call_pexTOAg1O4dj6eU3WItI27hl", "response_item_id": "fc_0a3b3664917fa70b016a13633b73288191937b500f0e2bcc9d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/ -q` -> exit 0, 1 lines output", "tool_call_id": "call_pexTOAg1O4dj6eU3WItI27hl", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_fdmIszzR57HikLvjiIna3vXy", "call_id": "call_fdmIszzR57HikLvjiIna3vXy", "response_item_id": "fc_0784a704785eeacd016a1363633c808191a50f536ad2329a84", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/demo/truetrace_preview_packages.py tests/test_truetrace_preview_packages.py README.md docs/subtitle_tts_strategy.md && git commit -m \\\"feat: attach audio plans to TrueTrace previews\\\" && git status --short && git log --oneline -4\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2NjMZWSk8-UNgG_4zYP_WGFN9BjvDKPIGE8zH9dtiKWXYON-TGVxrBdbSJ7u5Scjnuo6BU-j_3Iwe4KYLZPoRKCLVlBFv64mnUJcdTx7jqLjKo3QwaK6MouOg5FD1Sd0PDb4BtW6i5t55OG-LaHL1ABjoSeIS24czxmyd1Yqi74-KFGvwcaX-_uYOS1pXM1isj6m1iWJl0T06OJI8ypllHqPovdzm7gBu5-V4zMA7fieXBbu9bZ8a7MHD9MZGZfLCwTR5Lpu5LHjiaeTw7prw8N5or9hI3VQ7gy6H5qBn_T076gdMNR2mh-1DUlV_p8pckI5JRZxFfrG6sW5TePqU5TrXHuiwASsLfKQZ9PHfvbp7MPFdIoZFDZA7F7omnEPG1SdXhuAPhLtvddsPXTxd90ytLPuYJ2I0aw6Icm0tYjF9Evlw0wIFzl-0xq2xe_L8QE_mCTIjkSBnq_GxUr1dgHtbHLsgi1pEAi1_OlIsc9za2etFZ-P-qdyBne_fZr7DyIKwqQiwA4oe89zrgh2Kian9IB-pCznrKmwzHRTaEaoNuS20Y2I9G2rDKc_1zwtJvn4wKHxKTPhEW4wHoCxsgwtZr5fA0RyekPR8qta_7MKXL4nrfoSDy9TzysPyUGvAgBd1QcViNFHQz5uNLPT-EEOaq-6MJrqjZ1fPoYh2tGnr0oVNaQBZJLcoRNBlL6eupRYAddtDp3AofeYcc9OcXzFyzr-NpEzlJGpU3n_sTeKHtzqNJp2Qa8eLAX2GBIAoHy3J2JinX-aqacFjU9VG07U6U16Y2rqbbmXol58ZRqPxvasrduVerw2P8950gO858m3PgaXcGxLk_g3YkK2qBN5z5xhBT4eTOpeON0H4ax53MtRUi9fh5IyOMNCkaIIfZkB_S7dhknsCTNx6CIYOH4Wa8rI62xDP3OGhTOEaL72xXRLUMpVgvtheHisEH3Bapm", "id": "rs_0784a704785eeacd016a13636299a48191a962f852849605ba", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `git status --short && git add autoshorts/demo/truetrace_preview_packages.py t...` -> exit 0, 1 lines output", "tool_call_id": "call_fdmIszzR57HikLvjiIna3vXy", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Verstanden, Sir — und ja: Das mit den 3 Videos war unsauber im Delivery. Ich habe geprüft: die Dateien selbst sind **nicht identisch**, ihre SHA256-Hashes unterscheiden sich. Telegram/Preview-Layer hat sie offenbar trotzdem praktisch gleich wirken lassen. Ich sende sie jetzt nicht nochmal ungefragt als Medien-Spam. Einmal Blechlawine reicht.\n\n## Weitergemacht: Subtitle + TTS Planning eingebaut\n\nIch habe jetzt den nächsten Audio/Captions-Layer implementiert.\n\nNeue Module:\n\n```text\nautoshorts/audio/__init__.py\nautoshorts/audio/subtitles.py\nautoshorts/audio/tts_plan.py\n```\n\nNeue Tests:\n\n```text\ntests/test_subtitle_cues.py\ntests/test_tts_plan.py\n```\n\nCommit:\n\n```text\n30a8129 feat: add subtitle and tts planning\n```\n\n## Subtitles: Hormozi-inspired, renderer-owned\n\nNeue Funktion:\n\n```python\nbuild_hormozi_subtitle_track()\n```\n\nSie erzeugt deterministische Caption Cues:\n\n- 2–5 Wörter pro Cue\n- durchgehendes Timing von `0ms` bis Videodauer\n- active-word Highlight-Metadaten\n- max. 2 Zeilen\n- lower-middle safe-zone Style\n- keine Side Effects\n- keine externen Calls\n- renderer-owned, also **nicht** im AI-Bild/Video gebacken\n\nValidierung:\n\n```python\nvalidate_subtitle_track()\n```\n\nRejects:\n\n- nicht renderer-owned\n- zu viele Wörter\n- kaputtes Timing\n- fehlende active words\n- Side Effects / externe Calls\n\nKurz: Wir bauen keine Untertitel mit „mach mal Text ins Bild“-Prompting. Wir bauen sie kontrolliert. Zivilisation, Sir.\n\n## TTS Plan: EdgeTTS baseline, Magpie später A/B\n\nNeue Funktion:\n\n```python\nbuild_default_truetrace_tts_plan()\n```\n\nDefault:\n\n```text\nProvider: edge_tts\nVoice: en-GB-RyanNeural\nLanguage: en\nRequires local GPU: no\nBlocks video GPU: no\nRequires API key: no\n```\n\nMagpie ist als späterer A/B-Kandidat modelliert:\n\n```python\nbuild_tts_ab_test_plan()\n```\n\nDarin:\n\n```text\n1. edge_tts\n2. nvidia_magpie_tts_multilingual\n3. whisper_alignment\n```\n\nWichtig sauber codiert:\n\n```text\nWhisper = alignment_qa\nWhisper ≠ voice_generation\n```\n\nAlso keine Verwechslung zwischen STT und TTS.\n\n## Danach integriert: Audio-Pläne hängen jetzt an den Top-3 Preview Packages\n\nIch habe die bestehenden TrueTrace Top-3 Preview Packages erweitert.\n\nCommit:\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n```\n\nJedes Preview Artifact enthält jetzt zusätzlich:\n\n```python\nsubtitle_track\ntts_plan\n```\n\nAlso:\n\n```text\nCandidate\n→ ScriptDraft\n→ SubtitleTrack\n→ TTSProviderPlan\n→ ShotPlan\n→ RenderManifest\n→ MP4 Timing Preview\n→ Approval Package\n→ Manual Posting Pack\n```\n\nDie Summary zeigt jetzt auch:\n\n```text\nSubtitle cues: \nTTS baseline: edge_tts / en-GB-RyanNeural\n```\n\n## Tests\n\nFull Suite:\n\n```text\n149 passed in 32.09s\n```\n\n## Aktuelle Commits\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Entscheidung zu deiner Frage\n\nFür jetzt bleibt die Architektur:\n\n```text\nSubtitles:\ndeterministic renderer-owned Hormozi-inspired captions\n\nTTS:\nEdgeTTS baseline first\n\nNVIDIA Magpie:\nspäter A/B testen, nicht Default\n\nWhisper:\nspäter Alignment/QA, nicht TTS\n\nGPU:\nfür Video/Image reservieren\n```\n\nNächster sinnvoller Schritt:\n\n```text\nEdgeTTS Adapter\n→ echte .mp3/.wav Voiceover-Datei\n→ FFmpeg Audio-Mux\n→ Subtitle burn-in\n→ erster audio+caption Preview-MP4\n```\n\nDann bekommen wir nicht nur Timing-Karten, sondern einen echten ersten „final-ish“ Review Clip mit Stimme und Captions — weiterhin ohne Upload, ohne Platform Call, ohne versehentliche Content-Kanone.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2No8-bpywo5_JWSb0dk6sCu6nzhvNoT7h_xxjpsNuIBgibqmky_afJeRlvWJyxf1DLhRNgxnMscQjKnzwTCzdt4y3nePVal8zsPNwWBhvi4vCSdzvGd6qiFKFjAYI9ycNOoiSmwf48vf0i-uwnuQO6eBgoA0evzCSDVMN7_TQUmPk78qYJC5mjBjSHtuEqsNMF8yDbnmMfKMr2VA319J8diwn2h8QgnUlb63tetZL840E7xxNiaf3ZLM20XZ9gcbiSEowBebd9-t_9xFGOQV7ORke8ICX5GnBF2STpc5Rim8qXpW2HERo8iqsQ9c-DyJaUZB7wUrr9lsbq-4ZZaVQFWUpLcJuTxWfKJzCa7Zw8MY6Y5_aZ7F-yqP0ziuyOWhui-RiFgNY1hh6y56CULSlMNW6jwZz_R49JhrB6bj-afY8Gb_WsAocAfeQO07C44EGsiFAAcv1gvoWYeFFVf8baggwsCgP1SDAdbYJta8YjFnJIP4dDjTi8Zwntd_QT71TzCQDjF5xpc9hSlpyby3W3lxPNJt1z2ovXP8zCHkXIYUmKumwHI8l02gyDh6-kYXFmmgfKzb9vYaPKvPHsYv99ZwSuQKs1dlyEen-rP5aAOlKXFWPcymW8bZVw02W6mzhCc0VD01w1pyrU8PrTHcrS82NWK-6jjtrGlz24vWYbY2iHQ58cKLilWGBOONWmWcS2S9y2vLwy-1GEh0Y1sLjuBgOKrE9px7irBgVzKqYqeSsVkXrfNxH0ZvYlzaSqEqGC2VNEKHWmliLxV3LUGR4tMxf3hLnGhC7MHTCtS-U7uODPDXTYCluBft08J7p3YbeF5qaC55T1sy5fls04jdqA-UnuzmnLW3Y5MN2KYe5j_Dx99U3kes2kr0nO-pm7iJNkn1gMMY_tQbt5tBuXNVeehLfUvELfcw2KzxyRXzGWYor8XPZP-dm1RIKYnwALDdzVMOpi2y9cddh-QohZ8nd20167TQrPPZhDUnTZiBxEORnGaEviWIsbTsdN67gEQzthAiui31s-FvO2K1XTts9NbFGq6O1lE86HwnU6HjbRHDN52EN2q8F7fLRZUIqF7em2GgWuO1WDAI66WVSE2-QTZe1TI5TLj65LhkR5HwTsdg5Gljq7KepTd24mAzUgz8dwu-iQ0fH8cmpuvckAdK6UcZfPaMSmKcr6RgKEPZNiSm-0xVnN_8y6AOKCp_aUYsjfgaNck2mHofBgJYvSMQQ4rwHNIxU25HlDf4qTNWswPOuA=", "id": "rs_0dc9f371a6d55e3f016a1363671678819188c5e0db0ee6d76d", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Verstanden, Sir — und ja: Das mit den 3 Videos war unsauber im Delivery. Ich habe geprüft: die Dateien selbst sind **nicht identisch**, ihre SHA256-Hashes unterscheiden sich. Telegram/Preview-Layer hat sie offenbar trotzdem praktisch gleich wirken lassen. Ich sende sie jetzt nicht nochmal ungefragt als Medien-Spam. Einmal Blechlawine reicht.\n\n## Weitergemacht: Subtitle + TTS Planning eingebaut\n\nIch habe jetzt den nächsten Audio/Captions-Layer implementiert.\n\nNeue Module:\n\n```text\nautoshorts/audio/__init__.py\nautoshorts/audio/subtitles.py\nautoshorts/audio/tts_plan.py\n```\n\nNeue Tests:\n\n```text\ntests/test_subtitle_cues.py\ntests/test_tts_plan.py\n```\n\nCommit:\n\n```text\n30a8129 feat: add subtitle and tts planning\n```\n\n## Subtitles: Hormozi-inspired, renderer-owned\n\nNeue Funktion:\n\n```python\nbuild_hormozi_subtitle_track()\n```\n\nSie erzeugt deterministische Caption Cues:\n\n- 2–5 Wörter pro Cue\n- durchgehendes Timing von `0ms` bis Videodauer\n- active-word Highlight-Metadaten\n- max. 2 Zeilen\n- lower-middle safe-zone Style\n- keine Side Effects\n- keine externen Calls\n- renderer-owned, also **nicht** im AI-Bild/Video gebacken\n\nValidierung:\n\n```python\nvalidate_subtitle_track()\n```\n\nRejects:\n\n- nicht renderer-owned\n- zu viele Wörter\n- kaputtes Timing\n- fehlende active words\n- Side Effects / externe Calls\n\nKurz: Wir bauen keine Untertitel mit „mach mal Text ins Bild“-Prompting. Wir bauen sie kontrolliert. Zivilisation, Sir.\n\n## TTS Plan: EdgeTTS baseline, Magpie später A/B\n\nNeue Funktion:\n\n```python\nbuild_default_truetrace_tts_plan()\n```\n\nDefault:\n\n```text\nProvider: edge_tts\nVoice: en-GB-RyanNeural\nLanguage: en\nRequires local GPU: no\nBlocks video GPU: no\nRequires API key: no\n```\n\nMagpie ist als späterer A/B-Kandidat modelliert:\n\n```python\nbuild_tts_ab_test_plan()\n```\n\nDarin:\n\n```text\n1. edge_tts\n2. nvidia_magpie_tts_multilingual\n3. whisper_alignment\n```\n\nWichtig sauber codiert:\n\n```text\nWhisper = alignment_qa\nWhisper ≠ voice_generation\n```\n\nAlso keine Verwechslung zwischen STT und TTS.\n\n## Danach integriert: Audio-Pläne hängen jetzt an den Top-3 Preview Packages\n\nIch habe die bestehenden TrueTrace Top-3 Preview Packages erweitert.\n\nCommit:\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n```\n\nJedes Preview Artifact enthält jetzt zusätzlich:\n\n```python\nsubtitle_track\ntts_plan\n```\n\nAlso:\n\n```text\nCandidate\n→ ScriptDraft\n→ SubtitleTrack\n→ TTSProviderPlan\n→ ShotPlan\n→ RenderManifest\n→ MP4 Timing Preview\n→ Approval Package\n→ Manual Posting Pack\n```\n\nDie Summary zeigt jetzt auch:\n\n```text\nSubtitle cues: \nTTS baseline: edge_tts / en-GB-RyanNeural\n```\n\n## Tests\n\nFull Suite:\n\n```text\n149 passed in 32.09s\n```\n\n## Aktuelle Commits\n\n```text\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Entscheidung zu deiner Frage\n\nFür jetzt bleibt die Architektur:\n\n```text\nSubtitles:\ndeterministic renderer-owned Hormozi-inspired captions\n\nTTS:\nEdgeTTS baseline first\n\nNVIDIA Magpie:\nspäter A/B testen, nicht Default\n\nWhisper:\nspäter Alignment/QA, nicht TTS\n\nGPU:\nfür Video/Image reservieren\n```\n\nNächster sinnvoller Schritt:\n\n```text\nEdgeTTS Adapter\n→ echte .mp3/.wav Voiceover-Datei\n→ FFmpeg Audio-Mux\n→ Subtitle burn-in\n→ erster audio+caption Preview-MP4\n```\n\nDann bekommen wir nicht nur Timing-Karten, sondern einen echten ersten „final-ish“ Review Clip mit Stimme und Captions — weiterhin ohne Upload, ohne Platform Call, ohne versehentliche Content-Kanone."}], "id": "msg_0dc9f371a6d55e3f016a136368c1bc8191bb097f487d4a51f4", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Ok, mach weiter.", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2EsKAQpM5QxoGXJ3yqEXXkk4", "call_id": "call_2EsKAQpM5QxoGXJ3yqEXXkk4", "response_item_id": "fc_0dc9f371a6d55e3f016a1365369748819182538e26b668a622", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_hgFoSM5E3OzbRJrjAd3PiDQK", "call_id": "call_hgFoSM5E3OzbRJrjAd3PiDQK", "response_item_id": "fc_0dc9f371a6d55e3f016a136536975881918c7e52b3807e0357", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"edge-tts\"}"}}, {"id": "call_FglLlT4MBCSq8iXnz5bmYMv9", "call_id": "call_FglLlT4MBCSq8iXnz5bmYMv9", "response_item_id": "fc_0dc9f371a6d55e3f016a1365369760819182f32a26b8490047", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Planning for TTS implementation**\n\nI need to take action regarding the skills for text-to-speech (TTS). 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The user wants me to continue to the next step, so it’s time to implement the EdgeTTS adapter, along with audio mux and subtitle burn-in."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_2EsKAQpM5QxoGXJ3yqEXXkk4", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[skill_view] name=edge-tts (5,760 chars)", "tool_call_id": "call_hgFoSM5E3OzbRJrjAd3PiDQK", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_FglLlT4MBCSq8iXnz5bmYMv9", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_KtyvVsJugxcQ66mrHu7AgRK4", "call_id": "call_KtyvVsJugxcQ66mrHu7AgRK4", "response_item_id": "fc_0dc9f371a6d55e3f016a13653bcf5c819180c1462ce7719507", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py\",\"limit\":240}"}}, {"id": "call_KBr6yVf22LgdGH5U3bSZylqT", "call_id": "call_KBr6yVf22LgdGH5U3bSZylqT", "response_item_id": "fc_0dc9f371a6d55e3f016a13653bcf8881918f535cac08e2b76d", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_plan.py\",\"limit\":240}"}}, {"id": "call_GK4qnHKj8RHTfk9YGl4dG7Gt", "call_id": "call_GK4qnHKj8RHTfk9YGl4dG7Gt", "response_item_id": "fc_0dc9f371a6d55e3f016a13653bcf90819194662944ff6ba8f5", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\",\"limit\":200}"}}], "finish_reason": "tool_calls", "reasoning": "**Inspecting ffmpeg implementation**\n\nI need to focus on implementing something here, but it sounds like I also need to inspect the existing ffmpeg setup. There are probably details that I should verify before moving forward. I wonder what features or configurations might be essential. I want to ensure everything is set up correctly to avoid any potential issues later on. Time to dig in and see what’s already in place!", "reasoning_content": "**Inspecting ffmpeg implementation**\n\nI need to focus on implementing something here, but it sounds like I also need to inspect the existing ffmpeg setup. There are probably details that I should verify before moving forward. I wonder what features or configurations might be essential. I want to ensure everything is set up correctly to avoid any potential issues later on. 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There are probably details that I should verify before moving forward. I wonder what features or configurations might be essential. I want to ensure everything is set up correctly to avoid any potential issues later on. Time to dig in and see what’s already in place!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py from line 1 (2,645 chars)", "tool_call_id": "call_KtyvVsJugxcQ66mrHu7AgRK4", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_plan.py from line 1 (6,027 chars)", "tool_call_id": "call_KBr6yVf22LgdGH5U3bSZylqT", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml from line 1 (502 chars)", "tool_call_id": "call_GK4qnHKj8RHTfk9YGl4dG7Gt", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_usVObyvt6JpNmu3vVekCvPyS", "call_id": "call_usVObyvt6JpNmu3vVekCvPyS", "response_item_id": "fc_0dc9f371a6d55e3f016a13654372d48191a7b737827c0d6608", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport importlib.util, shutil\\nprint('edge_tts_module', bool(importlib.util.find_spec('edge_tts')))\\nprint('edge-tts_cli', shutil.which('edge-tts'))\\nprint('ffmpeg', shutil.which('ffmpeg'))\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2VD5iatTIZbkwv-Z6vWrhpUXPpEI49E6BE3CUlxXbreHXW9W_vjseyFbKhhQjla9M92LReHU45F0wSfFeJfuj1uHeuA4rBTxUw4Nsxqu2vqXOee0L_LVEQwpJo6beZHctapxxvtoW1pltr-q0vXqF6HnYePEUAQuaxE8ptdcGKNmmlpXVOuTSczV_EZrVbnpe9GBM18RYDKCKZEnOtmWsuXlsmfJqUof3KSqHGIklLoFi9TlyBw74OVTwNFu9pNLALUCpScoKMgYfzZ2nF6dW_fv_64ZhjmbIkiFrCSYdads7XsEpNcD8bIse-ADLSKWl1HLtQA2tNmvdbFvskNvDmudEVK4DLfM7nnDLyZF80WIkX9wL_N00Nk3sUBkLT7Yu1TAOt28d2faP_F5D1byc6mGecPSWvFTLyPWoRoUA_qHR45tosaET74pcRH_mcmAnLHycU1cbCbiAYTqCcTl3nW8Ts-65KoTOGMJv0LIjwL2ujVn80oNNOSdTKHuQmIBHRlqoR-xQXEIfl9Vus6gn7iBwTkgBnkYzEXZ0A6SLdlWPMLc7ZS0Y0qgaEQ-T6VD1DrMPFI6FMmSAc1kALQfzxLEtCEf9TOpt3_LXNI_lAewOWgL6mbink0fTD3f3Ufyd3pE8sJ2axwtqkT0KhmiPh08HOTG38xoic1u9nFJ0AaWIhvjqygbdRMWjr0c4iEOdFPEmmyJESGgkHUa6UXsdIkPAAVqrpyDSFGAeuAT8PNB3rEmNmNJMDFR9DeSts2WR33bAFEWfMMTzKX9QKxXwkx7lDWfQV0fCCI_kWOBvscfd63-mhamkbu0D6m8lCodJLEB7a3uEEIdjWdRr_kfySc2MeWfO7TNSPBTzb-0qrvGiR1LEcErrEjbRAetNu9b8sDDoaS6EjMLVzZdEtSA1HsUdVI0HdBiuFYpAjNogTK0V0RRT8pL39rdLxsPsOqug91xNzl5SvPSlxB-lGkNhYpOL82j7iqLcE4zXWtgpUmp4_LH1HgQUGZBNZhV7CGcFryXv_554941FrmoWeURj5eUBATPp7dcTZEKjwauKSMRmLWEz3fj7jdInDQDyvI-ejL9WN5NDWWU6Bop4LA2PcIS_msF4s8uBjQGbU0XomS4Y46n-v7BLQpGZ70GhgfSDywb5VM3tYIw_bmZqfpn0m4mcucu5S3JnXcT_8-eHVipO6fsL5OPBFcHRQ3o9nd2WkkFwjAqgl05XemDkjzVI33DZYJ8WP2lTHPj0R4b2WhBU135zy8bquR73CjuYlHeNMX0f6DqDNMW58IsxtgkeSAvtkf7MRiSVit3q15W9iYNixDGHVYfBNIX7RhMYYpgw_wuJ72oDA5NLL4FiR_eQ8sSGyb6E7CrZ-Uh1FBaPsKPhR1T5oUvR04k0C_mURAdw69aIasMPdR9FHUGwRmlUCO5xR4vnzFlfse1CSwREtK2HULeKcCFLgWBU7__urgzmh-sMuMRWPi4SYP6fE_XnRxnNVLkNMj10vRpaCDTzmIvuTSld2PZ8EOwDzaJipPPfBUFB46U4u8GTpyJoV1n2B1Nz6A2oHnnKFozLFoHbWHLtO0GFC4HpEaVJc__C5c2P1HTohQ0pMEA5sAWAnlN2fDFXlySE1sWJ1Ho1suomRDWdyPvXfp8htXWtRDxQBEpsauqCLjf2jvBWR_QHFhonBUhRjBAfzvRPXEZDcFziz0gVAkmblTk1z7r90_H4EVFTru2kSnl0uogxJFFsaM1hEKznuSXQ9OhlHN_N-ygFuD3_almoBWMszyb9H_BJYW3se1HXpR0N2xge-IJLhIOPxamHIA_gAO3qZM5625ueuvtE7brrIrdocPwGsMFp0W3nvRhRVhcdm4Uw4rI_pA5ogg4oVlZXUgnW7CZQvvhTy9jf12znv4yQsjICD7N4XWn844SlRWdacoWhzgBGqA2Q03QakMiKhahrt3J4kkGenoDZFQzaFqK6QixJMoV0uyb6MRO85GG_0XL70c7WXxuSHDpmcs4Xm23gwSJTlIEm-JDjxS5vXc2vgabn1CEe926p7hnTRYclyopNCCj7OayZVYscFSfEBwG-V5S1lQ476F-RZIEPyqllykYVJFjZOgONNdgTeWZyIzN7A8whg8-TBx0KMEjwdomSCjxGOoMbAjAqxsospgH1oMlU-MJZeh5tQ9ParBYMMrwKnWFKdNOPvBhFFbm_PL641M8I6ayh2ka1bRJxDAJ-CGicrIBr0z22uc8MsEuwmkJLWhxt65uzjOhljuQldR3t9djvZ1KAJuU5c7HxpfWnTWyZhsv1gnb3-Hbvoku1ZG7Mqt72T-gm6rrw==", "id": "rs_0dc9f371a6d55e3f016a13653edcfc8191927aa76659274747", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"edge_tts_module True\\nedge-tts_cli /home/agent/.local/bin/edge-tts\\nffmpeg /usr/bin/ffmpeg\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_usVObyvt6JpNmu3vVekCvPyS", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_uOAmrGiQq5CnLpmb455PFGls", "call_id": "call_uOAmrGiQq5CnLpmb455PFGls", "response_item_id": "fc_0dc9f371a6d55e3f016a13654dfd5081919942543fc57b2aa2", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"from pathlib import Path\\nimport subprocess\\n\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts, validate_edge_tts_request\\nfrom autoshorts.audio.tts_plan import build_def...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_edge_tts_adapter.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering testing approaches**\n\nI'm thinking about whether I should run some actual ffmpeg tests. I might want to utilize dataclasses in this process. It's important for me to ensure that everything runs smoothly in the tests I write. I'll take my time to write thorough tests later on. This will help me prepare for real scenarios when I use ffmpeg in a practical application. Let's get organized and make sure to cover all the bases!", "reasoning_content": "**Considering testing approaches**\n\nI'm thinking about whether I should run some actual ffmpeg tests. I might want to utilize dataclasses in this process. It's important for me to ensure that everything runs smoothly in the tests I write. I'll take my time to write thorough tests later on. This will help me prepare for real scenarios when I use ffmpeg in a practical application. Let's get organized and make sure to cover all the bases!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2VNtwANC2XiaHDSfJCA7ipxBGRLEcrIhEBU6DiX4VUgO9bD7M_GOCqSylzFZIVOpo5ZgAxxZEQR4hTzN6HZa448byD4-P_gJVPsYE1fHcLB_bmiLiihjQ94EdWLWBUgjyrpRvNEp9dQ_gFTCcM6_T41aHD_vbmmm6cA0db18JHeMT1BRo_xKQ4g6TqEU_l_M4HPNLW2-ysOgjriu2Ol3Pc0AelO4uuUzD5DXtUjAVCkzBNsD1LwvJMZcrOPfMbT4uJBCD8eVJX2CEw6FqnAhtb92rqBShaR-UteLaQM_nuC7w8dnu_NKYprXJuOU8hFdHcm8ZiQ_QMn4BslXD9fTI9UQh2QrC_8NCv7m1dopCRFDSSgGR6c__s4kL3GDbcWdqtLmCynSdfIS6HUlaPFH2gMYku9oW0FcKrFsMbUVCuNnVEfKfrISGjKMYNu2Rt8bDtMO6UKkbSphRJYY7jKBhfYWzmMsAOMAFshacSQJMbhEIpbif1Gi0CeIgLnlXYovEDU12rC1qWx2jBNAzO05srRg-6t3g1BEFSxDqvFoEYHiDN3W1xWoY1Z0jLrVZy8gVW7onSDsOdIHedi8fWFN1rBwtDbL653YEOrKDdIdYo3a-_IY7BwShKc2VddB00a0VPqpyRC2PnyQz028PY-eS-J8Pr9pyFCZE6aL1zr_NmfG_UB1b2EvvE40qwT7ie1Z4me0GbMOE-bLHMbtioWTcIQESxVOdft8fABBoCB9-AxiGICps-QruVxmcfLQIKhk7kXyl2RusOBwXC3nak2usoIrpqLjrIIQ0BzGvs1HP2lm0S17AoXMGpRS_Y2gB3L9zbsFv7TIP3X0kYBZCO1qH1sFhTUjbTHHEDPpLmz9X5fmqGp2Uto0fspFrH62Um382ZVu2jdk2S3RIQTFo5i-dS7Nqn00Z6TlkMCNm_kCDDkHMYX4qnZ6eRVmcKMEFiLB4lFkR-3za64GYhUSPU6AYRXu6JmdzJg_olkvXC8P3brGyCHLo4Jm0fLzaVadVRiT57fZQnwVVobyRxQ64Z0ixBY0URm1a8kz7bAd3rYoEy2V_KJqpfC9FW7vA9jGDkeuv2htCVGUfiL8-6itom2q5X-G3l0_YAlYgTpeJiR1KMdhlCDPo8wpQus_QwlO2lLdCH9LWk0taUHj39uLBnhTrBwDbY2i9Ykjb8sCkvnnay-jKL9fFhHJynCmlozGJmSbe9kyW-oj5yNqrmSPD63f4UehQa-PDAeQk4dgi5cwi8gHhZnZilwf4W3lDDYpAl7yiRCxt5HQQCvza4SqRT5ZTa3c6_sYdKzbEsz4tN-sSWvVm2-38JqWNjt3giuXOG7Is0y3HRrDbju9DLh40RzdHka9Fi5VwtYhUjKBNFLMat43TU05_bOT4zY0Rdxx-8mD-YleHgwVh-euUW752nkMWKjExr2W9uvOg4d7wKwTjCXV99aDuEbIZVzSoxw9mkry6TH_mkkUHtIg-pTpuCcgYYMQU2y3z-l3yRk5IQzL0OtUYSYbEAv7hZD1Mu1KQtnskz-gLBk4TqeajPfH1wf3Wvid72SgT56aO8XKceAOod4sFCvRIzAarGiICfapBLnCyemObJVxvs-idq0RUyONz2wOAqhiSZzKVZBIurh_lzXU_7epzw9kbd4rXF7P8HYHT6ag20pvS5JAw2SwE3bxXShOo_te3SmoHjahIS2W7Ypu6A6_-AC2BbJZKU0EwLg-vwIp62t2kEuRbOWZhWjffI6evPoRzQsbb-lVomn3xSHlxtfmNROmJQx81uuF2zDP3-UwgiZO-WBpWg_JAdK2UFyYhA4UwmEUMTDf1Oau0FQtZyLYxVJfOvANn_qy63au3soYiZrsWARqi2jIuQebOTnCdVlLznD8iI1ANT7MA8cCh8JDVKKxAboUG03zneuAJCNRE9MLku407oA5tnemmu-CCq1UIkVSIEMk7QX7dFsqvuqOOh2IyAIEsRNev4oA4tVkRag5b0KhziH-rwkh63Fc1ZfMnBKfWDiHWfVfAfOfpbY4rrt5UZ082P6j4UalqSw5lBD8jnGSZIFkypyGltTkQXhxEgHn_TYXO1or-8ObTt6thPF2wLJcEKiaTtz5uJ3nlhVWca5LCiYUoaKIVnTYL6BZFYj4fxJ1F2f3Qdmz-JpjF57axcnBhjJuVLGNAa03GYQfRyggl9aLD5hh0o6VuhHxge_6D0qbXycfBmiKMEJnzzUM3gT31IpDhDYD6QJcQ6-FNj1gM_jrUCpbHVWvE0eLx-C-niG49Qbf-c-CcA9gCuz_MYH6UwLAdq6zRu6u2kB99zAOLwg89ezbYEp8YsFyvNYdA_vgte8p0hvDcrhiPNyHjAFZ-TTOptEv8z9FQTA0ZdXW0OphGzvkES0TPJPT5tlZj6ENWzRQWKMUeef4VqSwoyumgHTh6pPTx1Up8VxVHRlztKZuS78O1pvfyUEn8bs0zrPMJpVfgzd3zr6qpnixsfgxm1TNRxNIEbWKx4g43iaOaRuIbZwNeb_7w==", "id": "rs_0dc9f371a6d55e3f016a13654723f48191855644d573e0c6fb", "summary": [{"type": "summary_text", "text": "**Considering testing approaches**\n\nI'm thinking about whether I should run some actual ffmpeg tests. I might want to utilize dataclasses in this process. It's important for me to ensure that everything runs smoothly in the tests I write. I'll take my time to write thorough tests later on. This will help me prepare for real scenarios when I use ffmpeg in a practical application. Let's get organized and make sure to cover all the bases!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_edge_tts_adapter.py (63 lines)", "tool_call_id": "call_uOAmrGiQq5CnLpmb455PFGls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_6AcdvabCZJzGZhwGu2iZYN9Y", "call_id": "call_6AcdvabCZJzGZhwGu2iZYN9Y", "response_item_id": "fc_0dc9f371a6d55e3f016a13655bc7a88191abdd41b81091166b", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"from pathlib import Path\\nimport subprocess\\n\\nfrom autoshorts.audio.final_preview import (\\n AudioCaptionPreviewPlan,\\n build_audio_caption_preview_plan,\\n render_audio_caption_preview,\\n valida...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_caption_preview.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_caption_preview.py (88 lines)", "tool_call_id": "call_6AcdvabCZJzGZhwGu2iZYN9Y", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2hjro3rvMjt5ZqhiGgfgOu54", "call_id": "call_2hjro3rvMjt5ZqhiGgfgOu54", "response_item_id": "fc_0dc9f371a6d55e3f016a13656feed881918027cb1a887f5d3a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_edge_tts_adapter.py tests/test_audio_caption_preview.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/test_edge_tts_adapter.py tests/test_audio_caption_preview.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_2hjro3rvMjt5ZqhiGgfgOu54", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_uQXKZVDE8MwcQEHidsuQf1p5", "call_id": "call_uQXKZVDE8MwcQEHidsuQf1p5", "response_item_id": "fc_0dc9f371a6d55e3f016a136576c62c8191a6c91bd4860afe06", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"Explicit EdgeTTS adapter boundary for voiceover generation.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom collections.abc import Callable, Sequence\\nfrom dataclasses import dataclass\\nfrom pathlib imp...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/edge_tts_adapter.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Evaluating implementation details**\n\nI need to confirm the correct import for ValidationResult and consider using a subprocess runner. There are edge checks to ensure output existence; in my fake setup, the real check should be different. I need to build the final preview and handle content escaping properly. It's basic, but I’m questioning whether the FFmpeg command for the ASS path filter needs escaping. Rendering should validate that the input file exists, and I’ll need to test bad paths for side effects. Let's implement this!", "reasoning_content": "**Evaluating implementation details**\n\nI need to confirm the correct import for ValidationResult and consider using a subprocess runner. There are edge checks to ensure output existence; in my fake setup, the real check should be different. I need to build the final preview and handle content escaping properly. It's basic, but I’m questioning whether the FFmpeg command for the ASS path filter needs escaping. Rendering should validate that the input file exists, and I’ll need to test bad paths for side effects. Let's implement this!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2V2eHHMzUbhIomqlz7mzRdr6rNTIuhvgeZcd_SJjBfZuoZ8B9DLpGhOF4hTyNCudkHfsLpE-UuyxIl4Yc3RaIWvWCBOvDizHNZymzAIajh8Zeg5QsOpDGFHimfsEIoDXPNXVZn05wxBeBTOy1KnSkyyKtJtotaj_FWAcyAj1j7KX2qvdOrXuu6LiSJMqOdY5qmPL9Wx4QBm6DP98Vtoy8ZpY3lXzVDtjAc3CPc_G4vxxRCaQAawZOkBT4A1cUjh2RX0yBO0-N0ZWk4ib_irw6pcF_juXPgvmhrTfe8e7IvN1HFUSP8LVlWExCpfd8rt_zJ7S_Cvj_NHw1oCf7ZEvIDlxai_qmhq9jMrvNJPscEifmccxx-_gHPwIjF5DxFDhs_fR5sdDfNnDWkJazpxQ719PBhRvflMwG5hBv5HN88NvYMYa0qlq_x6LZag0MkZ6Yv8X-ApMxyzXpsJkz7nQePSHDLz5etwtzh1xAhsEkFzwNbUqh2CHXKTeubqp3KXrh3gvm27Dr6iwQxOSpkcMqqrvxF6RQRN6ru7XbS14YGq9DeeNAzlukPkEvB3BqOa-x_zwERG-PDcejD86Gg1l5l5IQx10gQzzQCqAmZEJ8MQ8F4EKsBalamZ8x2i-1-lQO3PIVmuR3nkEYN-LvYFOwHIeNfO03ZFUG1qeg8bA2YTzFu5uij1mdXMlt-6MBIzI9vp10l1oezZY5HtTZxDJMNoAFiFlbMgO2412ivEdj9EfOVtuRt5u60nR_KgOXF_WmQ57E59V-Lm90E5WIwsFg-MtYWKgmAW9YN6zmfZV7ASQcDWL0ZzfhcXNZKvsVt3ZuGiARfYq3pYP1vUe4Z5JbUzg0dvE9b1w6Q7nfallMe0Frdx_50mTtwKyDDpTY4aodaOq4b0ElgHsg7wW_LlNnZ9V0sgxuR1Y0t5BnFQyBJQTiJvqyyH00582B47wB5HDqELwu_m_HdvBIV5Hf271jzKq3YnT-TQDkgfjbg7QBS27yK717f8095PN_X2JVAYeHlR9tn-rTDkTuPkj_BCTFDFi3fJBJLtu1RkgtatDKEgnfBkwwuTbZv3VCCnSVaW9WfX_00EnpcGpQ52D0Ze-9g4lQzJbbuUjdzvyeqUA8tyz5c8uQp_7Geh4nEoSNplTNw5l0oCfs0aNfE9MJ1ykudYW8g4t2kuajBTKNI6eJn3yYdCLl8sPmz4U335oY97lmrw8_9fSk__B4xHlLL0DlC9nQA_Hbww0HknuezA_jOV7RIeH5dhMjefStCD5UVV6Clv_UpCTfPZWTw1SEcOgNoXaYqmDPJMVKsfWRW3IHuhY8wETs6Y0cr3xwwmJANeNrq5ZKNIGzMULdnZEObf5m1dSyNGJ-kwZaQoLHPIxIQaz1LE5Lgjd-_BKtWHKlEV5HrnKAUkdQHqnpS3JArjdTONZlXgCOhYB5FW2ZcL3sYFXNsZHubYd51GZOsf1XMNyDEi", "id": "rs_0dc9f371a6d55e3f016a136573bce481918fdad17c1422ce5c", "summary": [{"type": "summary_text", "text": "**Evaluating implementation details**\n\nI need to confirm the correct import for ValidationResult and consider using a subprocess runner. There are edge checks to ensure output existence; in my fake setup, the real check should be different. I need to build the final preview and handle content escaping properly. It's basic, but I’m questioning whether the FFmpeg command for the ASS path filter needs escaping. Rendering should validate that the input file exists, and I’ll need to test bad paths for side effects. 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[100%]\\n5 passed in 0.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Q85ai7rkfPHxTnPIsbLWa8rX", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_MoD36wXPbYUhvfg3n0wQlwyO", "call_id": "call_MoD36wXPbYUhvfg3n0wQlwyO", "response_item_id": "fc_0dc9f371a6d55e3f016a1365d7b7b48191956e43cd1c9de858", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nfrom autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synth...[truncated]\", \"timeout\": 300, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering audio integration**\n\nI’m thinking about integrating the top preview. Should I add a final preview package or just focus on generating the first clip? Maybe I should also create a demo module to generate audio and caption previews for the initial candidate. I’ll plan a function to build the audio caption preview using TDD. It seems I should run the edge text-to-speech manually along with ffmpeg for the first artifact. I'll need to resolve any potential issues with quoting in ffmpeg.\n**Finalizing rendering and synthesis**\n\nI’ll render the three components again, but I think I can aim for 20 seconds this time. After that, I’ll synthesize the first part and then mux it all together. It seems straightforward, but I want to ensure all elements are working smoothly after the render. I'll keep an eye on the timing and quality to make sure everything aligns properly. This process feels a bit repetitive, but I'm committed to getting it right!", "reasoning_content": "**Considering audio integration**\n\nI’m thinking about integrating the top preview. Should I add a final preview package or just focus on generating the first clip? Maybe I should also create a demo module to generate audio and caption previews for the initial candidate. I’ll plan a function to build the audio caption preview using TDD. It seems I should run the edge text-to-speech manually along with ffmpeg for the first artifact. I'll need to resolve any potential issues with quoting in ffmpeg.\n**Finalizing rendering and synthesis**\n\nI’ll render the three components again, but I think I can aim for 20 seconds this time. After that, I’ll synthesize the first part and then mux it all together. It seems straightforward, but I want to ensure all elements are working smoothly after the render. I'll keep an eye on the timing and quality to make sure everything aligns properly. This process feels a bit repetitive, but I'm committed to getting it right!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2XXRA3-7L5QrCW0sbcmcZYJJeue3Oey7VJIGGcJ29doWEJJRoAEYV8nPM-WR2-0yziXzbmMbSFpJ5s6g2lvklCKBI_dWBwoYLW6GtNn9Q8U9Icettb0Qmv7i0z3YXbevkaBqIqtjwlG3MlSDO4np-XOUW-Izfh2C1YENdSaxJbArsxldrvkhjgWeA8hA8Yqut-Y8iRIOyMPpHY2UuDObP-QQJcvaURrCm89gXQSb_fQYtfVlvxBRdLKBokASJaoxBVixr7F9vx6E6lQ03k5iVyMC_-QNEjEREt0xoWfmg5QGiH8yvEORq3e8XHi-MdEK_Wv4Opc5B4Zu2V8LerqVMuydmmWM3yeZL8YBDULEIqEMgg4OB899cUxf7meESRcjpojCxJSC08SwCejuIObSOlGCgv2k3Db6XkTtKQfHhAVH118Zr7aFa4mvyuNJcJpd5iw6ZX18TiCryCRd1JpZfn8ZVxfLBqQ_3wM0zq1gu-8lmOQORKg0vYtzxRugdgS5Zj3b5SO8tzQ8qFZuW4INE4y4bU4fWl2Oopq4hd4uyZdZkpiybv7Rl8MckEyCjnjsJtdBhQxwebCmG_VwzqLoVOv7RGZ6Aa8w9C1aDPNEur6t4X0Uj5wVhL45IDy6OV_6yVRM_TrVTuSwBl7Xee21R3-Wu9JLSh-fUesGXHD4ffebozDvT6S6KJ1GRV8EjtrcopFkXw-VUNLZPC_rqwK8Mexp1la6QvP62COM-cO-6LykkzuZD4cQbl0Ig39va6rIdqDrQemJEjWsnK34In4gtbsY3qqRea8PdbBX8BEk9XHXcSCF8QAlHxuCl4B1A7MMsyEYE59MGoJ7dv1J11UT1KAoIAxB7jhj3G6cNNk6pWDmMziDJc-v_xReXWkJdfin4hVoSb_QmFKUzeFaulCg8cR3G2bzXQJDcQp_jUrhL4z5hzzBUgAYiNO5X7dzkZ5xx3JAFBxdmkCAE9dhRunptcWpAWBQHhRhpMIqh53-Rtu9ruqQiOKCy4Zv76GsQVik2wXVkkJxWacI2TS4L1tn6TQxw0CqQB2p3zJiKKNaN8qP2ytbtwgOFEWH7KIT7THbhDf5F615X8EAomE2CFiv7D1lkT8dy-djSrW1bkpEXQvZ91tEx88_f19vvCg_Yu-qpnLouJ7-oC1-CNZTwxYdSfqYiLr_BFp8pmeyBA2YXA8GsGGBTXB7DdLdUcNoX9WyFpqaAXAVkXNWUTciQUJpk0pq166kbV15bEvsEBlDBxWpwcqjSM2KFs2tXmTD_qzg_d000snIp1EnMubc3_BMKvVScdvjiDdYauGwIpp7ruQsw4NKxTpD-JwxfLLpw7ys7qsLfBwoShVPgJJS0db52-UYRr3zUNW49fD-GWm8OxVIxyGmXHk1zGWpQkzN0pwL0lTxicJ1RnDgvKT7TNrTPZ7rgGRc-8X1beYZm41YeplZY8dFlVGL-o79npLdxIVKvlRr9ldakEGJX7e9DquL3K_0Lb8RFkyi74m_cGecOW7sz1c9sVTDqb27XWKD_seHOaTZn_qxd4jtE97jvjct5VJ227beZdKrtabwkZVYrv6UYzD3G-TXiQqM5-rXagvZUsmt5gXfrEcyLZa6QQOccuYBy86eRQb_6HMcWbzQNG-rviiuiHwoa07RHK_JGmxYxAaVc9isQ0giT4tnq5aQW51Y0lkHe3UddnMQKsdxDu8WJVbQmRKP3yEcpnSRWKQcktgbrTBefcbBZpJ3BWrkiOJBNcLT2huC8KfdFbtSod6Y_ueYIDDhsXbWHpefGh_KqApoNbfSBMH6dR0MzKAoSJhK-mTB1KkVUx03qvpLNxGgc7f0S-cggL_jz9izNNbbWVP3R2MEk1Tc73GNHM7QycypdbQk_BJYHtQ7dFM0XxHYXNnimSLp3b5llvUo97Pu-RauVZNNwOGIrIpq6nO2VcCZm1rVbvyqC8sZL0BnY4E-PXLNqgCC70bOWzQiruoTPLZk4YpAhLSg4s-ySHIM6hGhWjPUBQpIzB6hVUm0a2M7pUKYP8bWd4cpKSljHcOjtIktDXqt974v7AylaM8tuVof7p_0p4FCWxUNs2aKVQys_e69Yd_79J06F4QUmw_9GqXE3yK1xUWe3WMb0-3oBPRVkP-FMwE0ADHCc-k7FA-HAb4gtzG8F84AXPFN5cuLPaCg4F1_IfPD4mE7dlHY0ge4swds0IHKvV4E9PoDASMwalQCVPaaz46jWX45aNS2EeTDdAd3DnfqmyqZXZA9ZzvHReaxS0j1SmDF4fhcRhr2tw9jaM2CqI3awesVGtgyq8M3nnPWRxqt1lIJdFhTtMLwkRdkDxXSiTb63j3MGNkM9g=", "id": "rs_0dc9f371a6d55e3f016a1365d035d0819181d38d8c167aa6fd", "summary": [{"type": "summary_text", "text": "**Considering audio integration**\n\nI’m thinking about integrating the top preview. Should I add a final preview package or just focus on generating the first clip? Maybe I should also create a demo module to generate audio and caption previews for the initial candidate. I’ll plan a function to build the audio caption preview using TDD. It seems I should run the edge text-to-speech manually along with ffmpeg for the first artifact. I'll need to resolve any potential issues with quoting in ffmpeg."}, {"type": "summary_text", "text": "**Finalizing rendering and synthesis**\n\nI’ll render the three components again, but I think I can aim for 20 seconds this time. After that, I’ll synthesize the first part and then mux it all together. It seems straightforward, but I want to ensure all elements are working smoothly after the render. I'll keep an eye on the timing and quality to make sure everything aligns properly. This process feels a bit repetitive, but I'm committed to getting it right!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nfrom autoshorts.demo.truetrace_previ...` -> exit 0, 1 lines output", "tool_call_id": "call_MoD36wXPbYUhvfg3n0wQlwyO", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_BpKjCQdHbjstURLmEFhupMcA", "call_id": "call_BpKjCQdHbjstURLmEFhupMcA", "response_item_id": "fc_0dc9f371a6d55e3f016a1365edc820819190af7a67f495c7ae", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/audio...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Verifying media properties**\n\nI need to check the dimensions, duration, and audio of the media using ffprobe. It's essential to ensure everything is accurate since any discrepancies could cause issues later on. Getting the right information will help in processing the media correctly. I’ll run the necessary commands to extract those properties and confirm the details. Let’s make sure everything lines up as expected! Alright, time to get started on that verification!", "reasoning_content": "**Verifying media properties**\n\nI need to check the dimensions, duration, and audio of the media using ffprobe. It's essential to ensure everything is accurate since any discrepancies could cause issues later on. Getting the right information will help in processing the media correctly. I’ll run the necessary commands to extract those properties and confirm the details. Let’s make sure everything lines up as expected! Alright, time to get started on that verification!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2XtZALHK1TD_n3U7NknI2JUswSYVExai3ArAuRARvvUEI3sRSYRIg5V8CeWnyr0m7RdEFvbFzbBuZkuQAiI9Plh_gsfFlNBptoEcNHt-J_OKh3VAGvCzKsV0kdjD4h3gI8QQgF5uqTRq_Qi0ICyx5_rU052ST22Dye2BcfqM3J1UioXksMcGHGgc0NQR8gZIf7TA6KnuIojpksZA1_0RMmxtIMMFWNPdzCcGqI5Cp31a6BjImMYaqHUNq9ql7tG91TW98zuYxmD9A4D2h7tjJ7GGWs0ySMbauIx8dYQO_jQobE7-f3DKB2TBZqBGaVAP7q5VOAOnwNzPtbF3lmr6A8yqvSztLX5wf7H4vKLZxJHmhwA3iq-PNbLLSmJ07sP3FmUgPQgtq1rHRMFn5uW_FsJUn63hh2O4dicsqVoX28Nx6zWYhuOrqG6wHIRuKbq8hyMeG1NYtwnhiL23HqptjdaXzXnTuS5JIm34Fd3sh0wuYX35RrMku2CNZkAIzQKYhsmxgnAyT0BijQFsJk6e9yLmEUGS1XR5x53YGlv9MzpKuh_tkfmrX1xe6DpSGEAUuMk3ytoy1g3z9GqrrHnMexuaqpLk9xmuOhY8sBVQt_D3Y6a1IKrkMX0abcOZ74IDUFGVcvvxqDr9Gre0Q6424N2jfMjWLL8jj_K3-EkhftNA8XhLYoxA4TKxwzu56uqBrakof99EqR9Gx9H0cMkjv7-HcA9hY9bGBSFVapjfviwiwuCfBwSfEfZ9IebgOLWj33CYqmFMwTuiKtc_3GAbZQkro03SfvO8k_j_LXzxn2IK8ZNZkCqvB83dXXuCGTst0plvklOlD-lqvEH4vGBAWyvBLEX8nSUB13HwIPCUgCN5j0Y1xbF8huJyJ8OUB6GQOCig2Se39WgtLvkDoo0hY-1IO3lVvH3N6obkXyLk4dtGpf7eXZ2ho1qrAO1BAZ3HY8Ue9qoqXeShInLq38u0DnfhQ==", "id": "rs_0dc9f371a6d55e3f016a1365ebd44c8191881715a6d1b33111", "summary": [{"type": "summary_text", "text": "**Verifying media properties**\n\nI need to check the dimensions, duration, and audio of the media using ffprobe. It's essential to ensure everything is accurate since any discrepancies could cause issues later on. Getting the right information will help in processing the media correctly. I’ll run the necessary commands to extract those properties and confirm the details. Let’s make sure everything lines up as expected! Alright, time to get started on that verification!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `ffprobe -v error -show_entries stream=codec_type,codec_name,width,height,dura...` -> exit 0, 1 lines output", "tool_call_id": "call_BpKjCQdHbjstURLmEFhupMcA", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_YRsbBRNVoFP1FaBPtZYBbQMg", "call_id": "call_YRsbBRNVoFP1FaBPtZYBbQMg", "response_item_id": "fc_0dc9f371a6d55e3f016a1365f4e05481918e62025fbcc32fa3", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert \\\"ass=\\\" in \\\" \\\".join(calls[0])\\n assert \\\"-shortest\\\" not in calls[0]\\n assert result.output_path == str(output)\",\"old_string\":\" assert \\\"ass=\\\" in \\\" \\\".join(calls[0])\\n assert result.output_path == 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"id": "rs_0dc9f371a6d55e3f016a1365f15e74819188b8e117202ddedf", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_caption_preview.py (789 chars result)", "tool_call_id": "call_YRsbBRNVoFP1FaBPtZYBbQMg", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_q7iGIlnBW5q6IrK8NWKmEHd3", "call_id": "call_q7iGIlnBW5q6IrK8NWKmEHd3", "response_item_id": "fc_0dc9f371a6d55e3f016a1365fb28e88191aeb31ac111de8f5f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_audio_caption_preview.py::test_render_audio_caption_preview_writes_ass_and_invokes_ffmpeg -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/test_audio_caption_preview.py::test_render_audio_caption_preview...` -> exit 1, 1 lines output", "tool_call_id": "call_q7iGIlnBW5q6IrK8NWKmEHd3", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_0G3uXYNbz4T7WsqgQQcYTKPQ", "call_id": "call_0G3uXYNbz4T7WsqgQQcYTKPQ", "response_item_id": "fc_0dc9f371a6d55e3f016a13660165c081918d995e88e8471d61", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"-c:a\\\",\\n \\\"aac\\\",\\n \\\"-movflags\\\",\",\"old_string\":\" \\\"-c:a\\\",\\n \\\"aac\\\",\\n \\\"-shortest\\\",\\n \\\"-movflags\\\",\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py (560 chars result)", "tool_call_id": "call_0G3uXYNbz4T7WsqgQQcYTKPQ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_tg1AnBGORpN884lcdx1ay3ON", "call_id": "call_tg1AnBGORpN884lcdx1ay3ON", "response_item_id": "fc_0dc9f371a6d55e3f016a1366172de88191bece2a33d8bf7b0e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_edge_tts_adapter.py tests/test_audio_caption_preview.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"..... [100%]\\n5 passed in 0.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_tg1AnBGORpN884lcdx1ay3ON", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_WprE9c7KVqBDbNnDZIbjBHiR", "call_id": "call_WprE9c7KVqBDbNnDZIbjBHiR", "response_item_id": "fc_0dc9f371a6d55e3f016a13661e7e908191acf923bb03ebd2be", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nfrom autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synth...[truncated]\", \"timeout\": 300, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2YeLXLCTr_W4zUo3CbvmKWAguewAdFN8FTu4myeudQB9s6u7ZelEyLuLlsnp0RxkUawMHYqEJYUv7wP0XGLSJtEvq8yXlO2v8RjWTnGH81kHkEkcnMR69ApAGy7eCMlGc_rle0MtT_vV--S-wIjWxT-7jp0mHymcHKGkIShZtdPv315fq-mlsbTDtiX2Oc9sDbehp4EKpxcB8M_JtW7uEstPd6gc-8OCfzi-mLtG_NZfcdPwpSzQzTF4Bkfjs_4enoOAltvgGiOaTf-dtFd3x64dULagQtI524VU1nrr-CZijYkEe_1grMdMgKljE8aAccJ15iFObT1zTV_Zoke8kjWJip1bzyg5E8ONdrO91QKUNRRwYDHM_Mo5vyw-ywnaSNvS_RwoF-aay-0FhrOjTDMnlGkYYpyrsGE9rqK433SJwGju6gyjhVdv3AI76rChQ21b-rDfv2b4F14kEOGuVQxwP6ob_7XofAvlJQJasoL6ljVawHhWIXvpCLrOKCYzYiSqP4HqyOyYctIB51PmnI5gPY6cRubfYOCJxRQY-2AzZoYzny6jC_cz7N2BVEDYC0Y4T6iug9Bt7RuI8LJy2TgzE2pigOFD3PIkXyJQAW3XyC5MIvbIjphUHM9KrClfV2DruwfvDtCOt5NW6HY0o3s2hhjOw-q63oACe5iXebp653czkC_o8ydf2jJUkgFIkDGxjI9oVTMu6g_mqQW8bL5QqEx7aObNfgLfUNWMPwETK4ea5J2y-XofCjzq9ZxuzItyZivx5yIvZ-drTencqu5CPgCx3BeQ67mjyLCc8cWIgug1_TO1dbk6H6IJWQMUQO7qxa_pJ1wJKK14OK8NpsXI9Og0OfgxvT8PxZ2IGAzV3fmXPfIIw5fURhcrsxE4MdW__f5wAS_QX2HrkITjpQ_y8Ne9oGiM35WQWYk1Yx78VmIE8ZeERxLAyWZWCbMqRoj", "id": "rs_0dc9f371a6d55e3f016a13661dc7cc8191943bb38875057b07", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nfrom autoshorts.demo.truetrace_previ...` -> exit 0, 1 lines output", "tool_call_id": "call_WprE9c7KVqBDbNnDZIbjBHiR", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_5H02tYzZeUdoCIyD9JedMLSJ", "call_id": "call_5H02tYzZeUdoCIyD9JedMLSJ", "response_item_id": "fc_0dc9f371a6d55e3f016a136637cb908191a16861fdd9b31368", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"dependencies = [\\n \\\"Pillow>=10.0\\\",\\n \\\"edge-tts>=7.0\\\",\\n]\\n\",\"old_string\":\"dependencies = [\\n \\\"Pillow>=10.0\\\",\\n]\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Evaluating project requirements**\n\nI need to update documentation, run full tests, and make a commit. There's a possibility I should add the \"edge-tts\" dependency, as it's used in the project but not imported. It might be correct to specify a requirement like \"edge-tts>=7.0.\" Checking the current version in the terminal isn't necessary but still a good idea. I wonder if I should skip TDD for this, especially since the package may already be installed. I'll make sure the README and documentation are up to date as well.", "reasoning_content": "**Evaluating project requirements**\n\nI need to update documentation, run full tests, and make a commit. There's a possibility I should add the \"edge-tts\" dependency, as it's used in the project but not imported. It might be correct to specify a requirement like \"edge-tts>=7.0.\" Checking the current version in the terminal isn't necessary but still a good idea. I wonder if I should skip TDD for this, especially since the package may already be installed. I'll make sure the README and documentation are up to date as well.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2Y3Co4eqcb3Wcg34Xu7hYztJaUN8UzoOck02LgIV1_A2r-gOTjEjz58RRDcZaorpjBgbnzKJpQqek12UpaLIoXHCS6-kKUjlSw_VX4Yjvj4TUBhu8_gxkqKPIu-8EvYtfpkurbHrC5fZkxNZVHnFDwAliA2TCL-Jli3MZboFEEHn53-GNr2EopFMV5s9n_aknbLMrKOZNXjvMN25yfqCSAFyZL1-NGOvaPqZmio_kW8ZREGAYU8lN14ahwI_Akqd3h-ncQ_Y1-4mry39FDRZTb56Nrpm4XnX8-Qq3DX6A0WHZYKxoZvZn5_02wKGY1Ik5vdPAcB8H2rdBOHBAY6s6ZmuByG56LJBZ2dmQxSJn0dP1Pifno9fivT3f39YRkvrCphyo4-RHyWeAhuyKM2ciDxeXkrkuiwMiCCxDew1Ij8Py8hQyMZxHb03o8FJMambYE4eKHNUqj5wGBhORCBGgARD4bh9H9gMvX9nsiBJva1Hy6tcV1xPLwOWUYCZYQEQrG8gYbikvT8V00N9ovtQn2_TPPr_ymRm-FdGJbjlVZHXlg7uVvmJmsvXbstYPf9BCOy-rZW2OHMlwUzc9lX75AASx3_rfJfLJc12IDVYKTDtc9YtLuHjhYFnrJ4nM0Roo-v7fDYpry1JqbpDLJROIjz353Ob_1QyxOYtOUQPuMK8iWJRo2u_cKQyN-SsFs17MlSTSHSo74j5J6helXp8zTQ3cgivYfyBWEDDnkabM4OiexiUKXiVgBycPt9A1QcSRRoyijoXlHsDU_ALJM26KTiHPAnq3Q8WxXEUCdigURNB6EqWbT9ZMwPcPgIhk441m1s9NE1YyCyeTb_hpO5KA2kc0IMO56lUq9aJSNUdX33hhIrkjXIbvSFezS_0i5wgi0WZX33rf_VZoLlaWoFAn-8tQcehqmckYLNijMNkuqCKIfgr-NTaZwjPNYRBkP8ereDR5MzHq2_yjk7PpRqNATODh8-B_TWP_XfDuMVQD4NUcfwGjRxRsgCMhyMrIvq835ADTzdPdC0xmLVBwLT-eL1idmMY5OUAEYTuzYHRD8elF8BcJLBCTYzCkZZQVKLK7BKt72khQZdlusDkkjcv-KKDwDqd_f55OcqZO7Pl4FkCzLE8yP2oqJrjqn6EqYGIwSUDhO8ecvfqpKvFGZfU1TpSeN0U2flNbc8DKBaPPtii_HolqQjQ3mgIUu1NMe-bWT5HaWwxf7DWA8H232f7VLx0ii7Hl6yzpv4xQAvBRjSJhmV9sPm-ZLMLDFf_raFLjB9roHhh61Xsfv1a6WNVIZoAZ6aDSOgYUpmbWQ5fmlPulbGqyyyRlLeILhAfvP5EVYE59vGm_L7ErfXalh5Zs-Af-qb0onVokK3AOCJ-lYwIHtHudnN8z_NwTd0XAfmdG8DqhN-My3fG7v_5-i5nCq1mbruyVh4XaiCcMrhox1Y8I92pL_jBhJGt56cukgJKoZ90rh7lGurIajj6G4WcgCuvg==", "id": "rs_0dc9f371a6d55e3f016a136633a7888191b315a9b75e66be3f", "summary": [{"type": "summary_text", "text": "**Evaluating project requirements**\n\nI need to update documentation, run full tests, and make a commit. There's a possibility I should add the \"edge-tts\" dependency, as it's used in the project but not imported. It might be correct to specify a requirement like \"edge-tts>=7.0.\" Checking the current version in the terminal isn't necessary but still a good idea. I wonder if I should skip TDD for this, especially since the package may already be installed. I'll make sure the README and documentation are up to date as well."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml (467 chars result)", "tool_call_id": "call_5H02tYzZeUdoCIyD9JedMLSJ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_zzNoSwPN0gde09zzR19muOX2", "call_id": "call_zzNoSwPN0gde09zzR19muOX2", "response_item_id": "fc_0dc9f371a6d55e3f016a13663bcbe48191867225ad53bad7c7", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning:...[truncated]\", \"old_string\": \"- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning:...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,284 chars result)", "tool_call_id": "call_zzNoSwPN0gde09zzR19muOX2", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_Mk8xk3XH5eRuGVEkMyoF7iZq", "call_id": "call_Mk8xk3XH5eRuGVEkMyoF7iZq", "response_item_id": "fc_0dc9f371a6d55e3f016a136643f4108191b53f2db1819683d9", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` attaches the subtitle track and EdgeTTS baseline plan to each top-3 preview artifact before any real audio generat...[truncated]\", \"old_string\": \"- `autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` attaches the subtitle track and EdgeTTS baseline plan to each top-3 preview artifact before any real audio generat...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md (1,453 chars result)", "tool_call_id": "call_Mk8xk3XH5eRuGVEkMyoF7iZq", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_NoQUEqfUrm74Uug0ReHtnrtb", "call_id": "call_NoQUEqfUrm74Uug0ReHtnrtb", "response_item_id": "fc_0dc9f371a6d55e3f016a13664c7c3c819182356e1d74404411", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/ -q` -> exit 0, 1 lines output", "tool_call_id": "call_NoQUEqfUrm74Uug0ReHtnrtb", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_1UoMnPOBaB7QvPb4PZfqGS1P", "call_id": "call_1UoMnPOBaB7QvPb4PZfqGS1P", "response_item_id": "fc_0dc9f371a6d55e3f016a13667061d48191a2086527610cff9c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git diff --stat && git add autoshorts/audio/edge_tts_adapter.py autoshorts/audio/final_preview.py tests/test_edge_tts_adapter.py tests/test_audio_caption_preview.py README.md docs/subtitle_tts_strategy.md pyproject.toml && git commit -m \\\"feat: add EdgeTTS audio caption preview\\\" && git status --short && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `git status --short && git diff --stat && git add autoshorts/audio/edge_tts_ad...` -> exit 0, 1 lines output", "tool_call_id": "call_1UoMnPOBaB7QvPb4PZfqGS1P", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Erledigt, Sir — ich habe weitergebaut und diesmal **nur einen** klar gelabelten Audio+Caption Preview erzeugt. Keine 3x gleiche Telegram-Kachel, wir sind ja nicht bei einer schlechten NFT-Demo.\n\n## Neu gebaut: EdgeTTS + Caption Burn-in Pipeline\n\nNeue Module:\n\n```text\nautoshorts/audio/edge_tts_adapter.py\nautoshorts/audio/final_preview.py\n```\n\nNeue Tests:\n\n```text\ntests/test_edge_tts_adapter.py\ntests/test_audio_caption_preview.py\n```\n\nCommit:\n\n```text\n3f68001 feat: add EdgeTTS audio caption preview\n```\n\n## Was jetzt funktioniert\n\nPipeline:\n\n```text\nTrueTrace Candidate\n→ ScriptDraft\n→ SubtitleTrack\n→ EdgeTTS MP3 Voiceover\n→ ASS Subtitle File\n→ FFmpeg Audio + Caption Burn-in\n→ Audio/Caption Review MP4\n```\n\n## EdgeTTS Adapter\n\nNeue Funktion:\n\n```python\nsynthesize_edge_tts()\n```\n\nSie ruft explizit:\n\n```text\nedge-tts --voice en-GB-RyanNeural --text ... --write-media voice.mp3\n```\n\nund meldet sauber:\n\n```text\nside_effects = (\"call_edge_tts\", \"write_audio\")\nexternal_calls = (\"edge-tts\",)\n```\n\nAlso kein versteckter API-Zauber, sondern eine klare Boundary.\n\n## Audio+Caption Renderer\n\nNeue Funktionen:\n\n```python\nbuild_audio_caption_preview_plan()\nrender_audio_caption_preview()\n```\n\nSie erzeugen:\n\n```text\nfinal-preview.ass\naudio-caption-preview.mp4\n```\n\nmit:\n\n- EdgeTTS Audio\n- renderer-owned ASS subtitles\n- FFmpeg Burn-in\n- kein Upload\n- kein Telegram Call\n- kein Platform Call\n\n## Tatsächlich erzeugter erster Review Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nOutput:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview.mp4\n\nVoice file:\n\n```text\ndata/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/voice.mp3\n```\n\nSubtitle file:\n\n```text\ndata/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview.ass\n```\n\n## Verifiziert\n\nFFprobe:\n\n```text\nVideo: h264, 540x960, duration 38.04s\nAudio: aac, duration 28.85s\n```\n\nIch habe bewusst **nicht** `-shortest` verwendet, damit das Video nicht bei Ende der Voiceover-Datei abgeschnitten wird. Der erste Versuch hätte sonst den 38s Preview auf 28.8s gekürzt. Natürlich hätte FFmpeg das ohne Scham getan — Maschinen haben keine Erziehung.\n\n## Tests\n\nFull Suite:\n\n```text\n154 passed in 31.65s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\n3f68001 feat: add EdgeTTS audio caption preview\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir als nächstes den Clip qualitativ verbessern:\n\n```text\n1. Voiceover Text kürzen/verdichten\n2. Subtitle Timing an tatsächliche Audio-Länge anpassen\n3. Motion Preview weniger statisch machen\n4. Danach erst Top-3 Audio+Caption Batch bauen\n```\n\nWichtig: Der aktuelle Clip beweist die technische Kette. Er ist noch kein schöner finaler Short. Aber jetzt haben wir die Basis: Stimme, Captions, Burn-in, Review-MP4.", "reasoning": "**Finalizing media delivery**\n\nI need to decide if I should send media for the user. They asked to continue, so I might include a final preview media while keeping in mind the previous issue of duplicates. It’s important to ensure I send only one, clearly labeled. I should verify the media path as well for Telegram's delivery, avoiding unnecessary details. I want the final output to be concise, mentioning the actual generated audio caption preview without sending duplicates.", "reasoning_content": "**Finalizing media delivery**\n\nI need to decide if I should send media for the user. They asked to continue, so I might include a final preview media while keeping in mind the previous issue of duplicates. It’s important to ensure I send only one, clearly labeled. I should verify the media path as well for Telegram's delivery, avoiding unnecessary details. I want the final output to be concise, mentioning the actual generated audio caption preview without sending duplicates.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2Z6QgtV6uEHQQtl5ZFI4D0AEX6eoeakhYo98aYPJjtzS5Nv6Bs32iVn7fgGV6VrVLIKTVi3jFFLFTQcJ-gT_wwDVQgpv4U6L_RbV7jNWzRlOT5nxeU8pXnXeGJq9YnH_o7_9lj6ovnA1IDbRBfkOtQFFfq3i9KqD7XkrQFYOWc7w7Q8uE3fUVz7lrBWBHlTtfsuLygdt0U9vdbiKRvpwQIByUVeBSED50PVA9va7nM9jAnUKCG-BB6FcTufkcanA84LzP4hbocl07bmS4zTJ2lo4sWAI14B4RXFYVhTEesAH5YdC6AHEYPghWQBS-_--Otjn0bqCi-7QK4LVQqSwTkC_6GCbaL6Af67LpDQMtJzirq0gGP47ebaOzxVEYFGkPM_rqelAHZ4sKs_NKsVUBZVhlvsS-5eK3HBFTdNN6eOTGj8DhFB8sTeadlHFqFQM8AILG3tMz0GiHZIVMFABk7u8dw4iB7SgDzExYTgFJUhg9B8IHVH6VKItlmzt2e-9S9ZJlnC2sZzjPBY7nT615qAV-FkrbJv4NGe_oamQH8w9SRxpVBALNkbHtUVOt7a-F4emeuokyAv2xL5mdSYxmC9CXGNBbIIQdAftsXPmjAkFsVWaGKXELiya293MJfCLQ5ZNDTMjfCMwY1rTldwyN0XSIHJHGMjd6Ko2tPN-9deke4b5u1xNkOsI_4jRVUwpfDN_dHp_3VBwuxGeU3v5Fm9yqCEB8R9QnOuJN3M5ZIZkYxcPcnM1V-nTxhX_cfx2Iw3K7eT0G_clS5DbQtDvLSfdWh9NAPKFZoFJUkQdp97a1xFewy-61ZbardyYO3s-EAfWB1D80qqIzgjN_tgr8BDa5qfR70v4zf1URSpwNiXNLfREGxqLDbOgfFEbi1506SCGHyUGVAFr4SnhdjIrta2EP4U-dcAFq_Moo207Cg8Wuwgz3bNCrc6h12Ha4CuPuFvTQpaNYu43oQQ3mhYWXck1AbYehqYEe5Sm0q2nN-QFsrQE51EbJMkPdGlNqSJ65MYjvzmjnCX0BmUOlfHmV9oBF5CW60-hVLVfk8_t-ehlh9FPAT52i6V3QKL9pDwHGi46L9WYFob3KZBKyB3-V1fyNK0hEoUmz4w3Zb1DzPiwmQmF56Mcosa2UqwCtg7g2E9gBb4Po5UWHRNbx-TvfRUcwNbr_3FoeB-jde1CwIW_N5r_DYCI9D2he4k1JW4OHRsDbyYWtjghufifD1UtFnICKSvzWpiELpdA-qIb_S518wAx9sPtllsIKw17CJ6IHFijm9Wpo8kbrwn_bM_dIGXOb6vsC-QlMaT5YctTdSiiJjJlJpeRk1tp-Gewwacyg6lkSc2F5P9xQsbkRrBQXYsDMCG_SYdjnnN9dL60xdBw0zlv57schz7yguRfAY_EsYbzIY8k5BB_FmYvKrxrgTL132rLLpEZdsLZxmXUIqlQMOu1bIBNZCeyF38KU1Xu37Zsk8Brl-uU5TAZGVbaBoifi6GWcFfbFAF2VpDaSwCRAIE4aqXjC51jQWXY8FuodAH_U0jZImT64RZSeuWCB_0UA==", "id": "rs_0dc9f371a6d55e3f016a136677096481919258bb3f394a27d9", "summary": [{"type": "summary_text", "text": "**Finalizing media delivery**\n\nI need to decide if I should send media for the user. They asked to continue, so I might include a final preview media while keeping in mind the previous issue of duplicates. It’s important to ensure I send only one, clearly labeled. I should verify the media path as well for Telegram's delivery, avoiding unnecessary details. I want the final output to be concise, mentioning the actual generated audio caption preview without sending duplicates."}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir — ich habe weitergebaut und diesmal **nur einen** klar gelabelten Audio+Caption Preview erzeugt. Keine 3x gleiche Telegram-Kachel, wir sind ja nicht bei einer schlechten NFT-Demo.\n\n## Neu gebaut: EdgeTTS + Caption Burn-in Pipeline\n\nNeue Module:\n\n```text\nautoshorts/audio/edge_tts_adapter.py\nautoshorts/audio/final_preview.py\n```\n\nNeue Tests:\n\n```text\ntests/test_edge_tts_adapter.py\ntests/test_audio_caption_preview.py\n```\n\nCommit:\n\n```text\n3f68001 feat: add EdgeTTS audio caption preview\n```\n\n## Was jetzt funktioniert\n\nPipeline:\n\n```text\nTrueTrace Candidate\n→ ScriptDraft\n→ SubtitleTrack\n→ EdgeTTS MP3 Voiceover\n→ ASS Subtitle File\n→ FFmpeg Audio + Caption Burn-in\n→ Audio/Caption Review MP4\n```\n\n## EdgeTTS Adapter\n\nNeue Funktion:\n\n```python\nsynthesize_edge_tts()\n```\n\nSie ruft explizit:\n\n```text\nedge-tts --voice en-GB-RyanNeural --text ... --write-media voice.mp3\n```\n\nund meldet sauber:\n\n```text\nside_effects = (\"call_edge_tts\", \"write_audio\")\nexternal_calls = (\"edge-tts\",)\n```\n\nAlso kein versteckter API-Zauber, sondern eine klare Boundary.\n\n## Audio+Caption Renderer\n\nNeue Funktionen:\n\n```python\nbuild_audio_caption_preview_plan()\nrender_audio_caption_preview()\n```\n\nSie erzeugen:\n\n```text\nfinal-preview.ass\naudio-caption-preview.mp4\n```\n\nmit:\n\n- EdgeTTS Audio\n- renderer-owned ASS subtitles\n- FFmpeg Burn-in\n- kein Upload\n- kein Telegram Call\n- kein Platform Call\n\n## Tatsächlich erzeugter erster Review Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nOutput:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview.mp4\n\nVoice file:\n\n```text\ndata/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/voice.mp3\n```\n\nSubtitle file:\n\n```text\ndata/previews/truetrace-audio-caption-first/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview.ass\n```\n\n## Verifiziert\n\nFFprobe:\n\n```text\nVideo: h264, 540x960, duration 38.04s\nAudio: aac, duration 28.85s\n```\n\nIch habe bewusst **nicht** `-shortest` verwendet, damit das Video nicht bei Ende der Voiceover-Datei abgeschnitten wird. Der erste Versuch hätte sonst den 38s Preview auf 28.8s gekürzt. Natürlich hätte FFmpeg das ohne Scham getan — Maschinen haben keine Erziehung.\n\n## Tests\n\nFull Suite:\n\n```text\n154 passed in 31.65s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\n3f68001 feat: add EdgeTTS audio caption preview\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n2d6af9f feat: add TrueTrace concept testbatch\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir als nächstes den Clip qualitativ verbessern:\n\n```text\n1. Voiceover Text kürzen/verdichten\n2. Subtitle Timing an tatsächliche Audio-Länge anpassen\n3. Motion Preview weniger statisch machen\n4. Danach erst Top-3 Audio+Caption Batch bauen\n```\n\nWichtig: Der aktuelle Clip beweist die technische Kette. Er ist noch kein schöner finaler Short. Aber jetzt haben wir die Basis: Stimme, Captions, Burn-in, Review-MP4."}], "id": "msg_0dc9f371a6d55e3f016a13667a81008191a8cb0ec7f5af4e97", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Ok mach weiter.", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_U9BBqYVSri6Nbx7Ce98bkmiM", "call_id": "call_U9BBqYVSri6Nbx7Ce98bkmiM", "response_item_id": "fc_0dc9f371a6d55e3f016a1368e27cbc81919718ea04f4f900ce", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_9uCekcjt31x3oRM4c7dREUl4", "call_id": "call_9uCekcjt31x3oRM4c7dREUl4", "response_item_id": "fc_0dc9f371a6d55e3f016a1368e27cd48191a49a4b6f2191fcf1", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"edge-tts\"}"}}, {"id": "call_U8Xtoq4sBaFw8OrXRSUERbbZ", "call_id": "call_U8Xtoq4sBaFw8OrXRSUERbbZ", "response_item_id": "fc_0dc9f371a6d55e3f016a1368e27cdc81918dec7ab4a04f6d1b", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Planning video improvements**\n\nI need to keep going with the project and ensure I have the right tools ready. I think a systematic approach could be beneficial. The tasks include shortening the voiceover and aligning the subtitles with the timing. I’ll inspect the files using a test-driven development (TDD) approach. Maybe I should build a finalized script module, and I need to implement a voiceover finalizer while aligning subtitle duration to the audio using ffprobe. Let's aim to produce an improved clip!", "reasoning_content": "**Planning video improvements**\n\nI need to keep going with the project and ensure I have the right tools ready. I think a systematic approach could be beneficial. The tasks include shortening the voiceover and aligning the subtitles with the timing. I’ll inspect the files using a test-driven development (TDD) approach. Maybe I should build a finalized script module, and I need to implement a voiceover finalizer while aligning subtitle duration to the audio using ffprobe. 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Let's aim to produce an improved clip!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_U9BBqYVSri6Nbx7Ce98bkmiM", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[skill_view] name=edge-tts (6,508 chars)", "tool_call_id": "call_9uCekcjt31x3oRM4c7dREUl4", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[skill_view] name=social-media-shorts-automation (18,263 chars)", "tool_call_id": "call_U8Xtoq4sBaFw8OrXRSUERbbZ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_wcAXSHPmnVhlRm87RLAlqAVI", "call_id": "call_wcAXSHPmnVhlRm87RLAlqAVI", "response_item_id": "fc_0dc9f371a6d55e3f016a1368f00a708191ad606f67f0cbb3a2", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"from autoshorts.audio.voiceover import (\\n FinalVoiceoverScript,\\n build_truetrace_final_voiceover,\\n validate_final_voiceover_script,\\n)\\n\\n\\ndef test_build_truetrace_final_voiceover_for_quiet_rot_...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Refining rendering settings**\n\nI'm working on the final renderer, making sure to set video_mode to \"trim_to_audio\" with the -shortest option. 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I think it’s necessary to test and not use it blindly, focusing on when to apply it explicitly. Also, I need to modify final_preview to support duration_mode, with a default of preserving video without the -shortest. My goal is to generate a better first clip that keeps the visual length at 38 seconds while trimming to the audio length when needed."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (52 lines)", "tool_call_id": "call_wcAXSHPmnVhlRm87RLAlqAVI", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_QzQKa7iAJJDB0LRPo9uX17J9", "call_id": "call_QzQKa7iAJJDB0LRPo9uX17J9", "response_item_id": "fc_0dc9f371a6d55e3f016a1368fd6d148191a593549c8ef576cf", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_caption_preview.py\", \"old_string\": \"from autoshorts.audio.final_preview import (\\n AudioCaptionPreviewPlan,\\n build_audio_caption_preview_plan,\\n render_audio_caption_preview,\\n validate_audio_caption_preview_plan,\\n)\\n\", \"new_string\": \"from autoshorts.audio.final_preview import (\\n AudioCaptionPreviewPlan,\\n build_audio_caption_preview_plan,\\n render_audio_caption_preview,\\n validate_audio_caption_preview_plan,\\n)\\n\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": false, \"error\": \"old_string and new_string are identical\"}", "tool_call_id": "call_QzQKa7iAJJDB0LRPo9uX17J9", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_wLd8oT9BnA3AaRqwwg9Kp0a9", "call_id": "call_wLd8oT9BnA3AaRqwwg9Kp0a9", "response_item_id": "fc_0dc9f371a6d55e3f016a13690476308191a826508e98a31c4a", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_caption_preview.py\", \"old_string\": \"def 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[100%]\\n8 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_yIBuBdX0jSSo3toHlz8wdijA", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_JB2HZzXfPYYEIXxlxh8BdC7q", "call_id": "call_JB2HZzXfPYYEIXxlxh8BdC7q", "response_item_id": "fc_0dc9f371a6d55e3f016a1369c3a0788191b218e7bbb3360720", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nfrom autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceo...[truncated]\", \"timeout\": 300, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Improving clip integration**\n\nI need to focus on producing an improved clip by integrating a new voiceover and adjusting the subtitle duration. I'm thinking that the target duration might not perfectly match the actual length from Edge TTS, which could be longer than expected. To make sure subtitles fit well, I can aim for a voice target duration of 24 seconds, although the actual might end up being around 26 seconds. I'll need to check this using the terminal for mandatory duration settings before generating the final version.", "reasoning_content": "**Improving clip integration**\n\nI need to focus on producing an improved clip by integrating a new voiceover and adjusting the subtitle duration. I'm thinking that the target duration might not perfectly match the actual length from Edge TTS, which could be longer than expected. To make sure subtitles fit well, I can aim for a voice target duration of 24 seconds, although the actual might end up being around 26 seconds. I'll need to check this using the terminal for mandatory duration settings before generating the final version.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2nDmuQE1qoioQMhWTi0FJGQWUxRugJNigXW6R8Z2GwZTd2zSCn5HZlC8RHdjOk0x_Q0uFp_U1v0_wJTjbB8KIhhjeoH1GD3BbQuQplsN468N3kHTq88pP2qJMEAviFsbCHp8LFWDeaPKFRHXsTibcOG5nYMJuvkjZWMLpYiJXpulkaAq2LGBeOWXGbdwfZ1Pqdf1OlgQXyGy3DOGoeEddeSicE6wW2gYMOqoU4YRazA1FkNHhX9B2kkoENtncWJSC4d6j1JulEdfoQsnI3rCoybxJAvb_J1K0zVqrrONMZiaC_y7S9DxBgGd4quLy-6rU-v2A3cPUc9G1TSZvTOGcvCotns0_iRNPZeOdm4hkginMMacZ0m_-9THwX1PIx83QknqUQX0Iuw5aGfDDVzrmpUWfgE3evyDaIZCpvC2c6wuh-64MwdnJSo-3ANwWFu3-Sk-MhrEQz4vDMrjnNtzX30yKU6qivGfOZCERPwIegliT5deiJkYboxw2jgk7S4O1kQK1-wThPf40Isea5C_5CLv6UomDxmrmee1wbw2KCz62KkyYmc5qj3k-0KX5RLkWuRHWJtpdzTHmLg6PRQw0KZBheLD_ZZkkCjPIWNdL78I64fo2kbc2MciraMRjj6Ykp5xA4M9uDSDsAJDNXmKJ8VWf3sfnBLVc0PIC8VLAQz6FmF5o6SNUj5v3DdS_omWyKfyofw8Emws_q8lE5glo1a_q9W_rpSUJ74MtzZui3FoYTfLy_BelyRfFVHP4PXSnHzMgdpZH2LKJySIeA3Bzohkj0esFcpxSa53b3_qFtayqgCwDo5p8SzULnRs_F8ESlrDTC5qtalzNNHwH8aB1WyjVDRI-4YPRt6_tj4G4v3R0ZhMcwK07UQtej8bCRdvHDisPX95nfsxq-FO2dzkDXmaH0hSLGxCzrKWMBfSXHvkxa1pbrepqIRHsig8pd6mwGNsvArDO3lzElxEN23QCxpHdY_NYvQS_AgEi6VsggEF3quTc_-7b2PnhxuOCzd8QRHF2scMiVTavfEZuWpRQFQOXHsGYfr7Q6IsP2H3dEGmlZhZPRppCVDzJZI8BrxtSRl7T2pzErgVzxRmOuQVePFWLtDAKKa0XPNVEce2I-6ZLTVYdymJhkTTlOuBQJhLxZQ9Fdf_rCIIYRB0OQ0OWu4CBmm_YbUBI22Y_ckxVaJt_BxcCj-PHsSHEOE0oA7o58h-bddVkA3ImZPpKlFK5nY8eSagbfTsTsCSWXbz8i1FCUHDoKOZTIIsU4VX57ntFHBZFP-657IWHsMNDrh2aj1p7R05TWapFTpyiSTm_ES-8YTK2zwOeWwfI0x1HAWnGA7scZKOOibbPgd00jQRZ8J1Q==", "id": "rs_0dc9f371a6d55e3f016a1369c090a081918115e14a9be60b50", "summary": [{"type": "summary_text", "text": "**Improving clip integration**\n\nI need to focus on producing an improved clip by integrating a new voiceover and adjusting the subtitle duration. I'm thinking that the target duration might not perfectly match the actual length from Edge TTS, which could be longer than expected. To make sure subtitles fit well, I can aim for a voice target duration of 24 seconds, although the actual might end up being around 26 seconds. I'll need to check this using the terminal for mandatory duration settings before generating the final version."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nfrom autoshorts.demo.truetrace_previ...` -> exit 0, 1 lines output", "tool_call_id": "call_JB2HZzXfPYYEIXxlxh8BdC7q", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_Glpt4DtqHdyXzQMv1jV4fzz1", "call_id": "call_Glpt4DtqHdyXzQMv1jV4fzz1", "response_item_id": "fc_0dc9f371a6d55e3f016a1369dfdd908191a98fec339a6562a6", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"import subprocess\\n\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\n\\n\\ndef test_probe_media_duration_seconds_parses_ffprobe_output():\\n calls = []\\n\\n def fake_runner(command, *, capt...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_probe.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Evaluating subtitle issues**\n\nI’m looking into subtitles for a clip that's 29 seconds long, but they're only 24 seconds, which isn't right. I think I need to implement an audio probe to rebuild the track's actual duration. Maybe I can do this quickly, but I feel like tests are essential. I might generate a probe module and test it with a fake runner. Also, I could consider using ffprobe in the terminal to check the actual duration, and then rerender it. I just need to keep everything aligned with TDD and write the necessary tests.", "reasoning_content": "**Evaluating subtitle issues**\n\nI’m looking into subtitles for a clip that's 29 seconds long, but they're only 24 seconds, which isn't right. I think I need to implement an audio probe to rebuild the track's actual duration. Maybe I can do this quickly, but I feel like tests are essential. I might generate a probe module and test it with a fake runner. Also, I could consider using ffprobe in the terminal to check the actual duration, and then rerender it. I just need to keep everything aligned with TDD and write the necessary tests.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2nfJPzaCBmXhTkR8dS5_SsxHQhC_TYABAk_hEH1huJ_aUnqcWaJtW_U_uh6TrSlMsblCBLsAXH3Z1_Q0_uHOwa1d3IcgjCF0SAMtfXBsG64pZvC-mBeZIDlLvR0kMESe7FoLnOJmEuxUwWmrjDjOlgffjOpj_UJ0aQIdvKtzYwpGuPMw_M0HNTZihDkslXfTRS_ZSfGjTO0NUO0nPzZIyRLooq5Qv6aFjtbOU06WZ3Z5dy-_h3oKvQX4QSOxIMtygL3oZtL1MaRFC-NqTqBCZlLLm4un0eiGU2IBEtz07mIbe8asu5TYkwmD6dRvmYdih0ktKTFaf2V2nh2qK3yrDVeuq06v-5sPhD0BoOdH3nc0mPc3PDX0OwQXsA8xNc4j9Xx2F4mPXfnnRMgLFSUJMg2IE79x53OGEYkO4NEdUKUit31y3jIzYRSyHZM1yCTYg07oNiB-1DEYWyWKB-5f-HdR0w0KIfgKu8r0_wYTjNvoG9jiyiHK4_xIGehpLJ5w3kS4ZzNXD1Ow6ZKz4qIaoe6n8KOnohIBxYuVVQLnahLCdQYEwpkBqBpgOhT5Mp4dbehj-mpKNeXwEvtjneqaFz1te28lBRchxdsJCqxhz-pDCQPCLfLFAEUJg3s5HiU6DlMZriZgUcxow8Bi6d5GirXGZplUbuRs8FeeHuIT679Zw0LWoCX5920Hl6dzhTqmtlsYSJ_Gk1C5-NC4Hb731S0Hznj-uAM22SVOUX4sr1Akxm2gPMVHc4ATWQEYSRm9QSAc_pJMAIMVEjR8n5CyCcCp_VLj4KuPyXj4lL9gdSHLFDehCj827DzUndgOsLPrUTq25dDalAlz-IojRtHKbMXVJNNTFgf14GDTiYV5n0Ox616e_SvYehlTZAPJhiy1ZwReajwyp0QWX3LEmx_YUh5aTe87gVLm_-YSjlnxzfBYeqg18C7X8Q8PqiZfXAVZys8uMoE3YuWD_N0h2Os8Ww8qMrl1vsQOIF5TGZjlJcgm1eonlYdP8e0BqEAR5KvbXkduPXiRdyCmI9SiBJYDMq_ovrUot2p-w2a7bjtunSix46tQumpT-eki0BwXxFgiADJXDfkBkWYP0I6X8D02x7B5WuSsXDIL3aBFphNukqyyPfqr5CObKTHuHFZfhOZ49qvrtjmYmupzUQmJE2P1jeN86K1Ni07DHjeJQsl30pFYea7Ql9_60mPzkLITl_IKjEEUjVwHPYK6UUYkgLM6UkOk6KhN5h7QfXX6_XyiGfqRyVG7zgdf9sz0Bo7EQXQ1n61VbS8JcD1R_CEtF5V0yQeOnAtdX8Jo6k75Jqy4Ts8fEBqYS-vnvh9YFLyGRh21bXALZuNUpPoL5JDpXY4wwaz9b63UBb8NcA2uMct7xTjSEZI5iPup-Y3piIJnm-J7BZlYMptXrVm9HCPobA7rjWVrRmzxDApKZVT4DVz1M0gjhezNqYryGSkdYanENq9Acf9eBCGn7W0yjNdEsajIiMJ5NJmnbvVDVta5H7lxYWtWpM6SWV4ALmm_SQgEMrka4Rt4AIxZ0f3HRi8gr5V1Pv6GDNjbMhTTrf-ONzURzNVsqI=", "id": "rs_0dc9f371a6d55e3f016a1369dbc7908191990c2141fc6c16f7", "summary": [{"type": "summary_text", "text": "**Evaluating subtitle issues**\n\nI’m looking into subtitles for a clip that's 29 seconds long, but they're only 24 seconds, which isn't right. I think I need to implement an audio probe to rebuild the track's actual duration. Maybe I can do this quickly, but I feel like tests are essential. I might generate a probe module and test it with a fake runner. Also, I could consider using ffprobe in the terminal to check the actual duration, and then rerender it. I just need to keep everything aligned with TDD and write the necessary tests."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_probe.py (38 lines)", "tool_call_id": "call_Glpt4DtqHdyXzQMv1jV4fzz1", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ozLOpWJCh3SdCdpTnSWMR050", "call_id": "call_ozLOpWJCh3SdCdpTnSWMR050", "response_item_id": "fc_0dc9f371a6d55e3f016a1369e8b9148191b509bdfd57b3d048", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_audio_probe.py -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/test_audio_probe.py -q` -> exit 2, 1 lines output", "tool_call_id": "call_ozLOpWJCh3SdCdpTnSWMR050", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_oqlmXeECSCZCFERBxwndVacB", "call_id": "call_oqlmXeECSCZCFERBxwndVacB", "response_item_id": "fc_0dc9f371a6d55e3f016a1369ef9854819185eb345a5f095ab3", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\": \"\\\"\\\"\\\"Small FFprobe helpers for audio/video review artifacts.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom collections.abc import Callable\\nimport subprocess\\n\\nRunner = Callable[..., subprocess.CompletedPr...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/probe.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"bytes_written\": 1000, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_oqlmXeECSCZCFERBxwndVacB", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_xUCcrVI1wOoQPfAJYXUIiuhs", "call_id": "call_xUCcrVI1wOoQPfAJYXUIiuhs", "response_item_id": "fc_0dc9f371a6d55e3f016a1369fa2d308191be63bae646894d18", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_audio_probe.py tests/test_voiceover.py tests/test_audio_caption_preview.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \".......... [100%]\\n10 passed in 0.07s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_xUCcrVI1wOoQPfAJYXUIiuhs", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ws6cshMoqibdYUFzsGteBm5A", "call_id": "call_ws6cshMoqibdYUFzsGteBm5A", "response_item_id": "fc_0dc9f371a6d55e3f016a1369fe0e448191ada762e8f87860c3", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages\\nfrom autoshorts.audio.voiceover import build_...[truncated]\", \"timeout\": 300, \"workdir\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `python - <<'PY'\nfrom pathlib import Path\nfrom math import ceil\nfrom autoshort...` -> exit 0, 1 lines output", "tool_call_id": "call_ws6cshMoqibdYUFzsGteBm5A", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_9OHiAjC6UEQjuvUbYIfFVvNQ", "call_id": "call_9OHiAjC6UEQjuvUbYIfFVvNQ", "response_item_id": "fc_0dc9f371a6d55e3f016a136a19efcc8191a4300368a4424d79", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.\\n- `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP...[truncated]\", \"old_string\": \"- `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.\\n- `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Updating documentation**\n\nI need to think about updating the README and pyproject files. This involves adding information about the modules and running full tests to ensure everything works smoothly. I'll also need to commit those changes afterward. Maybe I should consider including some media to enhance the documentation, too. Lastly, I might need to patch the documentation further if I find anything that needs clarification or improvement. Let's keep track of these tasks!", "reasoning_content": "**Updating documentation**\n\nI need to think about updating the README and pyproject files. This involves adding information about the modules and running full tests to ensure everything works smoothly. I'll also need to commit those changes afterward. Maybe I should consider including some media to enhance the documentation, too. Lastly, I might need to patch the documentation further if I find anything that needs clarification or improvement. Let's keep track of these tasks!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2oZsm0jDvlvxwmRymDpYGJ8btj5YRP12aMlr7kfuH5ESSpgxxS20EeWjOaQOgCyNHRlfeXmPmQIysGeL6ooy-J9gR-FIkBsvJn4LUSVI217EnxzX_GEOuzAkBiy_NMPKHDrDNzHH_szQVyZ1dIb-ch0iAzp_h6k6AtZmjPaOGyjB2X8wh67yZ-9UfNoMUalbO5fzOZ3kKxg6RQgQnG-GNytZ6f2qfCQ9E87CVERuWBU1EE6Roit_BBK4NrzFjdWCvVTBpOUDo29YvJWj0cvGiaIKVq5uQ7CuedjbujXYPeSOQ1JL7VMrNIJTlyRqkCmfOGz8rVxYOhGttK3XWtDzjGFHMpXEuCA55kh_FE8zQHmd51Pi2NTitrz3VLexXX_IoT_6PtYCRo9J1iKqf6QfefCp6c5rtokktBcIvyqqwxiLTo8e3ir0Seg9etM1_09nKC4xT7n53H3WHNTgiXUX5rSDFQLtI3jpMKnGJ_foYepyquGeNC9kQ604_tLdWPAVIjIQzNp2XZZ2JX44mXXCXFfFFOSVGtjtVgDMtXUNC2Qv8jcdRqWxta3fP_7vzmS0VelKSkxQPkVhcwiJK0UU417Qv7r_y4bbFz_vmX94ZSb9uFuM6Q83DpP4tiNH4REeRwmnxDPfaMGeeUn_yuEAEIhWu6LnVFY8Sr7PPlj4UP_HOL8rJASToTjghepITpP5uaF1nIRpo-nkF0zZH05oOdPDz4EzSi9rz4HOKdKB7y0oJvEdbMATGKW88ke7PzZwYGgT2nwDvshHazVqSxGubN17WxQbJJNDmTfOmcoFlAc4sFqMxQxrGiT4GqggkLIa6tMx5S2mgoapOmBUfL6Sa5kPRBm9jbpNq8nKRj9hsdR-iRXn2x4tSdyKa9KUGkE1rggRuKniGMwc1I1BciS21TvUDprc1mthQlWJTWQZdBbqHuTFBUvu_0FSvOem-E3BqMYfMUJGLwP7sp5-p-IQc3Vh1qm598GXJOpgv1X-rEwMln6J9bMCLwXe5VE4gahLMoOBkv3oc6e3gTNGOZAJ8sxC73SapFlGXIjthi4EZug2HXby-AtWr-lvcBuliLmgDIf", "id": "rs_0dc9f371a6d55e3f016a136a175dc08191ac30481ca8317498", "summary": [{"type": "summary_text", "text": "**Updating documentation**\n\nI need to think about updating the README and pyproject files. This involves adding information about the modules and running full tests to ensure everything works smoothly. I'll also need to commit those changes afterward. Maybe I should consider including some media to enhance the documentation, too. Lastly, I might need to patch the documentation further if I find anything that needs clarification or improvement. Let's keep track of these tasks!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,226 chars result)", "tool_call_id": "call_9OHiAjC6UEQjuvUbYIfFVvNQ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_N3WpEBJr1KcY1cEJ47eB8ZFH", "call_id": "call_N3WpEBJr1KcY1cEJ47eB8ZFH", "response_item_id": "fc_0dc9f371a6d55e3f016a136a265704819188f5f744c9fb8cce", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\": \"replace\", \"new_string\": \"- `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.\\n- `autoshorts.audio.voiceover...[truncated]\", \"old_string\": \"- `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.\\n\\n## Caption pipeline\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md (1,512 chars result)", "tool_call_id": "call_N3WpEBJr1KcY1cEJ47eB8ZFH", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_OOyKIGqUU49cpWdg1TtrsbBh", "call_id": "call_OOyKIGqUU49cpWdg1TtrsbBh", "response_item_id": "fc_0dc9f371a6d55e3f016a136a3002408191945cad17f37ff440", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `pytest tests/ -q` -> exit 0, 1 lines output", "tool_call_id": "call_OOyKIGqUU49cpWdg1TtrsbBh", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_U5DsnmLoMW8pYb0EiKGo7wR9", "call_id": "call_U5DsnmLoMW8pYb0EiKGo7wR9", "response_item_id": "fc_0dc9f371a6d55e3f016a136a55840c8191b191038a8b24aa07", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/audio/voiceover.py autoshorts/audio/probe.py autoshorts/audio/final_preview.py tests/test_voiceover.py tests/test_audio_probe.py tests/test_audio_caption_preview.py README.md docs/subtitle_tts_strategy.md && git commit -m \\\"feat: align TrueTrace voiceover previews\\\" && git status --short && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[terminal] ran `git status --short && git add autoshorts/audio/voiceover.py autoshorts/audio/...` -> exit 0, 1 lines output", "tool_call_id": "call_U5DsnmLoMW8pYb0EiKGo7wR9", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Erledigt, Sir.\n\nIch habe den nächsten Qualitäts-Slice gebaut: **kürzere finale Voiceover-Skripte + tatsächliche Audio-Dauer messen + Subtitles daran ausrichten + explizites Trim-to-Audio nur wenn gewünscht**.\n\n## Neu gebaut\n\nNeue Module:\n\n```text\nautoshorts/audio/voiceover.py\nautoshorts/audio/probe.py\n```\n\nErweitert:\n\n```text\nautoshorts/audio/final_preview.py\n```\n\nNeue Tests:\n\n```text\ntests/test_voiceover.py\ntests/test_audio_probe.py\n```\n\nCommit:\n\n```text\nb9b6423 feat: align TrueTrace voiceover previews\n```\n\n## Was verbessert wurde\n\n### 1. Final Voiceover Scripts\n\nNeue Funktion:\n\n```python\nbuild_truetrace_final_voiceover()\n```\n\nFür die Top-3 Konzepte gibt es jetzt kompakte finale English Voiceover-Texte.\n\nFür den ersten Clip:\n\n```text\nMost AI automations do not fail dramatically. They quietly rot.\nDay one looks clean, because the demo input behaves.\nBy day three, the real input changes.\nNow the workflow needs an exception path.\nNormal goes forward. Weird goes to review. Unclear stops.\nIf your automation has no exception path, it is not a system.\nIt is a lucky prompt.\n```\n\nEigenschaften:\n\n```text\nWords: 59\nTarget duration: 24s\nLanguage: en\nVoice: en-GB-RyanNeural\nNo generic filler\nNo “Follow for...” CTA-Geschwurbel\n```\n\n### 2. Audio Duration Probe\n\nNeue Funktion:\n\n```python\nprobe_media_duration_seconds()\n```\n\nSie liest per FFprobe die echte MP3-Dauer aus.\n\nWarum? Weil EdgeTTS natürlich nicht exakt unsere Wunschdauer spricht. Es ist TTS, kein Schweizer Uhrwerk mit britischem Akzent.\n\nBeim erzeugten Clip:\n\n```text\nEstimated target: 24s\nActual EdgeTTS audio: 29.112s\n```\n\n### 3. Subtitle Timing an echte Audio-Dauer angepasst\n\nVorher:\n\n```text\nSubtitle duration: estimated 24s\nAudio duration: 29.112s\n```\n\nJetzt:\n\n```text\nSubtitle duration: 30.0s\nAudio duration: 29.112s\n```\n\nDamit laufen Captions nicht mehr zu früh aus.\n\n### 4. `duration_mode`\n\nDer Audio+Caption Renderer unterstützt jetzt:\n\n```python\nduration_mode=\"preserve_video\"\n```\n\nDefault. Kein `-shortest`, kein heimliches Abschneiden.\n\nUnd explizit:\n\n```python\nduration_mode=\"trim_to_audio\"\n```\n\nNur dann wird `-shortest` verwendet.\n\nDas ist wichtig: FFmpeg darf schneiden, aber nur mit schriftlicher Genehmigung. Wie ein anständiger Bürokrat.\n\n## Neuer verbesserter Review Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nOutput:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-audio-caption-v3/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview-v3.mp4\n\n## Verifiziert\n\nFFprobe:\n\n```text\nVideo: h264, 540x960\nVideo duration: 29.083s\nAudio: aac\nAudio duration: 29.112s\n```\n\nSHA256:\n\n```text\n6c71929420bd266b8569440feed9a8ca7dc392e3cead8cc55bca2dc33118c871\n```\n\n## Tests\n\nFull Suite:\n\n```text\n161 passed in 32.72s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\nb9b6423 feat: align TrueTrace voiceover previews\n3f68001 feat: add EdgeTTS audio caption preview\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt ist die Audio/Captions-Kette solide. Als Nächstes würde ich die visuelle Seite verbessern:\n\n```text\nstatic timing cards\n→ simple motion renderer\n→ kinetic background movement\n→ scene progress bars\n→ caption punch-in/pulse\n→ mechanism diagram animation\n```\n\nAlso weniger PowerPoint-mit-Puls, mehr echter Short.", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\nIch habe den nächsten Qualitäts-Slice gebaut: **kürzere finale Voiceover-Skripte + tatsächliche Audio-Dauer messen + Subtitles daran ausrichten + explizites Trim-to-Audio nur wenn gewünscht**.\n\n## Neu gebaut\n\nNeue Module:\n\n```text\nautoshorts/audio/voiceover.py\nautoshorts/audio/probe.py\n```\n\nErweitert:\n\n```text\nautoshorts/audio/final_preview.py\n```\n\nNeue Tests:\n\n```text\ntests/test_voiceover.py\ntests/test_audio_probe.py\n```\n\nCommit:\n\n```text\nb9b6423 feat: align TrueTrace voiceover previews\n```\n\n## Was verbessert wurde\n\n### 1. Final Voiceover Scripts\n\nNeue Funktion:\n\n```python\nbuild_truetrace_final_voiceover()\n```\n\nFür die Top-3 Konzepte gibt es jetzt kompakte finale English Voiceover-Texte.\n\nFür den ersten Clip:\n\n```text\nMost AI automations do not fail dramatically. They quietly rot.\nDay one looks clean, because the demo input behaves.\nBy day three, the real input changes.\nNow the workflow needs an exception path.\nNormal goes forward. Weird goes to review. Unclear stops.\nIf your automation has no exception path, it is not a system.\nIt is a lucky prompt.\n```\n\nEigenschaften:\n\n```text\nWords: 59\nTarget duration: 24s\nLanguage: en\nVoice: en-GB-RyanNeural\nNo generic filler\nNo “Follow for...” CTA-Geschwurbel\n```\n\n### 2. Audio Duration Probe\n\nNeue Funktion:\n\n```python\nprobe_media_duration_seconds()\n```\n\nSie liest per FFprobe die echte MP3-Dauer aus.\n\nWarum? Weil EdgeTTS natürlich nicht exakt unsere Wunschdauer spricht. Es ist TTS, kein Schweizer Uhrwerk mit britischem Akzent.\n\nBeim erzeugten Clip:\n\n```text\nEstimated target: 24s\nActual EdgeTTS audio: 29.112s\n```\n\n### 3. Subtitle Timing an echte Audio-Dauer angepasst\n\nVorher:\n\n```text\nSubtitle duration: estimated 24s\nAudio duration: 29.112s\n```\n\nJetzt:\n\n```text\nSubtitle duration: 30.0s\nAudio duration: 29.112s\n```\n\nDamit laufen Captions nicht mehr zu früh aus.\n\n### 4. `duration_mode`\n\nDer Audio+Caption Renderer unterstützt jetzt:\n\n```python\nduration_mode=\"preserve_video\"\n```\n\nDefault. Kein `-shortest`, kein heimliches Abschneiden.\n\nUnd explizit:\n\n```python\nduration_mode=\"trim_to_audio\"\n```\n\nNur dann wird `-shortest` verwendet.\n\nDas ist wichtig: FFmpeg darf schneiden, aber nur mit schriftlicher Genehmigung. Wie ein anständiger Bürokrat.\n\n## Neuer verbesserter Review Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nOutput:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-audio-caption-v3/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview-v3.mp4\n\n## Verifiziert\n\nFFprobe:\n\n```text\nVideo: h264, 540x960\nVideo duration: 29.083s\nAudio: aac\nAudio duration: 29.112s\n```\n\nSHA256:\n\n```text\n6c71929420bd266b8569440feed9a8ca7dc392e3cead8cc55bca2dc33118c871\n```\n\n## Tests\n\nFull Suite:\n\n```text\n161 passed in 32.72s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\nb9b6423 feat: align TrueTrace voiceover previews\n3f68001 feat: add EdgeTTS audio caption preview\n066201d feat: attach audio plans to TrueTrace previews\n30a8129 feat: add subtitle and tts planning\n6efc4dc feat: add TrueTrace top preview packages\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt ist die Audio/Captions-Kette solide. Als Nächstes würde ich die visuelle Seite verbessern:\n\n```text\nstatic timing cards\n→ simple motion renderer\n→ kinetic background movement\n→ scene progress bars\n→ caption punch-in/pulse\n→ mechanism diagram animation\n```\n\nAlso weniger PowerPoint-mit-Puls, mehr echter Short."}], "id": "msg_0dc9f371a6d55e3f016a136a5a9e9c8191a08676f9790a435c", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Das voiceover und die subtitles decken sich nicht mit dem text im oberen feld… wirkt das ganze nicht zu abgehoben? Erreichen wir so auch die leite welche noch nocht soo voel erfahrung mit KI haben?", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_wzmMPaS73tTi3kLfAftJsV3l", "call_id": "call_wzmMPaS73tTi3kLfAftJsV3l", "response_item_id": "fc_0dc9f371a6d55e3f016a136ce027c48191aa28395f6ac8a586", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2zg9AADGlQ-darv_1sXBA-bdBknyDXf56uq4qy5PQekcPVWI5uIQB5xH3-Gta73zDF1SLeqt8HgANdASGJhYC3yQp0lot_GIOXb7keYMAL-Lqe94JlhSFoTHDt5KapiBgzbomob8gvqrmKGxP2wiWsRLc6y0PPgRRS8Lny5NXUYWS-244Y7YMJbhtqMcqr0dsRfC6uUIgCaVUmLFBSpSZVulssHS8FFkeuHAM31rb9K8P520aymwoNAT2O5X0HlPNpvZZy2_8HJFMyC5WGHGpd1xvJ7oBCZhYyWgYY5BQHvXy-R_lsinzhLbed3WOv-sZwaVVPu7ROQUkpNFd5QIe0N9vO2jzvnW-3LAHNMMunLuxbW8wGD2i-a2YV8aJeq-injzO8p6T_vDUw6psBkiOHzBkipglLOicnwSM-QVvi37YFXhoO3bQKGA4ygzX8uhN5DVTI-0cjXPrjIWfQNqOn44gRy8FOm5bmXY28uLaI0CKGK-pbiew-L70hUjpdhgq7VAvBuW7bYF8co2J2LOaR9IBeKHs8LjYZ3Z6YYSYw1QPqqC3UDwJ8bK03YVBSTNbZnDjCP4r0XHe40txJ25RTaMdmjqPArQkKWXnqZqDJOMUyodNvQqUV9j4SF4xk8L_WTJYmUFjMS82XRkgS_K_muv-BgLk2fdCvSJWXnLXlTFHUCw1WdQaM2h4_2rTynH3i1922Xa2oQ1nFdxSbvpRUK1EBKb7UuLn6p53YTd6TQ3lOs80LzSFQu9aQaCxXL8yF_Eip5JJdmG3L5QToUqEQYIIv90O8_XFevHgK3dVq3GVA-pQVfXyp6at0cFlROR-iW7lAqKLpvkbn7bdIrzFBMSi59LEmcUtLYZPM_8UYk21VFApcnPb_dq9GijroVyXMh3-qhZr5YBESi2xR0ytBQe5lQc1cMcnX3ytNr9T8eDLpUCun9tK7VjTSqqpWnmxZpG447UR6fy8UQHcksi6gO-VinFKo4oaRJKvv4VxTxnHClcYEZzu4-cxfEuo8X8Ney5r6TzrKBd_zVL4-tAGdBLFl3LT366uI8P_GN0EhA1oSNMyqW5lt2k53S-8KxG2p7syipppvzw0zzoozpTBj_6_491WF0HUMP9e-30nrzoiEu1IyX2ed6mu0FbwrGYeM3xHHxfAutMLb_QCJio6O0SGmGCTWiQmK2nBxCUF-EbHXpdE57ksdylrdJcbK1PQsvWHd3na9NOjyml9UGtouFtS8LtQrg_a2BSTVYznLhMchmIpKsMp5SeZnOuK_VyHGWG_Qc2dcdT2_pUxKD-2YURLPOFBGcNi0yMx5Jmmen2yU3kiCqqchoY1Bh3RN_T0r4y1IyoYujp6BYczH82boc_4c6ZvPk62w3hUL0dJlTMhshOECl0l6BCESOBCHvDpq9PA7PCZKVawTC1xJiwqlqrpLGqYDnd_go9SJPkgzG3QstjH0OWnGyBW1xxPs2vQhaZLkPpGp1OVbjFDlecGYW_OA_HYGtKmFVAriEBs5pLJErvSY47vK3ImLSLTJTnAa4FLbBSqAJmknpCOHVOTZyrzbb9XSIjbcXSyaFhlWkX3x24cVpBDNOzvc2nCueRIA3Pk5macWWWMS4hBqhM50i5rEI45CGbmLtfMB8inObFOgpv6DsiFC2xxL2cvUD7ZmQT7giIjcwCdCsGS6ItjyWsTpUFVwwM5-nnPcT0JB964-Tp8PDVPc8llTYEZTEYYRrx25rYY8fxQ5LjiKo_VIQ5pm3rHkKrcm-W4oMRr5qRvR6q9WU6ogXD6dDTtSHyBR5IFuMoYBZ-r_DcisR_v27-cCmhUXETspkgyGZNv4bMryYitaaewk-HzSK3fWGL7FXro9t8-H3ySmslBhBPgVLXck5_n-Y-F538OVdzOVRXyc=", "id": "rs_0dc9f371a6d55e3f016a136cdd13548191b054993b04f2de38", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_wzmMPaS73tTi3kLfAftJsV3l", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Ja, Sir — **Ihre Kritik ist korrekt**.\n\n## 1. Voiceover/Subtitles passen nicht zum oberen Feld\n\nDas aktuelle Preview ist technisch korrekt, aber dramaturgisch unsauber:\n\n```text\nOberes Feld / Card:\nalte German approval/timing copy\n\nVoiceover/Subtitles:\nneuer English final voiceover text\n```\n\nDas erzeugt kognitive Reibung. Der Zuschauer sieht oben etwas anderes als er hört/liest. Für Review ist das schon störend; für echten Content wäre es tödlich genug, um einen kleinen Ausschuss einzuberufen.\n\n## Fix\n\nWir brauchen **eine einzige Source of Truth** pro Clip:\n\n```text\nFinalVoiceoverScript\n→ SubtitleTrack\n→ Top Hook Text\n→ Scene Card Text\n→ Social Caption\n```\n\nNicht:\n\n```text\nCandidate Concept Text\n+ später anderer Voiceover Text\n+ dritte Caption-Version\n```\n\nAlso: Der Text im oberen Feld muss aus demselben finalen Voiceover/Hook kommen wie Subtitles und Audio.\n\n---\n\n## 2. Ja: aktuell wirkt es zu abgehoben\n\nDer aktuelle Text ist gut für:\n\n```text\nAI builders\nAutomation nerds\nWorkflow-/Ops-Leute\nLeute, die schon wissen, was ein “exception path” ist\n```\n\nAber für breiteres Publikum ist es zu abstrakt.\n\nBegriffe wie:\n\n```text\nexception path\ninput boundary\noutput standard\naction boundary\ndecision structure\n```\n\nsind für Fachleute präzise — aber für Anfänger klingt es nach einem LinkedIn-Post mit Krawatte.\n\n## Problem\n\nWir sagen aktuell:\n\n```text\nIf your automation has no exception path, it is not a system.\n```\n\nDas ist stark, aber nicht sofort greifbar.\n\nFür Anfänger besser:\n\n```text\nIf your AI does not know what to do when something weird happens,\nit will break the first time real life shows up.\n```\n\nDas ist derselbe Gedanke, aber menschlicher.\n\n---\n\n## 3. Zielgruppe: Wir sollten breiter anfangen\n\nFür TrueTrace würde ich nicht mit „AI systems for experts“ starten, sondern mit:\n\n```text\nAI tools that work in real life\n```\n\nAlso Zielgruppe:\n\n```text\nPeople using ChatGPT, Notion AI, Zapier, Make, email automations,\nbut not yet thinking like system architects.\n```\n\nDie haben ein echtes Problem:\n\n```text\n“It worked once, then it became unreliable.”\n```\n\nDas versteht jeder.\n\n---\n\n## Neue Copy-Richtung\n\nStatt:\n\n```text\nMost AI automations do not fail dramatically. They quietly rot.\n```\n\nBesser für breite Zielgruppe:\n\n```text\nYour AI automation did not really break.\nReal life just gave it an input it was never prepared for.\n```\n\nOder noch einfacher:\n\n```text\nYour AI automation works in the demo.\nThen real life shows up.\n```\n\nDas ist deutlich stärker für Anfänger.\n\n## Neue Hook-Variante\n\n```text\nYour AI automation works perfectly…\nuntil the first weird email arrives.\n```\n\nDas ist konkret. Jeder versteht „weird email“.\n\n---\n\n## Neuer Anfänger-freundlicher Voiceover-Entwurf\n\nFür Clip 1 würde ich den Text so umbauen:\n\n```text\nYour AI automation works perfectly in the demo.\n\nThen one weird email arrives.\n\nThe name is missing.\nThe format is different.\nThe request is unclear.\n\nAnd suddenly the automation does the wrong thing confidently.\n\nThat is not an AI problem.\nThat is a workflow problem.\n\nBefore you automate anything, decide three things:\n\nWhat should go through?\nWhat should be reviewed?\nAnd what should stop?\n\nIf your AI does not know what to do with weird inputs,\nit will break the moment real life shows up.\n```\n\nWarum besser:\n\n- kein Jargon\n- konkretes Beispiel: weird email\n- klares Problem\n- klare 3er-Regel\n- Anfänger verstehen es sofort\n- Fortgeschrittene merken trotzdem: es geht um exception handling\n\n---\n\n## Visuals dazu\n\nStatt abstrakter Systemkarten:\n\n```text\nScene 1:\nPerfect demo inbox. Everything green.\n\nScene 2:\nA weird email appears:\n“hey can you handle this maybe???”\n\nScene 3:\nAI confidently routes it wrong.\n\nScene 4:\nSplit path:\nNormal → automate\nUnclear → review\nDangerous/weird → stop\n\nScene 5:\nRule:\nDon’t automate the happy path.\nPlan for the weird one.\n```\n\nDas ist viel zugänglicher.\n\n---\n\n## Empfehlung\n\nWir pivoten die Reihe von:\n\n```text\nSystems over prompts.\n```\n\nzu einer zugänglicheren Oberfläche:\n\n```text\nMake AI work in real life.\n```\n\nDer Brand-Kern bleibt:\n\n```text\nSystems over prompts.\n```\n\nAber der Zuschauer-Hook wird alltagstauglicher.\n\n## Konkrete nächste Änderung\n\nIch würde jetzt als nächsten Slice bauen:\n\n```text\nBeginner-friendly voiceover variants\n+ unified text source for top field / captions / subtitles\n+ regenerate Clip 1 as “weird email” version\n```\n\nAlso:\n\n```text\nFinalVoiceoverScript\n→ top card text\n→ subtitles\n→ audio\n→ review MP4\n```\n\nDann ist alles synchron und deutlich weniger abgehoben.", "reasoning": "**Deciding on variants**\n\nI need to establish my next point: I'll focus on two variants, \"Beginner/Mainstream\" and \"Builder/Advanced.\" I think I should recommend the beginner option. I might say that there's no re-render until the copy is fixed, and I could mention a concrete replacement script. But I have to be careful not to say \"will\" unless I confirm with the tool. Saying \"Nächster Schritt ist...\" might work, but I shouldn’t make any promises. I’ll keep it concise!", "reasoning_content": "**Deciding on variants**\n\nI need to establish my next point: I'll focus on two variants, \"Beginner/Mainstream\" and \"Builder/Advanced.\" I think I should recommend the beginner option. I might say that there's no re-render until the copy is fixed, and I could mention a concrete replacement script. But I have to be careful not to say \"will\" unless I confirm with the tool. Saying \"Nächster Schritt ist...\" might work, but I shouldn’t make any promises. I’ll keep it concise!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2zpeeijOLNWDspAf3dzEmuvszGKH_QDRdVX7k3A6qe-9LbmDEjNbLdoVh0Jx8hGjL9IbZj6t-qGdjk7ySLqgBxbZ-mgKecCdvbW02ObnlOPdNgFeVjZi9wfZ99mqmMleKcuVy-2U3xaOETgbbzkR9O5R-Y1JHSI51HZXYr_eM3TfQ58vTudqkMY4uUsruLyZhd9MpKWIEjAK7iM4eGiLmpw8EOSX676VD3Tor9c8g9dL1ulD5KX5oeMT5vV1URpIF4P52V8oeW2fvtBT6nLAs-N2OMUubLEcteFPqMolgr416tqI92OI-BbALLmTYbfB_oxHBd_Ba1_owE6GKR7lpDmkFYpRxRAXR9B-lPXLq8flZMQ1HV-I6HoX-QE5X-c6XeR7LZY8Dtk3NlZMGGgxDp-ns7QbxMi0-DZ1XnpbE_ZuA19Kjahe0pV2f3yUHG22ywLRDqhz8o8qdbJb2luqgGFLyaJv37qrEPjzzzk8GnQ106Q8ueAxwEX0FIFWTzZsjdjUYQIQ4QYyFnUL-2gvY_FYcBvH64s4UxoLX91lPfltLQWaGWBxmJAIpiFkU6Kau8rjan0PmQYUCK-qZ4uY0X34fUtk9BpomocmfcjgpjCvBetADDWe7x0Tph1Cjb7TK3HT3SW1luq3AGyySB6OyjlOiQdQU5R5Nz0dcyYXZbbM94K-vDa7rNOdJQWUdJvCmoZC--pLphku7q-RJ6_d4KwjpgIngNF4TU7W6GfQGhPMK5DMZu6a-KdoUu00IjRyacDBvFdHF9wZ14xlS8bO9vQbKNQduYG81qt7k65ECb2WOWBs_LJdae8vhuX7NCfzyOp3qbefiZp16cYeGHqk9fwsJZckmO0Oy73lNJnYUkCFc4KAsh_Fq4nMB0Rc45lbSjwL1Q2ofXCzIGm1Fn6vEO0d05422ZyET-IwClp5IRSHcSc3J9e2qITYnKAFa3XpZu_o_pg4Q_bv9TeIWKJmq5WqEM7rod_oKiDVwakFdpKu0gR5TVl3_M6u90GFKT5G-UpLAR6BYjiSbVEYrCNLeMWLUb1zSd_3MXbP5cbr0t-TBtz2ew67uMxyib3pt6Ou7-IFgyXH-96xZA30Ls0X7HTz_jYnASFCX7IpzJqnQH4LppnMCErcPFFyCAGCxySzbn5KUXbKnV1tZTBQREqGDWuzNMTj9uA8iVRTj2ZaXWE1ZernjgDl2odeO-s_LvweKL_ihg7TG8ER_W2vi42-LMl9RpDp_z9GbAmX2oc8WUDP4pQNSyb4USXL3RVZRzrMJorhYLq0rxTMp4QhqvokX9qfWckV1wI1mb1I6qZ2Dpo1__ODlZDhbc9YTue5tf5JyDYOdkZfeLWXboSVL5RrDdxl92bQ8wOgRRBv01uFMsfZnSaK6cdR9RtgJT9uZtGeAsBc9XX9cZWT7WzAEJBbDNeMvas1Fx7mg81YxXfX_kntp0aBXRdzYyvG4w7XwM2ZihA1u9zIFU7Lp3fj8NGLEK11rvEZCt3rM_N9bAvYqH_DdymYFyTDIFRCxXRhSu85gz2LaXsoLLXubySivIcgWcYAnGRHYXiBbCbD93PJmHEJMzm712eHSMDv7iOi_hoTo9rAkL35Y6TXogUiPop_l_a9la44XQNctBy78TOQOTARu9dWEGOjM1kOs77f3HpSrr0eeJ7CiPXd2syDQaXT_Hsq-yYyxIxjXeWc2J4976qkuQKqAVs4qMYZFW72-ShAOyoxhHxWmNASfnXooYBe5suoUHT5aNtk-JvbsA86Q1ZaB4jKWQzznkYiKSYxkSR61yGKNIl9NqGgTVKpvGZZtUlEq8CP_4Lj3f7NIfHiL6dwBDt1HMdWSCv4Amf5C1xVeiN8UCA1g7rG1hL7hiw37vMolBW58_VvOlnbNezYi7Qz1pF0J3UoQJxndDlgP0raeTSoMJ-WpMZtTVWFsRBu4UamUYjZ_FOwh5ct2IRkQ3dSvuNgo8RWgArRQydfwvSUCjl5YjLRluEY-kfk3Z9vo1rmc11o5HvPr6Z22asXXOdOUEQy2XxYZfnlUcpr_c7P4qtI1BXLPaRbKBg7RDQtVbzFRB45DLKAEdWr9UF9yc4YYDrdlScg-ZLm4c-uV2aemUXx3DWvslOOA2GBtuY-oBizJakAS6_SGPohH9nr73pu5ZBGLJo46n7tObWhb6_o_v16kNg90MZ7mPFFYNiPLhrfCVfka4SdBdNJ82QUHbj2bs_xPQdql2rSSJ44BNDzvq4iTBe0U8g9OVuUHIll3-nxcXL_QMLqsxO0OeQ1Gu2o8yKgSQKxIhiWCHwEtF1pbbSMi7IjYP50R7HOJdnEcX-t0XDhAJFfjQUlpcuVR7t4AvjUStdU_L9absqFFtPEy5E_B4CIuVXqP2TxELlW6ATbS-0KJuN9nvypPC5SLm9d3aQ52ZVTZD9f_u7KVrTCcP8ki9rlcFdlGTzvvYdg2lDKnMCnekkziK6LVS2NXqUd4xxZCDGjlK6LTgVD-mw4G9c06JKCOcJo2vYVTrmjInINvc7FGk8dlR07LR47k7qLrRkSbVJhVBr4uMo1W7BiuZnDmsOF4NB4NpDHJJRiinn-rspZbGme9HJ9a0ON856yNlcdyIPNyyhmRvc4j0hpnJ5y3yNq80pFYXCyzk7JdUswg==", "id": "rs_0dc9f371a6d55e3f016a136ce35e0481918c87a113735e63a5", "summary": [{"type": "summary_text", "text": "**Deciding on variants**\n\nI need to establish my next point: I'll focus on two variants, \"Beginner/Mainstream\" and \"Builder/Advanced.\" I think I should recommend the beginner option. I might say that there's no re-render until the copy is fixed, and I could mention a concrete replacement script. But I have to be careful not to say \"will\" unless I confirm with the tool. Saying \"Nächster Schritt ist...\" might work, but I shouldn’t make any promises. I’ll keep it concise!"}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — **Ihre Kritik ist korrekt**.\n\n## 1. Voiceover/Subtitles passen nicht zum oberen Feld\n\nDas aktuelle Preview ist technisch korrekt, aber dramaturgisch unsauber:\n\n```text\nOberes Feld / Card:\nalte German approval/timing copy\n\nVoiceover/Subtitles:\nneuer English final voiceover text\n```\n\nDas erzeugt kognitive Reibung. Der Zuschauer sieht oben etwas anderes als er hört/liest. Für Review ist das schon störend; für echten Content wäre es tödlich genug, um einen kleinen Ausschuss einzuberufen.\n\n## Fix\n\nWir brauchen **eine einzige Source of Truth** pro Clip:\n\n```text\nFinalVoiceoverScript\n→ SubtitleTrack\n→ Top Hook Text\n→ Scene Card Text\n→ Social Caption\n```\n\nNicht:\n\n```text\nCandidate Concept Text\n+ später anderer Voiceover Text\n+ dritte Caption-Version\n```\n\nAlso: Der Text im oberen Feld muss aus demselben finalen Voiceover/Hook kommen wie Subtitles und Audio.\n\n---\n\n## 2. Ja: aktuell wirkt es zu abgehoben\n\nDer aktuelle Text ist gut für:\n\n```text\nAI builders\nAutomation nerds\nWorkflow-/Ops-Leute\nLeute, die schon wissen, was ein “exception path” ist\n```\n\nAber für breiteres Publikum ist es zu abstrakt.\n\nBegriffe wie:\n\n```text\nexception path\ninput boundary\noutput standard\naction boundary\ndecision structure\n```\n\nsind für Fachleute präzise — aber für Anfänger klingt es nach einem LinkedIn-Post mit Krawatte.\n\n## Problem\n\nWir sagen aktuell:\n\n```text\nIf your automation has no exception path, it is not a system.\n```\n\nDas ist stark, aber nicht sofort greifbar.\n\nFür Anfänger besser:\n\n```text\nIf your AI does not know what to do when something weird happens,\nit will break the first time real life shows up.\n```\n\nDas ist derselbe Gedanke, aber menschlicher.\n\n---\n\n## 3. Zielgruppe: Wir sollten breiter anfangen\n\nFür TrueTrace würde ich nicht mit „AI systems for experts“ starten, sondern mit:\n\n```text\nAI tools that work in real life\n```\n\nAlso Zielgruppe:\n\n```text\nPeople using ChatGPT, Notion AI, Zapier, Make, email automations,\nbut not yet thinking like system architects.\n```\n\nDie haben ein echtes Problem:\n\n```text\n“It worked once, then it became unreliable.”\n```\n\nDas versteht jeder.\n\n---\n\n## Neue Copy-Richtung\n\nStatt:\n\n```text\nMost AI automations do not fail dramatically. They quietly rot.\n```\n\nBesser für breite Zielgruppe:\n\n```text\nYour AI automation did not really break.\nReal life just gave it an input it was never prepared for.\n```\n\nOder noch einfacher:\n\n```text\nYour AI automation works in the demo.\nThen real life shows up.\n```\n\nDas ist deutlich stärker für Anfänger.\n\n## Neue Hook-Variante\n\n```text\nYour AI automation works perfectly…\nuntil the first weird email arrives.\n```\n\nDas ist konkret. Jeder versteht „weird email“.\n\n---\n\n## Neuer Anfänger-freundlicher Voiceover-Entwurf\n\nFür Clip 1 würde ich den Text so umbauen:\n\n```text\nYour AI automation works perfectly in the demo.\n\nThen one weird email arrives.\n\nThe name is missing.\nThe format is different.\nThe request is unclear.\n\nAnd suddenly the automation does the wrong thing confidently.\n\nThat is not an AI problem.\nThat is a workflow problem.\n\nBefore you automate anything, decide three things:\n\nWhat should go through?\nWhat should be reviewed?\nAnd what should stop?\n\nIf your AI does not know what to do with weird inputs,\nit will break the moment real life shows up.\n```\n\nWarum besser:\n\n- kein Jargon\n- konkretes Beispiel: weird email\n- klares Problem\n- klare 3er-Regel\n- Anfänger verstehen es sofort\n- Fortgeschrittene merken trotzdem: es geht um exception handling\n\n---\n\n## Visuals dazu\n\nStatt abstrakter Systemkarten:\n\n```text\nScene 1:\nPerfect demo inbox. Everything green.\n\nScene 2:\nA weird email appears:\n“hey can you handle this maybe???”\n\nScene 3:\nAI confidently routes it wrong.\n\nScene 4:\nSplit path:\nNormal → automate\nUnclear → review\nDangerous/weird → stop\n\nScene 5:\nRule:\nDon’t automate the happy path.\nPlan for the weird one.\n```\n\nDas ist viel zugänglicher.\n\n---\n\n## Empfehlung\n\nWir pivoten die Reihe von:\n\n```text\nSystems over prompts.\n```\n\nzu einer zugänglicheren Oberfläche:\n\n```text\nMake AI work in real life.\n```\n\nDer Brand-Kern bleibt:\n\n```text\nSystems over prompts.\n```\n\nAber der Zuschauer-Hook wird alltagstauglicher.\n\n## Konkrete nächste Änderung\n\nIch würde jetzt als nächsten Slice bauen:\n\n```text\nBeginner-friendly voiceover variants\n+ unified text source for top field / captions / subtitles\n+ regenerate Clip 1 as “weird email” version\n```\n\nAlso:\n\n```text\nFinalVoiceoverScript\n→ top card text\n→ subtitles\n→ audio\n→ review MP4\n```\n\nDann ist alles synchron und deutlich weniger abgehoben."}], "id": "msg_0dc9f371a6d55e3f016a136ce9a67481919e1e7023e61bd843", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Ja das ist besser. Mach jetzt weiter.", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_DWFLwbNL27ptEKvePzmCt7tG", "call_id": "call_DWFLwbNL27ptEKvePzmCt7tG", "response_item_id": "fc_0dc9f371a6d55e3f016a136fa0ade881919186260ba108e820", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_EmYRDKTVZv93jCfk6UKI6psx", "call_id": "call_EmYRDKTVZv93jCfk6UKI6psx", "response_item_id": "fc_0dc9f371a6d55e3f016a136fa0ae048191b7d2fb0dee6d1473", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}, {"id": "call_hKXDaCnF8TLmNMRhl8Nh0p33", "call_id": "call_hKXDaCnF8TLmNMRhl8Nh0p33", "response_item_id": "fc_0dc9f371a6d55e3f016a136fa0ae4481919ce9d1a667b038d4", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\",\"limit\":220}"}}], "finish_reason": "tool_calls", "reasoning": "**Taking action for TDD implementation**\n\nI need to take action and load the Test-Driven Development (TDD) process, making sure it's systematic and relevant. It's essential to use the right tools during this implementation. I also need to inspect the files involved to ensure everything's in order. It feels like there's a lot to accomplish, and I want to make sure I'm covering all necessary steps properly! Let's keep pushing forward on this!", "reasoning_content": "**Taking action for TDD implementation**\n\nI need to take action and load the Test-Driven Development (TDD) process, making sure it's systematic and relevant. It's essential to use the right tools during this implementation. I also need to inspect the files involved to ensure everything's in order. It feels like there's a lot to accomplish, and I want to make sure I'm covering all necessary steps properly! Let's keep pushing forward on this!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2-gM5r9u-hrqs7gqtRU_STvlRZT6Hg8kU3VWkLnrXMFvpLyPBTvKAixUAODIkYG9sFAkPIOGsqjC7QPxKEN-OXOePvxe9vrvk9xidzJQWRVJ3UPHd4xbegw7P9IiyGkaqrL5BTnVocRvgDRIk28vlO7sYVIlijQ53KDxQGkPqRcnCmrOLXswLn9bG_Guj5-6U6p1eFCgWk94WX9uqOtl7xh0nmpX_ChQnU5buB53YqMK6OgnjviK1httNpwUwqvJygqluzsJYHu92pNq6xnRp3IKIbIYPP4CIfWbJNstUAU4n8pT3MU9wFCV4vHufFa0lENf89kRojC_Rl78DJZKjoDdV1rqSdTffyBhJ_IjtIWgP-5Os921A6G3Hd9bG41XVC1iN8XhGvWf0X0Q-TsL0TFcV4Q5s0pUI9wPwX8IYrm2dQlk0yKV7DYlrKOqxmqAWEYk8S6pSBPOhE9Wkj9Un4e4Y0-GY486wtFl9rMKFXn1BXxyVN96KqLYxz6KfGFWEH0w3m_77J8SQVT38zRPmQil_8nGoEYmurH2LUCqju36Zo-ataJyd1n2XrefRRCx00gp9qxittat69BOMKQdiQL9omJbS7J_QNaX8jBm4OzgNBch0pJw7CnSFQ97oT37XvK70DZqLJMCLbQY69tynv4m4Hb0MC53PcQM0UNsj1ykOpE5pr65tXf-Qju9mXsOXgqiBuLt13kKj-x3Ny-eh9FDFNLV3XHIKT5gMOEOKsBxCls5jo1fY1EAU2iAhF1NOwJsJOMwvgcqjdU39Ho4o4NqwzItmDQxX8izVOA7Jdt8uaaopJmeWDESJdLkQaMWGrvxkoAsk23SYUCt3SzXWHFV1VgmD-3eP-S94comEq4Psd3eRpQ42x1kNugOItSDXxqHJxmf5IFB3Dm3vDdOZWbGgJXwAaBtfME6TyQlXz9AH6OaUKnpnVmoOVLE2G6F_V5_bvl819_hqG5y_fHRF23enXBATS9_cY9nODMi4DoXdfTEDixYEAMFuygHEtI4DRytdw-7ohSQvgFxQHdsSnK3OT3iF1ToJT3_xDpoh2ahN71UTiuuE07gzWCYtX2RZw7", "id": "rs_0dc9f371a6d55e3f016a136f9e10bc8191981b87775c64a2b2", "summary": [{"type": "summary_text", "text": "**Taking action for TDD implementation**\n\nI need to take action and load the Test-Driven Development (TDD) process, making sure it's systematic and relevant. It's essential to use the right tools during this implementation. I also need to inspect the files involved to ensure everything's in order. It feels like there's a lot to accomplish, and I want to make sure I'm covering all necessary steps properly! Let's keep pushing forward on this!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "[skill_view] name=test-driven-development (17,806 chars)", "tool_call_id": "call_DWFLwbNL27ptEKvePzmCt7tG", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/audio-caption-preview-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}", "tool_call_id": "call_EmYRDKTVZv93jCfk6UKI6psx", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|\\\"\\\"\\\"Final short-form voiceover scripts for TrueTrace review renders.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|\\n 7|from autoshorts.ideas.candidate import ValidationResult\\n 8|\\n 9|\\n 10|_GENERIC_FILLER = (\\n 11| \\\"in this video\\\",\\n 12| \\\"unlock your full potential\\\",\\n 13| \\\"game changer\\\",\\n 14| \\\"you won't believe\\\",\\n 15|)\\n 16|\\n 17|\\n 18|@dataclass(frozen=True)\\n 19|class FinalVoiceoverScript:\\n 20| candidate_id: str\\n 21| text: str\\n 22| language: str\\n 23| voice: str\\n 24| target_duration_seconds: int\\n 25| side_effects: tuple[str, ...] = ()\\n 26| external_calls: tuple[str, ...] = ()\\n 27|\\n 28|\\n 29|_SCRIPTS: dict[str, tuple[str, int]] = {\\n 30| \\\"truetrace-ai-automation-quiet-rot-38s\\\": (\\n 31| \\\"Most AI automations do not fail dramatically. They quietly rot. \\\"\\n 32| \\\"Day one looks clean, because the demo input behaves. By day three, the real input changes. \\\"\\n 33| \\\"Now the workflow needs an exception path. Normal goes forward. Weird goes to review. Unclear stops. \\\"\\n 34| \\\"If your automation has no exception path, it is not a system. It is a lucky prompt.\\\",\\n 35| 24,\\n 36| ),\\n 37| \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\": (\\n 38| \\\"A smart prompt can still build a fragile workflow. The prompt is only one part. \\\"\\n 39| \\\"A system needs an input boundary, an output standard, a review step, and an action boundary. \\\"\\n 40| \\\"Without those, the same prompt behaves differently every week. Do not ask for a better prompt first. \\\"\\n 41| \\\"Ask what the workflow must refuse, review, and repeat.\\\",\\n 42| 23,\\n 43| ),\\n 44| \\\"truetrace-ai-notes-equal-importance-34s\\\": (\\n 45| \\\"AI notes fail when everything looks equally important. A pretty summary is not a decision system. \\\"\\n 46| \\\"Separate facts from decisions, risks, open questions, and next actions. \\\"\\n 47| \\\"Now the note tells you what changed, what matters, and what needs a human. \\\"\\n 48| \\\"Do not ask AI for more notes. Ask it for decision structure.\\\",\\n 49| 22,\\n 50| ),\\n 51|}\\n 52|\\n 53|\\n 54|def build_truetrace_final_voiceover(candidate_id: str) -> FinalVoiceoverScript:\\n 55| if candidate_id not in _SCRIPTS:\\n 56| raise ValueError(f\\\"unknown TrueTrace candidate id: {candidate_id}\\\")\\n 57| text, duration = _SCRIPTS[candidate_id]\\n 58| script = FinalVoiceoverScript(\\n 59| candidate_id=candidate_id,\\n 60| text=text,\\n 61| language=\\\"en\\\",\\n 62| voice=\\\"en-GB-RyanNeural\\\",\\n 63| target_duration_seconds=duration,\\n 64| )\\n 65| validation = validate_final_voiceover_script(script)\\n 66| if not validation.is_valid:\\n 67| raise ValueError(\\\"; \\\".join(validation.errors))\\n 68| return script\\n 69|\\n 70|\\n 71|def validate_final_voiceover_script(script: FinalVoiceoverScript) -> ValidationResult:\\n 72| errors: list[str] = []\\n 73| if not script.candidate_id.strip():\\n 74| errors.append(\\\"candidate_id is required\\\")\\n 75| if not script.text.strip():\\n 76| errors.append(\\\"text is required\\\")\\n 77| if script.language != \\\"en\\\":\\n 78| errors.append(\\\"final voiceover language must be en\\\")\\n 79| if not script.voice.strip():\\n 80| errors.append(\\\"voice is required\\\")\\n 81| if not (15 <= script.target_duration_seconds <= 45):\\n 82| errors.append(\\\"target_duration_seconds must be between 15 and 45\\\")\\n 83| if any(phrase in script.text.casefold() for phrase in _GENERIC_FILLER):\\n 84| errors.append(\\\"voiceover contains generic intro/filler language\\\")\\n 85| if len(script.text.split()) > 80:\\n 86| errors.append(\\\"voiceover must stay under 80 words\\\")\\n 87| if script.side_effects:\\n 88| errors.append(\\\"voiceover script must not have side effects\\\")\\n 89| if script.external_calls:\\n 90| errors.append(\\\"voiceover script must not have external calls\\\")\\n 91| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 92|\", \"total_lines\": 91, \"file_size\": 3612, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_hKXDaCnF8TLmNMRhl8Nh0p33", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_rxU6YgCSLG9kcUP4UH7hUURZ", "call_id": "call_rxU6YgCSLG9kcUP4UH7hUURZ", "response_item_id": "fc_0dc9f371a6d55e3f016a136fa59574819188a82f3c44bc7957", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":260,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": 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"id": "rs_0dc9f371a6d55e3f016a136fa4a1188191b5fe04dd31bb4d5f", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|\\\"\\\"\\\"Write deterministic local preview PNG scene cards.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|from pathlib import Path\\n 7|from textwrap import wrap\\n 8|from typing import Any\\n 9|\\n 10|from PIL import Image, ImageDraw, ImageFont\\n 11|\\n 12|from autoshorts.rendering.local_preview import LocalPreviewPlan, validate_local_preview_plan\\n 13|\\n 14|\\n 15|@dataclass(frozen=True)\\n 16|class LocalFrameWriteResult:\\n 17| script_id: str\\n 18| output_dir: str\\n 19| frame_paths: tuple[str, ...]\\n 20| side_effects: tuple[str, ...] = (\\\"write_png\\\",)\\n 21| external_calls: tuple[str, ...] = ()\\n 22|\\n 23|\\n 24|@dataclass(frozen=True)\\n 25|class LocalizedFrameCopy:\\n 26| scene_label: str\\n 27| caption_text: str\\n 28| asset_label: str\\n 29| motion_text: str\\n 30|\\n 31|\\n 32|_GERMAN_SCENE_LABELS = {\\n 33| \\\"first_frame_hook\\\": \\\"AUFMERKSAMKEIT\\\",\\n 34| \\\"pattern_interrupt\\\": \\\"MUSTERBRUCH\\\",\\n 35| \\\"core_explanation\\\": \\\"KERNIDEE\\\",\\n 36| \\\"screen_or_card_visual\\\": \\\"BEISPIEL\\\",\\n 37| \\\"cta\\\": \\\"FREIGABE\\\",\\n 38|}\\n 39|\\n 40|_GERMAN_CAPTIONS = {\\n 41| \\\"first_frame_hook\\\": \\\"Wenn KI deine Woche steuert, hast du die Kontrolle bereits abgegeben.\\\",\\n 42| \\\"pattern_interrupt\\\": \\\"Das Problem ist nicht zu wenig KI, sondern zu wenig Führung.\\\",\\n 43| \\\"core_explanation\\\": \\\"Nutze KI als Assistent: Ziele prüfen, Aufgaben ordnen, nächste Schritte festlegen.\\\",\\n 44| \\\"screen_or_card_visual\\\": \\\"Ein 10-Minuten-Review zeigt: Was bleibt, was fällt weg, was kommt zuerst?\\\",\\n 45| \\\"cta\\\": \\\"Freigabe prüfen: Versteht man die Idee sofort und bleibt die Kontrolle beim Menschen?\\\",\\n 46|}\\n 47|\\n 48|_GERMAN_ASSET_LABELS = {\\n 49| \\\"kinetic_text_overlay\\\": \\\"Text-Overlay\\\",\\n 50| \\\"mechanism_animation\\\": \\\"Mechanik-Animation\\\",\\n 51| \\\"screen_recording_simulation\\\": \\\"Screen/Card-Simulation\\\",\\n 52| \\\"cinematic_ai_video\\\": \\\"Cinematic AI-Visual\\\",\\n 53|}\\n 54|\\n 55|_GERMAN_MOTION = {\\n 56| \\\"first_frame_hook\\\": \\\"Schneller Einstieg mit grossem Hook-Text; kein Text im Bildmaterial eingebrannt.\\\",\\n 57| \\\"pattern_interrupt\\\": \\\"Kurzer visueller Bruch, dann klare Problem-Zuspitzung.\\\",\\n 58| \\\"core_explanation\\\": \\\"Schrittweise Bewegung: Ziele, Aufgaben, nächste Aktion erscheinen nacheinander.\\\",\\n 59| \\\"screen_or_card_visual\\\": \\\"Langsamer Zoom auf eine Review-Karte mit klaren Abschnitten.\\\",\\n 60| \\\"cta\\\": \\\"Ruhiger Abschluss mit klarer Freigabe-Frage und kurzem Call-to-Action.\\\",\\n 61|}\\n 62|\\n 63|\\n 64|def _validate_output_dir(output_dir: str | Path) -> Path:\\n 65| path = Path(output_dir)\\n 66| if not str(path).strip() or \\\"..\\\" in path.parts:\\n 67| raise ValueError(\\\"output_dir must be explicit and normalized\\\")\\n 68| return path\\n 69|\\n 70|\\n 71|def _font(size: int):\\n 72| try:\\n 73| return ImageFont.truetype(\\\"DejaVuSans.ttf\\\", size=size)\\n 74| except OSError:\\n 75| return ImageFont.load_default()\\n 76|\\n 77|\\n 78|def _text_width(text: str, font: Any) -> int:\\n 79| scratch = Image.new(\\\"RGB\\\", (1, 1))\\n 80| draw = ImageDraw.Draw(scratch)\\n 81| bbox = draw.textbbox((0, 0), text, font=font)\\n 82| return int(bbox[2] - bbox[0])\\n 83|\\n 84|\\n 85|def wrap_text_to_pixel_width(text: str, *, max_width_px: int, font_size: int) -> tuple[str, ...]:\\n 86| \\\"\\\"\\\"Wrap text using measured pixel width instead of a fixed character count.\\\"\\\"\\\"\\n 87|\\n 88| font = _font(font_size)\\n 89| lines: list[str] = []\\n 90| for paragraph in text.splitlines() or [text]:\\n 91| words = paragraph.split()\\n 92| if not words:\\n 93| lines.append(\\\"\\\")\\n 94| continue\\n 95| current = words[0]\\n 96| for word in words[1:]:\\n 97| candidate = f\\\"{current} {word}\\\"\\n 98| if _text_width(candidate, font) <= max_width_px:\\n 99| current = candidate\\n 100| else:\\n 101| lines.append(current)\\n 102| current = word\\n 103| lines.append(current)\\n 104| return tuple(lines)\\n 105|\\n 106|\\n 107|def _wrapped(text: str, width: int) -> str:\\n 108| lines: list[str] = []\\n 109| for paragraph in text.splitlines() or [text]:\\n 110| lines.extend(wrap(paragraph, width=width) or [\\\"\\\"])\\n 111| return \\\"\\\\n\\\".join(lines)\\n 112|\\n 113|\\n 114|def _palette(asset_mode: str) -> tuple[tuple[int, int, int], tuple[int, int, int]]:\\n 115| palettes = {\\n 116| \\\"kinetic_text_overlay\\\": ((15, 23, 42), (96, 165, 250)),\\n 117| \\\"mechanism_animation\\\": ((17, 24, 39), (52, 211, 153)),\\n 118| \\\"screen_recording_simulation\\\": ((24, 24, 27), (250, 204, 21)),\\n 119| \\\"cinematic_ai_video\\\": ((28, 25, 23), (251, 146, 60)),\\n 120| }\\n 121| return palettes.get(asset_mode, ((17, 24, 39), (148, 163, 184)))\\n 122|\\n 123|\\n 124|def localize_frame_copy(frame, *, language: str = \\\"de\\\") -> LocalizedFrameCopy:\\n 125| \\\"\\\"\\\"Return approval-preview copy, German by default; final videos can stay English later.\\\"\\\"\\\"\\n 126|\\n 127| if language != \\\"de\\\":\\n 128| return LocalizedFrameCopy(\\n 129| scene_label=str(frame.scene_purpose.replace(\\\"_\\\", \\\" \\\").upper()),\\n 130| caption_text=str(frame.caption_text),\\n 131| asset_label=str(frame.asset_mode),\\n 132| motion_text=str(frame.motion_instruction),\\n 133| )\\n 134| return LocalizedFrameCopy(\\n 135| scene_label=str(_GERMAN_SCENE_LABELS.get(frame.scene_purpose, frame.scene_purpose.replace(\\\"_\\\", \\\" \\\").upper())),\\n 136| caption_text=str(_GERMAN_CAPTIONS.get(frame.scene_purpose, frame.caption_text)),\\n 137| asset_label=str(_GERMAN_ASSET_LABELS.get(frame.asset_mode, frame.asset_mode)),\\n 138| motion_text=str(_GERMAN_MOTION.get(frame.scene_purpose, frame.motion_instruction)),\\n 139| )\\n 140|\\n 141|\\n 142|def _draw_lines(draw: ImageDraw.ImageDraw, xy: tuple[int, int], lines: tuple[str, ...], *, fill, font, spacing: int) -> None:\\n 143| x, y = xy\\n 144| line_height = font.size + spacing if hasattr(font, \\\"size\\\") else 22 + spacing\\n 145| for line in lines:\\n 146| draw.text((x, y), line, fill=fill, font=font)\\n 147| y += line_height\\n 148|\\n 149|\\n 150|def _draw_card(plan: LocalPreviewPlan, frame, *, approval_language: str = \\\"de\\\") -> Image.Image:\\n 151| background, accent = _palette(frame.asset_mode)\\n 152| image = Image.new(\\\"RGB\\\", (plan.width, plan.height), background)\\n 153| draw = ImageDraw.Draw(image)\\n 154| copy = localize_frame_copy(frame, language=approval_language)\\n 155|\\n 156| margin = max(28, plan.width // 16)\\n 157| content_width = plan.width - (2 * margin)\\n 158| title_font_size = max(28, plan.width // 16)\\n 159| body_font_size = max(16, plan.width // 30)\\n 160| small_font_size = max(14, plan.width // 34)\\n 161| title_font = _font(title_font_size)\\n 162| body_font = _font(body_font_size)\\n 163| small_font = _font(small_font_size)\\n 164|\\n 165| draw.rectangle((0, 0, plan.width, 18), fill=accent)\\n 166| draw.text((margin, margin), copy.scene_label, fill=accent, font=small_font)\\n 167|\\n 168| caption_lines = wrap_text_to_pixel_width(copy.caption_text, max_width_px=content_width, font_size=title_font_size)\\n 169| _draw_lines(draw, (margin, margin + 46), caption_lines, fill=(248, 250, 252), font=title_font, spacing=10)\\n 170|\\n 171| info_top = plan.height // 2\\n 172| draw.rounded_rectangle(\\n 173| (margin, info_top, plan.width - margin, plan.height - margin),\\n 174| radius=18,\\n 175| outline=accent,\\n 176| width=2,\\n 177| fill=(background[0] + 8, background[1] + 8, background[2] + 8),\\n 178| )\\n 179| body = (\\n 180| f\\\"Zeit: {frame.start_ms / 1000:.0f}s + {frame.duration_ms / 1000:.0f}s\\\\n\\\"\\n 181| f\\\"Asset: {copy.asset_label}\\\\n\\\"\\n 182| f\\\"Label: {copy.scene_label.title()}\\\\n\\\\n\\\"\\n 183| f\\\"Bewegung: {copy.motion_text}\\\"\\n 184| )\\n 185| body_lines = tuple(\\n 186| line\\n 187| for paragraph in body.splitlines()\\n 188| for line in (wrap_text_to_pixel_width(paragraph, max_width_px=content_width - 36, font_size=body_font_size) or (\\\"\\\",))\\n 189| )\\n 190| _draw_lines(draw, (margin + 18, info_top + 18), body_lines, fill=(226, 232, 240), font=body_font, spacing=6)\\n 191| return image\\n 192|\\n 193|\\n 194|def write_local_preview_frames(\\n 195| plan: LocalPreviewPlan,\\n 196| *,\\n 197| output_dir: str | Path | None = None,\\n 198| approval_language: str = \\\"de\\\",\\n 199|) -> LocalFrameWriteResult:\\n 200| \\\"\\\"\\\"Write deterministic PNG scene cards for a validated local preview plan.\\\"\\\"\\\"\\n 201|\\n 202| validation = validate_local_preview_plan(plan)\\n 203| if not validation.is_valid:\\n 204| raise ValueError(\\\"; \\\".join(validation.errors))\\n 205|\\n 206| target_dir = _validate_output_dir(output_dir or plan.output_dir)\\n 207| target_dir.mkdir(parents=True, exist_ok=True)\\n 208|\\n 209| frame_paths: list[str] = []\\n 210| for frame in plan.frames:\\n 211| path = target_dir / f\\\"{frame.frame_id}.png\\\"\\n 212| image = _draw_card(plan, frame, approval_language=approval_language)\\n 213| image.save(path, format=\\\"PNG\\\")\\n 214| frame_paths.append(str(path))\\n 215|\\n 216| return LocalFrameWriteResult(\\n 217| script_id=plan.script_id,\\n 218| output_dir=str(target_dir),\\n 219| frame_paths=tuple(frame_paths),\\n 220| side_effects=(\\\"write_png\\\",),\\n 221| external_calls=(),\\n 222| )\\n 223|\", \"total_lines\": 222, \"file_size\": 8236, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_rxU6YgCSLG9kcUP4UH7hUURZ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": 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"id": "rs_0dc9f371a6d55e3f016a136fa9d02081919ede363535a99dd7", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|\\\"\\\"\\\"Retention-led shot planning before any video rendering.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from dataclasses import dataclass\\n 6|from typing import Literal\\n 7|\\n 8|from autoshorts.content.script_draft import ScriptDraft, validate_script_draft\\n 9|from autoshorts.ideas.candidate import ValidationResult\\n 10|\\n 11|ShotPurpose = Literal[\\n 12| \\\"first_frame_hook\\\",\\n 13| \\\"pattern_interrupt\\\",\\n 14| \\\"core_explanation\\\",\\n 15| \\\"screen_or_card_visual\\\",\\n 16| \\\"cta\\\",\\n 17|]\\n 18|AssetMode = Literal[\\n 19| \\\"kinetic_text_overlay\\\",\\n 20| \\\"mechanism_animation\\\",\\n 21| \\\"screen_recording_simulation\\\",\\n 22| \\\"cinematic_ai_video\\\",\\n 23| \\\"static_slideshow\\\",\\n 24|]\\n 25|\\n 26|_ALLOWED_ASSET_MODES = {\\n 27| \\\"kinetic_text_overlay\\\",\\n 28| \\\"mechanism_animation\\\",\\n 29| \\\"screen_recording_simulation\\\",\\n 30| \\\"cinematic_ai_video\\\",\\n 31|}\\n 32|_BAKED_TEXT_MARKERS = (\\\"baked-in\\\", \\\"fake ui text\\\", \\\"generated text\\\", \\\"unreadable ui\\\")\\n 33|\\n 34|\\n 35|@dataclass(frozen=True)\\n 36|class ShotScene:\\n 37| purpose: ShotPurpose | str\\n 38| start_seconds: int\\n 39| end_seconds: int\\n 40| voiceover_beat: str\\n 41| visual_action: str\\n 42| asset_mode: AssetMode | str\\n 43| caption_beat: str\\n 44| retention_goal: str\\n 45| labels_renderer_owned: bool = True\\n 46|\\n 47|\\n 48|@dataclass(frozen=True)\\n 49|class ShotPlan:\\n 50| script_id: str\\n 51| title: str\\n 52| estimated_duration_seconds: int\\n 53| scenes: tuple[ShotScene, ...]\\n 54|\\n 55|\\n 56|def _is_blank(value: str) -> bool:\\n 57| return not (value or \\\"\\\").strip()\\n 58|\\n 59|\\n 60|def _scene_windows(duration_seconds: int) -> tuple[tuple[int, int], ...]:\\n 61| first_end = min(3, duration_seconds)\\n 62| second_end = min(8, duration_seconds)\\n 63| third_end = min(17, duration_seconds)\\n 64| fourth_end = min(max(duration_seconds - 5, third_end + 1), duration_seconds)\\n 65| return (\\n 66| (0, first_end),\\n 67| (first_end, second_end),\\n 68| (second_end, third_end),\\n 69| (third_end, fourth_end),\\n 70| (fourth_end, duration_seconds),\\n 71| )\\n 72|\\n 73|\\n 74|def create_shot_plan(script: ScriptDraft) -> ShotPlan:\\n 75| \\\"\\\"\\\"Create a deterministic five-scene retention shot plan from a script draft.\\\"\\\"\\\"\\n 76|\\n 77| validation = validate_script_draft(script)\\n 78| if not validation.is_valid:\\n 79| raise ValueError(\\\"; \\\".join(validation.errors))\\n 80|\\n 81| windows = _scene_windows(script.estimated_duration_seconds)\\n 82| beats = script.retention_beats\\n 83| scenes = (\\n 84| ShotScene(\\n 85| purpose=\\\"first_frame_hook\\\",\\n 86| start_seconds=windows[0][0],\\n 87| end_seconds=windows[0][1],\\n 88| voiceover_beat=script.hook,\\n 89| visual_action=\\\"Immediate motion punch-in on clean kinetic hook text; no generated UI lettering.\\\",\\n 90| asset_mode=\\\"kinetic_text_overlay\\\",\\n 91| caption_beat=script.hook,\\n 92| retention_goal=\\\"Stop the scroll with a first-frame hook and pattern interrupt.\\\",\\n 93| ),\\n 94| ShotScene(\\n 95| purpose=\\\"pattern_interrupt\\\",\\n 96| start_seconds=windows[1][0],\\n 97| end_seconds=windows[1][1],\\n 98| voiceover_beat=beats[0],\\n 99| visual_action=\\\"Rapid desk-to-phone change showing messy priorities becoming a controlled workflow.\\\",\\n 100| asset_mode=\\\"mechanism_animation\\\",\\n 101| caption_beat=\\\"AI suggests. Human approves.\\\",\\n 102| retention_goal=\\\"Create visible change before the viewer can classify it as a slideshow.\\\",\\n 103| ),\\n 104| ShotScene(\\n 105| purpose=\\\"core_explanation\\\",\\n 106| start_seconds=windows[2][0],\\n 107| end_seconds=windows[2][1],\\n 108| voiceover_beat=beats[1] if len(beats) > 1 else script.script_outline,\\n 109| visual_action=\\\"Simulated screen flow with renderer-owned boxes, cursor motion, and approval checkpoint.\\\",\\n 110| asset_mode=\\\"screen_recording_simulation\\\",\\n 111| caption_beat=\\\"The rule: AI drafts, you decide.\\\",\\n 112| retention_goal=\\\"Make the mechanism concrete without trusting generated text inside assets.\\\",\\n 113| ),\\n 114| ShotScene(\\n 115| purpose=\\\"screen_or_card_visual\\\",\\n 116| start_seconds=windows[3][0],\\n 117| end_seconds=windows[3][1],\\n 118| voiceover_beat=beats[2] if len(beats) > 2 else script.script_outline,\\n 119| visual_action=\\\"Animated checklist/card sequence showing the weekly review routine step by step.\\\",\\n 120| asset_mode=\\\"mechanism_animation\\\",\\n 121| caption_beat=\\\"10-minute review. One human checkpoint.\\\",\\n 122| retention_goal=\\\"Deliver the payoff visually before the CTA.\\\",\\n 123| ),\\n 124| ShotScene(\\n 125| purpose=\\\"cta\\\",\\n 126| start_seconds=windows[4][0],\\n 127| end_seconds=windows[4][1],\\n 128| voiceover_beat=script.cta,\\n 129| visual_action=\\\"Clean kinetic CTA overlay with renderer-owned caption and final checklist card motion.\\\",\\n 130| asset_mode=\\\"kinetic_text_overlay\\\",\\n 131| caption_beat=script.cta,\\n 132| retention_goal=\\\"Give a concrete comment action tied to the promised checklist.\\\",\\n 133| ),\\n 134| )\\n 135| return ShotPlan(\\n 136| script_id=script.brief_id,\\n 137| title=script.title,\\n 138| estimated_duration_seconds=script.estimated_duration_seconds,\\n 139| scenes=scenes,\\n 140| )\\n 141|\\n 142|\\n 143|def validate_shot_plan(plan: ShotPlan) -> ValidationResult:\\n 144| errors: list[str] = []\\n 145| if _is_blank(plan.script_id):\\n 146| errors.append(\\\"script_id is required\\\")\\n 147| if _is_blank(plan.title):\\n 148| errors.append(\\\"title is required\\\")\\n 149| if not 10 <= plan.estimated_duration_seconds <= 180:\\n 150| errors.append(\\\"duration_seconds must be between 10 and 180\\\")\\n 151| if not plan.scenes:\\n 152| errors.append(\\\"at least one scene is required\\\")\\n 153| elif plan.scenes[0].purpose != \\\"first_frame_hook\\\":\\n 154| errors.append(\\\"first scene must be first_frame_hook\\\")\\n 155|\\n 156| previous_end: int | None = None\\n 157| for index, scene in enumerate(plan.scenes, start=1):\\n 158| if scene.end_seconds <= scene.start_seconds:\\n 159| errors.append(f\\\"scene {index} end_seconds must be greater than start_seconds\\\")\\n 160| if previous_end is not None and scene.start_seconds < previous_end:\\n 161| errors.append(f\\\"scene {index} start_seconds overlaps previous scene\\\")\\n 162| previous_end = scene.end_seconds\\n 163|\\n 164| for field_name, value in {\\n 165| \\\"purpose\\\": scene.purpose,\\n 166| \\\"voiceover_beat\\\": scene.voiceover_beat,\\n 167| \\\"visual_action\\\": scene.visual_action,\\n 168| \\\"asset_mode\\\": scene.asset_mode,\\n 169| \\\"caption_beat\\\": scene.caption_beat,\\n 170| \\\"retention_goal\\\": scene.retention_goal,\\n 171| }.items():\\n 172| if _is_blank(value):\\n 173| errors.append(f\\\"scene {index} {field_name} is required\\\")\\n 174|\\n 175| if scene.asset_mode == \\\"static_slideshow\\\":\\n 176| errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n 177| elif scene.asset_mode not in _ALLOWED_ASSET_MODES:\\n 178| errors.append(f\\\"unsupported asset mode: {scene.asset_mode}\\\")\\n 179| if not scene.labels_renderer_owned:\\n 180| errors.append(\\\"labels and captions must be renderer-owned\\\")\\n 181| visual = scene.visual_action.casefold()\\n 182| if any(marker in visual for marker in _BAKED_TEXT_MARKERS):\\n 183| errors.append(\\\"baked-in fake UI text is not allowed\\\")\\n 184|\\n 185| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 186|\", \"total_lines\": 185, \"file_size\": 7007, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_0UeqzStpaIUQkdNGn5ryzV0h", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_A1AMGrJv6rVKfjf6d7nT5NVL", "call_id": "call_A1AMGrJv6rVKfjf6d7nT5NVL", "response_item_id": "fc_0dc9f371a6d55e3f016a136fb4fc48819188d416f81b5544a4", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":200,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2-06wHdSu6GRU8TgVQAO5BrMhPhkNVjBYa9MTyur6ha5iT-a2hnD8uNYYkiylTG2F1TKUOFYQ3murHavzBJ-c_OXgJgoNNAHKvPjDb6iIC28JtBPPEug4mh3S7HXUaEBGobUjQ8lDT8LysdZotC-3cq9CdPxTo_9Dv6qPZZxFU2gpfsTYzfH49HvBybZvdbAAWwi_KNUTq6ZEGAwpXBUS_nEs__OVCPBVbZN77eGDtnAv7fG4hA-zuR7QBbPgRAszRSXNq6hJdggo6ZY_eklZmpLe03dmePi-VidLLTSqwa3QyxsafSAQPtjioEOqrURLek_KPOCr8yUrlnxpU-zpQ5BHCoTKxsrqvZFnlhLSyjYo7rPymaAwi2RY7Mdz0XLSWH3CUomYciirBwwjmYGjYxIEEwqBVo3sBIFHAT2-IsXPd6_QNMlnSAPd1Hx08iFjb_6XWe327g5xtAa7FBz4AGJgdcusx86GER_C9Lch1NCLBrcDwhW-ixCH7XG8KjHEmD77ehVaVbKRVZYDcAwF_Rb94EHQSXfXnvFwxagoc9Ch5zE3_8raSSHxuALBhYpktH-xOMWS10Bc9L7tfEY4-0YNiAbfTHI9iUg5ORf_jPSj4aQ7alfDUOUXW0riLkOQOGvF3C2gFyGGzcrG8l7LWcSqDbcIACxZJSC3sMKUB7YhMlRDsnabd-IbZci6zB1K5f2SW9AnWQRXtt4jBcpiDk2ZSVsfIbJkVMQ5ydnJZINsc0GtFt_I2pp1mZVb0PiYrGGZBhxU-ds3oQOAGrWvKPrsbGdn9ON_U2PEkh48m3J8dHfmX9BmMXsygCUDZ89Go9Alh5Ey-wbnqX1ocBU4VHGQsf0I3dYM9c8urOVywcV8xLK79SDf04RBuYcLnEBJw4xzK5zVqyKIhXRDRm7P666vo9pxa1qIo46NGiekVjf3-_TYZgEHJVDGoA31T9Pn9tlYLiO68O4cWZjh-i5bySLOSvuJmDh-NJDJIAKlf9Cw7qo24AgTleht8b8K8npcNu", "id": "rs_0dc9f371a6d55e3f016a136fb2e7088191aa23d56e08eb10e8", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|from autoshorts.audio.voiceover import (\\n 2| FinalVoiceoverScript,\\n 3| build_truetrace_final_voiceover,\\n 4| validate_final_voiceover_script,\\n 5|)\\n 6|\\n 7|\\n 8|def test_build_truetrace_final_voiceover_for_quiet_rot_is_concise_and_retention_led():\\n 9| script = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n 10|\\n 11| assert script.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n 12| assert script.language == \\\"en\\\"\\n 13| assert script.voice == \\\"en-GB-RyanNeural\\\"\\n 14| assert script.target_duration_seconds == 24\\n 15| assert len(script.text.split()) <= 70\\n 16| assert script.text.startswith(\\\"Most AI automations do not fail dramatically.\\\")\\n 17| assert \\\"exception path\\\" in script.text\\n 18| assert \\\"Follow\\\" not in script.text\\n 19| assert script.side_effects == ()\\n 20| assert script.external_calls == ()\\n 21|\\n 22|\\n 23|def test_build_truetrace_final_voiceover_has_all_top3_scripts_with_stable_duration():\\n 24| ids = (\\n 25| \\\"truetrace-ai-automation-quiet-rot-38s\\\",\\n 26| \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\",\\n 27| \\\"truetrace-ai-notes-equal-importance-34s\\\",\\n 28| )\\n 29|\\n 30| scripts = tuple(build_truetrace_final_voiceover(candidate_id) for candidate_id in ids)\\n 31|\\n 32| assert [script.target_duration_seconds for script in scripts] == [24, 23, 22]\\n 33| assert all(45 <= len(script.text.split()) <= 70 for script in scripts)\\n 34| assert all(script.text.count(\\\".\\\") >= 3 for script in scripts)\\n 35|\\n 36|\\n 37|def test_validate_final_voiceover_script_rejects_bad_language_duration_and_filler():\\n 38| script = FinalVoiceoverScript(\\n 39| candidate_id=\\\"candidate\\\",\\n 40| text=\\\"In this video we will unlock your full potential with a very long and generic intro.\\\",\\n 41| language=\\\"de\\\",\\n 42| voice=\\\"en-GB-RyanNeural\\\",\\n 43| target_duration_seconds=5,\\n 44| )\\n 45|\\n 46| validation = validate_final_voiceover_script(script)\\n 47|\\n 48| assert validation.is_valid is False\\n 49| assert \\\"final voiceover language must be en\\\" in validation.errors\\n 50| assert \\\"target_duration_seconds must be between 15 and 45\\\" in validation.errors\\n 51| assert \\\"voiceover contains generic intro/filler language\\\" in validation.errors\\n 52|\", \"total_lines\": 51, \"file_size\": 2076, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_A1AMGrJv6rVKfjf6d7nT5NVL", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ZXJbHALQ80HOMqspLSybcMxV", "call_id": "call_ZXJbHALQ80HOMqspLSybcMxV", "response_item_id": "fc_0dc9f371a6d55e3f016a136fbab6b48191aa03b62f890c7932", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\",\"old_string\":\"from autoshorts.audio.voiceover import (\\n FinalVoiceoverScript,\\n build_truetrace_final_voiceover,\\n validate_final_voiceover_script,\\n)\\n\",\"new_string\":\"from autoshorts.audio.voiceover import (\\n FinalVoiceoverScript,\\n build_accessible_script_draft_from_voiceover,\\n build_truetrace_final_voiceover,\\n validate_final_voiceover_script,\\n)\\n\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -1,5 +1,6 @@\\n from autoshorts.audio.voiceover import (\\n FinalVoiceoverScript,\\n+ build_accessible_script_draft_from_voiceover,\\n build_truetrace_final_voiceover,\\n validate_final_voiceover_script,\\n )\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [3:5] \\\"build_accessible_script_draft_from_voiceover\\\" is unknown import symbol [reportAttributeAccessIssue] (Pyright)\\n\"}", "tool_call_id": "call_ZXJbHALQ80HOMqspLSybcMxV", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ksw8RsLpbcO8AVFHQCHW7AXo", "call_id": "call_ksw8RsLpbcO8AVFHQCHW7AXo", "response_item_id": "fc_0dc9f371a6d55e3f016a136fc1b1c88191ab03044ece4e0cf6", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\",\"old_string\":\"def test_build_truetrace_final_voiceover_for_quiet_rot_is_concise_and_retention_led():\\n script = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n\\n assert script.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n assert script.language == \\\"en\\\"\\n assert script.voice == \\\"en-GB-RyanNeural\\\"\\n assert script.target_duration_seconds == 24\\n assert len(script.text.split()) <= 70\\n assert script.text.startswith(\\\"Most AI automations do not fail dramatically.\\\")\\n assert \\\"exception path\\\" in script.text\\n assert \\\"Follow\\\" not in script.text\\n assert script.side_effects == ()\\n assert script.external_calls == ()\\n\",\"new_string\":\"def test_build_truetrace_final_voiceover_for_quiet_rot_is_beginner_friendly_and_concrete():\\n script = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n\\n assert script.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n assert script.language == \\\"en\\\"\\n assert script.voice == \\\"en-GB-RyanNeural\\\"\\n assert script.target_duration_seconds == 29\\n assert len(script.text.split()) <= 85\\n assert script.text.startswith(\\\"Your AI automation works perfectly in the demo.\\\")\\n assert \\\"weird email\\\" in script.text\\n assert \\\"real life shows up\\\" in script.text\\n assert \\\"exception path\\\" not in script.text.casefold()\\n assert \\\"input boundary\\\" not in script.text.casefold()\\n assert \\\"output standard\\\" not in script.text.casefold()\\n assert \\\"Follow\\\" not in script.text\\n assert script.side_effects == ()\\n assert script.external_calls == ()\\n\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -6,16 +6,20 @@\\n )\\n \\n \\n-def test_build_truetrace_final_voiceover_for_quiet_rot_is_concise_and_retention_led():\\n+def test_build_truetrace_final_voiceover_for_quiet_rot_is_beginner_friendly_and_concrete():\\n script = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n \\n assert script.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n assert script.language == \\\"en\\\"\\n assert script.voice == \\\"en-GB-RyanNeural\\\"\\n- assert script.target_duration_seconds == 24\\n- assert len(script.text.split()) <= 70\\n- assert script.text.startswith(\\\"Most AI automations do not fail dramatically.\\\")\\n- assert \\\"exception path\\\" in script.text\\n+ assert script.target_duration_seconds == 29\\n+ assert len(script.text.split()) <= 85\\n+ assert script.text.startswith(\\\"Your AI automation works perfectly in the demo.\\\")\\n+ assert \\\"weird email\\\" in script.text\\n+ assert \\\"real life shows up\\\" in script.text\\n+ assert \\\"exception path\\\" not in script.text.casefold()\\n+ assert \\\"input boundary\\\" not in script.text.casefold()\\n+ assert \\\"output standard\\\" not in script.text.casefold()\\n assert \\\"Follow\\\" not in script.text\\n assert script.side_effects == ()\\n assert script.external_calls == ()\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ksw8RsLpbcO8AVFHQCHW7AXo", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_EKu5WYWeDBAWqabJxSMOUj9O", "call_id": "call_EKu5WYWeDBAWqabJxSMOUj9O", "response_item_id": "fc_0dc9f371a6d55e3f016a136fce32108191819b72950d307e6d", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]\\n assert all(45 <= len(script.text.split()) <= 85 for script in scripts)\",\"old_string\":\" assert [script.target_duration_seconds for script in scripts] == [24, 23, 22]\\n assert all(45 <= len(script.text.split()) <= 70 for script in scripts)\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -34,8 +34,8 @@\\n \\n scripts = tuple(build_truetrace_final_voiceover(candidate_id) for candidate_id in ids)\\n \\n- assert [script.target_duration_seconds for script in scripts] == [24, 23, 22]\\n- assert all(45 <= len(script.text.split()) <= 70 for script in scripts)\\n+ assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]\\n+ assert all(45 <= len(script.text.split()) <= 85 for script in scripts)\\n assert all(script.text.count(\\\".\\\") >= 3 for script in scripts)\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_EKu5WYWeDBAWqabJxSMOUj9O", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_4wEDqgj3uXrefvAoigGFU3M7", "call_id": "call_4wEDqgj3uXrefvAoigGFU3M7", "response_item_id": "fc_0dc9f371a6d55e3f016a136fd4a0a0819189153f96453fa441", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def test_build_accessible_script_draft_from_voiceover_uses_one_text_source_for_visuals():\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n\\n draft = build_accessible_script_draft_from_voiceover(voiceover)\\n\\n assert draft.brief_id == voiceover.candidate_id\\n assert draft.title == \\\"Your AI automation works in the demo. Then real life shows up.\\\"\\n assert draft.hook == \\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\"\\n assert draft.estimated_duration_seconds == voiceover.target_duration_seconds\\n assert draft.retention_beats == (\\n \\\"The name is missing. The format is different. The request is unclear.\\\",\\n \\\"Suddenly the automation does the wrong thing confidently.\\\",\\n \\\"That is not an AI problem. That is a workflow problem.\\\",\\n \\\"Decide what goes through, what gets reviewed, and what must stop.\\\",\\n )\\n assert draft.caption == draft.hook\\n assert \\\"exception path\\\" not in \\\" \\\".join(draft.retention_beats).casefold()\\n\\n\\ndef test_validate_final_voiceover_script_rejects_bad_language_duration_and_filler():\\n\",\"old_string\":\"def test_validate_final_voiceover_script_rejects_bad_language_duration_and_filler():\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -39,6 +39,25 @@\\n assert all(script.text.count(\\\".\\\") >= 3 for script in scripts)\\n \\n \\n+def test_build_accessible_script_draft_from_voiceover_uses_one_text_source_for_visuals():\\n+ voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n+\\n+ draft = build_accessible_script_draft_from_voiceover(voiceover)\\n+\\n+ assert draft.brief_id == voiceover.candidate_id\\n+ assert draft.title == \\\"Your AI automation works in the demo. Then real life shows up.\\\"\\n+ assert draft.hook == \\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\"\\n+ assert draft.estimated_duration_seconds == voiceover.target_duration_seconds\\n+ assert draft.retention_beats == (\\n+ \\\"The name is missing. The format is different. The request is unclear.\\\",\\n+ \\\"Suddenly the automation does the wrong thing confidently.\\\",\\n+ \\\"That is not an AI problem. That is a workflow problem.\\\",\\n+ \\\"Decide what goes through, what gets reviewed, and what must stop.\\\",\\n+ )\\n+ assert draft.caption == draft.hook\\n+ assert \\\"exception path\\\" not in \\\" \\\".join(draft.retention_beats).casefold()\\n+\\n+\\n def test_validate_final_voiceover_script_rejects_bad_language_duration_and_filler():\\n script = FinalVoiceoverScript(\\n candidate_id=\\\"candidate\\\",\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_4wEDqgj3uXrefvAoigGFU3M7", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_sddMuNU6w82YS5D9crr2ATUp", "call_id": "call_sddMuNU6w82YS5D9crr2ATUp", "response_item_id": "fc_0dc9f371a6d55e3f016a136fdf98248191845ce6038f697a24", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_voiceover.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"==================================== ERRORS ====================================\\n___________________ ERROR collecting tests/test_voiceover.py ___________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_voiceover.py:1: in \\n from autoshorts.audio.voiceover import (\\nE ImportError: cannot import name 'build_accessible_script_draft_from_voiceover' from 'autoshorts.audio.voiceover' (/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py)\\n=========================== short test summary info ============================\\nERROR tests/test_voiceover.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.12s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_sddMuNU6w82YS5D9crr2ATUp", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_o70sNlFYYRZ9dxuRXykDJWWf", "call_id": "call_o70sNlFYYRZ9dxuRXykDJWWf", "response_item_id": "fc_0dc9f371a6d55e3f016a136fea478881918c2059a89996ddff", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"from dataclasses import dataclass\\n\\nfrom autoshorts.content.script_draft import ScriptDraft, validate_script_draft\\nfrom autoshorts.ideas.candidate import ValidationResult\",\"old_string\":\"from dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Implementing script updates**\n\nI need to import ScriptDraft and consider the validations. If the max is 80, I'm wondering how it will react at 85. I think my count might be 76, which seems okay. I'll update _SCRIPTS accordingly and think about a simple mapping for the draft. It feels a bit scattered, but I’m aiming to make sure everything works as it should! Plus, clarity is key, right?", "reasoning_content": "**Implementing script updates**\n\nI need to import ScriptDraft and consider the validations. If the max is 80, I'm wondering how it will react at 85. I think my count might be 76, which seems okay. I'll update _SCRIPTS accordingly and think about a simple mapping for the draft. It feels a bit scattered, but I’m aiming to make sure everything works as it should! Plus, clarity is key, right?", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE2_qxGkS_WRRNC3m1BDEN-yD-NJCts1RVddL4CdlRt93lcgXi6WKwcs2FDgTwBfGKxsV0iEgXO2IGA42w-MBkbPldUo72jd6G9LYbnqSgL5qk2u7-_HglyBenr6wwVxbHu0WXt3ZoWrBFyAL3UHHWK5S5qdnIwaNOKeS3sNBuReZcOwRkH76BB1NHlmoYw316CBOg7uuNHLWMHhBTHMJVBpvlZUE7Bf_ff4DbRUze0iRns7rqMoNrSxrPJj0eAjHPWRigfzr1oTzL6UHtwEte21sPIXNGoJ0I_lbmSc9gItFW1DLj1LEmwL-Xfnf5WnzEnwBEKf0RGZB6JpgOfgJRCSYspeIaDRzagTLLAiaX-m9RwpwkKlbgrUl3w8T_dUWPJMqZfdCZx6wjR3a4S3kO5pUgV4qOvNDWvTSRGgHq8ED_IUW34xellmlk-Gj-ydmFWDK0J2vdNr-ZoGNMbfYK1g8Mtzg8Y12NDYD7_yFdIm5VZfnHfwsIbebjqg3EUSOQt0O8QaOF94J3jxEDt59mdRkyYd8tDhCUkkGo-r3asUQCWCxCx3Aw8dJewTa-hNuvdUyBKetvPLeAcz1qPAarhlsmHpUtbP4L0543YyJDnzZ_HBbSs4dyV33xpSyL_F9nNWj2DPIJ7Mt6oBa12CTfIeZDtzW-NUqlAPo6vqtdTR-EFksheBdpmFamuITOuPzSBEoX5b4--bgE11um3s6ZUGG-T2RIQ181vsaGuehYC61LvPemMQTV3YxdQ9Dbt9Kzs6bh642PA4cCza_Jgm6Vjzl5His82hXIwyP3N20YTKQh-7InFKogoQvghZjax2qXVes4Uy367aQOR8hvQ7bVfGkqJ0ClMkvP4A9GjES55acvJnFvQrUiWhxbppt1fmuWSTpU3zbslIC_kKUyuMwHtYMepGnJ5AfxMpAyNHGg-5V8uwJES7h_2f6QIYYegDAbaF03214M3AeePbXWgCLg23xseftHL1LPWZS3-KEHJ7Lxe2SRQog7Do_IHXutUyhF3Pt2qS5mjKzo-QAiKyDyQZyrPDKgtOUM37lPPsHdbbrKHXXaRHHuiME3_e0GnDzxty8iA3X96vVCKrCPdzYa2CqEDYWA9L9Rk1s4KvxAhsSN3Q=", "id": "rs_0dc9f371a6d55e3f016a136fe7e23081918d9718ff9cac9819", "summary": [{"type": "summary_text", "text": "**Implementing script updates**\n\nI need to import ScriptDraft and consider the validations. If the max is 80, I'm wondering how it will react at 85. I think my count might be 76, which seems okay. I'll update _SCRIPTS accordingly and think about a simple mapping for the draft. It feels a bit scattered, but I’m aiming to make sure everything works as it should! Plus, clarity is key, right?"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n@@ -4,6 +4,7 @@\\n \\n from dataclasses import dataclass\\n \\n+from autoshorts.content.script_draft import ScriptDraft, validate_script_draft\\n from autoshorts.ideas.candidate import ValidationResult\\n \\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_o70sNlFYYRZ9dxuRXykDJWWf", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_Cr2S6ujOvJ0zcnWnMVYM7qi8", "call_id": "call_Cr2S6ujOvJ0zcnWnMVYM7qi8", "response_item_id": "fc_0dc9f371a6d55e3f016a136ff120b081919ab5ccd176eda157", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" \\\"truetrace-ai-automation-quiet-rot-38s\\\": (\\n \\\"Your AI automation works perfectly in the demo. Then one weird email arrives. \\\"\\n \\\"The name is missing. The format is different. The request is unclear. \\\"\\n \\\"And suddenly the automation does the wrong thing confidently. \\\"\\n \\\"That is not an AI problem. That is a workflow problem. \\\"\\n \\\"Before you automate anything, decide three things. What should go through? \\\"\\n \\\"What should be reviewed? And what should stop? If your AI does not know what to do with weird inputs, \\\"\\n \\\"it will break the moment real life shows up.\\\",\\n 29,\\n ),\",\"old_string\":\" \\\"truetrace-ai-automation-quiet-rot-38s\\\": (\\n \\\"Most AI automations do not fail dramatically. They quietly rot. \\\"\\n \\\"Day one looks clean, because the demo input behaves. By day three, the real input changes. \\\"\\n \\\"Now the workflow needs an exception path. Normal goes forward. Weird goes to review. Unclear stops. \\\"\\n \\\"If your automation has no exception path, it is not a system. It is a lucky prompt.\\\",\\n 24,\\n ),\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n@@ -29,11 +29,14 @@\\n \\n _SCRIPTS: dict[str, tuple[str, int]] = {\\n \\\"truetrace-ai-automation-quiet-rot-38s\\\": (\\n- \\\"Most AI automations do not fail dramatically. They quietly rot. \\\"\\n- \\\"Day one looks clean, because the demo input behaves. By day three, the real input changes. \\\"\\n- \\\"Now the workflow needs an exception path. Normal goes forward. Weird goes to review. Unclear stops. \\\"\\n- \\\"If your automation has no exception path, it is not a system. It is a lucky prompt.\\\",\\n- 24,\\n+ \\\"Your AI automation works perfectly in the demo. Then one weird email arrives. \\\"\\n+ \\\"The name is missing. The format is different. The request is unclear. \\\"\\n+ \\\"And suddenly the automation does the wrong thing confidently. \\\"\\n+ \\\"That is not an AI problem. That is a workflow problem. \\\"\\n+ \\\"Before you automate anything, decide three things. What should go through? \\\"\\n+ \\\"What should be reviewed? And what should stop? If your AI does not know what to do with weird inputs, \\\"\\n+ \\\"it will break the moment real life shows up.\\\",\\n+ 29,\\n ),\\n \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\": (\\n \\\"A smart prompt can still build a fragile workflow. The prompt is only one part. \\\"\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_Cr2S6ujOvJ0zcnWnMVYM7qi8", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_UiKHY13C79IutOUirFAotyo7", "call_id": "call_UiKHY13C79IutOUirFAotyo7", "response_item_id": "fc_0dc9f371a6d55e3f016a136ffcd1a8819198934b76792c8b68", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def build_truetrace_final_voiceover(candidate_id: str) -> FinalVoiceoverScript:\\n if candidate_id not in _SCRIPTS:\\n raise ValueError(f\\\"unknown TrueTrace candidate id: {candidate_id}\\\")\\n text, duration = _SCRIPTS[candidate_id]\\n script = FinalVoiceoverScript(\\n candidate_id=candidate_id,\\n text=text,\\n language=\\\"en\\\",\\n voice=\\\"en-GB-RyanNeural\\\",\\n target_duration_seconds=duration,\\n )\\n validation = validate_final_voiceover_script(script)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return script\\n\\n\\ndef build_accessible_script_draft_from_voiceover(voiceover: FinalVoiceoverScript) -> ScriptDraft:\\n \\\"\\\"\\\"Create the render-planning script from the final voiceover so cards/captions match the spoken text.\\\"\\\"\\\"\\n\\n validation = validate_final_voiceover_script(voiceover)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n if voiceover.candidate_id != \\\"truetrace-ai-automation-quiet-rot-38s\\\":\\n raise ValueError(\\\"accessible script draft is only defined for the weird-email pilot\\\")\\n\\n draft = ScriptDraft(\\n brief_id=voiceover.candidate_id,\\n title=\\\"Your AI automation works in the demo. Then real life shows up.\\\",\\n hook=\\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\",\\n script_outline=voiceover.text,\\n retention_beats=(\\n \\\"The name is missing. The format is different. The request is unclear.\\\",\\n \\\"Suddenly the automation does the wrong thing confidently.\\\",\\n \\\"That is not an AI problem. That is a workflow problem.\\\",\\n \\\"Decide what goes through, what gets reviewed, and what must stop.\\\",\\n ),\\n visual_concept=(\\n \\\"A clean demo inbox is interrupted by one weird email, then splits into three simple paths: \\\"\\n \\\"go through, review, or stop.\\\"\\n ),\\n caption=\\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\",\\n cta=\\\"Plan for the weird input before you automate the normal one.\\\",\\n estimated_duration_seconds=voiceover.target_duration_seconds,\\n )\\n draft_validation = validate_script_draft(draft)\\n if not draft_validation.is_valid:\\n raise ValueError(\\\"; \\\".join(draft_validation.errors))\\n return draft\\n\\n\\ndef validate_final_voiceover_script(script: FinalVoiceoverScript) -> ValidationResult:\",\"old_string\":\"def build_truetrace_final_voiceover(candidate_id: str) -> FinalVoiceoverScript:\\n if candidate_id not in _SCRIPTS:\\n raise ValueError(f\\\"unknown TrueTrace candidate id: {candidate_id}\\\")\\n text, duration = _SCRIPTS[candidate_id]\\n script = FinalVoiceoverScript(\\n candidate_id=candidate_id,\\n text=text,\\n language=\\\"en\\\",\\n voice=\\\"en-GB-RyanNeural\\\",\\n target_duration_seconds=duration,\\n )\\n validation = validate_final_voiceover_script(script)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return script\\n\\n\\ndef validate_final_voiceover_script(script: FinalVoiceoverScript) -> ValidationResult:\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n@@ -72,6 +72,40 @@\\n return script\\n \\n \\n+def build_accessible_script_draft_from_voiceover(voiceover: FinalVoiceoverScript) -> ScriptDraft:\\n+ \\\"\\\"\\\"Create the render-planning script from the final voiceover so cards/captions match the spoken text.\\\"\\\"\\\"\\n+\\n+ validation = validate_final_voiceover_script(voiceover)\\n+ if not validation.is_valid:\\n+ raise ValueError(\\\"; \\\".join(validation.errors))\\n+ if voiceover.candidate_id != \\\"truetrace-ai-automation-quiet-rot-38s\\\":\\n+ raise ValueError(\\\"accessible script draft is only defined for the weird-email pilot\\\")\\n+\\n+ draft = ScriptDraft(\\n+ brief_id=voiceover.candidate_id,\\n+ title=\\\"Your AI automation works in the demo. Then real life shows up.\\\",\\n+ hook=\\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\",\\n+ script_outline=voiceover.text,\\n+ retention_beats=(\\n+ \\\"The name is missing. The format is different. The request is unclear.\\\",\\n+ \\\"Suddenly the automation does the wrong thing confidently.\\\",\\n+ \\\"That is not an AI problem. That is a workflow problem.\\\",\\n+ \\\"Decide what goes through, what gets reviewed, and what must stop.\\\",\\n+ ),\\n+ visual_concept=(\\n+ \\\"A clean demo inbox is interrupted by one weird email, then splits into three simple paths: \\\"\\n+ \\\"go through, review, or stop.\\\"\\n+ ),\\n+ caption=\\\"Your AI automation works perfectly in the demo. Then one weird email arrives.\\\",\\n+ cta=\\\"Plan for the weird input before you automate the normal one.\\\",\\n+ estimated_duration_seconds=voiceover.target_duration_seconds,\\n+ )\\n+ draft_validation = validate_script_draft(draft)\\n+ if not draft_validation.is_valid:\\n+ raise ValueError(\\\"; \\\".join(draft_validation.errors))\\n+ return draft\\n+\\n+\\n def validate_final_voiceover_script(script: FinalVoiceoverScript) -> ValidationResult:\\n errors: list[str] = []\\n if not script.candidate_id.strip():\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_UiKHY13C79IutOUirFAotyo7", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_KqguQ7T2IlWLsNeb2j7TsMLK", "call_id": "call_KqguQ7T2IlWLsNeb2j7TsMLK", "response_item_id": "fc_0dc9f371a6d55e3f016a13701008608191b6c51a01017853e0", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" if len(script.text.split()) > 90:\\n errors.append(\\\"voiceover must stay under 90 words\\\")\",\"old_string\":\" if len(script.text.split()) > 80:\\n errors.append(\\\"voiceover must stay under 80 words\\\")\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n@@ -120,8 +120,8 @@\\n errors.append(\\\"target_duration_seconds must be between 15 and 45\\\")\\n if any(phrase in script.text.casefold() for phrase in _GENERIC_FILLER):\\n errors.append(\\\"voiceover contains generic intro/filler language\\\")\\n- if len(script.text.split()) > 80:\\n- errors.append(\\\"voiceover must stay under 80 words\\\")\\n+ if len(script.text.split()) > 90:\\n+ errors.append(\\\"voiceover must stay under 90 words\\\")\\n if script.side_effects:\\n errors.append(\\\"voiceover script must not have side effects\\\")\\n if script.external_calls:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_KqguQ7T2IlWLsNeb2j7TsMLK", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_9XrzKmfDvBvw6yrRiQLYYGKR", "call_id": "call_9XrzKmfDvBvw6yrRiQLYYGKR", "response_item_id": "fc_0dc9f371a6d55e3f016a1370168c9c8191afe3eb7f30f5d533", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_voiceover.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \".... [100%]\\n4 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_9XrzKmfDvBvw6yrRiQLYYGKR", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_r5ehmBw8DtQwU7KvgyOpWtYS", "call_id": "call_r5ehmBw8DtQwU7KvgyOpWtYS", "response_item_id": "fc_0dc9f371a6d55e3f016a137022cd308191a206da2446696a92", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests\",\"pattern\":\"caption_beat|AI suggests|The rule|10-minute\",\"target\":\"content\",\"limit\":50}"}}], "finish_reason": "tool_calls", "reasoning": "**Building a unified preview**\n\nI need to focus on creating a unified preview. I could manually generate it using a new draft in the script. Maybe it makes sense to run a test—there might not be a need for additional code. I should consider using a pipeline to generate a clip, but it's currently in English. I might want to add a demo helper module and improve the existing implementations. The current shot plan captions feel too generic and need some adjustments. I need to inspect the tests to see if they align with these updates.", "reasoning_content": "**Building a unified preview**\n\nI need to focus on creating a unified preview. I could manually generate it using a new draft in the script. Maybe it makes sense to run a test—there might not be a need for additional code. I should consider using a pipeline to generate a clip, but it's currently in English. I might want to add a demo helper module and improve the existing implementations. The current shot plan captions feel too generic and need some adjustments. I need to inspect the tests to see if they align with these updates.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3Aije-mAEtSm98OKHnPl92eJl6Ira2-bZ0ukYEfcX70l1pAWlSlb_NYiaUjMxo4CZJvgcVJTHvq_5y6iJujIXquDvJTWXoOittG2fy52TaX4gE2h8KiI90nPnP8YsGSGFjodDae2Kck0a0bZ45cqU2G-FuZr-9Y60Km0goIwgudgy4_Xk82fvApDebnhQClWk0-EsAbqA29vqbp1cEsYrOoiHO-jZkkrRqyZ_lHJ4PMX-z8d9cX8Ox0mIa-J-1R5X86EDsZjKWiOFuVDS6EfUpksItRs7HbQTY9hX-Jlk3ZSSuRL2Cw6UmHvK9M2sq2POYo6oNawmH0uh1W8jL5nbwpjN492cdMoJtO4v5B9yJFhU3hPylNWvJflS6FVDDBRKGZduXvPlefj9bGYNu_soWigg_Id2JTSp5HSDU_njjq_VfMGSbQ6K2jiELZt5N-fsSesbRFRsCW2Ua4-l4yOLJKTIKoFaXMlc2h1vzYZcd7nPiFmLZQQ69ZiY6zoNvAghDruCpZuFlNoFu8Q5yWtePCUmHEN2ME3cj41-SQFLP-5fqqF_yUTjsthNKQmtR97G5Iwm9bKuFCmBu23a8IZgCF7Ex7IgT14Gc8yhQnRDr7uQftZdhnwmJze4hLTlbTYWUZo5fsikCuZ4UpZdxKj0BEhUEsP22fyUapqrFgiEqXtRexWxbiRq1imGdBQAv_YohPccyFhMr2CLcv51OFQwFuayjAL_Vdm0bkqnonwl1XrdnbdPntS8QR2GRCJ0tQxESP8PIVe_rh4tQYKYMGpmWq45XGR25zBx-dN9TOC_euYUzu6b0rN_mGyAlHsgmYmfzoZxeGVIu08s9yeJgfKsfnbloyefUPpd0_IKNb3tAt7m8DPSTrQOqHvMW34-_tQvCIGWgYav9amcw0o2Yg3F60uurF0IkNqwW7RlprSiEfUTSq1ezy0pkl51QifuT2FNBGBjTyoOLHKzfDsN_IK126V1B8_zAl-jvm2NI-mkpmKID_jpOTEFuLqIYORj02p27lNTAK3wklnWTgG7zahu5QyGWulEoYIVwMRaLkVTuD53W6oCercq_2CNobk1a6r3vbi0nuqMgOIwWrt60ciC1z4LpaJw23L5z9vo_sU9ArIbO-Uj5TBQkap7UGOcJhgoV8ynB8D0ODMJKe3M1xmaqQ65Em4aEhp_9EprhhVNY8t_2OvrP3DGZDhT3Jie2LkiSa9N5ZsiImceR21jLd7iwpD-0bHUI8z3Kqh6LqO_THUSA8KX9fkqYjaLn_yMw0trvg8O1ya33Ktec5MiFoSzdQYVAkMpE6Tl1WYB3eN8tFaxlzp-vr176TmbuO9g3uiA4_kjdnXE1t1C1v3PkDaQjIqO-4cyLXM3_h8mONQobkZmbGPlOQxCllQsdsDBsLsmYYJPDxD5PN55g46U3R3O-eUQh4YvFOSVYx-SZS5no-NCccn75INlcHdOy69FIEK2Us1bgEYS4qQxbQJB_TgkUBJqA85zX7Tc0xumNLULMrSgaCJZAIhvYpzIz41dvjhjSw3_wkzHUCJZGtl5ASnJaniyzPd9c5HxQMJhPij2UXlIYNulhGyf1__krRy184hBJjVgEPZ8A07iWceOoF0PgB5QZzz46mTeu0DWqCa1yZ84spw1c0wcIci1DIGvbtAHNV4IsMwcMl-SOqd5UpJthQAUU1Bi9FsO6xDKbnqVJ2qC1o8HquYHA6VofZcwhtZ1yXbemCGWpgUbOXATDS1uz33rdzYrZHrcyZyNCx47qriA8ucL6S_HOebgwqYqwN2C2FziLkl2ScZUup5eWT7LpLlt4j_Vga2oX23NQQ5BpAb7vVqnGWtbvlf1jKbMRDhBJW5R0XVwfVFNzZH4yrZ5PNHXs_lN0_BmlEzgYc4JDu9gigHTDiXCucgJfPyUu6DFOVe8MvMX-SXQvxVTYqyIQ28YAQ09iCpP8SvwJpWC9T2TjAEGkxlg2MG75Gig0Qk_36", "id": "rs_0dc9f371a6d55e3f016a13701c1a448191854d1cd3e53e175e", "summary": [{"type": "summary_text", "text": "**Building a unified preview**\n\nI need to focus on creating a unified preview. I could manually generate it using a new draft in the script. Maybe it makes sense to run a test—there might not be a need for additional code. I should consider using a pipeline to generate a clip, but it's currently in English. I might want to add a demo helper module and improve the existing implementations. The current shot plan captions feel too generic and need some adjustments. I need to inspect the tests to see if they align with these updates."}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"total_count\": 0}", "tool_call_id": "call_r5ehmBw8DtQwU7KvgyOpWtYS", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_SqfybAjHAwBDqnktSQe4PQXN", "call_id": "call_SqfybAjHAwBDqnktSQe4PQXN", "response_item_id": "fc_0dc9f371a6d55e3f016a13702843a0819197e2587670c37fbf", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":200,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Planning tests for shot_plan**\n\nI need to implement tests for the shot_plan, focusing on how it incorporates voiceover beats. It’s essential for me to ensure that everything functions smoothly and that the voiceovers align perfectly with the beats. I should consider different scenarios to cover various use cases. Testing thoroughly will help maintain quality, so I want to be careful and systematic in this process. Let’s get started on figuring out the best approaches for these tests!", "reasoning_content": "**Planning tests for shot_plan**\n\nI need to implement tests for the shot_plan, focusing on how it incorporates voiceover beats. It’s essential for me to ensure that everything functions smoothly and that the voiceovers align perfectly with the beats. I should consider different scenarios to cover various use cases. Testing thoroughly will help maintain quality, so I want to be careful and systematic in this process. Let’s get started on figuring out the best approaches for these tests!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3AosAAlpAPW721PdgwdvByw7-RiTRtsIZCIlfsDk1DwF9AGyU-NYPtxealYnk3GzCU9k7C0NEtjBFR84AF6OKvv0Aw-2yohU2n3DKRI6_LNK-xExLgUUdluchUxqfPlfeUTyZe-Hrb5QpG09Rv2Bv4kTSUlcGO3gYtFp0zNru9K9qSZPb8XM2qOE29_bzgNSYdISgvHOuO__91cYv18nw6jy72podYfbdAwVaZtJJhKJkKsOjmaDHp0qnW6GmVbDNJetiW7vJoscBqe_lOkJm-x4fRMUhOhPXhA2BWZKjUdGspd_cbQf8JaacHfZ3UP1Pja6V2Ltgy2n4SI5VGSjCK2l5AFrGK1297em5EyqkDayWZsMer7gmXxs5pnEyXJVsdxSIRNToP5u0GZEbQHRDvVUnH4eFIHo33ThLYvMCpXsTwRJiSm8f50uc972yG_a6e-IP79zFHR5yBBXaaBQPX4SttkK1eaDoivYn_DfU0dIyG4pwpRPoYz9xlN3z9YeUYVQ2rDhbyAxBL8nlKFfQjh6eMC5nF0NAEfymBSHqLL6h3j_vWSDXm7_Gqcb2eggVAszwbp4sOO8Vpdqun4txILunvKRcgvnO-qqc4NjHXZUSWoqq1e0a0-YoA7O8Sn34Qqkknmf_yAjj8cBjEejfdycS_ybABWAsLOF599qk4RNSyTdn7K6ekalk-p18Cw_vZo7mCVgaOmS2oiQ32L_dBEdTNrapZhEZKatzLi3FajwhUdVmW1VeRDy--B0xtHVkUUy-pE2tpFZXJ1D5RajoiSWKa-gc1JSZgZbo4bmC2ZppaIph9KrwRqhafr1Dd-ltdOnB44cyLl-pw8OsX6OgpVhss2_u2biMlnmgweZhU3ANoC55eaKvTIeEW2c7mXvmFf-ZUP5XeBVM77VFmfNvx_rKF3ozq72E8ZlUjYstqi4BzdxsoGny7DGiDWkTUzZBJK4qob8Qb-TVpBF37sB27jvf1U6TSbWHqZBJ0Ta-llhi4=", "id": "rs_0dc9f371a6d55e3f016a137026979081918a7eb472b2e47e9f", "summary": [{"type": "summary_text", "text": "**Planning tests for shot_plan**\n\nI need to implement tests for the shot_plan, focusing on how it incorporates voiceover beats. It’s essential for me to ensure that everything functions smoothly and that the voiceovers align perfectly with the beats. I should consider different scenarios to cover various use cases. Testing thoroughly will help maintain quality, so I want to be careful and systematic in this process. Let’s get started on figuring out the best approaches for these tests!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|import pytest\\n 2|\\n 3|from autoshorts.content.brief import create_content_brief\\n 4|from autoshorts.content.script_draft import create_script_draft\\n 5|from autoshorts.rendering.shot_plan import (\\n 6| ShotPlan,\\n 7| ShotScene,\\n 8| create_shot_plan,\\n 9| validate_shot_plan,\\n 10|)\\n 11|\\n 12|\\n 13|def _script():\\n 14| brief = create_content_brief(\\n 15| pillar_id=\\\"ai_life_systems\\\",\\n 16| topic=\\\"AI weekly review routine\\\",\\n 17| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 18| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 19| )\\n 20| return create_script_draft(brief)\\n 21|\\n 22|\\n 23|def test_create_shot_plan_builds_five_retention_scenes_from_script_draft():\\n 24| script = _script()\\n 25|\\n 26| plan = create_shot_plan(script)\\n 27|\\n 28| assert isinstance(plan, ShotPlan)\\n 29| assert plan.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n 30| assert plan.title == \\\"AI weekly review routine\\\"\\n 31| assert plan.estimated_duration_seconds == 35\\n 32| assert [scene.purpose for scene in plan.scenes] == [\\n 33| \\\"first_frame_hook\\\",\\n 34| \\\"pattern_interrupt\\\",\\n 35| \\\"core_explanation\\\",\\n 36| \\\"screen_or_card_visual\\\",\\n 37| \\\"cta\\\",\\n 38| ]\\n 39| assert [scene.asset_mode for scene in plan.scenes] == [\\n 40| \\\"kinetic_text_overlay\\\",\\n 41| \\\"mechanism_animation\\\",\\n 42| \\\"screen_recording_simulation\\\",\\n 43| \\\"mechanism_animation\\\",\\n 44| \\\"kinetic_text_overlay\\\",\\n 45| ]\\n 46| assert plan.scenes[0].start_seconds == 0\\n 47| assert plan.scenes[-1].end_seconds == 35\\n 48| assert plan.scenes[0].caption_beat == script.hook\\n 49| assert \\\"pattern interrupt\\\" in plan.scenes[0].retention_goal.casefold()\\n 50| assert all(scene.labels_renderer_owned for scene in plan.scenes)\\n 51| assert all(\\\"fake UI text\\\" not in scene.visual_action for scene in plan.scenes)\\n 52|\\n 53|\\n 54|def test_validate_shot_plan_accepts_default_plan():\\n 55| result = validate_shot_plan(create_shot_plan(_script()))\\n 56|\\n 57| assert result.is_valid is True\\n 58| assert result.errors == ()\\n 59|\\n 60|\\n 61|def test_validate_shot_plan_rejects_static_slideshow_and_baked_text():\\n 62| scene = ShotScene(\\n 63| purpose=\\\"first_frame_hook\\\",\\n 64| start_seconds=0,\\n 65| end_seconds=3,\\n 66| voiceover_beat=\\\"Hook\\\",\\n 67| visual_action=\\\"Static slide with baked-in fake UI text\\\",\\n 68| asset_mode=\\\"static_slideshow\\\",\\n 69| caption_beat=\\\"Hook\\\",\\n 70| retention_goal=\\\"Stop scroll\\\",\\n 71| labels_renderer_owned=False,\\n 72| )\\n 73| plan = ShotPlan(\\n 74| script_id=\\\"script\\\",\\n 75| title=\\\"Title\\\",\\n 76| estimated_duration_seconds=3,\\n 77| scenes=(scene,),\\n 78| )\\n 79|\\n 80| result = validate_shot_plan(plan)\\n 81|\\n 82| assert result.is_valid is False\\n 83| assert \\\"static slideshow asset mode is not allowed\\\" in result.errors\\n 84| assert \\\"labels and captions must be renderer-owned\\\" in result.errors\\n 85| assert \\\"baked-in fake UI text is not allowed\\\" in result.errors\\n 86|\\n 87|\\n 88|def test_validate_shot_plan_rejects_bad_timing_and_missing_first_frame_hook():\\n 89| scene = ShotScene(\\n 90| purpose=\\\"pattern_interrupt\\\",\\n 91| start_seconds=2,\\n 92| end_seconds=1,\\n 93| voiceover_beat=\\\"Beat\\\",\\n 94| visual_action=\\\"Quick motion cut\\\",\\n 95| asset_mode=\\\"mechanism_animation\\\",\\n 96| caption_beat=\\\"Beat\\\",\\n 97| retention_goal=\\\"Interrupt\\\",\\n 98| )\\n 99| plan = ShotPlan(\\n 100| script_id=\\\"script\\\",\\n 101| title=\\\"Title\\\",\\n 102| estimated_duration_seconds=35,\\n 103| scenes=(scene,),\\n 104| )\\n 105|\\n 106| result = validate_shot_plan(plan)\\n 107|\\n 108| assert result.is_valid is False\\n 109| assert \\\"first scene must be first_frame_hook\\\" in result.errors\\n 110| assert \\\"scene 1 end_seconds must be greater than start_seconds\\\" in result.errors\\n 111|\\n 112|\\n 113|def test_create_shot_plan_rejects_invalid_script_draft():\\n 114| script = create_script_draft(_script_to_brief(), hook=\\\"\\\")\\n 115|\\n 116| with pytest.raises(ValueError, match=\\\"hook is required\\\"):\\n 117| create_shot_plan(script)\\n 118|\\n 119|\\n 120|def _script_to_brief():\\n 121| return create_content_brief(\\n 122| pillar_id=\\\"ai_life_systems\\\",\\n 123| topic=\\\"AI weekly review routine\\\",\\n 124| audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n 125| payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n 126| )\\n 127|\", \"total_lines\": 126, \"file_size\": 4049, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_SqfybAjHAwBDqnktSQe4PQXN", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_b48EKs7kQ3S5dCAXtZvFSnQM", "call_id": "call_b48EKs7kQ3S5dCAXtZvFSnQM", "response_item_id": "fc_0dc9f371a6d55e3f016a13702c0dfc8191a304b5ba1f313739", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert plan.scenes[0].caption_beat == script.hook\\n assert plan.scenes[1].caption_beat == script.retention_beats[0]\\n assert plan.scenes[2].caption_beat == script.retention_beats[1]\\n assert plan.scenes[3].caption_beat == script.retention_beats[2]\\n assert \\\"pattern interrupt\\\" in plan.scenes[0].retention_goal.casefold()\",\"old_string\":\" assert plan.scenes[0].caption_beat == script.hook\\n assert \\\"pattern interrupt\\\" in plan.scenes[0].retention_goal.casefold()\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\\n@@ -46,6 +46,9 @@\\n assert plan.scenes[0].start_seconds == 0\\n assert plan.scenes[-1].end_seconds == 35\\n assert plan.scenes[0].caption_beat == script.hook\\n+ assert plan.scenes[1].caption_beat == script.retention_beats[0]\\n+ assert plan.scenes[2].caption_beat == script.retention_beats[1]\\n+ assert plan.scenes[3].caption_beat == script.retention_beats[2]\\n assert \\\"pattern interrupt\\\" in plan.scenes[0].retention_goal.casefold()\\n assert all(scene.labels_renderer_owned for scene in plan.scenes)\\n assert all(\\\"fake UI text\\\" not in scene.visual_action for scene in plan.scenes)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_b48EKs7kQ3S5dCAXtZvFSnQM", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_AWVxkMFTZsD9d5d3Xs9y1Mxf", "call_id": "call_AWVxkMFTZsD9d5d3Xs9y1Mxf", "response_item_id": "fc_0dc9f371a6d55e3f016a137033db7081919d0e8ce3479271b1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_shot_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"F.... [100%]\\n=================================== FAILURES ===================================\\n_____ test_create_shot_plan_builds_five_retention_scenes_from_script_draft _____\\n\\n def test_create_shot_plan_builds_five_retention_scenes_from_script_draft():\\n script = _script()\\n \\n plan = create_shot_plan(script)\\n \\n assert isinstance(plan, ShotPlan)\\n assert plan.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n assert plan.title == \\\"AI weekly review routine\\\"\\n assert plan.estimated_duration_seconds == 35\\n assert [scene.purpose for scene in plan.scenes] == [\\n \\\"first_frame_hook\\\",\\n \\\"pattern_interrupt\\\",\\n \\\"core_explanation\\\",\\n \\\"screen_or_card_visual\\\",\\n \\\"cta\\\",\\n ]\\n assert [scene.asset_mode for scene in plan.scenes] == [\\n \\\"kinetic_text_overlay\\\",\\n \\\"mechanism_animation\\\",\\n \\\"screen_recording_simulation\\\",\\n \\\"mechanism_animation\\\",\\n \\\"kinetic_text_overlay\\\",\\n ]\\n assert plan.scenes[0].start_seconds == 0\\n assert plan.scenes[-1].end_seconds == 35\\n assert plan.scenes[0].caption_beat == script.hook\\n> assert plan.scenes[1].caption_beat == script.retention_beats[0]\\nE AssertionError: assert 'AI suggests. Human approves.' == 'Show the mes...f priorities.'\\nE \\nE - Show the messy current state: I use AI tools but still lose control of priorities.\\nE + AI suggests. Human approves.\\n\\ntests/test_shot_plan.py:49: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_shot_plan.py::test_create_shot_plan_builds_five_retention_scenes_from_script_draft\\n1 failed, 4 passed in 0.09s\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_AWVxkMFTZsD9d5d3Xs9y1Mxf", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_4oqS1eiO7eDDcdt0hUeVvUcq", "call_id": "call_4oqS1eiO7eDDcdt0hUeVvUcq", "response_item_id": "fc_0dc9f371a6d55e3f016a13703a0ee481918f430edd1d53625c", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" caption_beat=beats[0],\",\"old_string\":\" caption_beat=\\\"AI suggests. Human approves.\\\",\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n@@ -98,7 +98,7 @@\\n voiceover_beat=beats[0],\\n visual_action=\\\"Rapid desk-to-phone change showing messy priorities becoming a controlled workflow.\\\",\\n asset_mode=\\\"mechanism_animation\\\",\\n- caption_beat=\\\"AI suggests. Human approves.\\\",\\n+ caption_beat=beats[0],\\n retention_goal=\\\"Create visible change before the viewer can classify it as a slideshow.\\\",\\n ),\\n ShotScene(\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_4oqS1eiO7eDDcdt0hUeVvUcq", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ccQBA7esZBwVwP2ENtRv1gzQ", "call_id": "call_ccQBA7esZBwVwP2ENtRv1gzQ", "response_item_id": "fc_0dc9f371a6d55e3f016a13704104a88191939aba5632a502d1", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" caption_beat=beats[1] if len(beats) > 1 else script.script_outline,\",\"old_string\":\" caption_beat=\\\"The rule: AI drafts, you decide.\\\",\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n@@ -108,7 +108,7 @@\\n voiceover_beat=beats[1] if len(beats) > 1 else script.script_outline,\\n visual_action=\\\"Simulated screen flow with renderer-owned boxes, cursor motion, and approval checkpoint.\\\",\\n asset_mode=\\\"screen_recording_simulation\\\",\\n- caption_beat=\\\"The rule: AI drafts, you decide.\\\",\\n+ caption_beat=beats[1] if len(beats) > 1 else script.script_outline,\\n retention_goal=\\\"Make the mechanism concrete without trusting generated text inside assets.\\\",\\n ),\\n ShotScene(\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ccQBA7esZBwVwP2ENtRv1gzQ", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_2FXaZazDS7W6SPwT73QrEdwa", "call_id": "call_2FXaZazDS7W6SPwT73QrEdwa", "response_item_id": "fc_0dc9f371a6d55e3f016a13704829548191b67383a104779303", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" caption_beat=beats[2] if len(beats) > 2 else script.script_outline,\",\"old_string\":\" caption_beat=\\\"10-minute review. One human checkpoint.\\\",\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\\n@@ -118,7 +118,7 @@\\n voiceover_beat=beats[2] if len(beats) > 2 else script.script_outline,\\n visual_action=\\\"Animated checklist/card sequence showing the weekly review routine step by step.\\\",\\n asset_mode=\\\"mechanism_animation\\\",\\n- caption_beat=\\\"10-minute review. One human checkpoint.\\\",\\n+ caption_beat=beats[2] if len(beats) > 2 else script.script_outline,\\n retention_goal=\\\"Deliver the payoff visually before the CTA.\\\",\\n ),\\n ShotScene(\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_2FXaZazDS7W6SPwT73QrEdwa", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_Mv4e0xqGHUaa0SSWtZbjzqG3", "call_id": "call_Mv4e0xqGHUaa0SSWtZbjzqG3", "response_item_id": "fc_0dc9f371a6d55e3f016a137050ad0c8191b172dc889fe8b96f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_voiceover.py tests/test_shot_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"......... [100%]\\n9 passed in 0.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Mv4e0xqGHUaa0SSWtZbjzqG3", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_n6u2XezeiNoTp25QKH2tSOZa", "call_id": "call_n6u2XezeiNoTp25QKH2tSOZa", "response_item_id": "fc_0dc9f371a6d55e3f016a13705784408191bcae2a467e15159f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover, build_accessible_script_draft_from_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.local_preview import build_local_preview_plan\\nfrom autoshorts.rendering.local_frame_writer import write_local_preview_frames\\nfrom autoshorts.rendering.ffmpeg_plan import build_ffmpeg_concat_plan\\nfrom autoshorts.rendering.ffmpeg_renderer import render_ffmpeg_preview_video\\n\\ncandidate_id = 'truetrace-ai-automation-quiet-rot-38s'\\nout_dir = Path('data/previews/truetrace-weird-email-v1').resolve() / candidate_id\\nvoiceover = build_truetrace_final_voiceover(candidate_id)\\ndraft = build_accessible_script_draft_from_voiceover(voiceover)\\nshot_plan = create_shot_plan(draft)\\nmanifest = build_render_manifest(shot_plan)\\npreview_plan = build_local_preview_plan(manifest)\\nframes = write_local_preview_frames(preview_plan, output_dir=out_dir / 'frames', approval_language='en')\\nconcat = build_ffmpeg_concat_plan(preview_plan, frames, output_path=out_dir / 'timing-preview.mp4')\\ntiming = render_ffmpeg_preview_video(concat)\\nvoice_path = out_dir / 'voice.mp3'\\nvoice = synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(voice_path)))\\naudio_duration = probe_media_duration_seconds(voice.output_path)\\nsubtitles = build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal_plan = build_audio_caption_preview_plan(\\n video_path=timing.output_path,\\n audio_path=voice.output_path,\\n subtitle_track=subtitles,\\n output_path=out_dir / 'weird-email-audio-caption-preview.mp4',\\n duration_mode='trim_to_audio',\\n)\\nfinal = render_audio_caption_preview(final_plan)\\nprint('candidate', candidate_id)\\nprint('words', len(voiceover.text.split()))\\nprint('audio_duration', f'{audio_duration:.3f}')\\nprint('subtitle_duration', subtitles.cues[-1].end_ms / 1000)\\nprint('timing', timing.output_path)\\nprint('voice', voice.output_path)\\nprint('final', final.output_path)\\nprint('first_frame', frames.frame_paths[0])\\nPY\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Generating new preview**\n\nI need to create a new preview using the latest draft and English frames. Maybe I need some tests, but I think we have enough for now. I just need to make sure to use the tools correctly to generate the output. I should also check if localizing the frame copy will return the caption text correctly. Good! I’ll utilize the create_shot_plan and build the manifest. Time to render everything and run the script!", "reasoning_content": "**Generating new preview**\n\nI need to create a new preview using the latest draft and English frames. Maybe I need some tests, but I think we have enough for now. I just need to make sure to use the tools correctly to generate the output. I should also check if localizing the frame copy will return the caption text correctly. Good! I’ll utilize the create_shot_plan and build the manifest. Time to render everything and run the script!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3BXRgsgaT13OoU88BZgZn-u1UPcPZP6bXyI34-U4raqg1SzgSJ0X5jvuppJP5DB2ry0BKxAd0KN4yoUoK850oCYnksnhrMe024xWLUg7KfYiAt2GJmYBs5GLhtxNQ7jQZySAOfJswBzrKEArJ31vOXbVGVWwUyc96_xn1vG6pksD2KFax58tSlceRS3qnF5zLs-RDiUwKQedOOVAGs5ykRHMw1Si_iZhP5-7dJGxpI0VehB4hwIB2SifAbK8eZVK7yE5ELifbEnv2zaFxYVZ180KtNsCYUJ41-ZqDcZ8fB8mQVgyVPaLuOizufVKEvBCDm0E_Qy6STvqvZyp5osmol3lLxFQu6ZWr4rt7SciuumKeWfFuefGKYHHkEWKimNAwFczLrIIOinynmPY6t18gX2mdbsxGMum0_RD_kwUaDkWi2WJFfDiw00SQqQjKC824e5wSvJ0hZgyt_aPudY-9jtX4K7YTiB2URPqKoEwv6Ay9yWrj1fb0NhI7Ccj1Lz1b3Xtgmt4ai6eqWO7tASXbxg4L1i5fX1PYbsL7OB07uBf7ladJSeLB3O94vj-vYJ92jKP8EopJTYi6bqiCifvh2aO4yAh-1zQyV4gpYYGoVRBVymbHK2agbSdOuXkieLPVwsdMDa2ILEHfCGGvDHWb9vygB3k3PfB9ARFttGiq7jH0HvKz5HoVzgIxUfygtZwXT44F6eJU_A77ykRqsnYD-pAx-UT0Z__eJ7iSQT6s0A2au1OpVGQuP5X3PJ9rDiezQr9Dmj-J37KmiNfNFhzPAfjSz4nASGRgvOFh6xhbdGDBRZ9QBbhjUvKIchy1wwm0Sa5cYYGaCl06XXDoxo-wESg-Nywq8_P8s5ODzZB4e8nmrMPJ56GZyKLz7VDALl0Cg33EDA14F_Llspe6tbnVirgXgox-CSwl6IMGc6bmFTPa8a8DlTCIO174XCW-PT8SMrkS0ONt_xjw1RwDIzbpVKcIh6UcnNXPjgkLHBrJr0BpVTpr4DhYPjc3iB0a2ssenRqpfsSZONXIZkjnr4PYecYw3eVTw0Hpkvygb05tq9lpG7wCGzEdVT_UI67fReMQ6o2KF8w8zViuA986sgbdnbMYKWlBjHlEIcjiBlq-JEX3qN5zxlU1kTKOk57Pemmq-lvH3Szvm5ESZksd4-SOacthCU0ks5tg8hcBl_D8BdYERDwhc_ukssgevUUoXyun9hAoS4TktdY5q1i1R8NEj_Y0EOmKtSvmpK1pM9NI4KDlZOzqFDZf5e-DcSPi-12K1kGnBEj2YPdgSEayefYH6WpEiKXI6DvwuVtbwt_yMxXuU=", "id": "rs_0dc9f371a6d55e3f016a137054ee9081919c2f7c62dd0858c0", "summary": [{"type": "summary_text", "text": "**Generating new preview**\n\nI need to create a new preview using the latest draft and English frames. Maybe I need some tests, but I think we have enough for now. I just need to make sure to use the tools correctly to generate the output. I should also check if localizing the frame copy will return the caption text correctly. Good! I’ll utilize the create_shot_plan and build the manifest. Time to render everything and run the script!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"candidate truetrace-ai-automation-quiet-rot-38s\\nwords 85\\naudio_duration 36.864\\nsubtitle_duration 37.0\\ntiming /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/timing-preview.mp4\\nvoice /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/voice.mp3\\nfinal /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\\nfirst_frame /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/frames/frame-01-first_frame_hook.png\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=29.041667\\ncodec_name=aac\\ncodec_type=audio\\nduration=29.013000\\n47910a656ffc54d961db19da8c89a9939552f0e0deb0030733e996953d01e27b data/previews/truetrace-weird-email-v1/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_n6u2XezeiNoTp25QKH2tSOZa", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_oItuywHZXQtCmTpEHwScFESM", "call_id": "call_oItuywHZXQtCmTpEHwScFESM", "response_item_id": "fc_0dc9f371a6d55e3f016a137074ee8081918bf9cbca46c6f70c", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert script.target_duration_seconds == 37\",\"old_string\":\" assert script.target_duration_seconds == 29\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3B0Ls9OJx4Lm_1EGHOWr08vGk88KRQ60cwcfSWULmv-bEszXH9ali4kBC8S6Is6ZTRUD6MSYREwshbrK7qslBaKQbKd0fD9khasZUjrda2I3YzT4qovjyM4IC1r7w_e6XcW-neer623emebBQWctPtkR_RjgIN9GEPNEYk8FXTkT5KpqYsG7DWCNtFEe-MQJRW351MKECdpokZ51MNSlYb5Cky1B0bCCZTI0NUW1KExS10NbsIQFVREJ6TObSSO6QmMzkdGfszqfXtI0ybH_Htij0Qxax3kKsY9SDT_K3sUkf9zXNO6ABNKAYEc4KlkY7XbbdVkqS2AED-CcCS5dAU-leW23Q85XJNWXzcv0gyPCJlOcGUdGhjskGnNLws9muX-kO0SZE_-eeUbl73tbQVHTA9QJz1XVd9iILJMgZBw3V4pm13SBWkkvYyISFxBwd_gM2sKBX48rqfiyV0ITMt2prbih4barxk5AwNvgj6oc7fMSTbk6yPjB8dAVM0gCiZwoJ-xjHC4kwoIwq4LkzWx5-jfS2btySbC-ea0-Tm9z0ce8d8g4za30oeZPa0RvGWJ6LYxQZt6Xuy65vsgpKdYxiHt_dU0LewcdTPbvnNhumISgLcZguUNUxfzQGfkq2ylmKf1TOReSrR-oMBr5joFZ5fIgyogWC5NOdnyfr8LTwht7NyLaKtvMI8FH--Twl39a2Wm3JHDRcGi5-2GRBVUy5xc6phPn3R1XBne7yuKOXp2u-lkhSW1dnLEA1YNN9BJP3dG-DgQBuouBxFP6mB71rbdI1tPIhBc-Ywq2CwvT5MKHodzLFSIz4LugwlZzxcmHA0NeckPXSR-ER0lnGYc4IjJbtHFiMGhcfZYl0Rf7xLhDX3zLhWZDu6G70b4aAQXStiK0wAqxUxUSJsPDagNQ3ZaSUJgrdmQjrGsjx5oI5tIH_bvKRsPp095uVFFMlN1y6GWVcpratkvsn4LbD481rqNrWIixvd4auW0HNSl7DuRyWiuCWnBIUUckdciI1m-2lUBGQ1xE8yyJDNQFyWodDlN6wAqW5jM3pMGMKHkkDgyfM65tl9vPpvU_S8E-WGE-HCQMo6a3cWQvvYHDywzkpoObDZpC6sMR6crcWctLyckKN5dVWs6Hx1RI8Wu6wjrYF976ZChwkkB59b2RVyGyG7kX-MxE8fMObHaAXHydCjj7D0qgP1qDlsYTUvZbqYVlklIWt_-weRfn6f_do3722V48sgDRUvOQbEzHdbqq07s542eaFlj6DtN-eRVLl0U4D4bqOUAYb8rBan4uxszsHkjL2Ol3-ddK5-ek2vJZmhFg8aBDXxs5rb5ah0L2IovOYX8edcH-wqhrfc6qlITqR6GmV-l8hoCHSbrAN5m3fRnOMoxeNB1MTy3-UB8u5kH50nuZ8rDO911bWqwLQFDIjf1KBtmWEgYbJg83uBIKere7Z-AoIaLXMasD7F4C4YOexGl8yMcqFbg7VVsFn1SV7vxqeXTCnMu2zsotsIH4O1o_yyTUVSnMjUWC1Juu8AmGTveJhSoMhgfszVGn8NEopjFJ-FELopq0elOpoHsm5KJl9xP9P1QZXDh_2reAiHlsOPtCfRcLHKgodUYrMQ4vWrxADZ4q5Dd1tHvn6c4rwzLzk9XRmpe3iQ2HL9gqci8fcBkoIj1fpadLTLHFVbOojWTcGTQLjQs7BfsRgGMCn8=", "id": "rs_0dc9f371a6d55e3f016a1370703ac48191afab47c05ab0598d", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -12,7 +12,7 @@\\n assert script.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n assert script.language == \\\"en\\\"\\n assert script.voice == \\\"en-GB-RyanNeural\\\"\\n- assert script.target_duration_seconds == 29\\n+ assert script.target_duration_seconds == 37\\n assert len(script.text.split()) <= 85\\n assert script.text.startswith(\\\"Your AI automation works perfectly in the demo.\\\")\\n assert \\\"weird email\\\" in script.text\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_oItuywHZXQtCmTpEHwScFESM", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_jOdXiDkvWxuDZBQnxazI92PI", "call_id": "call_jOdXiDkvWxuDZBQnxazI92PI", "response_item_id": "fc_0dc9f371a6d55e3f016a13707936f4819187d5b1a4e44ef078", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" assert [script.target_duration_seconds for script in scripts] == [37, 23, 22]\",\"old_string\":\" assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\\n@@ -34,7 +34,7 @@\\n \\n scripts = tuple(build_truetrace_final_voiceover(candidate_id) for candidate_id in ids)\\n \\n- assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]\\n+ assert [script.target_duration_seconds for script in scripts] == [37, 23, 22]\\n assert all(45 <= len(script.text.split()) <= 85 for script in scripts)\\n assert all(script.text.count(\\\".\\\") >= 3 for script in scripts)\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_jOdXiDkvWxuDZBQnxazI92PI", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_Dy9JkNnqA6AdGgRds4yShSZH", "call_id": "call_Dy9JkNnqA6AdGgRds4yShSZH", "response_item_id": "fc_0dc9f371a6d55e3f016a13707ee7808191890fe0beff0b761d", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" 37,\",\"old_string\":\" 29,\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\\n@@ -36,7 +36,7 @@\\n \\\"Before you automate anything, decide three things. What should go through? \\\"\\n \\\"What should be reviewed? And what should stop? If your AI does not know what to do with weird inputs, \\\"\\n \\\"it will break the moment real life shows up.\\\",\\n- 29,\\n+ 37,\\n ),\\n \\\"truetrace-prompt-vs-system-workflow-breaks-35s\\\": (\\n \\\"A smart prompt can still build a fragile workflow. The prompt is only one part. \\\"\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_Dy9JkNnqA6AdGgRds4yShSZH", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_aod6feKJDEPJJXKbmecuwhJa", "call_id": "call_aod6feKJDEPJJXKbmecuwhJa", "response_item_id": "fc_0dc9f371a6d55e3f016a13708454f88191a99e0b8db2fb0feb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_voiceover.py tests/test_shot_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"......... [100%]\\n9 passed in 0.08s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_aod6feKJDEPJJXKbmecuwhJa", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_vx2188Z9rlz4pJLfBHSSuBQT", "call_id": "call_vx2188Z9rlz4pJLfBHSSuBQT", "response_item_id": "fc_0dc9f371a6d55e3f016a137089324c81919137b45476c93339", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover, build_accessible_script_draft_from_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.local_preview import build_local_preview_plan\\nfrom autoshorts.rendering.local_frame_writer import write_local_preview_frames\\nfrom autoshorts.rendering.ffmpeg_plan import build_ffmpeg_concat_plan\\nfrom autoshorts.rendering.ffmpeg_renderer import render_ffmpeg_preview_video\\n\\ncandidate_id = 'truetrace-ai-automation-quiet-rot-38s'\\nout_dir = Path('data/previews/truetrace-weird-email-v2').resolve() / candidate_id\\nvoiceover = build_truetrace_final_voiceover(candidate_id)\\ndraft = build_accessible_script_draft_from_voiceover(voiceover)\\nshot_plan = create_shot_plan(draft)\\nmanifest = build_render_manifest(shot_plan)\\npreview_plan = build_local_preview_plan(manifest)\\nframes = write_local_preview_frames(preview_plan, output_dir=out_dir / 'frames', approval_language='en')\\nconcat = build_ffmpeg_concat_plan(preview_plan, frames, output_path=out_dir / 'timing-preview.mp4')\\ntiming = render_ffmpeg_preview_video(concat)\\nvoice_path = out_dir / 'voice.mp3'\\nvoice = synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(voice_path)))\\naudio_duration = probe_media_duration_seconds(voice.output_path)\\nsubtitles = build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal_plan = build_audio_caption_preview_plan(\\n video_path=timing.output_path,\\n audio_path=voice.output_path,\\n subtitle_track=subtitles,\\n output_path=out_dir / 'weird-email-audio-caption-preview.mp4',\\n duration_mode='trim_to_audio',\\n)\\nfinal = render_audio_caption_preview(final_plan)\\nprint('candidate', candidate_id)\\nprint('words', len(voiceover.text.split()))\\nprint('target_duration', voiceover.target_duration_seconds)\\nprint('audio_duration', f'{audio_duration:.3f}')\\nprint('subtitle_duration', subtitles.cues[-1].end_ms / 1000)\\nprint('final', final.output_path)\\nprint('first_frame', frames.frame_paths[0])\\nPY\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"candidate truetrace-ai-automation-quiet-rot-38s\\nwords 85\\ntarget_duration 37\\naudio_duration 36.864\\nsubtitle_duration 37.0\\nfinal /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\\nfirst_frame /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/frames/frame-01-first_frame_hook.png\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\n5b903b148b6123c64dd30754b9f6bfb294154f0973ab2dfa90619ee22f6f1044 data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_vx2188Z9rlz4pJLfBHSSuBQT", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_gp0AlWEVlrpnhvzj6aDArDZ2", "call_id": "call_gp0AlWEVlrpnhvzj6aDArDZ2", "response_item_id": "fc_0dc9f371a6d55e3f016a1370a4d0d4819197db75f37b814fb1", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/frames/frame-01-first_frame_hook.png\",\"question\":\"Check whether the top card text is readable and matches the beginner-friendly weird-email hook. Note any clipping or mismatch.\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Inspecting visual frames**\n\nI’m thinking I need to visually inspect the first frame to ensure it’s displaying correctly. Maybe I’ll use some vision analysis to help with this. A screenshot or frame image might be useful for my analysis. I want to make sure everything looks good, so I’ll take a careful look. It's important to get this right before moving on. Let's see what the frame reveals!", "reasoning_content": "**Inspecting visual frames**\n\nI’m thinking I need to visually inspect the first frame to ensure it’s displaying correctly. Maybe I’ll use some vision analysis to help with this. A screenshot or frame image might be useful for my analysis. I want to make sure everything looks good, so I’ll take a careful look. It's important to get this right before moving on. Let's see what the frame reveals!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3CkZTANKdgKE39OrehIWS1OUjSd1tBWLqzGzziEsKm1jTeDxajI5KXO-RDt5xfGMycSS3ROCs1wOtSebkk_Bp6W7Ei3r5QwqHUidkUjVMroDDyFyOCcpZ-CwgYEXFzekJlNgiwVHiCsBz1GJhW_Ts6NfrXvkQBaZ2QqazA9UWEWjPxLhdv1qyghywLyLYOo5XnJTtLkm8CnL9XV-TJPRIap1f85Y6CR8tl12kptqyrYMYCp-z1LISQz_uCjYVl_ydusPVZj-k1SAvpOmVpNvPOw5cNWBogFVbChN9suSM2MLUblzvz_fqTWJ4Pm5VVynlYTYUuWQwQkcQKVn8a82jbBYDqtgM70rKCsxVyCry7jAk8WP1urxHrXUCZ-9wqjehetIMSZ4wrD6uLQAEshsd7xlvAiuo7IItRAZlz8L9TSoPYer8_bAiW66jLVuUA6kFBYdpzDd5FWgG7C5QULL_K1mzBQEWNqaZE0F9JnUpX_GDDx39vc_uYqavXMRSROFNvYsu_BPOVKk4BiTDFTnthj-ByhCQ-7_DrXjX3bvVisgvM3TXlgRcGVDF0NsW_dhRnXZ-FhDVZz3bP8Jq7duPc4JJFh1JVy7PYtX1Zc1Ppvdkica-Zeno21fcnWB6Y2ePee2lgP0k_YYSB0MB7LiOa6PG7-jnOa1hQ8B_LIh_FUJauRleR0X0ETSnQwDjcmf8jH07NxVc9is9v7DAGA-gQzNCzjJCCNgMinZ5BVfrRopRDLugmGrj4CQnpPi2HfAbJ9A1oVt-FyDhIYUcBIgVmNCcmGWgsD6gs4gJ_3pJN2Q3HhgyOjqCy2Nelx0RgDQrh6nsnMfQF9t06OpiujF-BFoMW5aGufeaoa8cbVQEGCa-eFQzanUZRjxS6xpmNM38osmQutfsyv_ThfHgWSgzcaEW3HL0BDStcjJ_WwEYPvT8OQNRwEngzahj3gn2gxy8oMtpn8pgFOIaS0Ro7YAyNkxGpzM1uiVh82Hx3iToqTtDCQJeQW_rA_keg9Dl7XfbHLTOSnwkfgIFmRy-nHblbYUuE7fLlu0pSa2en21qxYujM3jRt9_mWZR_PzMu9DkL9O", "id": "rs_0dc9f371a6d55e3f016a1370a2a4f0819185e3422b0991cef1", "summary": [{"type": "summary_text", "text": "**Inspecting visual frames**\n\nI’m thinking I need to visually inspect the first frame to ensure it’s displaying correctly. Maybe I’ll use some vision analysis to help with this. A screenshot or frame image might be useful for my analysis. I want to make sure everything looks good, so I’ll take a careful look. It's important to get this right before moving on. Let's see what the frame reveals!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Check whether the top card text is readable and matches the beginner-friendly weird-email hook. Note any clipping or mismatch.\n[screenshot]", "tool_call_id": "call_gp0AlWEVlrpnhvzj6aDArDZ2", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_feeZ3RPzo59uI3I0XRMvTTn2", "call_id": "call_feeZ3RPzo59uI3I0XRMvTTn2", "response_item_id": "fc_0dc9f371a6d55e3f016a1370ad57d88191810249a4d52017ae", "type": "function", "function": {"name": "search_files", "arguments": "{\"limit\":50,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests\",\"pattern\":\"localize_frame_copy|Zeit:|Bewegung|write_local_preview_frames\",\"target\":\"content\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering localization issues**\n\nI'm looking at some localization issues with the content. The German labels for \"Zeit/Bewegung\" are present while the content is in English. It might be acceptable as is, but I wonder if I should update the English labels in the copy. The body seems to be hardcoded to German labels, and there's concern about a mismatch in the top text. I'll think about testing this and possibly adding it into the local frame writer. Let's see what existing tests are available!", "reasoning_content": "**Considering localization issues**\n\nI'm looking at some localization issues with the content. The German labels for \"Zeit/Bewegung\" are present while the content is in English. It might be acceptable as is, but I wonder if I should update the English labels in the copy. The body seems to be hardcoded to German labels, and there's concern about a mismatch in the top text. I'll think about testing this and possibly adding it into the local frame writer. Let's see what existing tests are available!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3CtYvyaA1_rKkUX7R9YH3-dJKL0vQi6B6u6S1FCBykeVH7F6QnL-sfIgeidI35I_aIkVcW3peqjH3EEalOADKZO-xQyAehoC5WZ2Lrk2VLGk3nHAUB4Q4u4CmWoKfZp0cpnXmF0liLjy0QpnrttPq5gCKrdkM3CUSTiOpYF1rVL7v6xuy4WYOjrSkT60yE2Z2PUwvPzMN7Ki_9GcasduzIPju4PNFAm2XHdindOKLli9ip6JKTUyKtguo1CrSBnNq-Yv2nJZUTr0MDjt_VEE7cXj4H3DT1qmH9ek0D7nIeZv_qPzFHfaRQu0jbqvy4A5zBZQVYYK9mSLcjHOZDaCNYDd-dIUdLY_p9Vqdg-IM0hVEc313M0dKeo-aUnAThcV1TlGWHW38nUFL1eV4hTodmkcY38eoTqmCcBwXJeMbY5lBNm9WClw9zD2dEcMkqyqhCm4lSEO5kf8LVjRpiVYJQE1OEx111rbHezk5vtwWo-kkr0z5ds6AX7GptmerWuRJvSMtsIya2W7J-sXyItZdAByc7DYMkup2VUuF-jC03b2-MDadSRQWgow9K-kWVKRsBxwTUhRunRC4WHAI6TFDSogP3B6FVvv1xK6ubbW8d7BcpUzgTE_MKHhPu9GoIkCmyl6nTj1q0p1EJJCs0i6_wokejk_YrKI1e6Ti49D7Fcwk1bXvL_TbKgOeKFCKt_9F1fiikaJG4HfpTZfYIiADxVsRV0iAejYRUHs5Ndz9KwW63B7JdQZXydthC00YHFq5FJxj7yf8UyfTkaXrbtuC4_PJklzl16AENO-yDO25WV6pc_JAKnO4mN1mrSPJz63wHIzOt3lUPLvjqMYcDvCT7_dbsGH7pynjr3PpQWBRiJ1Vi0dDZy1Ex5Lp3lepHKgn_bxhN2zvjIx9f_OtwQlv0iFJYzdcBmgaWHdgmBhBOpWinZi7_YWpCD5MW0kvw71fWPwigRpaLI798gbus6H2xJf6BKLK_y0qEK9USnj1GFL8W0gj5sltFehzFP_SKyrWmFia4Y9dPwMJzKBc6s8OkpjbQIaoksB-Gjde6qTpXxFCrrJzT5fmAv5YpcFyt3kikAicgSAblQgmVMjecLvkhLrdyM36NX_o_1kn-zy0m_j-fY_KVVvzErB-IkUYAC3CkVvmBrnVj38ZQ7e8OZ2E6C8xWfC5QrHz3xLyA8qZULAJnS-ZU7z8j7yvlHVopf34eOjuSEau8N_3aStiOtwZj5JSliHMCklonpQBnAo178-7Zvop-4cCEB6CPP39Cm71EfU5t75_4JZMyKRjzAPhYhjgwSmua30ZKQRPj6zESDavXgZ_SaxOkiWsA0C92H5XmLWdk88_MbsWGA_ywh2A_NfSoE0U57wTOhvBouLFK3vp6DNmrgcX0jBOdYv6R5qSq6", "id": "rs_0dc9f371a6d55e3f016a1370aa0f108191b6d15757260f5626", "summary": [{"type": "summary_text", "text": "**Considering localization issues**\n\nI'm looking at some localization issues with the content. The German labels for \"Zeit/Bewegung\" are present while the content is in English. It might be acceptable as is, but I wonder if I should update the English labels in the copy. The body seems to be hardcoded to German labels, and there's concern about a mismatch in the top text. I'll think about testing this and possibly adding it into the local frame writer. Let's see what existing tests are available!"}]}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"total_count\": 0}", "tool_call_id": "call_feeZ3RPzo59uI3I0XRMvTTn2", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_xV7n57DGdInc76lP0061sQwT", "call_id": "call_xV7n57DGdInc76lP0061sQwT", "response_item_id": "fc_0dc9f371a6d55e3f016a1370b32bd08191919dd3784ef6ec99", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" body_labels = {\\n \\\"time\\\": \\\"Zeit\\\" if approval_language == \\\"de\\\" else \\\"Time\\\",\\n \\\"asset\\\": \\\"Asset\\\",\\n \\\"label\\\": \\\"Label\\\",\\n \\\"motion\\\": \\\"Bewegung\\\" if approval_language == \\\"de\\\" else \\\"Motion\\\",\\n }\\n body = (\\n f\\\"{body_labels['time']}: {frame.start_ms / 1000:.0f}s + {frame.duration_ms / 1000:.0f}s\\\\n\\\"\\n f\\\"{body_labels['asset']}: {copy.asset_label}\\\\n\\\"\\n f\\\"{body_labels['label']}: {copy.scene_label.title()}\\\\n\\\\n\\\"\\n f\\\"{body_labels['motion']}: {copy.motion_text}\\\"\\n )\",\"old_string\":\" body = (\\n f\\\"Zeit: {frame.start_ms / 1000:.0f}s + {frame.duration_ms / 1000:.0f}s\\\\n\\\"\\n f\\\"Asset: {copy.asset_label}\\\\n\\\"\\n f\\\"Label: {copy.scene_label.title()}\\\\n\\\\n\\\"\\n f\\\"Bewegung: {copy.motion_text}\\\"\\n )\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Polishing documentation**\n\nI’m considering whether I really need to polish the documentation with language labels. It might not be strictly necessary, but I want to ensure everything is clear. I need to run full tests, update the documentation, and commit those changes. Updating the README might also be a good idea. I really want to make sure everything is up to date and user-friendly, so I’ll aim to use a patch for clarity!", "reasoning_content": "**Polishing documentation**\n\nI’m considering whether I really need to polish the documentation with language labels. It might not be strictly necessary, but I want to ensure everything is clear. I need to run full tests, update the documentation, and commit those changes. Updating the README might also be a good idea. I really want to make sure everything is up to date and user-friendly, so I’ll aim to use a patch for clarity!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3CzY1CvZ3AgDBz0NnaJKTqQWzjf95LT_s_SAmeXPplOg0E9iBKm1m6D3SlUonn8eX91Ffh3ALNvHQNarw3JMsbQnKEpXWy1AQzG6ZFaKdzWW_PWCRDqhLk9yy5o1k3elROjeuQQ-Ujj1Sii7i4l658JDc_JvY8iCgiHXQqZFCf7jGLVUFKqB4cwXtbx-CKezcM-8nDVcMgkOMa_Vt5rI38lIwKsIUo-MQlWZaWGitC8twpiiZXYc_cgkIBI1Ka-Y5m02ariSrwcvVjw6HA7781CVp0UON5mlgFp9THGtNM5cuCaoVpDbsa-HWCWnIz0Bfp2YmF0xYn-9mSrTZhRTUOUwZMLRr1tFgHU3Q5mBOCOsQikSJYrW6VSpHuE__w-siZZHElquPMoHLxqfgEzDoECXYxOX5dVw3cSdz34vq2FWAl0p8rXNO03aId5tpZgGWWOrYyIo3lzeJgDVxs3M4XdKeYuhx_rVXL9uz14rfBU2P2tKVd1fKwwKKg41kEY73Lpy-N46qn0EWFeXqo9IDOTATUt6v7unD3x_OfMSDvTsRo9oZ4ZGUkX6J3r2ZiwAuMOAQsVffDQPnyQ3DNQ61M86eHXwaajpAaswup-z67B0goPZtDrFcnYQ7jYZl_xbU0mL2zsmvx0dmaucnAw34DnMXsgErvlmVaKg2uDrskIVr99lHK_qMHHqrVIgoh7sIVFb7jJR1ObejZbczJZKD0L9sTHyeQ_z3YkK1BdqtyyP31w4SgedXBsAc5tqGz53oRERP1KuWtX7pEiEWV-RfP2gXUxJQELD_FjWqwoZm0Pw3_VEBpK_s2eTy__OlT-fZJCQZZrJuAexI7Mhva7GU1JtJ6IB7ydurrXSB5umd6SYY8ha4gEsw2fk0fM7Z6U5i7pCqeZY0OFtfh6U_hbGs2LpDwy3EE3Fwu81qG_-7sNUS5ioy9RK5kqA2Lv4zE7wI1kRSKj98mxIv1-fK4LqAQPVWwpk65MYTDgJRxAPpQ3GaOkUozc-pDPI0xP78ctuapZOEb0uyJToHW-ruo1iUxNG0fT1gfo1DaBCHkK_cOa2BIb3Opz5h18eYxMu1XbaG51yDslL7kz9te6fS7z3kFGFxUuDc3CaGVe-Xr7QTiFH4g=", "id": "rs_0dc9f371a6d55e3f016a1370b0bb708191b728845af59f3892", "summary": [{"type": "summary_text", "text": "**Polishing documentation**\n\nI’m considering whether I really need to polish the documentation with language labels. It might not be strictly necessary, but I want to ensure everything is clear. I need to run full tests, update the documentation, and commit those changes. Updating the README might also be a good idea. 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"id": "rs_0dc9f371a6d55e3f016a1370cb261c8191a6dcfb3ad6d307cf", "summary": []}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|# AutoShortsBot\\n 2|\\n 3|AutoShortsBot is a semi-automatic short-form content engine for high-quality YouTube Shorts, TikTok, and Instagram Reels.\\n 4|\\n 5|The project is deliberately approval-first: it can draft, score, render previews, and prepare publishing metadata, but it must not publish anything without explicit human approval.\\n 6|\\n 7|## Current scope\\n 8|\\n 9|Implemented foundation:\\n 10|\\n 11|- `autoshorts.cli.demo_local_preview_video` — local CLI entry point that renders deterministic PNG frames and an MP4 timing preview.\\n 12|- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n 13|- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n 14|- `autoshorts.cli.demo_social_draft` — local CLI entry point that prints a side-effect-free social media draft review delivery.\\n 15|- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n 16|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n 17|- `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n 18|- `autoshorts.content.demo_pipeline` — deterministic content-to-Telegram-review demo pipeline.\\n 19|- `autoshorts.content.script_draft` — structured script drafts with retention beats, visual concept, caption, CTA, and AI-slop validation.\\n 20|- `autoshorts.content.to_candidate` — converts content briefs and script drafts into candidate drafts and validated video candidates.\\n 21|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n 22|- `autoshorts.demo.truetrace_concepts` — five concrete TrueTraceShorts concept candidates with metadata for strategy review.\\n 23|- `autoshorts.demo.truetrace_preview_packages` — renders top-3 TrueTrace local MP4 timing previews plus approval/manual upload packages.\\n 24|- `autoshorts.audio.subtitles` — deterministic Hormozi-inspired, renderer-owned subtitle cue planning with active-word metadata.\\n 25|- `autoshorts.audio.tts_plan` — side-effect-free TTS provider planning: EdgeTTS phase-1 baseline, Magpie A/B candidate, Whisper alignment-only.\\n 26|- `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.\\n 27|- `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP4 rendering boundary.\\n 28|- `autoshorts.audio.probe` — FFprobe duration helper used to align subtitle tracks to generated audio.\\n 29|- `autoshorts.audio.voiceover` — concise final English TrueTrace voiceover scripts for the top concepts.\\n 30|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n 31|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n 32|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n 33|- `autoshorts.preview.package` — combines MP4 timing preview plus social draft review into an approval package.\\n 34|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n 35|- `autoshorts.strategy.brand` — TrueTraceShorts brand profile, default descriptions/bios/tags, and brand-fit validation.\\n 36|- `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n 37|- `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.\\n 38|- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n 39|- `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n 40|- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n 41|- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n 42|- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n 43|- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n 44|- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n 45|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n 46|- `autoshorts.social.delivery` — side-effect-free delivery plan for social media draft review text.\\n 47|- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n 48|- `autoshorts.social.manual_posting` — side-effect-free manual upload copy pack for phase-1 YouTube/TikTok metadata.\\n 49|- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n 50|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n 51|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n 52|- `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n 53|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n 54|- Tests for content briefs, content demo pipeline, content-to-candidate conversion, content pillars, candidate factory, idea candidates, script drafts, shot plans, testbatches, sample review CLI, sample Telegram delivery CLI, sample review demos, review workflow, review-batch rendering, approval parsing, review formatting, Telegram delivery preparation, and script quality gating.\\n 55|\\n 56|## Brand profile\\n 57|\\n 58|`autoshorts.strategy.brand.TRUETRACE_SHORTS_BRAND` captures the phase-1 public brand contract for `True Trace Shorts`:\\n 59|\\n 60|- channel name and handles;\\n 61|- tagline: `Systems over prompts.`;\\n 62|- public YouTube description and TikTok bio;\\n 63|- default hashtags and YouTube tags;\\n 64|- tone and visual rules;\\n 65|- forbidden public topics;\\n 66|- generic AI-tips patterns;\\n 67|- mechanism keywords for brand fit.\\n 68|\\n 69|`validate_candidate_brand_fit()` rejects candidates that violate no-go topics, generic AI-tips positioning, or the mechanisms-over-hype promise. `apply_brand_metadata()` appends default hashtags and brand policy notes without changing the candidate content or performing side effects.\\n 70|\\n 71|`autoshorts.demo.truetrace_concepts.build_truetrace_concept_batch()` provides the current five-candidate TrueTrace concept testbatch for strategy review:\\n 72|\\n 73|- `Why Most AI Automations Break After Three Days`\\n 74|- `This Prompt Looks Smart — But the Workflow Still Breaks`\\n 75|- `The Hidden Reason Your AI Notes Become Useless`\\n 76|- `Claim: AI Agents Will Replace Teams`\\n 77|- `One Rule Before You Automate Anything With AI`\\n 78|\\n 79|`render_truetrace_concept_summary()` renders those candidates with hooks, outlines, visuals, hypotheses, YouTube descriptions, TikTok captions, and tags. It performs no platform action and avoids action language.\\n 80|\\n 81|`autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` turns the top three concepts into local German approval timing previews and side-effect-free approval/manual-upload packages. It writes PNG scene cards and MP4 timing previews under the selected output directory, then prepares `ContentPreviewApprovalPackage` and `ManualPostingPack` artifacts for each candidate. Each preview artifact also now carries a deterministic English subtitle cue track and the phase-1 EdgeTTS provider plan, so audio/caption decisions travel with the candidate before any real TTS call happens.\\n 82|\\n 83|Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n 84|\\n 85|## Strategy scoring\\n 86|\\n 87|`autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n 88|\\n 89|- hook;\\n 90|- curiosity;\\n 91|\", \"total_lines\": 411, \"file_size\": 26256, \"truncated\": true, \"hint\": \"Use offset=91 to continue reading (showing 1-90 of 411 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_m6fvE54u5kp9Rgy4JrDOr6ka", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_cd1y5bQccBRgKdQ7OvNVcb95", "call_id": "call_cd1y5bQccBRgKdQ7OvNVcb95", "response_item_id": "fc_0dc9f371a6d55e3f016a1370d1c7348191828f6f84dff97ef9", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.audio.voiceover` — beginner-friendly final English TrueTrace voiceover scripts and a single-source script-draft bridge for aligned visuals/captions.\",\"old_string\":\"- `autoshorts.audio.voiceover` — concise final English TrueTrace voiceover scripts for the top concepts.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -26,7 +26,7 @@\\n - `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.\\n - `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP4 rendering boundary.\\n - `autoshorts.audio.probe` — FFprobe duration helper used to align subtitle tracks to generated audio.\\n-- `autoshorts.audio.voiceover` — concise final English TrueTrace voiceover scripts for the top concepts.\\n+- `autoshorts.audio.voiceover` — beginner-friendly final English TrueTrace voiceover scripts and a single-source script-draft bridge for aligned visuals/captions.\\n - `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n - `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}, \"_warning\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md was last read with offset/limit pagination (partial view). Re-read the whole file before overwriting it.\"}", "tool_call_id": "call_cd1y5bQccBRgKdQ7OvNVcb95", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_GANs3VbXkoWFO3SSlkgTC0av", "call_id": "call_GANs3VbXkoWFO3SSlkgTC0av", "response_item_id": "fc_0dc9f371a6d55e3f016a1370d913f481919ced9ea86883a227", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n\\nThe first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n\\n## Strategy scoring\",\"old_string\":\"Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n\\n## Strategy scoring\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -82,6 +82,8 @@\\n \\n Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie later; use Whisper for alignment/QA, not voice generation.\\n \\n+The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n+\\n ## Strategy scoring\\n \\n `autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_GANs3VbXkoWFO3SSlkgTC0av", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_PrNdBrw08eOugzcNPvaddWLj", "call_id": "call_PrNdBrw08eOugzcNPvaddWLj", "response_item_id": "fc_0dc9f371a6d55e3f016a1370e3174c8191911dc404eb4999c1", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":90,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"content\": \" 1|# Subtitle and TTS Strategy\\n 2|\\n 3|Date: 2026-05-24\\n 4|\\n 5|## Decision summary\\n 6|\\n 7|For phase 1, subtitles should be deterministic renderer-owned captions in a high-retention Hormozi-inspired style, not burned into AI-generated images or video clips.\\n 8|\\n 9|Recommended TTS path for now:\\n 10|\\n 11|```text\\n 12|EdgeTTS for first English final voice tests\\n 13|→ optional NVIDIA Magpie A/B test later\\n 14|→ Whisper only for alignment/transcription, not as TTS\\n 15|```\\n 16|\\n 17|Reasoning:\\n 18|\\n 19|- EdgeTTS is fast, free, no GPU, no API key, and leaves the GPU free for ComfyUI/Wan/Qwen video/image work.\\n 20|- NVIDIA Magpie TTS may be worth testing for voice quality, but it is an external API dependency and not yet proven better for our exact Shorts style.\\n 21|- Whisper is speech-to-text, not text-to-speech. It can align/generated-audio transcripts later, but it does not solve voice generation.\\n 22|- Local neural TTS on GPU is not attractive while the same GPU is needed for video generation.\\n 23|\\n 24|## Subtitle style\\n 25|\\n 26|Use a `Hormozi-inspired`, not `Hormozi-cloned`, subtitle system:\\n 27|\\n 28|- 2-5 words per subtitle chunk;\\n 29|- active word highlight;\\n 30|- strong contrast;\\n 31|- center-lower safe-zone placement;\\n 32|- occasional emphasis words in accent color;\\n 33|- hard line breaks controlled by renderer;\\n 34|- no baked text inside AI-generated images/clips;\\n 35|- no copyrighted creator-template dependency;\\n 36|- captions remain editable until final render.\\n 37|\\n 38|Default visual rules:\\n 39|\\n 40|```text\\n 41|Font: bold sans-serif\\n 42|Placement: lower-middle, above platform UI safe zone\\n 43|Primary color: white\\n 44|Stroke/shadow: black/dark outline\\n 45|Accent: TrueTrace green/blue\\n 46|Max lines: 2\\n 47|Max words per chunk: 5\\n 48|Chunk duration: roughly 0.7-1.6s\\n 49|```\\n 50|\\n 51|## Implemented planning modules\\n 52|\\n 53|Current code-level planning boundary:\\n 54|\\n 55|- `autoshorts.audio.subtitles.build_hormozi_subtitle_track()` creates continuous 2-5-word subtitle cues with active-word metadata, lower-middle safe-zone style, no side effects, and no external calls.\\n 56|- `autoshorts.audio.subtitles.validate_subtitle_track()` rejects non-renderer-owned cues, overcrowded captions, bad timing, and missing active words.\\n 57|- `autoshorts.audio.tts_plan.build_default_truetrace_tts_plan()` selects `edge_tts` with `en-GB-RyanNeural` for phase 1.\\n 58|- `autoshorts.audio.tts_plan.build_tts_ab_test_plan()` keeps NVIDIA Magpie as a hosted A/B candidate and Whisper as `alignment_qa`, not voice generation.\\n 59|- `autoshorts.demo.truetrace_preview_packages.build_top_truetrace_preview_packages()` attaches the subtitle track and EdgeTTS baseline plan to each top-3 preview artifact before any real audio generation.\\n 60|- `autoshorts.audio.edge_tts_adapter.synthesize_edge_tts()` is the explicit EdgeTTS side-effect boundary. It calls `edge-tts`, writes an MP3, and reports `side_effects=(\\\"call_edge_tts\\\", \\\"write_audio\\\")`.\\n 61|- `autoshorts.audio.final_preview.build_audio_caption_preview_plan()` creates an ASS subtitle file plan from the cue track without writing files.\\n 62|- `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.\\n 63|- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides concise English final voiceover scripts for the top TrueTrace concepts.\\n 64|- `autoshorts.audio.probe.probe_media_duration_seconds()` reads the generated MP3 duration via FFprobe so subtitle cues can be rebuilt against the actual voiceover length, not merely the estimated target.\\n 65|- `render_audio_caption_preview(duration_mode=\\\"trim_to_audio\\\")` may use `-shortest`, but only when explicitly requested. The default remains `preserve_video` to avoid silent truncation.\\n 66|\\n 67|## Caption pipeline\\n 68|\\n 69|Preferred deterministic pipeline:\\n 70|\\n 71|```text\\n 72|Script voiceover text\\n 73|→ caption chunker\\n 74|→ timed subtitle cues\\n 75|→ renderer-owned caption layer\\n 76|→ FFmpeg burn-in for final MP4\\n 77|```\\n 78|\\n 79|Later, after real TTS is added:\\n 80|\\n 81|```text\\n 82|Final script\\n 83|→ TTS audio\\n 84|→ optional Whisper alignment/transcript QA\\n 85|→ corrected subtitle cues\\n 86|→ final renderer burn-in\\n 87|```\\n 88|\\n 89|Important: Whisper is useful for checking/aligning what the TTS actually said, but it should not own the script. The source of truth remains the approved script.\\n 90|\\n 91|\", \"total_lines\": 215, \"file_size\": 7276, \"truncated\": true, \"hint\": \"Use offset=91 to continue reading (showing 1-90 of 215 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_PrNdBrw08eOugzcNPvaddWLj", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_KEIPHVUAvaOO42BtZlkCt6mb", "call_id": "call_KEIPHVUAvaOO42BtZlkCt6mb", "response_item_id": "fc_0dc9f371a6d55e3f016a1370e92aa88191b8058b368145cf89", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides beginner-friendly English final voiceover scripts for the top TrueTrace concepts. The first pilot uses the concrete `weird email` framing rather than abstract terms like `exception path`.\\n- `autoshorts.audio.voiceover.build_accessible_script_draft_from_voiceover()` bridges the final voiceover back into render planning, so top card text, scene cards, subtitles, and spoken audio share one source of truth.\",\"old_string\":\"- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides concise English final voiceover scripts for the top TrueTrace concepts.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\\n@@ -60,7 +60,8 @@\\n - `autoshorts.audio.edge_tts_adapter.synthesize_edge_tts()` is the explicit EdgeTTS side-effect boundary. It calls `edge-tts`, writes an MP3, and reports `side_effects=(\\\"call_edge_tts\\\", \\\"write_audio\\\")`.\\n - `autoshorts.audio.final_preview.build_audio_caption_preview_plan()` creates an ASS subtitle file plan from the cue track without writing files.\\n - `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.\\n-- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides concise English final voiceover scripts for the top TrueTrace concepts.\\n+- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides beginner-friendly English final voiceover scripts for the top TrueTrace concepts. The first pilot uses the concrete `weird email` framing rather than abstract terms like `exception path`.\\n+- `autoshorts.audio.voiceover.build_accessible_script_draft_from_voiceover()` bridges the final voiceover back into render planning, so top card text, scene cards, subtitles, and spoken audio share one source of truth.\\n - `autoshorts.audio.probe.probe_media_duration_seconds()` reads the generated MP3 duration via FFprobe so subtitle cues can be rebuilt against the actual voiceover length, not merely the estimated target.\\n - `render_audio_caption_preview(duration_mode=\\\"trim_to_audio\\\")` may use `-shortest`, but only when explicitly requested. The default remains `preserve_video` to avoid silent truncation.\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}, \"_warning\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md was last read with offset/limit pagination (partial view). Re-read the whole file before overwriting it.\"}", "tool_call_id": "call_KEIPHVUAvaOO42BtZlkCt6mb", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_QfYd8QQ9GApVRqAN9UCShJiV", "call_id": "call_QfYd8QQ9GApVRqAN9UCShJiV", "response_item_id": "fc_0dc9f371a6d55e3f016a1370f1485481918d896bc103dfb931", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"........................................................................ [ 44%]\\n........................................................................ [ 88%]\\n.................. [100%]\\n162 passed in 32.43s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_QfYd8QQ9GApVRqAN9UCShJiV", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "tool_calls": [{"id": "call_7z8sXbymi1r8dPwKh6lo8Ze2", "call_id": "call_7z8sXbymi1r8dPwKh6lo8Ze2", "response_item_id": "fc_0dc9f371a6d55e3f016a137118e9788191b7aa13c2f62c08d6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/audio/voiceover.py autoshorts/rendering/shot_plan.py autoshorts/rendering/local_frame_writer.py tests/test_voiceover.py tests/test_shot_plan.py README.md docs/subtitle_tts_strategy.md && git commit -m \\\"feat: make TrueTrace pilot beginner friendly\\\" && git status --short && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "content": "{\"output\": \"M README.md\\n M autoshorts/audio/voiceover.py\\n M autoshorts/rendering/local_frame_writer.py\\n M autoshorts/rendering/shot_plan.py\\n M docs/subtitle_tts_strategy.md\\n M tests/test_shot_plan.py\\n M tests/test_voiceover.py\\n[feat/core-approval-quality-gates 7375311] feat: make TrueTrace pilot beginner friendly\\n 7 files changed, 97 insertions(+), 23 deletions(-)\\n7375311 feat: make TrueTrace pilot beginner friendly\\nb9b6423 feat: align TrueTrace voiceover previews\\n3f68001 feat: add EdgeTTS audio caption preview\\n066201d feat: attach audio plans to TrueTrace previews\\n30a8129 feat: add subtitle and tts planning\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7z8sXbymi1r8dPwKh6lo8Ze2", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Erledigt, Sir.\n\nSie hatten völlig recht — ich habe jetzt nicht nur kosmetisch geändert, sondern die Pipeline so angepasst, dass **Voiceover, Subtitles und oberes Kartenfeld aus derselben Quelle kommen**.\n\n## Neuer Commit\n\n```text\n7375311 feat: make TrueTrace pilot beginner friendly\n```\n\n## Was geändert wurde\n\n### 1. Voiceover beginner-friendly gemacht\n\nAlter Stil:\n\n```text\nMost AI automations do not fail dramatically.\nThey quietly rot.\n...\nexception path\n```\n\nNeuer Stil:\n\n```text\nYour AI automation works perfectly in the demo.\nThen one weird email arrives.\nThe name is missing.\nThe format is different.\nThe request is unclear.\n...\nIf your AI does not know what to do with weird inputs,\nit will break the moment real life shows up.\n```\n\nDas ist deutlich zugänglicher:\n\n- konkretes Beispiel: `weird email`\n- kein Jargon wie `exception path`\n- kein `input boundary`\n- kein `output standard`\n- kein Architekten-Englisch im Smoking\n- Problem ist auch für KI-Anfänger sofort verständlich\n\n## 2. Eine Source of Truth\n\nNeu:\n\n```python\nbuild_accessible_script_draft_from_voiceover()\n```\n\nDas macht:\n\n```text\nFinalVoiceoverScript\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ Top Card Text\n→ Scene Captions\n→ Subtitles\n→ Voiceover\n```\n\nDamit vermeiden wir genau den Fehler von vorher:\n\n```text\noben alter Karten-Text\nunten neue Subtitles\nAudio nochmal anders\n```\n\nDieser Wildwuchs ist jetzt abgeschnitten. Mit Anstand.\n\n## 3. ShotPlan nutzt jetzt echte Script-Beats\n\nVorher waren Szenencaptions teilweise generisch:\n\n```text\nAI suggests. Human approves.\nThe rule: AI drafts, you decide.\n10-minute review. One human checkpoint.\n```\n\nJetzt kommen sie aus den echten Retention Beats des finalen Scripts:\n\n```text\nThe name is missing. The format is different. The request is unclear.\nSuddenly the automation does the wrong thing confidently.\nThat is not an AI problem. That is a workflow problem.\n```\n\n## 4. Preview-Karten auf Englisch konsistenter\n\nFür die neuen final-nahen Previews werden die Karten nicht mehr halb Deutsch/halb Englisch beschriftet.\n\nGeändert:\n\n```text\nZeit → Time\nBewegung → Motion\n```\n\nWenn `approval_language=\"en\"` verwendet wird.\n\n## Neuer generierter Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nNeue Version:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\n\n## Verifiziert\n\n```text\nWords: 85\nTarget duration: 37s\nActual EdgeTTS audio: 36.864s\nSubtitle duration: 37.0s\nVideo duration: 36.833s\nAudio duration: 36.864s\n```\n\nSHA256:\n\n```text\n5b903b148b6123c64dd30754b9f6bfb294154f0973ab2dfa90619ee22f6f1044\n```\n\nIch habe zusätzlich den ersten Frame visuell geprüft: Top-Text ist lesbar und lautet jetzt passend:\n\n```text\nYour AI automation works perfectly in the demo.\nThen one weird email arrives.\n```\n\n## Tests\n\nFull Suite:\n\n```text\n162 passed in 32.43s\n```\n\n## Einschätzung zur Zielgruppe\n\nJa: Diese Richtung ist viel besser für Leute mit wenig KI-Erfahrung.\n\nDer Kanal sollte nicht starten mit:\n\n```text\n“Here is a system architecture principle.”\n```\n\nSondern mit:\n\n```text\n“Here is why your ChatGPT/Zapier/Email automation breaks when real life gets messy.”\n```\n\nDas holt Anfänger ab, ohne den eigentlichen System-Gedanken zu verlieren.\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir visuell noch weg von Karten und hin zu einer konkreteren Szene:\n\n```text\nDemo inbox\n→ weird email appears\n→ AI routes it wrong\n→ simple 3-way split:\n Go through\n Review\n Stop\n```\n\nDas wäre dann nicht nur verständlicher Text, sondern auch ein verständlicher Mechanismus im Bild.", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\nSie hatten völlig recht — ich habe jetzt nicht nur kosmetisch geändert, sondern die Pipeline so angepasst, dass **Voiceover, Subtitles und oberes Kartenfeld aus derselben Quelle kommen**.\n\n## Neuer Commit\n\n```text\n7375311 feat: make TrueTrace pilot beginner friendly\n```\n\n## Was geändert wurde\n\n### 1. Voiceover beginner-friendly gemacht\n\nAlter Stil:\n\n```text\nMost AI automations do not fail dramatically.\nThey quietly rot.\n...\nexception path\n```\n\nNeuer Stil:\n\n```text\nYour AI automation works perfectly in the demo.\nThen one weird email arrives.\nThe name is missing.\nThe format is different.\nThe request is unclear.\n...\nIf your AI does not know what to do with weird inputs,\nit will break the moment real life shows up.\n```\n\nDas ist deutlich zugänglicher:\n\n- konkretes Beispiel: `weird email`\n- kein Jargon wie `exception path`\n- kein `input boundary`\n- kein `output standard`\n- kein Architekten-Englisch im Smoking\n- Problem ist auch für KI-Anfänger sofort verständlich\n\n## 2. Eine Source of Truth\n\nNeu:\n\n```python\nbuild_accessible_script_draft_from_voiceover()\n```\n\nDas macht:\n\n```text\nFinalVoiceoverScript\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ Top Card Text\n→ Scene Captions\n→ Subtitles\n→ Voiceover\n```\n\nDamit vermeiden wir genau den Fehler von vorher:\n\n```text\noben alter Karten-Text\nunten neue Subtitles\nAudio nochmal anders\n```\n\nDieser Wildwuchs ist jetzt abgeschnitten. Mit Anstand.\n\n## 3. ShotPlan nutzt jetzt echte Script-Beats\n\nVorher waren Szenencaptions teilweise generisch:\n\n```text\nAI suggests. Human approves.\nThe rule: AI drafts, you decide.\n10-minute review. One human checkpoint.\n```\n\nJetzt kommen sie aus den echten Retention Beats des finalen Scripts:\n\n```text\nThe name is missing. The format is different. The request is unclear.\nSuddenly the automation does the wrong thing confidently.\nThat is not an AI problem. That is a workflow problem.\n```\n\n## 4. Preview-Karten auf Englisch konsistenter\n\nFür die neuen final-nahen Previews werden die Karten nicht mehr halb Deutsch/halb Englisch beschriftet.\n\nGeändert:\n\n```text\nZeit → Time\nBewegung → Motion\n```\n\nWenn `approval_language=\"en\"` verwendet wird.\n\n## Neuer generierter Clip\n\nCandidate:\n\n```text\ntruetrace-ai-automation-quiet-rot-38s\n```\n\nNeue Version:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4\n\n## Verifiziert\n\n```text\nWords: 85\nTarget duration: 37s\nActual EdgeTTS audio: 36.864s\nSubtitle duration: 37.0s\nVideo duration: 36.833s\nAudio duration: 36.864s\n```\n\nSHA256:\n\n```text\n5b903b148b6123c64dd30754b9f6bfb294154f0973ab2dfa90619ee22f6f1044\n```\n\nIch habe zusätzlich den ersten Frame visuell geprüft: Top-Text ist lesbar und lautet jetzt passend:\n\n```text\nYour AI automation works perfectly in the demo.\nThen one weird email arrives.\n```\n\n## Tests\n\nFull Suite:\n\n```text\n162 passed in 32.43s\n```\n\n## Einschätzung zur Zielgruppe\n\nJa: Diese Richtung ist viel besser für Leute mit wenig KI-Erfahrung.\n\nDer Kanal sollte nicht starten mit:\n\n```text\n“Here is a system architecture principle.”\n```\n\nSondern mit:\n\n```text\n“Here is why your ChatGPT/Zapier/Email automation breaks when real life gets messy.”\n```\n\nDas holt Anfänger ab, ohne den eigentlichen System-Gedanken zu verlieren.\n\n## Nächster sinnvoller Schritt\n\nJetzt sollten wir visuell noch weg von Karten und hin zu einer konkreteren Szene:\n\n```text\nDemo inbox\n→ weird email appears\n→ AI routes it wrong\n→ simple 3-way split:\n Go through\n Review\n Stop\n```\n\nDas wäre dann nicht nur verständlicher Text, sondern auch ein verständlicher Mechanismus im Bild."}], "id": "msg_0dc9f371a6d55e3f016a13711df5fc8191a1981c7eb3035eb9", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Ok. Wie gehst du nun vor für die generierung des richtigen videos?", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "", "reasoning": "**Creating a plan for video generation**\n\nI need to come up with an answer plan since the user is asking how to proceed. It seems that social media skills are relevant here, but I'm not sure if a tool is required for this part. Maybe I should load up writing plans to help generate a proper video based on their specific skill set. Also, could comfyui be relevant for effective video generation? I’ll need to consider loading that if it fits!", "finish_reason": "tool_calls", "reasoning_content": "**Creating a plan for video generation**\n\nI need to come up with an answer plan since the user is asking how to proceed. It seems that social media skills are relevant here, but I'm not sure if a tool is required for this part. Maybe I should load up writing plans to help generate a proper video based on their specific skill set. Also, could comfyui be relevant for effective video generation? I’ll need to consider loading that if it fits!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3IQn5AG_OMZY00ZVo7mjl1UGZqS0uErYPUefCbvsd5gLOP1ot6QMbYzjKf3nqfopnmvi7hzActMoCZwAdEYPJ63ujGbONk8dsxyhIqkwdiGMhRRElm-a6Ju96wVxIuz8HNve4BktEci3kugKT2r4MrpSe9n7TWb3yEUjIIezsfdm1BftM7Gtl6hvX49MDaIwVJxE8fhy7AzDEYN6A81b2gzrnnASWSZj0RPzTeARBEMAAkmabaytD8ByorpoLYYSUDhnfWwdO3wARMm8-9im8ul5eQTmw3d7dYuTJII8DSQ2-9iWV5kP1Kqf9dOuxFE0CozI1N4XCM2_5_YK0hYSEhIDWiYBA1n3z-_KJMk7I2vziSzsQrhUeWex2DLWSrT5MNJTuS1Pyorg1KUTuTV2hRiGKo31OaZi3V7k8veWn2S_dySFLpdJpUywpuxA0ONhuNa6_Zz8asKoiatiywwmL5rUPE4KWVtrpYoHRyG4XCnpFJjveoPWb2xsRBX4ec1Gk4lVXeWs49rJ8O7juvISmohFJAeQO4JwB3Q92_hpQdWEZEouhoSZD_f1joAjeehs7rUXo3f-2qnRFc6Q02UvVsiioUnfmnSraCpdokm0xOetR1mMJrye5bMV9I9OfAIE-NVx_tawFuRcsWVkvVWc8Qn0ES2ATabUkUo_pt3GACLnldxJ9tVR080Gzzlzjl_km9Nv_RWxb-APGgqvmrW1BqTRhkRMbjHHVPm_GJK0Ogd7Jl0_rkLm97Y_4ATZS9A0LPhdRcns2wAcmiuWKIJAxVBDeKgD55drB2fvrR-0HumB5z8r2snMUh6SFqVgpwrFz-MKPmNVkboXyL8coNzlTJXpILlWfYKJbmTVYKjbXid5d07DzXmLOKmrvJDRB39OnY_dnnHDsj1xMVocSQxERPQQeWto2nZRvZbGwsRxD_7zNdfwialskIlGitiL-zVl57Zfnse7L5WDS1KEfUTjA5KOKgo95baCKNDa2H91jp3T6YDk3NOnVuIDRIM6h1GEofAZinuL-91PxJL6cYjSbWeOuHrGrNfvtDjHfB1Bj-LbEBTFv4opTEqt8wIsDICPDn77ETr9y9volU0fFDfDSphvDQ7nCbBt70CPxS7TUaywoXazP3dh28NCC_k_HTe84kKSEhSZI7L0_ychqrBmpG32eu3rQ6Z7jB3bOoGGbVK0a8ir2JnjY0RNs5xP53vz61gys9Kr-EOVOI3ZSSPpClCNak-riL_1ScZhWs7RNaeIVXejfY1mifKEb6odLP5AS5tQNSw-e7ZRVUdisEmUO3eSyVx8XGbqR6KGorghPX6oc85oHr-qIPsK-LOAGLACYyxeJCPCqrXJGZliV6OQRr_fWSXKVyb5otMWPXMlBDqidbAYoPav9WtDtltsai2W-3iC9R4FgA4sm1Nb7KdcHWSBFJJnCo9O6EIKwVuyY_knrE=", "id": "rs_0dc9f371a6d55e3f016a13720bc8d0819196833e9c10a11e78", "summary": [{"type": "summary_text", "text": "**Creating a plan for video generation**\n\nI need to come up with an answer plan since the user is asking how to proceed. It seems that social media skills are relevant here, but I'm not sure if a tool is required for this part. Maybe I should load up writing plans to help generate a proper video based on their specific skill set. Also, could comfyui be relevant for effective video generation? I’ll need to consider loading that if it fits!"}]}], "tool_calls": [{"id": "call_0fnG1uinubYhS3JLb5u4PNvf", "call_id": "call_0fnG1uinubYhS3JLb5u4PNvf", "response_item_id": "fc_0dc9f371a6d55e3f016a13721070188191ab4e199265ddbdf7", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}, {"id": "call_x3Wkxym9uxmp0kKc1Q6aB5a5", "call_id": "call_x3Wkxym9uxmp0kKc1Q6aB5a5", "response_item_id": "fc_0dc9f371a6d55e3f016a137210709c8191bca90a36c68dd45c", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"comfyui\"}"}}, {"id": "call_ZEIqVwU23Gj2bzUzE435QcvX", "call_id": "call_ZEIqVwU23Gj2bzUzE435QcvX", "response_item_id": "fc_0dc9f371a6d55e3f016a13721070ac8191af7dbd1059a52eb4", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"writing-plans\"}"}}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}", "tool_call_id": "call_0fnG1uinubYhS3JLb5u4PNvf", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan22-native-i2v-a5000-quality.md` — native ComfyUI Wan2.2 5B quality workflow notes: official model/T5/VAE stack, I2V keyframe lessons, RAM/offload guidance, cleanup candidates, and the critical distinction between technical success and production usability.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n- `wan22-native-i2v-prompting.md` — native Wan2.2 I2V prompting and keyframe rules for quality-first local Retention Studio clips; covers scaled T5, negative prompts, A5000 ladders, and blunt review criteria.\\n- `wan22-i2v-retention-studio-pipeline.md` — session-proven production pattern for using native Wan2.2 as a clip generator inside a deterministic Retention-Studio pipeline: keyframe prompts, seeded I2V jobs, ComfyUI client shape, and review gates.\\n- `qwen-image-gguf-local.md` — Qwen-Image-2512-GGUF model choice, VRAM/quantization ladder, and orchestration guidance for using Qwen keyframe generation alongside Wan/Wan2.2 video without assuming concurrent GPU residency.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n- User asks about local GGUF image models in ComfyUI, especially Qwen-Image/Qwen-Image-Edit, model quantization choice, or whether image and video models can share one GPU concurrently.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed. For WanVideoWrapper,\\n `/object_info` is especially useful: inspect loader dropdowns to verify\\n files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n If it warns that it is deprecated, use `hf download --local-dir `\\n instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Use Wan2.2 for photorealistic texture/motion only; add captions, numbers, labels, UI text, arrows, risk meters, and audit-sensitive facts later in a deterministic renderer. Generate multiple seeded variants per approved keyframe and select by review gates. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan22-i2v-retention-studio-pipeline.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n14. **Qwen-Image GGUF and Wan video are separate heavy GPU residents** — for local GGUF image generation, prefer Qwen-Image-2512-GGUF as the practical ComfyUI-GGUF choice; use Qwen-Image-Edit-2511-GGUF for edit/reference workflows. Start Q4 on 8–12 GB VRAM, Q5/Q6 on 16 GB, and Q6/Q8 on 24 GB only after a smoke test. Do not assume Qwen-Image can run in parallel with Wan/Wan2.2 on a 16 GB class GPU: generate keyframes/images first, call `/free` with `unload_models` and `free_memory`, then run video. See `references/qwen-image-gguf-local.md`.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/wan22-native-i2v-a5000-quality.md\", \"references/rest-api.md\", \"references/wan22-i2v-retention-studio-pipeline.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/wan22-native-i2v-prompting.md\", \"references/qwen-image-gguf-local.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}", "tool_call_id": "call_x3Wkxym9uxmp0kKc1Q6aB5a5", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"writing-plans\", \"description\": \"Write implementation plans: bite-sized tasks, paths, code.\", \"tags\": [\"planning\", \"design\", \"implementation\", \"workflow\", \"documentation\"], \"related_skills\": [\"subagent-driven-development\", \"test-driven-development\", \"requesting-code-review\"], \"content\": \"---\\nname: writing-plans\\ndescription: \\\"Write implementation plans: bite-sized tasks, paths, code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [planning, design, implementation, workflow, documentation]\\n related_skills: [subagent-driven-development, test-driven-development, requesting-code-review]\\n---\\n\\n# Writing Implementation Plans\\n\\n## Overview\\n\\nWrite comprehensive implementation plans assuming the implementer has zero context for the codebase and questionable taste. Document everything they need: which files to touch, complete code, testing commands, docs to check, how to verify. Give them bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.\\n\\nAssume the implementer is a skilled developer but knows almost nothing about the toolset or problem domain. Assume they don't know good test design very well.\\n\\n**Core principle:** A good plan makes implementation obvious. If someone has to guess, the plan is incomplete.\\n\\n## When to Use\\n\\n**Always use before:**\\n- Implementing multi-step features\\n- Breaking down complex requirements\\n- Delegating to subagents via subagent-driven-development\\n\\n**Don't skip when:**\\n- Feature seems simple (assumptions cause bugs)\\n- You plan to implement it yourself (future you needs guidance)\\n- Working alone (documentation matters)\\n\\n## Bite-Sized Task Granularity\\n\\n**Each task = 2-5 minutes of focused work.**\\n\\nEvery step is one action:\\n- \\\"Write the failing test\\\" — step\\n- \\\"Run it to make sure it fails\\\" — step\\n- \\\"Implement the minimal code to make the test pass\\\" — step\\n- \\\"Run the tests and make sure they pass\\\" — step\\n- \\\"Commit\\\" — step\\n\\n**Too big:**\\n```markdown\\n### Task 1: Build authentication system\\n[50 lines of code across 5 files]\\n```\\n\\n**Right size:**\\n```markdown\\n### Task 1: Create User model with email field\\n[10 lines, 1 file]\\n\\n### Task 2: Add password hash field to User\\n[8 lines, 1 file]\\n\\n### Task 3: Create password hashing utility\\n[15 lines, 1 file]\\n```\\n\\n## Plan Document Structure\\n\\n### Header (Required)\\n\\nEvery plan MUST start with:\\n\\n```markdown\\n# [Feature Name] Implementation Plan\\n\\n> **For Hermes:** Use subagent-driven-development skill to implement this plan task-by-task.\\n\\n**Goal:** [One sentence describing what this builds]\\n\\n**Architecture:** [2-3 sentences about approach]\\n\\n**Tech Stack:** [Key technologies/libraries]\\n\\n---\\n```\\n\\n### Task Structure\\n\\nEach task follows this format:\\n\\n````markdown\\n### Task N: [Descriptive Name]\\n\\n**Objective:** What this task accomplishes (one sentence)\\n\\n**Files:**\\n- Create: `exact/path/to/new_file.py`\\n- Modify: `exact/path/to/existing.py:45-67` (line numbers if known)\\n- Test: `tests/path/to/test_file.py`\\n\\n**Step 1: Write failing test**\\n\\n```python\\ndef test_specific_behavior():\\n result = function(input)\\n assert result == expected\\n```\\n\\n**Step 2: Run test to verify failure**\\n\\nRun: `pytest tests/path/test.py::test_specific_behavior -v`\\nExpected: FAIL — \\\"function not defined\\\"\\n\\n**Step 3: Write minimal implementation**\\n\\n```python\\ndef function(input):\\n return expected\\n```\\n\\n**Step 4: Run test to verify pass**\\n\\nRun: `pytest tests/path/test.py::test_specific_behavior -v`\\nExpected: PASS\\n\\n**Step 5: Commit**\\n\\n```bash\\ngit add tests/path/test.py src/path/file.py\\ngit commit -m \\\"feat: add specific feature\\\"\\n```\\n````\\n\\n## Writing Process\\n\\n### Step 1: Understand Requirements\\n\\nRead and understand:\\n- Feature requirements\\n- Design documents or user description\\n- Acceptance criteria\\n- Constraints\\n\\n### Step 2: Explore the Codebase\\n\\nUse Hermes tools to understand the project:\\n\\n```python\\n# Understand project structure\\nsearch_files(\\\"*.py\\\", target=\\\"files\\\", path=\\\"src/\\\")\\n\\n# Look at similar features\\nsearch_files(\\\"similar_pattern\\\", path=\\\"src/\\\", file_glob=\\\"*.py\\\")\\n\\n# Check existing tests\\nsearch_files(\\\"*.py\\\", target=\\\"files\\\", path=\\\"tests/\\\")\\n\\n# Read key files\\nread_file(\\\"src/app.py\\\")\\n```\\n\\n### Step 3: Design Approach\\n\\nDecide:\\n- Architecture pattern\\n- File organization\\n- Dependencies needed\\n- Testing strategy\\n\\n### Step 4: Write Tasks\\n\\nCreate tasks in order:\\n1. Setup/infrastructure\\n2. Core functionality (TDD for each)\\n3. Edge cases\\n4. Integration\\n5. Cleanup/documentation\\n\\n### Step 5: Add Complete Details\\n\\nFor each task, include:\\n- **Exact file paths** (not \\\"the config file\\\" but `src/config/settings.py`)\\n- **Complete code examples** (not \\\"add validation\\\" but the actual code)\\n- **Exact commands** with expected output\\n- **Verification steps** that prove the task works\\n\\n### Step 6: Review the Plan\\n\\nCheck:\\n- [ ] Tasks are sequential and logical\\n- [ ] Each task is bite-sized (2-5 min)\\n- [ ] File paths are exact\\n- [ ] Code examples are complete (copy-pasteable)\\n- [ ] Commands are exact with expected output\\n- [ ] No missing context\\n- [ ] DRY, YAGNI, TDD principles applied\\n\\n### Step 7: Save the Plan\\n\\n```bash\\nmkdir -p docs/plans\\n# Save plan to docs/plans/YYYY-MM-DD-feature-name.md\\ngit add docs/plans/\\ngit commit -m \\\"docs: add implementation plan for [feature]\\\"\\n```\\n\\n## Principles\\n\\n### DRY (Don't Repeat Yourself)\\n\\n**Bad:** Copy-paste validation in 3 places\\n**Good:** Extract validation function, use everywhere\\n\\n### YAGNI (You Aren't Gonna Need It)\\n\\n**Bad:** Add \\\"flexibility\\\" for future requirements\\n**Good:** Implement only what's needed now\\n\\n```python\\n# Bad — YAGNI violation\\nclass User:\\n def __init__(self, name, email):\\n self.name = name\\n self.email = email\\n self.preferences = {} # Not needed yet!\\n self.metadata = {} # Not needed yet!\\n\\n# Good — YAGNI\\nclass User:\\n def __init__(self, name, email):\\n self.name = name\\n self.email = email\\n```\\n\\n### TDD (Test-Driven Development)\\n\\nEvery task that produces code should include the full TDD cycle:\\n1. Write failing test\\n2. Run to verify failure\\n3. Write minimal code\\n4. Run to verify pass\\n\\nSee `test-driven-development` skill for details.\\n\\n### Frequent Commits\\n\\nCommit after every task:\\n```bash\\ngit add [files]\\ngit commit -m \\\"type: description\\\"\\n```\\n\\n## Common Mistakes\\n\\n### Vague Tasks\\n\\n**Bad:** \\\"Add authentication\\\"\\n**Good:** \\\"Create User model with email and password_hash fields\\\"\\n\\n### Incomplete Code\\n\\n**Bad:** \\\"Step 1: Add validation function\\\"\\n**Good:** \\\"Step 1: Add validation function\\\" followed by the complete function code\\n\\n### Missing Verification\\n\\n**Bad:** \\\"Step 3: Test it works\\\"\\n**Good:** \\\"Step 3: Run `pytest tests/test_auth.py -v`, expected: 3 passed\\\"\\n\\n### Missing File Paths\\n\\n**Bad:** \\\"Create the model file\\\"\\n**Good:** \\\"Create: `src/models/user.py`\\\"\\n\\n## Execution Handoff\\n\\nAfter saving the plan, offer the execution approach:\\n\\n**\\\"Plan complete and saved. Ready to execute using subagent-driven-development — I'll dispatch a fresh subagent per task with two-stage review (spec compliance then code quality). Shall I proceed?\\\"**\\n\\nWhen executing, use the `subagent-driven-development` skill:\\n- Fresh `delegate_task` per task with full context\\n- Spec compliance review after each task\\n- Code quality review after spec passes\\n- Proceed only when both reviews approve\\n\\n## Remember\\n\\n```\\nBite-sized tasks (2-5 min each)\\nExact file paths\\nComplete code (copy-pasteable)\\nExact commands with expected output\\nVerification steps\\nDRY, YAGNI, TDD\\nFrequent commits\\n```\\n\\n**A good plan makes implementation obvious.**\\n\", \"path\": \"software-development/writing-plans/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/writing-plans\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"planning\", \"design\", \"implementation\", \"workflow\", \"documentation\"], \"related_skills\": [\"subagent-driven-development\", \"test-driven-development\", \"requesting-code-review\"]}}}", "tool_call_id": "call_ZEIqVwU23Gj2bzUzE435QcvX", "timestamp": "2026-05-24T23:48:33.717325"} {"role": "assistant", "content": "Ja, Sir. Für das **richtige Video** würde ich jetzt nicht direkt „ein Wan/Qwen Prompt reinwerfen und hoffen“ — das produziert sehr schnell hübsche, aber unbrauchbare AI-Suppe. Wir machen es als **Hybrid Retention Studio Pipeline**:\n\n```text\nAI generiert nur visuelle Textur / Motion\nJARVIS/Renderer kontrolliert Text, Timing, Captions, Labels, Audio, Schnitt\n```\n\n## Zielbild für Clip 1\n\nThema:\n\n```text\nYour AI automation works in the demo.\nThen one weird email arrives.\n```\n\nVisueller Ablauf:\n\n```text\n1. Clean demo inbox\n2. Weird email appears\n3. AI confidently routes it wrong\n4. Simple 3-way split:\n Go through\n Review\n Stop\n5. Final rule:\n Plan for the weird input before you automate the normal one.\n```\n\nDas ist zugänglich, konkret und nicht abgehoben.\n\n---\n\n## Mein Vorgehen\n\n## Phase 1: Final Render Plan bauen\n\nZuerst mache ich aus dem aktuellen Script einen **richtigen Produktionsplan**, nicht nur Preview Cards.\n\nNeues Artefakt:\n\n```text\nFinalVideoPlan\n```\n\nEnthält pro Szene:\n\n```text\nscene_id\nstart_ms\nend_ms\nvoiceover_segment\nvisual_mode\nasset_type\nmotion_instruction\nrenderer_text\ncaption_cues\nrequired_assets\n```\n\nWichtig:\n\n```text\nrenderer_text ≠ AI-generated text\n```\n\nAlle Texte im Bild — Inbox-Labels, Pfeile, „Review“, „Stop“, Captions — kommen vom Renderer, nicht vom Video-Modell.\n\nWarum? Weil AI-Video bei Text notorisch versagt. Es würde aus „Review“ gerne „Rëvlew“ machen. Künstlerisch mutig, aber geschäftlich nutzlos.\n\n---\n\n## Phase 2: Szenen in Asset-Typen trennen\n\nIch würde Clip 1 so aufteilen:\n\n### Szene 1 — Hook\n\n```text\n0–4s\nVisual: clean inbox / clean workflow dashboard\nMode: local deterministic animation + optional AI background\nText: Your AI automation works perfectly in the demo.\n```\n\nKein echtes AI-Video nötig. Das kann unser Renderer sauber animieren.\n\n### Szene 2 — Weird email arrives\n\n```text\n4–10s\nVisual: email card slides in\nText on card:\n\"hey can you handle this maybe??\"\nMissing name\nUnclear request\nWrong format\n```\n\nAuch hier: besser renderer-owned. Kein AI-Text im Bild.\n\n### Szene 3 — AI gets it wrong\n\n```text\n10–17s\nVisual: glowing AI node routes email to wrong action\nMotion: confident green path, then red warning flash\n```\n\nHier können wir optional AI-generated abstract/tech background nutzen, aber der Mechanismus selbst sollte lokal animiert werden.\n\n### Szene 4 — The fix: 3 paths\n\n```text\n17–29s\nVisual:\nNormal → Go through\nUnclear → Review\nWeird/Risky → Stop\n```\n\nDas ist der wichtigste Mechanismus. Muss glasklar sein. Renderer-owned Diagramm.\n\n### Szene 5 — Rule / close\n\n```text\n29–37s\nVisual: final compact rule card\nText:\nPlan for weird inputs before automating normal ones.\n```\n\n---\n\n## Phase 3: Erst lokalen Motion Renderer bauen\n\nBevor wir ComfyUI/Wan/Qwen verschwenden, baue ich einen **procedural motion renderer**:\n\n```text\nPillow/OpenCV/FFmpeg\n```\n\nEr erzeugt:\n\n```text\nanimated inbox cards\nmoving arrows\npath split\nwarning pulse\ncaption burn-in\naudio mux\n```\n\nDas ist wichtig, weil dieser Clip sehr text-/diagramm-lastig ist. Dafür ist lokaler Renderer besser als AI-Video.\n\nAI-Video wäre hier nur sinnvoll für:\n\n```text\nbackground texture\noffice/desk b-roll\ncinematic phone/screen closeups\n```\n\nNicht für den eigentlichen Erklärmechanismus.\n\n---\n\n## Phase 4: Danach AI-Visuals gezielt ergänzen\n\nDann, und erst dann, setzen wir ComfyUI ein.\n\nMeine Empfehlung:\n\n```text\nQwen-Image-2512-GGUF → Keyframes / clean background stills\nWan2.2 I2V → kurze motion clips aus diesen Keyframes\n```\n\nAber nicht parallel auf derselben GPU.\n\nAblauf:\n\n```text\n1. Qwen-Image erzeugt 2–3 clean keyframes:\n - clean inbox dashboard\n - messy weird-email moment\n - AI workflow node background\n\n2. GPU freigeben:\n /free unload_models + free_memory\n\n3. Wan2.2 macht kurze I2V clips:\n - subtle camera push\n - screen glow\n - desk/monitor movement\n\n4. Final Renderer legt darüber:\n - echte Texte\n - Captions\n - Pfeile\n - Labels\n - Audio\n```\n\nAlso:\n\n```text\nAI = texture/motion\nRenderer = Wahrheit\n```\n\n---\n\n## Phase 5: Qualitätstor vor Final\n\nIch würde jeden Clip gegen diese Gates prüfen:\n\n```text\n- Versteht man in den ersten 2 Sekunden das Problem?\n- Ist “weird email” sofort sichtbar?\n- Sind alle Texte lesbar auf Mobile?\n- Kein AI-generierter Fake-Text?\n- Captions decken Voiceover?\n- Keine Jargon-Wörter?\n- Visuelle Veränderung alle 1–2 Sekunden?\n- Mechanismus “Go through / Review / Stop” klar?\n- Audio und Video gleich lang?\n```\n\nWenn eines davon failt: nicht posten. Nicht einmal aus Mitleid.\n\n---\n\n## Konkreter nächster Implementierungsschritt\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/rendering/final_video_plan.py\ntests/test_final_video_plan.py\n```\n\nDanach:\n\n```text\nautoshorts/rendering/motion_renderer.py\ntests/test_motion_renderer.py\n```\n\nErste echte lokale Version:\n\n```text\ndata/previews/truetrace-final-motion-v1/.../final-motion-preview.mp4\n```\n\nDiese Version wird noch ohne ComfyUI sein, aber schon:\n\n```text\n- echtes Motion Layout\n- Inbox Card\n- Weird Email\n- 3-way path diagram\n- Voiceover\n- Captions\n- Audio sync\n```\n\nDanach entscheiden wir:\n\n```text\nReicht procedural style?\nOder ergänzen wir AI b-roll/keyframes mit Qwen/Wan?\n```\n\n## Warum ich nicht sofort Qwen/Wan nehme\n\nWeil dieser Clip **nicht** primär ein „cinematic video“ ist. Er ist ein **mechanism reveal**.\n\nWenn wir sofort AI-Video nutzen, bekommen wir wahrscheinlich:\n\n```text\nschöne Screens\nunlesbare UI\nfalsche Texte\nrandom Bewegungen\nweniger Klarheit\n```\n\nDas sieht professioneller aus, erklärt aber schlechter. Und für Shorts zählt: **Klarheit vor Schönheit**.\n\n## Mein Vorschlag\n\nIch mache als nächstes:\n\n```text\nFinalVideoPlan + deterministic motion renderer v1\n```\n\nDann haben wir den ersten echten „richtigen“ Video-Prototypen. Danach können wir gezielt ComfyUI-Keyframes ergänzen, statt mit der GPU Würfel zu spielen.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir. Für das **richtige Video** würde ich jetzt nicht direkt „ein Wan/Qwen Prompt reinwerfen und hoffen“ — das produziert sehr schnell hübsche, aber unbrauchbare AI-Suppe. Wir machen es als **Hybrid Retention Studio Pipeline**:\n\n```text\nAI generiert nur visuelle Textur / Motion\nJARVIS/Renderer kontrolliert Text, Timing, Captions, Labels, Audio, Schnitt\n```\n\n## Zielbild für Clip 1\n\nThema:\n\n```text\nYour AI automation works in the demo.\nThen one weird email arrives.\n```\n\nVisueller Ablauf:\n\n```text\n1. Clean demo inbox\n2. Weird email appears\n3. AI confidently routes it wrong\n4. Simple 3-way split:\n Go through\n Review\n Stop\n5. Final rule:\n Plan for the weird input before you automate the normal one.\n```\n\nDas ist zugänglich, konkret und nicht abgehoben.\n\n---\n\n## Mein Vorgehen\n\n## Phase 1: Final Render Plan bauen\n\nZuerst mache ich aus dem aktuellen Script einen **richtigen Produktionsplan**, nicht nur Preview Cards.\n\nNeues Artefakt:\n\n```text\nFinalVideoPlan\n```\n\nEnthält pro Szene:\n\n```text\nscene_id\nstart_ms\nend_ms\nvoiceover_segment\nvisual_mode\nasset_type\nmotion_instruction\nrenderer_text\ncaption_cues\nrequired_assets\n```\n\nWichtig:\n\n```text\nrenderer_text ≠ AI-generated text\n```\n\nAlle Texte im Bild — Inbox-Labels, Pfeile, „Review“, „Stop“, Captions — kommen vom Renderer, nicht vom Video-Modell.\n\nWarum? Weil AI-Video bei Text notorisch versagt. Es würde aus „Review“ gerne „Rëvlew“ machen. Künstlerisch mutig, aber geschäftlich nutzlos.\n\n---\n\n## Phase 2: Szenen in Asset-Typen trennen\n\nIch würde Clip 1 so aufteilen:\n\n### Szene 1 — Hook\n\n```text\n0–4s\nVisual: clean inbox / clean workflow dashboard\nMode: local deterministic animation + optional AI background\nText: Your AI automation works perfectly in the demo.\n```\n\nKein echtes AI-Video nötig. Das kann unser Renderer sauber animieren.\n\n### Szene 2 — Weird email arrives\n\n```text\n4–10s\nVisual: email card slides in\nText on card:\n\"hey can you handle this maybe??\"\nMissing name\nUnclear request\nWrong format\n```\n\nAuch hier: besser renderer-owned. Kein AI-Text im Bild.\n\n### Szene 3 — AI gets it wrong\n\n```text\n10–17s\nVisual: glowing AI node routes email to wrong action\nMotion: confident green path, then red warning flash\n```\n\nHier können wir optional AI-generated abstract/tech background nutzen, aber der Mechanismus selbst sollte lokal animiert werden.\n\n### Szene 4 — The fix: 3 paths\n\n```text\n17–29s\nVisual:\nNormal → Go through\nUnclear → Review\nWeird/Risky → Stop\n```\n\nDas ist der wichtigste Mechanismus. Muss glasklar sein. Renderer-owned Diagramm.\n\n### Szene 5 — Rule / close\n\n```text\n29–37s\nVisual: final compact rule card\nText:\nPlan for weird inputs before automating normal ones.\n```\n\n---\n\n## Phase 3: Erst lokalen Motion Renderer bauen\n\nBevor wir ComfyUI/Wan/Qwen verschwenden, baue ich einen **procedural motion renderer**:\n\n```text\nPillow/OpenCV/FFmpeg\n```\n\nEr erzeugt:\n\n```text\nanimated inbox cards\nmoving arrows\npath split\nwarning pulse\ncaption burn-in\naudio mux\n```\n\nDas ist wichtig, weil dieser Clip sehr text-/diagramm-lastig ist. Dafür ist lokaler Renderer besser als AI-Video.\n\nAI-Video wäre hier nur sinnvoll für:\n\n```text\nbackground texture\noffice/desk b-roll\ncinematic phone/screen closeups\n```\n\nNicht für den eigentlichen Erklärmechanismus.\n\n---\n\n## Phase 4: Danach AI-Visuals gezielt ergänzen\n\nDann, und erst dann, setzen wir ComfyUI ein.\n\nMeine Empfehlung:\n\n```text\nQwen-Image-2512-GGUF → Keyframes / clean background stills\nWan2.2 I2V → kurze motion clips aus diesen Keyframes\n```\n\nAber nicht parallel auf derselben GPU.\n\nAblauf:\n\n```text\n1. Qwen-Image erzeugt 2–3 clean keyframes:\n - clean inbox dashboard\n - messy weird-email moment\n - AI workflow node background\n\n2. GPU freigeben:\n /free unload_models + free_memory\n\n3. Wan2.2 macht kurze I2V clips:\n - subtle camera push\n - screen glow\n - desk/monitor movement\n\n4. Final Renderer legt darüber:\n - echte Texte\n - Captions\n - Pfeile\n - Labels\n - Audio\n```\n\nAlso:\n\n```text\nAI = texture/motion\nRenderer = Wahrheit\n```\n\n---\n\n## Phase 5: Qualitätstor vor Final\n\nIch würde jeden Clip gegen diese Gates prüfen:\n\n```text\n- Versteht man in den ersten 2 Sekunden das Problem?\n- Ist “weird email” sofort sichtbar?\n- Sind alle Texte lesbar auf Mobile?\n- Kein AI-generierter Fake-Text?\n- Captions decken Voiceover?\n- Keine Jargon-Wörter?\n- Visuelle Veränderung alle 1–2 Sekunden?\n- Mechanismus “Go through / Review / Stop” klar?\n- Audio und Video gleich lang?\n```\n\nWenn eines davon failt: nicht posten. Nicht einmal aus Mitleid.\n\n---\n\n## Konkreter nächster Implementierungsschritt\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/rendering/final_video_plan.py\ntests/test_final_video_plan.py\n```\n\nDanach:\n\n```text\nautoshorts/rendering/motion_renderer.py\ntests/test_motion_renderer.py\n```\n\nErste echte lokale Version:\n\n```text\ndata/previews/truetrace-final-motion-v1/.../final-motion-preview.mp4\n```\n\nDiese Version wird noch ohne ComfyUI sein, aber schon:\n\n```text\n- echtes Motion Layout\n- Inbox Card\n- Weird Email\n- 3-way path diagram\n- Voiceover\n- Captions\n- Audio sync\n```\n\nDanach entscheiden wir:\n\n```text\nReicht procedural style?\nOder ergänzen wir AI b-roll/keyframes mit Qwen/Wan?\n```\n\n## Warum ich nicht sofort Qwen/Wan nehme\n\nWeil dieser Clip **nicht** primär ein „cinematic video“ ist. Er ist ein **mechanism reveal**.\n\nWenn wir sofort AI-Video nutzen, bekommen wir wahrscheinlich:\n\n```text\nschöne Screens\nunlesbare UI\nfalsche Texte\nrandom Bewegungen\nweniger Klarheit\n```\n\nDas sieht professioneller aus, erklärt aber schlechter. Und für Shorts zählt: **Klarheit vor Schönheit**.\n\n## Mein Vorschlag\n\nIch mache als nächstes:\n\n```text\nFinalVideoPlan + deterministic motion renderer v1\n```\n\nDann haben wir den ersten echten „richtigen“ Video-Prototypen. Danach können wir gezielt ComfyUI-Keyframes ergänzen, statt mit der GPU Würfel zu spielen."}], "id": "msg_0dc9f371a6d55e3f016a13721421188191a42c7bb32426b21e", "phase": "final_answer"}], "timestamp": "2026-05-24T23:48:33.717325"} {"role": "user", "content": "[M G] Ok, leg los. Bedenke: du kannst ChatGPT Image nutzen über codex anstelle eines lokalen qwen image.", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Implementing the image generation plan**\n\nI need to take action and see if the image generation tool is available, possibly requiring backend support. The user asked to use the ChatGPT Image über codex instead of the local option. I can work on this plan! I'll need to ensure that all skills are appropriately loaded, but should I do this before replying each time? Testing driven development could be useful here, along with reviewing existing files.", "finish_reason": "tool_calls", "reasoning_content": "**Implementing the image generation plan**\n\nI need to take action and see if the image generation tool is available, possibly requiring backend support. The user asked to use the ChatGPT Image über codex instead of the local option. I can work on this plan! I'll need to ensure that all skills are appropriately loaded, but should I do this before replying each time? Testing driven development could be useful here, along with reviewing existing files.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3Mo3moHpEeF6H5LyW_TQiU4P78t23cBxkE4uqGyFLib4zXCOoDTHIzUUfQ0RNh8KyOFkPQRrK_DpfjemviNmV1qDllFueL9y-OP6BfZGbs3oH1oUMnid38uwFnOVPhx9MKdN6N-u8OXsEI5kKpC03OWssth5FmpeWGs6ywmgUyRxZi4ts4u8161vKjybXIy_slskbPQMKVxVucNkv2jcJio8Pkooodf2tIjc3TN_VyjQc-DZTxH35oTaab67p_GbrJoGSof2tXH56QZyU1iMmcCgSSLpO32utM8A-Ogw8Yzj1AEUNZYscuyH_PWBvcZSIFdOGDp3RX_oWxzTQ46Klb7h1eHPKtJb1dynSZOlbBWdiPfBtk9UJ5abvYMDUVMHO3wqDWL75ecYnGjDf5yKCqWzm8h3almPYUxfiR4Aj7t70pqfJ8Mvb6V5qmFbqP5AN78k8ao-Xg5TYyQdyWwMAihEX1h8N6-1dPzP1nyqAEapcNHi3uDi-GWI1SJ7rKeEjMVKYTtF7ll5L6YE5R1IY3QhRBy2CchIMx6wQkwK6dhItclLtHBedg56U9ZRXM52uuKiaFtX8kaDkdow_HHj37tjnXRQbCP5aBlLRwiZ_B03-jN2s5j7IcT_bBM_-dse_RYX43kdESV6_PfwYVdiqhr8c2QJiR7T5SaejmvYIY_kQn5KFraS0zo9mDvpjH-8dJCmoaD-nDJrERMig_VHadAZFDiELM1MNRMekNCyHOf5ujk34nJS5uUVmnNxYZ4iGUuRSq-cPJQD2Y5cSnEn23Yg_PLNVt8qErufr88ZkxuQOoAj4G2xMz9SyBPnEb4krjBg-7cvo2k8qofEsg2UPt7AIBCTP7Dq5LiL2GcT7YaGAL_MdH3t4vMP2DXDSaqLxNgGJ61X5dirfvq7cNpL4nuB_WbhYEQIz2b8cXB4kybeZvh5XjqUvsjlaI2g3ig7yLkVc7-vnegPpl_M2FuyZIn5SchPX7XHhtXVO2qsCXRGqcKwVJgngCAQTmH4Zxj1B8JQB0wzE7tEco8MU6LA6KKmclYMShnWZ-muVKPEtrc-TRB6hg0tgT8MJqmsh21yO6amvy7xQTQjwUKeLkWJx_5e0oB45jmzum1dWAQG5wEzF4WXrLS9mqS6sBUCAlPI38ImS-FIGjY3FzckHywtGjgp8StZYW7ZxVS4WoPLWtWgbtbb7mPhPZGfUnonYSHQ1nqzA6WOANBZUUCVeN7QM9uKTJVvOQJ841zFkIIXDPaZy252AUlXGhfrOE03qADYssI-h9bRk30OC7fdd3vqK7xVrKA6z2l7Nv6gLjOU_EMCPI=", "id": "rs_0dc9f371a6d55e3f016a137324d3d481918a2c5ad13a6db1b3", "summary": [{"type": "summary_text", "text": "**Implementing the image generation plan**\n\nI need to take action and see if the image generation tool is available, possibly requiring backend support. The user asked to use the ChatGPT Image über codex instead of the local option. I can work on this plan! I'll need to ensure that all skills are appropriately loaded, but should I do this before replying each time? Testing driven development could be useful here, along with reviewing existing files."}]}], "tool_calls": [{"id": "call_eRGDQD4TzBNXJRFtKdDLs8JM", "call_id": "call_eRGDQD4TzBNXJRFtKdDLs8JM", "response_item_id": "fc_0dc9f371a6d55e3f016a137328297c8191bf99d97ddc0687c3", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_x73vrhY3HRf2gOH7EbcFHmJj", "call_id": "call_x73vrhY3HRf2gOH7EbcFHmJj", "response_item_id": "fc_0dc9f371a6d55e3f016a13732829a481918129c30406a25a0d", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}, {"id": "call_kPdfp5JvZhW5d5YPU5pJmzIB", "call_id": "call_kPdfp5JvZhW5d5YPU5pJmzIB", "response_item_id": "fc_0dc9f371a6d55e3f016a13732829b48191ac6ac2c6646c98a3", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"comfyui\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### CLI Entry Points\\n\\nFor new `python -m package.cli.command` style entry points, keep the TDD loop boring and side-effect free:\\n\\n1. Write a test that imports `main` from the missing CLI module and calls `main([])`.\\n2. Capture stdout/stderr with the project test framework (`capsys` in pytest) or inject a `stdout` file-like object if supported.\\n3. RED should usually be an import failure for the missing CLI module/package, not a vague end-to-end shell failure.\\n4. Implement `main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int` so tests can call it without spawning a subprocess.\\n5. Add the `if __name__ == \\\"__main__\\\": raise SystemExit(main())` guard only after the callable path is green.\\n6. Verify both the unit test and the real module invocation, e.g. `python -m package.cli.command`, before committing.\\n\\nThis catches command wiring while avoiding accidental network calls, publishing actions, or other theatrical nonsense masquerading as a CLI test.\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Demo / Fixture Features\\n\\nWhen adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.\\n\\n## Side-Effect-Free Integration Boundaries\\n\\nWhen building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:\\n\\n1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.\\n2. RED should be a missing module/API or missing validation failure, not a real network/send failure.\\n3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.\\n4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.\\n5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.\\n6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.\\n\\nThis pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.\\n\\n### Media / Rendering Pipeline Boundaries\\n\\nWhen building toward image/video generation or renderer integration, add deterministic planning artifacts before any GPU/API work:\\n\\n1. Write tests for the wished-for pure API first, e.g. `create_shot_plan(script)`, `build_render_manifest(plan)`, `build_local_preview_plan(manifest)`, or `build_ffmpeg_concat_plan(plan, frames, output_path)`.\\n2. RED should be a missing module/API or missing validation behavior — never a failed ComfyUI/Wan/Qwen/API invocation.\\n3. Keep each stage inspectable and side-effect-free until the explicit adapter layer: `ScriptDraft -> ShotPlan -> RenderManifest -> PreviewPlan/PreviewArtifact -> renderer adapter`.\\n4. Assert stable IDs/order, scene counts, timing continuity, vertical settings (`1080x1920`, `30fps` when building Shorts/Reels/TikTok), reduced preview settings (`540x960`, `24fps` when building fast local timing previews), and placeholder/runtime paths before generating media.\\n5. Add guardrail tests that reject static slideshows, baked-in/generated text inside assets, non-renderer-owned captions/labels, unsafe render settings, missing fields, bad timings, non-PNG frame paths, non-MP4 output paths, and mismatched frame counts.\\n6. Split artifact-writing/rendering into explicit boundaries after the pure plans are green: frame writer returns `side_effects=(\\\"write_png\\\",)`, concat planning remains side-effect-free, and the FFmpeg renderer returns `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` plus `external_calls=(\\\"ffmpeg\\\",)`.\\n7. Only after the manifest/preview boundary is green should a later adapter call local GPU tools or external services. Planning first; GPU roulette later.\\n\\nThis prevents wasting render time on unclear direction and keeps expensive media generation behind a small, audited boundary.\\n\\n### Approval-First Media/Social Pipelines\\n\\nWhen a feature crosses from content planning toward media/social delivery, prefer many small RED-GREEN-README-COMMIT slices instead of one large integration jump:\\n\\n1. typed artifact/model (`SocialMediaDraft`, `RenderManifest`, etc.)\\n2. renderer/formatter for human review text\\n3. side-effect-free delivery/preparation boundary\\n4. demo CLI that prints metadata first, then payload\\n\\nFor each slice, write the missing import/API test first, verify the expected import/API failure, implement only that slice, run the focused test, run the full suite, update docs, and commit. Keep invariant tests explicit: `requires_human_approval=True`, `side_effects=()`, no external calls, no credential access, no file writes unless that adapter is the explicit feature, and no accidental action language such as `publish`/`post` in review/delivery text.\\n\\nThis staged approach keeps approval semantics auditable and prevents a later renderer, Telegram sender, or platform adapter from being smuggled into formatting code.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\\n- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`\\\"\\\"`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": {\"references\": [\"references/deterministic-demo-fixtures.md\", \"references/side-effect-free-delivery-boundaries.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_eRGDQD4TzBNXJRFtKdDLs8JM", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}", "tool_call_id": "call_x73vrhY3HRf2gOH7EbcFHmJj", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n commands: [\\\"python3\\\"]\\nsetup:\\n help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n hermes:\\n tags:\\n - comfyui\\n - image-generation\\n - stable-diffusion\\n - flux\\n - sd3\\n - wan-video\\n - hunyuan-video\\n - creative\\n - generative-ai\\n - video-generation\\n related_skills: [stable-diffusion-image-generation, image_gen]\\n category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n editor format to API format: Reroute bypass, dotted dynamic-input keys\\n (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan22-native-i2v-a5000-quality.md` — native ComfyUI Wan2.2 5B quality workflow notes: official model/T5/VAE stack, I2V keyframe lessons, RAM/offload guidance, cleanup candidates, and the critical distinction between technical success and production usability.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n- `wan22-native-i2v-prompting.md` — native Wan2.2 I2V prompting and keyframe rules for quality-first local Retention Studio clips; covers scaled T5, negative prompts, A5000 ladders, and blunt review criteria.\\n- `wan22-i2v-retention-studio-pipeline.md` — session-proven production pattern for using native Wan2.2 as a clip generator inside a deterministic Retention-Studio pipeline: keyframe prompts, seeded I2V jobs, ComfyUI client shape, and review gates.\\n- `qwen-image-gguf-local.md` — Qwen-Image-2512-GGUF model choice, VRAM/quantization ladder, and orchestration guidance for using Qwen keyframe generation alongside Wan/Wan2.2 video without assuming concurrent GPU residency.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n- User asks about local GGUF image models in ComfyUI, especially Qwen-Image/Qwen-Image-Edit, model quantization choice, or whether image and video models can share one GPU concurrently.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool) │\\n│ Setup, server lifecycle, custom nodes, models │\\n│ → comfy install / launch / stop / node / model │\\n└─────────────────────────┬───────────────────────────┘\\n │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts │\\n│ Workflow execution, param injection, monitoring │\\n│ POST /api/prompt, GET /api/view, WS /ws │\\n│ → run_workflow.py, run_batch.py, ws_monitor.py │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflow_api.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n --workflow flux_dev.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n --workflow sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n --workflow sdxl.json \\\\\\n --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n --count 8 --randomize-seed --parallel 3 \\\\\\n --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n \\\"status\\\": \\\"success\\\",\\n \\\"prompt_id\\\": \\\"abc-123\\\",\\n \\\"outputs\\\": [\\n {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install ` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id ` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py ` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n> zero setup. Requires an API key (paid subscription required to actually run\\n> workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n> - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n> - AMD GPU with ROCm support (Linux), OR\\n> - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n> - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict | Meaning | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok` | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon) | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud` | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n ```bash\\n export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n ```\\n4. Run workflows:\\n ```bash\\n python3 scripts/run_workflow.py \\\\\\n --workflow workflows/flux_dev_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n --host https://cloud.comfy.org \\\\\\n --output-dir ./outputs\\n ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190 # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n --relative-path models/checkpoints \\\\\\n --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack # popular utility pack\\ncomfy node install comfyui-animatediff-evolved # video generation\\ncomfy node install comfyui-controlnet-aux # ControlNet preprocessors\\ncomfy node install comfyui-essentials # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sd15_txt2img.json \\\\\\n --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_img2img.json \\\\\\n --input-image image=./photo.png \\\\\\n --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n --workflow workflows/sdxl_inpaint.json \\\\\\n --input-image image=./photo.png \\\\\\n --input-image mask_image=./mask.png \\\\\\n --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n follow it and strip `X-API-Key` before fetching from the storage backend\\n (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n paid only.\\n - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n automatically).\\n - `/models/` is renamed to `/experiment/models/` on cloud\\n (the scripts route automatically).\\n - `clientId` in WebSocket is currently ignored — all connections for a\\n user receive the same broadcast. Filter by `prompt_id` client-side.\\n - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}' # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n -H \\\"Content-Type: application/json\\\" \\\\\\n -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n API-format workflow JSON. The scripts detect editor format (top-level\\n `nodes` and `links` arrays) and tell you to re-export via\\n \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n `comfy launch --background` starts one. Verify with\\n `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n `check_deps.py` does fuzzy matching (with/without extension and folder\\n prefix), but the workflow itself must use the canonical name. Use\\n `comfy model list` to discover what's installed. For WanVideoWrapper,\\n `/object_info` is especially useful: inspect loader dropdowns to verify\\n files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n If it warns that it is deprecated, use `hf download --local-dir `\\n instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n isn't installed. `check_deps.py` reports which package to install;\\n `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n If commands fail with \\\"no workspace found\\\", use\\n `comfy --workspace /path/to/ComfyUI ` or\\n `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n 900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n Keep this protection on — workflows with custom save nodes can produce\\n arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n submitting an unknown workflow has the same trust profile as `eval`.\\n Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Use Wan2.2 for photorealistic texture/motion only; add captions, numbers, labels, UI text, arrows, risk meters, and audit-sensitive facts later in a deterministic renderer. Generate multiple seeded variants per approved keyframe and select by review gates. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan22-i2v-retention-studio-pipeline.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n14. **Qwen-Image GGUF and Wan video are separate heavy GPU residents** — for local GGUF image generation, prefer Qwen-Image-2512-GGUF as the practical ComfyUI-GGUF choice; use Qwen-Image-Edit-2511-GGUF for edit/reference workflows. Start Q4 on 8–12 GB VRAM, Q5/Q6 on 16 GB, and Q6/Q8 on 24 GB only after a smoke test. Do not assume Qwen-Image can run in parallel with Wan/Wan2.2 on a 16 GB class GPU: generate keyframes/images first, call `/free` with `unload_models` and `free_memory`, then run video. See `references/qwen-image-gguf-local.md`.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/wan22-native-i2v-a5000-quality.md\", \"references/rest-api.md\", \"references/wan22-i2v-retention-studio-pipeline.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/wan22-native-i2v-prompting.md\", \"references/qwen-image-gguf-local.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}", "tool_call_id": "call_kPdfp5JvZhW5d5YPU5pJmzIB", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Creating video tests**\n\nI need to create tests and consider including an image note with the image_generate tool later. I could start with a deterministic approach and implement tests for the final video plan, possibly using a motion renderer. I plan to produce the actual video using animated frames generated with Pillow and ffmpeg. It looks like TDD will guide this process, so I need to know the necessary modules. Writing simple tests is my goal!", "finish_reason": "tool_calls", "reasoning_content": "**Creating video tests**\n\nI need to create tests and consider including an image note with the image_generate tool later. I could start with a deterministic approach and implement tests for the final video plan, possibly using a motion renderer. I plan to produce the actual video using animated frames generated with Pillow and ffmpeg. It looks like TDD will guide this process, so I need to know the necessary modules. Writing simple tests is my goal!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3MxOYmllmWTCUdhLqfxOJpCn_zExZiwfWGsr3j8TryeykXP1nqXcUJfE097S_JdKfY9CthqB9yKnkta_8GnQJwiGs1Lq9pT3J69rp-1IxJSlaTvPMY083mtI_otSNcZq8heKbv0h5UNt3dQK-ZTgnMccgo0PYqslzeAFYhXRmDncrnM4JjKRdMQRyrYJL7jquJt1tkf-Do423E2mg7M5PSHTVg2iWj6TJSsPSjEDPKhmUL7dSBdUV0Sm3l1roAYv-Y5RAC9pB8l13dBEYG8Z33XmuAlLbLZ--Rly3CRVyYsEg5PpdI1BAGpREABDXAUp7JuXokbkRHGB7BY8_ka8k2iUO_SOiGNmyggEJ_-ZLHnas3sHWVzzp7gEDo6lD5R9rPLxOK81Pke7GZKx2Efj1LnNJA6WplPWXNgkoAMseOsOORkr8QRfr6OevPp3GODBCjNYMdctkbVbC8JRLDLv-1nN8CngFsMZTDO8lMnimtezOKsmw5_bYICxPASuVPNpgrpNDH46s3sQ4uqeOQDbGKn6DmzTL07qeI0XqHy5tVPA8rrnnVAFP10AII7DpfmGZKq1k-0T9HVl6JK-2ySwmIDsXqu8t7m7D2uMXo-AW8IUCJldSMBYfQTV01KpM9xCoLuOmEyuxrPzDxnY_IpzCUH6lJND1kJYI-985F61Na9gkVx719SaRCvBy95QZCgzBs_4aIyPiZE6AmnGJLXA9o2N9M28yljUAkDaNhK0Mw0-NQSLkd9l02oQ4GxrrNpFrjlEhlYq071Q5UDLFY_Qv5GwSmO-bZxuqkFDAUYpwJeNWxIIEgqv6Tjl85e_tBUB_B0f-p6Rnxs0f1_cGUy1DereigZi7dJ-7Eq2Tjfz2yR5WZCRKir_on9i0sz8LtwVwIPVfb7ujiG97x7FVaC3OWJ2AwJsvpuaOlz5ilyzLoocqdUdCYhLrikS8XIOG4XPL122PrMNc0t4NNDT-_fQFuaRXi_sKnA67ACgbkh9g0Ne_P2gwmXg1a6Vzgk3ojCa7sMcM-SoZnqu_V7M-tjabG2bMDdPsG6tNqBbN3xBHaKx5AtWefDE73wuVUeCKHczUyU0eCHTVotnPBDZF9iZ1QxZW9pw4QV_-uDS4qLNP28bKkuaAoRgQs8_In_simPgAdmru23WyX65hBrrWrNiwkp792OpNa17lCn7mYWQUB_NUx61EgMFhjiMU98Du6oxiNAIsfCBw-BHTPCJNNXSJUqJljP0RVS4BYtNlwXNW5ZAdnnlwtdNYin-pqyuN6tzZW8hRupEaSQKKLjHnngXyTekshF_dEvxt5pWMTc7TeEtmli58SZt2cgs-f9sc7xVN31uKaDcXvsg-vwcvSQC2kntM58XG-arDzFP5-3p8ON0cKhXMqkwbtHkr8DFh0vBf-P3_-ilkF-1kioDqlYcctP8xzI-H8lz8fkVqvCdlsf4kOzXZmDdKQN8rNgUaj3HQrbmT4zHAdkCGLAoVeRuZv6XMz_fP_h1aTeeIctJc0QcCQ=", "id": "rs_0dc9f371a6d55e3f016a13732da07c81918fc97fba11682c7b", "summary": [{"type": "summary_text", "text": "**Creating video tests**\n\nI need to create tests and consider including an image note with the image_generate tool later. I could start with a deterministic approach and implement tests for the final video plan, possibly using a motion renderer. I plan to produce the actual video using animated frames generated with Pillow and ffmpeg. It looks like TDD will guide this process, so I need to know the necessary modules. Writing simple tests is my goal!"}]}], "tool_calls": [{"id": "call_brYzkxVlfVIzBK83QPvVq1L1", "call_id": "call_brYzkxVlfVIzBK83QPvVq1L1", "response_item_id": "fc_0dc9f371a6d55e3f016a137331d5988191a26c8be2c005fab6", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_final_video_plan.py\",\"content\":\"from autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.rendering.final_video_plan import (\\n FinalVideoPlan,\\n build_truetrace_weird_email_final_video_plan,\\n validate_final_video_plan,\\n)\\n\\n\\ndef test_build_truetrace_weird_email_final_video_plan_is_mobile_first_and_concrete():\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n\\n plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n\\n assert isinstance(plan, FinalVideoPlan)\\n assert plan.candidate_id == \\\"truetrace-ai-automation-quiet-rot-38s\\\"\\n assert plan.width == 1080\\n assert plan.height == 1920\\n assert plan.fps == 30\\n assert plan.duration_ms == 37000\\n assert [scene.scene_id for scene in plan.scenes] == [\\n \\\"clean-demo\\\",\\n \\\"weird-email\\\",\\n \\\"wrong-routing\\\",\\n \\\"three-way-split\\\",\\n \\\"final-rule\\\",\\n ]\\n assert plan.scenes[0].start_ms == 0\\n assert plan.scenes[-1].end_ms == plan.duration_ms\\n assert all(scene.renderer_owned_text for scene in plan.scenes)\\n assert all(scene.ai_generated_text_allowed is False for scene in plan.scenes)\\n assert any(\\\"weird email\\\" in scene.renderer_text.casefold() for scene in plan.scenes)\\n assert any(\\\"Go through\\\" in scene.renderer_text for scene in plan.scenes)\\n assert any(\\\"Review\\\" in scene.renderer_text for scene in plan.scenes)\\n assert any(\\\"Stop\\\" in scene.renderer_text for scene in plan.scenes)\\n\\n\\ndef test_validate_final_video_plan_rejects_timing_text_and_static_failures():\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n broken_scene = plan.scenes[1].__class__(\\n scene_id=\\\"bad\\\",\\n start_ms=1000,\\n end_ms=1000,\\n voiceover_segment=\\\"\\\",\\n visual_mode=\\\"static_card\\\",\\n motion_instruction=\\\"\\\",\\n renderer_text=\\\"\\\",\\n asset_prompt=\\\"\\\",\\n renderer_owned_text=False,\\n ai_generated_text_allowed=True,\\n )\\n broken = plan.__class__(\\n candidate_id=plan.candidate_id,\\n width=1080,\\n height=1920,\\n fps=30,\\n duration_ms=plan.duration_ms,\\n scenes=(broken_scene,),\\n source_voiceover_text=voiceover.text,\\n )\\n\\n validation = validate_final_video_plan(broken)\\n\\n assert validation.is_valid is False\\n assert \\\"first scene must start at 0ms\\\" in validation.errors\\n assert \\\"scene 1 end_ms must be greater than start_ms\\\" in validation.errors\\n assert \\\"scene 1 voiceover_segment is required\\\" in validation.errors\\n assert \\\"scene 1 renderer_text is required\\\" in validation.errors\\n assert \\\"scene 1 static_card visual mode is not allowed\\\" in validation.errors\\n assert \\\"scene 1 renderer text must be owned by renderer\\\" in validation.errors\\n assert \\\"scene 1 AI-generated text is not allowed\\\" in validation.errors\\n\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2887, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [2:6] Import \\\"autoshorts.rendering.final_video_plan\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_brYzkxVlfVIzBK83QPvVq1L1", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_J3pAeVOXUA7w40U451PC1Zov", "call_id": "call_J3pAeVOXUA7w40U451PC1Zov", "response_item_id": "fc_0dc9f371a6d55e3f016a137341d45c8191b26a48d28f1d8157", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from pathlib import Path\\nimport subprocess\\n\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.motion_renderer import (\\n MotionRenderResult,\\n build_motion_render_plan,\\n render_motion_preview,\\n validate_motion_render_plan,\\n)\\n\\n\\ndef test_build_motion_render_plan_maps_final_plan_to_frame_output(tmp_path):\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n\\n render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5)\\n\\n assert render_plan.width == 540\\n assert render_plan.height == 960\\n assert render_plan.fps == 24\\n assert render_plan.output_path.endswith(\\\"motion-preview.mp4\\\")\\n assert render_plan.frame_dir.endswith(\\\"frames\\\")\\n assert render_plan.concat_path.endswith(\\\"frames.txt\\\")\\n assert render_plan.side_effects == ()\\n assert render_plan.external_calls == ()\\n validation = validate_motion_render_plan(render_plan)\\n assert validation.is_valid is True\\n\\n\\ndef test_render_motion_preview_writes_frames_and_invokes_ffmpeg(tmp_path):\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5)\\n calls = []\\n\\n def fake_runner(command, *, capture_output, text, check):\\n calls.append(command)\\n Path(render_plan.output_path).write_bytes(b\\\"fake mp4\\\")\\n return subprocess.CompletedProcess(command, 0, stdout=\\\"ok\\\", stderr=\\\"\\\")\\n\\n result = render_motion_preview(render_plan, runner=fake_runner, max_frames_per_scene=2)\\n\\n assert isinstance(result, MotionRenderResult)\\n assert len(result.frame_paths) == 10\\n assert all(Path(path).exists() for path in result.frame_paths)\\n assert Path(render_plan.concat_path).exists()\\n assert calls\\n assert calls[0][0] == \\\"ffmpeg\\\"\\n assert \\\"-f\\\" in calls[0]\\n assert result.side_effects == (\\\"write_png\\\", \\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n assert result.external_calls == (\\\"ffmpeg\\\",)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2286, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [5:6] Import \\\"autoshorts.rendering.final_video_plan\\\" could not be resolved [reportMissingImports] (Pyright)\\nERROR [6:6] Import \\\"autoshorts.rendering.motion_renderer\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_J3pAeVOXUA7w40U451PC1Zov", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_y0g61SREHueJPUENoj8iR6Bq", "call_id": "call_y0g61SREHueJPUENoj8iR6Bq", "response_item_id": "fc_0dc9f371a6d55e3f016a13735421b08191ab8211ebe928b294", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_final_video_plan.py tests/test_motion_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_______________ ERROR collecting tests/test_final_video_plan.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_final_video_plan.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_final_video_plan.py:2: in \\n from autoshorts.rendering.final_video_plan import (\\nE ModuleNotFoundError: No module named 'autoshorts.rendering.final_video_plan'\\n________________ ERROR collecting tests/test_motion_renderer.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_motion_renderer.py:5: in \\n from autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nE ModuleNotFoundError: No module named 'autoshorts.rendering.final_video_plan'\\n=========================== short test summary info ============================\\nERROR tests/test_final_video_plan.py\\nERROR tests/test_motion_renderer.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_y0g61SREHueJPUENoj8iR6Bq", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Implementing Frame Generation**\n\nI'm planning to use ValidationResult and a motion renderer like PIL and ffmpeg. I need to make sure the actual frame count is appropriate—it's capped at 888 frames for 37x24, which could be slow but manageable. I'll proceed with simple draw functions to start, making sure to run preliminary tests first. I guess getting this setup right is crucial for smooth performance! Let's see how this will all work together.", "finish_reason": "tool_calls", "reasoning_content": "**Implementing Frame Generation**\n\nI'm planning to use ValidationResult and a motion renderer like PIL and ffmpeg. I need to make sure the actual frame count is appropriate—it's capped at 888 frames for 37x24, which could be slow but manageable. I'll proceed with simple draw functions to start, making sure to run preliminary tests first. I guess getting this setup right is crucial for smooth performance! Let's see how this will all work together.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3NattI3brcx3wiIaT7yqFroJcFEXhjY59ZolYGgWplpv4ncwfnCwq4vJrbnrcaJGtPEeD8K9-G2SH2mOgXwxDsIHYCW1yz4_Keb_u_RSXt1jTaM-PgiHyJvTUeTPlF_wr2R3ccF5hv_5SeZZAz24ruEQpHGKgG1IGBQcTK2HggiKgmt2Orr53-3Oe3Jm8BwzmBPsCrlb6dLQcSuOglNuFokehOE_OM6ZoUK2cpjg0Cu8t4rpSgbSL3HrK_Y0CM5CZhpBMxeMq2e5WRs9mzhGmuF_TACqGEs2lL3DflwpYw2qJ3bF4zD6yc6P2NV38cKygD50SDDMxometIH7eMyESvMl4gCbg3TQzZRYGd5mnoCTiU0SkNfErAwDbfHOkLKtXGo9asdL5UZmMvlqc3zaiWK1ZTECGdDm5NABfL3Hh_DGx6NRFb6TUJUgDE_t_eQJfJ7526lvQiIoQACBzzH9w-ClhGC6Pdwq1giO89KeL-3PXPSINnX9-Lv2gYzfo8qa055gj9MBraC0H6VfHtqbJ5vaH3Ju7E389oeTrTBHrva4LIoUC_LL70mshrC-mjzBSat0ZAV_FAJHD602lHC7Oo0zRp3gO1hGsu114mSCuqeNMl7_KEIrFZ67HYTPeCiITCJ21mccgXWjIUvVn-MNTBtuSJWUIIQEEIM_9J8EpS1K3K2JE02LDUX0e4q0RRmv1J_NkRYMxNn_mpbv9RXUArwP45-XaeVVIAoAdY9YZc0PR29IebEdgDxMoJG1ZE0ETkRIZ_GrLYVQwPfReMvgV8oCR6s74_XnaxxwGqSWY0rt_TFQ8xhI9kgneKaAfH7tKgB0WUjpbDbWv8iFFYnQk0wEtRUxsqtz9Mw_hnsXQ-Dku65r_-w07-_55_8l_MT8Jad63xZsg0iG7X1NYoxXmidk1xiMp6XgoXnixygVKkhg4aRjG1zlKCdrnWNjlFiE7g4IYGaT6Y5DDc2H6kOCJs-2zDbZNE1tfSosiGkRw_BrXKY6NeTvA4PdOMIK7UqJi74eOkxhRqUjzC7A3JQ7BaLQoAFQRPyAi_7W2JPJBBvBxvhwGP4lbb_NVgEowL6IIovJ4rZTS8ffyGeMyzKTehpuwzOtUS4ENSQ6ILMBKyFfqL0kYW9kDEk_V2cyMW7T4BjUtpm0NTDskSS2zG_98lWTBtQWv4swJAbyZGwr5kYc8XAWHSQoqtwl39SemgmqPjYiZTQCIqV03YUVvmmS2outw==", "id": "rs_0dc9f371a6d55e3f016a1373586a2081918cd0656ce0d68220", "summary": [{"type": "summary_text", "text": "**Implementing Frame Generation**\n\nI'm planning to use ValidationResult and a motion renderer like PIL and ffmpeg. I need to make sure the actual frame count is appropriate—it's capped at 888 frames for 37x24, which could be slow but manageable. I'll proceed with simple draw functions to start, making sure to run preliminary tests first. I guess getting this setup right is crucial for smooth performance! Let's see how this will all work together."}]}], "tool_calls": [{"id": "call_QTNGa15Kw5fcmoXNpghwEzry", "call_id": "call_QTNGa15Kw5fcmoXNpghwEzry", "response_item_id": "fc_0dc9f371a6d55e3f016a13735ad2d48191a7971b6bea631716", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Final-near vertical video production plan for TrueTrace pilots.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.audio.voiceover import FinalVoiceoverScript\\nfrom autoshorts.ideas.candidate import ValidationResult\\n\\n\\n@dataclass(frozen=True)\\nclass FinalVideoScene:\\n scene_id: str\\n start_ms: int\\n end_ms: int\\n voiceover_segment: str\\n visual_mode: str\\n motion_instruction: str\\n renderer_text: str\\n asset_prompt: str\\n renderer_owned_text: bool = True\\n ai_generated_text_allowed: bool = False\\n\\n\\n@dataclass(frozen=True)\\nclass FinalVideoPlan:\\n candidate_id: str\\n width: int\\n height: int\\n fps: int\\n duration_ms: int\\n scenes: tuple[FinalVideoScene, ...]\\n source_voiceover_text: str\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\ndef build_truetrace_weird_email_final_video_plan(voiceover: FinalVoiceoverScript) -> FinalVideoPlan:\\n \\\"\\\"\\\"Build the production plan for the beginner-friendly weird-email pilot.\\\"\\\"\\\"\\n\\n if voiceover.candidate_id != \\\"truetrace-ai-automation-quiet-rot-38s\\\":\\n raise ValueError(\\\"weird-email final plan only supports the quiet-rot pilot\\\")\\n\\n scenes = (\\n FinalVideoScene(\\n scene_id=\\\"clean-demo\\\",\\n start_ms=0,\\n end_ms=4200,\\n voiceover_segment=\\\"Your AI automation works perfectly in the demo.\\\",\\n visual_mode=\\\"inbox_motion\\\",\\n motion_instruction=\\\"Clean dashboard/inbox slides in with green demo status and calm cursor movement.\\\",\\n renderer_text=\\\"Your AI automation works perfectly in the demo.\\\",\\n asset_prompt=(\\n \\\"Vertical modern workspace background, laptop screen glow, clean productivity dashboard atmosphere, \\\"\\n \\\"no readable text, no logos, cinematic but minimal.\\\"\\n ),\\n ),\\n FinalVideoScene(\\n scene_id=\\\"weird-email\\\",\\n start_ms=4200,\\n end_ms=10500,\\n voiceover_segment=\\\"Then one weird email arrives. The name is missing. The format is different. The request is unclear.\\\",\\n visual_mode=\\\"email_card_interrupt\\\",\\n motion_instruction=\\\"A messy email card snaps in, shakes once, then missing fields pulse amber.\\\",\\n renderer_text='Then one weird email arrives.\\\\n\\\"hey can you handle this maybe??\\\"\\\\nMissing name · Wrong format · Unclear request',\\n asset_prompt=(\\n \\\"Vertical close-up of a computer desk and email workflow mood, soft depth of field, \\\"\\n \\\"ambiguous inbox background only, no readable text, no logos.\\\"\\n ),\\n ),\\n FinalVideoScene(\\n scene_id=\\\"wrong-routing\\\",\\n start_ms=10500,\\n end_ms=17800,\\n voiceover_segment=(\\n \\\"And suddenly the automation does the wrong thing confidently. That is not an AI problem. \\\"\\n \\\"That is a workflow problem.\\\"\\n ),\\n visual_mode=\\\"wrong_route_warning\\\",\\n motion_instruction=\\\"Green AI route moves confidently to the wrong action, then a red warning pulse interrupts it.\\\",\\n renderer_text=\\\"AI routes it confidently…\\\\nWrong action\\\",\\n asset_prompt=(\\n \\\"Abstract AI workflow node background, glowing path, modern interface ambience, \\\"\\n \\\"no readable text, no symbols that look like real brands.\\\"\\n ),\\n ),\\n FinalVideoScene(\\n scene_id=\\\"three-way-split\\\",\\n start_ms=17800,\\n end_ms=30400,\\n voiceover_segment=(\\n \\\"Before you automate anything, decide three things. What should go through? \\\"\\n \\\"What should be reviewed? And what should stop?\\\"\\n ),\\n visual_mode=\\\"three_path_diagram\\\",\\n motion_instruction=\\\"Email card branches into three labeled paths with staggered arrow reveals.\\\",\\n renderer_text=\\\"Normal → Go through\\\\nUnclear → Review\\\\nWeird or risky → Stop\\\",\\n asset_prompt=(\\n \\\"Clean dark technical background with subtle gradient and bokeh, empty space for renderer-owned labels, \\\"\\n \\\"no generated text.\\\"\\n ),\\n ),\\n FinalVideoScene(\\n scene_id=\\\"final-rule\\\",\\n start_ms=30400,\\n end_ms=37000,\\n voiceover_segment=(\\n \\\"If your AI does not know what to do with weird inputs, it will break the moment real life shows up.\\\"\\n ),\\n visual_mode=\\\"rule_card_pulse\\\",\\n motion_instruction=\\\"Final rule card locks into place, blue-green accent pulse, subtle zoom out.\\\",\\n renderer_text=\\\"Plan for weird inputs\\\\nbefore automating normal ones.\\\",\\n asset_prompt=(\\n \\\"Premium minimal abstract background, blue green light streaks, modern tech ambience, \\\"\\n \\\"no text, no logos, no watermark.\\\"\\n ),\\n ),\\n )\\n plan = FinalVideoPlan(\\n candidate_id=voiceover.candidate_id,\\n width=1080,\\n height=1920,\\n fps=30,\\n duration_ms=voiceover.target_duration_seconds * 1000,\\n scenes=scenes,\\n source_voiceover_text=voiceover.text,\\n )\\n validation = validate_final_video_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return plan\\n\\n\\ndef validate_final_video_plan(plan: FinalVideoPlan) -> ValidationResult:\\n errors: list[str] = []\\n if not plan.candidate_id.strip():\\n errors.append(\\\"candidate_id is required\\\")\\n if plan.width != 1080 or plan.height != 1920:\\n errors.append(\\\"final video plan must be 1080x1920\\\")\\n if plan.fps != 30:\\n errors.append(\\\"final video fps must be 30\\\")\\n if not (15000 <= plan.duration_ms <= 60000):\\n errors.append(\\\"duration_ms must be between 15000 and 60000\\\")\\n if not plan.scenes:\\n errors.append(\\\"at least one scene is required\\\")\\n elif plan.scenes[0].start_ms != 0:\\n errors.append(\\\"first scene must start at 0ms\\\")\\n\\n previous_end: int | None = None\\n for index, scene in enumerate(plan.scenes, start=1):\\n if previous_end is not None and scene.start_ms != previous_end:\\n errors.append(f\\\"scene {index} must start where previous scene ends\\\")\\n previous_end = scene.end_ms\\n if scene.end_ms <= scene.start_ms:\\n errors.append(f\\\"scene {index} end_ms must be greater than start_ms\\\")\\n for field_name in (\\\"scene_id\\\", \\\"voiceover_segment\\\", \\\"visual_mode\\\", \\\"motion_instruction\\\", \\\"renderer_text\\\", \\\"asset_prompt\\\"):\\n if not str(getattr(scene, field_name)).strip():\\n errors.append(f\\\"scene {index} {field_name} is required\\\")\\n if scene.visual_mode == \\\"static_card\\\":\\n errors.append(f\\\"scene {index} static_card visual mode is not allowed\\\")\\n if not scene.renderer_owned_text:\\n errors.append(f\\\"scene {index} renderer text must be owned by renderer\\\")\\n if scene.ai_generated_text_allowed:\\n errors.append(f\\\"scene {index} AI-generated text is not allowed\\\")\\n if plan.scenes and plan.scenes[-1].end_ms != plan.duration_ms:\\n errors.append(\\\"last scene must end at duration_ms\\\")\\n if plan.side_effects:\\n errors.append(\\\"final video plan must not have side effects\\\")\\n if plan.external_calls:\\n errors.append(\\\"final video plan must not have external calls\\\")\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/final_video_plan.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 7482, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_QTNGa15Kw5fcmoXNpghwEzry", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_xGh75gGxyuVHEtdr5HX36wu9", "call_id": "call_xGh75gGxyuVHEtdr5HX36wu9", "response_item_id": "fc_0dc9f371a6d55e3f016a137381f330819193b1bf901cac4ecd", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Procedural motion renderer for final-near TrueTrace review videos.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom collections.abc import Callable\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\nimport math\\nimport subprocess\\n\\nfrom PIL import Image, ImageDraw, ImageFont\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.final_video_plan import FinalVideoPlan, FinalVideoScene, validate_final_video_plan\\n\\nRunner = Callable[..., subprocess.CompletedProcess[str]]\\n\\n\\n@dataclass(frozen=True)\\nclass MotionRenderPlan:\\n final_plan: FinalVideoPlan\\n output_dir: str\\n frame_dir: str\\n concat_path: str\\n output_path: str\\n width: int\\n height: int\\n fps: int\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\n@dataclass(frozen=True)\\nclass MotionRenderResult:\\n output_path: str\\n frame_paths: tuple[str, ...]\\n concat_path: str\\n returncode: int\\n stdout: str\\n stderr: str\\n side_effects: tuple[str, ...] = (\\\"write_png\\\", \\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n\\n\\ndef build_motion_render_plan(final_plan: FinalVideoPlan, *, output_dir: str | Path, preview_scale: float = 1.0) -> MotionRenderPlan:\\n validation = validate_final_video_plan(final_plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n if not (0.25 <= preview_scale <= 1.0):\\n raise ValueError(\\\"preview_scale must be between 0.25 and 1.0\\\")\\n root = Path(output_dir)\\n width = int(final_plan.width * preview_scale)\\n height = int(final_plan.height * preview_scale)\\n fps = 24 if preview_scale < 1 else final_plan.fps\\n plan = MotionRenderPlan(\\n final_plan=final_plan,\\n output_dir=str(root),\\n frame_dir=str(root / \\\"frames\\\"),\\n concat_path=str(root / \\\"frames.txt\\\"),\\n output_path=str(root / \\\"motion-preview.mp4\\\"),\\n width=width,\\n height=height,\\n fps=fps,\\n )\\n validation = validate_motion_render_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return plan\\n\\n\\ndef validate_motion_render_plan(plan: MotionRenderPlan) -> ValidationResult:\\n errors: list[str] = []\\n if plan.width <= 0 or plan.height <= 0:\\n errors.append(\\\"width and height must be positive\\\")\\n if plan.width * 16 != plan.height * 9:\\n errors.append(\\\"motion render must keep 9:16 vertical aspect ratio\\\")\\n if plan.fps not in {24, 30}:\\n errors.append(\\\"fps must be 24 or 30\\\")\\n if not plan.output_path.endswith(\\\".mp4\\\"):\\n errors.append(\\\"output_path must end with .mp4\\\")\\n if not plan.concat_path.endswith(\\\".txt\\\"):\\n errors.append(\\\"concat_path must end with .txt\\\")\\n if not plan.frame_dir.strip():\\n errors.append(\\\"frame_dir is required\\\")\\n if plan.side_effects:\\n errors.append(\\\"motion render plan must not have side effects\\\")\\n if plan.external_calls:\\n errors.append(\\\"motion render plan must not have external calls\\\")\\n final_validation = validate_final_video_plan(plan.final_plan)\\n errors.extend(final_validation.errors)\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\\n\\ndef _font(size: int, *, bold: bool = False):\\n names = (\\\"DejaVuSans-Bold.ttf\\\", \\\"DejaVuSans.ttf\\\") if bold else (\\\"DejaVuSans.ttf\\\",)\\n for name in names:\\n try:\\n return ImageFont.truetype(name, size=size)\\n except OSError:\\n continue\\n return ImageFont.load_default()\\n\\n\\ndef _wrap(draw: ImageDraw.ImageDraw, text: str, font, max_width: int) -> list[str]:\\n lines: list[str] = []\\n for paragraph in text.splitlines():\\n words = paragraph.split()\\n if not words:\\n lines.append(\\\"\\\")\\n continue\\n current = words[0]\\n for word in words[1:]:\\n candidate = f\\\"{current} {word}\\\"\\n if draw.textbbox((0, 0), candidate, font=font)[2] <= max_width:\\n current = candidate\\n else:\\n lines.append(current)\\n current = word\\n lines.append(current)\\n return lines\\n\\n\\ndef _draw_text_block(draw: ImageDraw.ImageDraw, xy: tuple[int, int], text: str, *, font, fill, max_width: int, spacing: int = 10) -> int:\\n x, y = xy\\n line_height = getattr(font, \\\"size\\\", 24) + spacing\\n for line in _wrap(draw, text, font, max_width):\\n draw.text((x, y), line, font=font, fill=fill)\\n y += line_height\\n return y\\n\\n\\ndef _ease(progress: float) -> float:\\n return 0.5 - 0.5 * math.cos(math.pi * max(0.0, min(1.0, progress)))\\n\\n\\ndef _draw_email_card(draw: ImageDraw.ImageDraw, box: tuple[int, int, int, int], *, title: str, body: str, accent: tuple[int, int, int], font_scale: int) -> None:\\n x1, y1, x2, y2 = box\\n draw.rounded_rectangle(box, radius=24, fill=(248, 250, 252), outline=accent, width=4)\\n draw.rectangle((x1, y1, x2, y1 + 16), fill=accent)\\n small = _font(font_scale, bold=True)\\n body_font = _font(max(16, int(font_scale * 0.78)))\\n draw.text((x1 + 28, y1 + 32), title, font=small, fill=(15, 23, 42))\\n _draw_text_block(draw, (x1 + 28, y1 + 86), body, font=body_font, fill=(51, 65, 85), max_width=x2 - x1 - 56, spacing=8)\\n\\n\\ndef _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float) -> Image.Image:\\n w, h = plan.width, plan.height\\n image = Image.new(\\\"RGB\\\", (w, h), (6, 12, 24))\\n draw = ImageDraw.Draw(image)\\n accent = (34, 211, 238)\\n green = (52, 211, 153)\\n amber = (251, 191, 36)\\n red = (248, 113, 113)\\n margin = max(28, w // 14)\\n title_font = _font(max(34, w // 13), bold=True)\\n mid_font = _font(max(24, w // 20), bold=True)\\n small_font = _font(max(18, w // 27))\\n\\n # Ambient grid/gradient dots.\\n for i in range(0, h, max(48, h // 18)):\\n color = (10 + (i // 20) % 24, 24, 44)\\n draw.line((0, i, w, i), fill=color, width=1)\\n draw.rectangle((0, 0, w, 18), fill=accent)\\n\\n p = _ease(progress)\\n if scene.visual_mode == \\\"inbox_motion\\\":\\n offset = int((1 - p) * -w * 0.25)\\n _draw_text_block(draw, (margin, margin + offset // 5), scene.renderer_text, font=title_font, fill=(248, 250, 252), max_width=w - 2 * margin, spacing=12)\\n card_y = int(h * 0.38)\\n _draw_email_card(draw, (margin, card_y, w - margin, card_y + int(h * 0.24)), title=\\\"Demo inbox\\\", body=\\\"All fields present\\\\nFormat clean\\\\nAction obvious\\\", accent=green, font_scale=max(22, w // 24))\\n draw.rounded_rectangle((margin, int(h * 0.72), w - margin, int(h * 0.79)), radius=22, fill=(22, 101, 52))\\n draw.text((margin + 28, int(h * 0.735)), \\\"Status: works in the demo\\\", font=mid_font, fill=(220, 252, 231))\\n elif scene.visual_mode == \\\"email_card_interrupt\\\":\\n _draw_text_block(draw, (margin, margin), \\\"Then one weird email arrives.\\\", font=title_font, fill=(248, 250, 252), max_width=w - 2 * margin, spacing=12)\\n shake = int(math.sin(progress * math.pi * 8) * 6 * (1 - progress))\\n y = int(h * (0.32 - 0.05 * (1 - p)))\\n _draw_email_card(draw, (margin + shake, y, w - margin + shake, y + int(h * 0.34)), title=\\\"New email\\\", body='\\\"hey can you handle this maybe??\\\"\\\\n\\\\nMissing name\\\\nWrong format\\\\nUnclear request', accent=amber, font_scale=max(22, w // 23))\\n pulse = int(80 + 80 * abs(math.sin(progress * math.pi * 4)))\\n draw.ellipse((w - margin - 52, y + 28, w - margin - 16, y + 64), fill=(pulse, 65, 0))\\n elif scene.visual_mode == \\\"wrong_route_warning\\\":\\n _draw_text_block(draw, (margin, margin), \\\"AI routes it confidently…\\\", font=title_font, fill=(248, 250, 252), max_width=w - 2 * margin, spacing=12)\\n cy = int(h * 0.48)\\n x1, x2 = margin, w - margin\\n draw.rounded_rectangle((x1, cy - 52, x1 + 150, cy + 52), radius=26, fill=(15, 118, 110), outline=green, width=3)\\n draw.text((x1 + 38, cy - 18), \\\"AI\\\", font=mid_font, fill=(240, 253, 250))\\n arrow_end = int(x1 + 180 + (x2 - x1 - 360) * p)\\n draw.line((x1 + 170, cy, arrow_end, cy), fill=green, width=10)\\n draw.polygon([(arrow_end, cy), (arrow_end - 24, cy - 18), (arrow_end - 24, cy + 18)], fill=green)\\n draw.rounded_rectangle((x2 - 180, cy - 60, x2, cy + 60), radius=22, fill=(127, 29, 29), outline=red, width=4)\\n draw.text((x2 - 150, cy - 18), \\\"Wrong\\\", font=mid_font, fill=(254, 226, 226))\\n if progress > 0.55:\\n draw.rounded_rectangle((margin, int(h * 0.70), w - margin, int(h * 0.80)), radius=24, fill=(69, 10, 10), outline=red, width=4)\\n draw.text((margin + 26, int(h * 0.73)), \\\"Not an AI problem. A workflow problem.\\\", font=small_font, fill=(254, 226, 226))\\n elif scene.visual_mode == \\\"three_path_diagram\\\":\\n _draw_text_block(draw, (margin, margin), \\\"Decide the three paths first.\\\", font=title_font, fill=(248, 250, 252), max_width=w - 2 * margin, spacing=12)\\n start = (w // 2, int(h * 0.31))\\n draw.rounded_rectangle((start[0] - 120, start[1] - 48, start[0] + 120, start[1] + 48), radius=20, fill=(30, 41, 59), outline=accent, width=3)\\n draw.text((start[0] - 72, start[1] - 16), \\\"Input\\\", font=mid_font, fill=(226, 232, 240))\\n labels = [(\\\"Normal\\\", \\\"Go through\\\", green), (\\\"Unclear\\\", \\\"Review\\\", amber), (\\\"Weird/Risky\\\", \\\"Stop\\\", red)]\\n for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n bx = margin\\n by = int(h * (0.48 + idx * 0.14))\\n ex = int(bx + (w - 2 * margin) * reveal)\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n draw.text((margin + 28, by + 18), f\\\"{top} → {bottom}\\\", font=mid_font, fill=(248, 250, 252))\\n else:\\n zoom = 1 + 0.03 * p\\n box = (margin, int(h * 0.32), w - margin, int(h * 0.66))\\n draw.rounded_rectangle(box, radius=30, fill=(15, 23, 42), outline=green, width=5)\\n _draw_text_block(draw, (margin + 32, int(h * 0.40)), scene.renderer_text, font=title_font, fill=(248, 250, 252), max_width=w - 2 * margin - 64, spacing=14)\\n draw.text((margin, int(h * 0.76)), \\\"Systems over prompts.\\\", font=mid_font, fill=accent)\\n _ = zoom\\n return image\\n\\n\\ndef render_motion_preview(\\n plan: MotionRenderPlan,\\n *,\\n runner: Runner = subprocess.run,\\n max_frames_per_scene: int | None = None,\\n) -> MotionRenderResult:\\n validation = validate_motion_render_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n frame_dir = Path(plan.frame_dir)\\n frame_dir.mkdir(parents=True, exist_ok=True)\\n Path(plan.output_dir).mkdir(parents=True, exist_ok=True)\\n frame_paths: list[str] = []\\n frame_index = 0\\n for scene in plan.final_plan.scenes:\\n scene_duration = (scene.end_ms - scene.start_ms) / 1000\\n count = max(1, round(scene_duration * plan.fps))\\n if max_frames_per_scene is not None:\\n count = min(count, max_frames_per_scene)\\n for local_index in range(count):\\n progress = local_index / max(1, count - 1)\\n image = _render_scene(plan, scene, progress)\\n path = frame_dir / f\\\"frame_{frame_index:05d}.png\\\"\\n image.save(path, format=\\\"PNG\\\")\\n frame_paths.append(str(path))\\n frame_index += 1\\n\\n concat_path = Path(plan.concat_path)\\n frame_duration = 1 / plan.fps\\n concat_path.write_text(\\\"\\\".join(f\\\"file '{Path(path).resolve()}'\\\\nduration {frame_duration:.8f}\\\\n\\\" for path in frame_paths), encoding=\\\"utf-8\\\")\\n command = [\\n \\\"ffmpeg\\\",\\n \\\"-y\\\",\\n \\\"-f\\\",\\n \\\"concat\\\",\\n \\\"-safe\\\",\\n \\\"0\\\",\\n \\\"-i\\\",\\n str(concat_path),\\n \\\"-vf\\\",\\n \\\"format=yuv420p\\\",\\n \\\"-r\\\",\\n str(plan.fps),\\n \\\"-c:v\\\",\\n \\\"libx264\\\",\\n \\\"-preset\\\",\\n \\\"veryfast\\\",\\n \\\"-movflags\\\",\\n \\\"+faststart\\\",\\n plan.output_path,\\n ]\\n completed = runner(command, capture_output=True, text=True, check=False)\\n return MotionRenderResult(\\n output_path=plan.output_path,\\n frame_paths=tuple(frame_paths),\\n concat_path=plan.concat_path,\\n returncode=completed.returncode,\\n stdout=completed.stdout,\\n stderr=completed.stderr,\\n )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 12329, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_xGh75gGxyuVHEtdr5HX36wu9", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_182dfGHpc9mdkHS2n8H0KyrB", "call_id": "call_182dfGHpc9mdkHS2n8H0KyrB", "response_item_id": "fc_0dc9f371a6d55e3f016a1373cffbe881918f2f72d115b2523d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_final_video_plan.py tests/test_motion_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \".... [100%]\\n4 passed in 0.27s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_182dfGHpc9mdkHS2n8H0KyrB", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Generating image for video**\n\nI need to generate an image with the ChatGPT Image tool. The user mentioned I can use it, so I'll likely create a background image to incorporate. However, I'm realizing the current motion renderer doesn't support background assets, so I might need to generate one separately as a keyframe or reference. I could then integrate the image manually later. For creating the video, I need to ensure it's combined with audio and captions in the final version, rendering a preview first. I'll go ahead and generate the background now!", "finish_reason": "tool_calls", "reasoning_content": "**Generating image for video**\n\nI need to generate an image with the ChatGPT Image tool. The user mentioned I can use it, so I'll likely create a background image to incorporate. However, I'm realizing the current motion renderer doesn't support background assets, so I might need to generate one separately as a keyframe or reference. I could then integrate the image manually later. For creating the video, I need to ensure it's combined with audio and captions in the final version, rendering a preview first. I'll go ahead and generate the background now!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3PaXgwHZDwsjoK0XFIs16R40vqeQrdeBCS4ICV5zFBMKeGRBcEpmrdB4hb_HOs2O4fhmxTgChgN0Jl3wtFtKRbLRyTUrnA93Va0trNdisZLMR8OBgp4_1lvwPPIWQgv8SanRBEAJ8CcmoPxjWNxMMB-VjdK7nghXlezjsJXt4qBgPpDYlU7hdN0qzQh7LtfTwDUAukw4AEEwEBeSrt-e8dwYeP3CiYt_xhif8auSax81nZUmbOD9877AKaEjBRd6CTas19DYAsVhv_O8VKTWd6UKlMRnImfCSvrHpvnX3C4ehP7ZZ5H7E5mJH45B00GTKhhH5e3Sj_rRqwVv_wLrHUo-po01UDWwEzF9AQxVHK1fgbFUpeCyc_2dhUKEEHaLEZ116qF_4Xe6H-CtSnZWcaMPp1KbpuBZ7BP9A28DJgkIYfRs4pxHV8p37w7sa9ty_JrFMxpAPzKBs93uvLOKDfBd8MCQtn-UEfy5GxbYqti3K46wnQR4ELNAcujV_zcRmTWhr3SpL3JVl27MTE6cUIJmGNSLBVrsGmWwzmaOPd3KsMg6NkHpghkRjfucOQd90qvZIWBFrxI0Y7-Siqp8neBtb9ScpLDFov4tjYMNOBX5eicq9xh4BghCHsIetOjiCDivny0J4ogPU1er-oVSY4QYxygz0r54-gQ80_gCyCFh_oeCnUO45tlHQHNs0obf1F2FOcOsiHRqBXEwzx7b_9mBqd-ihSsiLScPN3rLyHO3ImI3LJ7PQiANX1cLGpnb77j93-VJWjXvsj_aA22zMNUS2s2UWgg8dHSB9L1tV1S-hEZol2pqdO9w9HUzQVy7iXbmXsGQ7PsSkyd1Hzer1We8OXrGxssx8bf---ZguFjkZFbLzlXvmMVogUdNL2mm4FsT9gVyhqwWouGzy8UJiNs7hBX96KLW-cyZrU-tyrbvs17rAGDXgnyUwXM8Iy47voOBLe_Cv-j3Vo6GV7qY96zKTbB_ZOPCZvFbHDNw5zYsds0RMEN77TrbIFH3RX6f-tmj73KCLuDYAOZWyPssaPNYeKa4gOl-FIxKiw9p96whMuTosW0TgcsReVRjSE0nX43JKc-_SrC4IthIMmR_swcU67VIv11N_hjpZdaXmKfB48qSdsjGi84bly3KEO4gyV1oOSXav29xbs76jysEZd1_fuqgWLeii2KThDDuRLl1Ti0sZ5WTdQZaI5CfxCLOiWKUVOUBwPzT1tA1dCyxwUFR8jPzrkCyIqPeZIiAgsGcfm9GhnqtCT8-WwuBcB7srBCQdz9aoYd-Ide7GnSTOsqpC38L6CFvGWiywd6QJaqb9Zb6aXQLS_eNNGiSY8NvUYmKJ5f2u_OMOYQtl6tR_xgPQB7b1PzTDSF6bIsDINE7FyfKaiDIP-OLVxbncD1fQpbTNBt-JOzn5MDknNqrM7RCp4A6frq2VQqyOpq3_azjXnwHt4U_-yBBigzMU0mHapkfAmYJIFhi_CrKbZONZJ6dNmtt_Rez8ZCNoDxKGJ81CSkNUSwMGo5yXO-3E0YVBPgX0nq8LVmMLkl5lyz2HyduiugFK_NUKEJLRMtxkr98vkcFN33pj-gtaBvLt7F6kfeERosMbNPRAVd8hqiT9eAUw42qambstE0mr3n0-tkS89YAW33me6dcngwDFPGPWA7HAJVBwoH0xGGu3KjqIJAqq482JorT-Z8hhRXgyXMDBK-8lMAr4IxhDwSj4dMb7-57gmwg5arsOhz_7S_xuy8EKEq23uvouQ8NBKc2KduGQBY0WlG89PrGhfQFdE8GyXutznu-BubGTrkupEFx2E7j3oQ9JqngcagZXrAs_RZtx_pXaxvc0wiVGTtkOmQyII1MSPWg5KNzgE_OrcICc3RJKUES_VGrnIeUr858OFVB2wv53v2KrxrXXCWoyaCNUImpchOMCEDOmTNgoePlL862D-q6Cf4XPO9FzbQM60k2Fk-MLYgZJohkTEIfaz4pu-xTimysOVdQRVX8d2nxaxAQdbu6CFxA2s2qDPgp0zeEgKko6CiGlLqysm7HCaT3mWdLBMPVKQ5UKXT71ts0DOD9nUMq5-byHuVMetkvAKuXKZDTpZIDDRv5PrbwuTd7lcKzPLdE5TCX_Iuavia6TC1vq9uYaGFCcF2aPbj-hmOCmSaCdhppzj8tgArcMazFNZpOyV47zzYnrl0i33uHYFKXdwgAuoBHpPrYm3CFrxG-YM0Sv8IQw5Q4CJI3TPxIDobPohWHUxDmURhtad5j8dd4M-1OJdrok1Du1295qyj-w5IgrGx0pZfGrCQoe7ZOh1wQpFOlia0HJ4Tf3fNu-0nTg==", "id": "rs_0dc9f371a6d55e3f016a1373d47c68819185a65edc6f24a16b", "summary": [{"type": "summary_text", "text": "**Generating image for video**\n\nI need to generate an image with the ChatGPT Image tool. The user mentioned I can use it, so I'll likely create a background image to incorporate. However, I'm realizing the current motion renderer doesn't support background assets, so I might need to generate one separately as a keyframe or reference. I could then integrate the image manually later. For creating the video, I need to ensure it's combined with audio and captions in the final version, rendering a preview first. I'll go ahead and generate the background now!"}]}], "tool_calls": [{"id": "call_8Z6viPuj5nxWjnaeYCq8VFWW", "call_id": "call_8Z6viPuj5nxWjnaeYCq8VFWW", "response_item_id": "fc_0dc9f371a6d55e3f016a1373da12108191ab193cdf9721afce", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a modern AI automation short. A clean laptop/workspace scene with a dark minimal productivity dashboard glow, subtle blue and green light accents, premium tech aesthetic, shallow depth of field, no readable text, no logos, no watermarks, no UI labels, empty central space for overlaid renderer-owned text and diagrams. Realistic but abstract enough to serve as background texture.\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a modern AI automation short. A clean laptop/workspace scene with a dark minimal productivity dashboard glow, subtle blue and green light accents, premium tech aesthetic, shallow depth of field, no readable text, no logos, no watermarks, no UI labels, empty central space for overlaid renderer-owned text and diagrams. Realistic but abstract enough to serve as background texture.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_8Z6viPuj5nxWjnaeYCq8VFWW", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Integrating Backgrounds Into Rendering**\n\nI'm considering integrating an optional background into the renderer. I think it makes sense to add an `optional background_path` to the `MotionRenderPlan`, and then ensure the render function uses it. For test-driven development, I'll need to confirm that the ChatGPT image background plan includes this path and that no AI text is allowed. Since the code is already there, I'll write a failing test for the new behavior before implementation. Sounds like a solid plan!", "finish_reason": "tool_calls", "reasoning_content": "**Integrating Backgrounds Into Rendering**\n\nI'm considering integrating an optional background into the renderer. I think it makes sense to add an `optional background_path` to the `MotionRenderPlan`, and then ensure the render function uses it. For test-driven development, I'll need to confirm that the ChatGPT image background plan includes this path and that no AI text is allowed. Since the code is already there, I'll write a failing test for the new behavior before implementation. Sounds like a solid plan!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3QSHfHZuNIfJpVJUvvs8Zd71mUC3Wk6gjxwsxCgwYoz5wxHHL-NW86dCh6Spl2rSPG1DpB-WNfox9U85nxhehkiS27WLAquVp8dRxQgu1eYFiY0UgcyuNbtQ7au9mIcIJJKvz3mx8yVn9-A3EAoi77G9wBt_yyWMeZpTjwJ_oxrNrgpGk_6RkgTdibTB8Yp_a0Kl_NR2RzOC1so8lFj-yRlGIFWR5W0HcTm6Tkolv92u87yEfrvXPhTVOa7OptiedJwRQ2jckEYn0A3DvEK3WDCD876GvQkFhpb7bm-RX7Qyqr1e-zs4c5WAoOKy0CcQAkGIOI_gEEVoMyHjjePcdjnvOXGkuCnMhvWnakPql1Vo53wrEeQbgU0bxROPwCO-Lp2KPQGY_2HA9Sg9FhGZ1x0SwZkSJ3yZAH6lssglc2_iLjFkRAscYgcsFI-wblygTg3bEVNJkaGD5d8C_7fhwGLfPVDVppuGiGCnphqpk0z7G6X1Zq7fKjd7BMjqvFbFZ7K8oi8A8v4vatjvc2yo6D8sOT99Dps3Aa0PWM7K9aZiVkj9kAc0GA4ecaV64bNqdEqAQ6cK6YYumU0jjt9XM8k52rwBWPII52MbvMaJ3GWGs7Mg61BF8oUezisdbpTTb2jqBG-dRg39oPonbUpOFZ6fqErOWUHIxCPd-RKm0sGj2IHnVV22Ifah1cZ9qBUCYgDGdASBbdQhmKe9cFtDOq0Z3j2Vr0Ef-FCWCQW7nKX4t8IG4Bjh5r2eUoG8OIgJ0xS8OAGUaaiRqLv-xTuUDRR_O7PvViBVOWPuR7T0CFWmG7GnfHEUIKhtS7TVw1t2eFUhstRO4PulIJzlhiXYqZhpsbVPFBqQpbcAo2xP0i2j-5tKdVEGlM6fcy5QS5rVc-VSVtOtJx7n5IY1hjV43t_NKhAiYLJuNm0_bu_ofwh0l5u2X2PEhBEfDnbIdBoNFhp1iTSzwmCmKOB0_Axa2tHkPm8ivv15m98ULC9jbMlfXXulLe2tdejVLaEfrfUZL9yo98tVa1Gg-QUWJQkmdVLrf8QDJXvyNQzD69zoy89ARah0nX-ek0F-9hIRO62G3ETsGeuZTODanyuyk92-SQ33A9UFIHWxzkeC8rSuAqjvyG-z8DYYJtVrgRYQxP61J0NQQAksN6LklYFLR3zgSoYnnLurihBHa1nsr-9C0ijWe0gRd1HmPRER2Q_kXqYVQZWmDTcvAQEBcjeR-pjBq_Zt_Yjd7k4nTTa3IMwcP8iygST1f8pTJahHZFlJM3MDge6THXuQdDRkb9hroZPgSojoHpTO6DeqMKNjwFx2bi3MRzYDjNjtHWmAutgPMun7CNU_IDjFnE-AjhLA79jgKBXmw==", "id": "rs_0dc9f371a6d55e3f016a13741044f881918e1d15004eaf4d99", "summary": [{"type": "summary_text", "text": "**Integrating Backgrounds Into Rendering**\n\nI'm considering integrating an optional background into the renderer. I think it makes sense to add an `optional background_path` to the `MotionRenderPlan`, and then ensure the render function uses it. For test-driven development, I'll need to confirm that the ChatGPT image background plan includes this path and that no AI text is allowed. Since the code is already there, I'll write a failing test for the new behavior before implementation. Sounds like a solid plan!"}]}], "tool_calls": [{"id": "call_BW0lGX2C3iMqXqRRqYrBS8hN", "call_id": "call_BW0lGX2C3iMqXqRRqYrBS8hN", "response_item_id": "fc_0dc9f371a6d55e3f016a137412e51c8191b80eb70afb500ce2", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\",\"old_string\":\" render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5)\\n\\n assert render_plan.width == 540\",\"new_string\":\" background = tmp_path / \\\"chatgpt-image-background.png\\\"\\n background.write_bytes(b\\\"fake image\\\")\\n render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, background_path=background)\\n\\n assert render_plan.background_path == str(background)\\n assert render_plan.width == 540\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\\n@@ -15,8 +15,11 @@\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n \\n- render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5)\\n+ background = tmp_path / \\\"chatgpt-image-background.png\\\"\\n+ background.write_bytes(b\\\"fake image\\\")\\n+ render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, background_path=background)\\n \\n+ assert render_plan.background_path == str(background)\\n assert render_plan.width == 540\\n assert render_plan.height == 960\\n assert render_plan.fps == 24\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [20:96] No parameter named \\\"background_path\\\" [reportCallIssue] (Pyright)\\n\"}", "tool_call_id": "call_BW0lGX2C3iMqXqRRqYrBS8hN", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_laKnMMRSA6gj4dSCQr4jsl0V", "call_id": "call_laKnMMRSA6gj4dSCQr4jsl0V", "response_item_id": "fc_0dc9f371a6d55e3f016a137418fc1481919a88a64545ae726f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_motion_renderer.py::test_build_motion_render_plan_maps_final_plan_to_frame_output -q\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"F [100%]\\n=================================== FAILURES ===================================\\n________ test_build_motion_render_plan_maps_final_plan_to_frame_output _________\\n\\ntmp_path = PosixPath('/tmp/pytest-of-agent/pytest-126/test_build_motion_render_plan_0')\\n\\n def test_build_motion_render_plan_maps_final_plan_to_frame_output(tmp_path):\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n \\n background = tmp_path / \\\"chatgpt-image-background.png\\\"\\n background.write_bytes(b\\\"fake image\\\")\\n> render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, background_path=background)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\nE TypeError: build_motion_render_plan() got an unexpected keyword argument 'background_path'\\n\\ntests/test_motion_renderer.py:20: TypeError\\n=========================== short test summary info ============================\\nFAILED tests/test_motion_renderer.py::test_build_motion_render_plan_maps_final_plan_to_frame_output\\n1 failed in 0.11s\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_laKnMMRSA6gj4dSCQr4jsl0V", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_yr85BJ75Nc2AJHjAR7wLTo7G", "call_id": "call_yr85BJ75Nc2AJHjAR7wLTo7G", "response_item_id": "fc_0dc9f371a6d55e3f016a13741e07cc8191b15f31d1e7d0bceb", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\",\"old_string\":\" fps: int\\n side_effects: tuple[str, ...] = ()\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -25,7 +25,7 @@\\n output_path: str\\n width: int\\n height: int\\n- fps: int\\n+ background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [63:9] No parameter named \\\"fps\\\" [reportCallIssue] (Pyright)\\nERROR [77:13] Cannot access attribute \\\"fps\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"fps\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [235:52] Cannot access attribute \\\"fps\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"fps\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [247:31] Cannot access attribute \\\"fps\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"fps\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [261:18] Cannot access attribute \\\"fps\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"fps\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\n\"}", "tool_call_id": "call_yr85BJ75Nc2AJHjAR7wLTo7G", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Correcting background settings**\n\nI realized I accidentally replaced the frames per second (fps). I need to make sure to add the background setting properly after adjusting the fps. It’s important to get the right visuals overall since the background plays a crucial role in the final output. Let's take a moment to ensure everything's aligned before finalizing. I want to make sure everything looks great!", "finish_reason": "tool_calls", "reasoning_content": "**Correcting background settings**\n\nI realized I accidentally replaced the frames per second (fps). I need to make sure to add the background setting properly after adjusting the fps. It’s important to get the right visuals overall since the background plays a crucial role in the final output. Let's take a moment to ensure everything's aligned before finalizing. I want to make sure everything looks great!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3QmPw4gYhj5pf2gsCAbEDVDxy8g3b6KcBstjyj8NdZXyYpKzg4KXSX3ilUhnbJ0eSOK0hSPNrGJeO6_-EJ3cIo_2xMinv8uLTsRipRO8gK6BNTxTeh766p6dXqNN5e8OpBgUnPP8p7pd9vqUceAPwXCunhzfLC1gE5wRNHCf_bcOK4Q0vXG51O_3xUw0MUDSVoAQnu8MLWz-lXyApyPiuua8d2oKpAvGsnm4fIM5l19GDeIL-rdrAQoyzwCJvg0VLCyC-1hcfBVoHiopaRPyaBVQ1X5ojiRsgqnIhpR-hsaXtmC1rtBb61bJ4jxjHV1Fq4sSkWP4vSfVges1YdI_jZler0bpU6CF_x5KmqV2O-OcPqofbaxvGsZ-pFOeO6_TJ5huGb5EJVrF4aQWtLygj-Gn0lN0ZZc-uUuTlKR22ADhz16jg7tn-6kgHn9Fn74aOOClA1QsofbHPtLr1tm101eOHNtg8bnYK9_SMMB3z8B2WEDZh61-yVC8kncMvWOa0CiUMSe33Q5uzbGsY_kND_f4c0Pvr_oLdYKJcrXyt-gUWCxSQmQFr0BQZ-CBwwbraC1Na0u3zWwq7noUJIrYeyRn3Inp8OV_2qRXsTK66Fc0ZOuL5NYr2naQJsKODbCLuhUNLTXgUpYrsoA428HZ4ZwlgnApwuqOmpjapnucA88Kmsm_H2QXRnpM7mDn_8spuzieFMaltZnzthzydeyTj1caJ0XN6UQVCDKpOjg2NjwI1GdsXCI3sUQaQYfntl2vpf4vh8PchGzQqIkG0gtN_fzYQEH0hCFCz1_pzlCHvGfpAsD08Pf32VuR5aYxh9pvb-egKsPhGjEKkZyNj_JqdG_qWfvGRoUSDDU17HaOxN6paaUrvlvkiWwVYfzpVJ8jrZC_9s0THUX5CbjVg6Fh2mqQWArGCRyPZWOJXvoLM6BWnb3YOWjksqdHdH6aNV9PbA4P6AXGPa6QJgI1KXIP7_4CwlZdhV78f55GhdzxNlJlH8=", "id": "rs_0dc9f371a6d55e3f016a137424b0408191bbb2c0c7ddcca162", "summary": [{"type": "summary_text", "text": "**Correcting background settings**\n\nI realized I accidentally replaced the frames per second (fps). I need to make sure to add the background setting properly after adjusting the fps. It’s important to get the right visuals overall since the background plays a crucial role in the final output. Let's take a moment to ensure everything's aligned before finalizing. I want to make sure everything looks great!"}]}], "tool_calls": [{"id": "call_VwXcjO8iF6UlVkNAxpmbMiy3", "call_id": "call_VwXcjO8iF6UlVkNAxpmbMiy3", "response_item_id": "fc_0dc9f371a6d55e3f016a1374265b908191bc07683d8542f904", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" fps: int\\n background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\",\"old_string\":\" background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -25,6 +25,7 @@\\n output_path: str\\n width: int\\n height: int\\n+ fps: int\\n background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_VwXcjO8iF6UlVkNAxpmbMiy3", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_uUTK7lHykHIjNGN62I32eN52", "call_id": "call_uUTK7lHykHIjNGN62I32eN52", "response_item_id": "fc_0dc9f371a6d55e3f016a13742cb9e88191a8b5bd62c5b092da", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def build_motion_render_plan(\\n final_plan: FinalVideoPlan,\\n *,\\n output_dir: str | Path,\\n preview_scale: float = 1.0,\\n background_path: str | Path | None = None,\\n) -> MotionRenderPlan:\",\"old_string\":\"def build_motion_render_plan(final_plan: FinalVideoPlan, *, output_dir: str | Path, preview_scale: float = 1.0) -> MotionRenderPlan:\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -43,7 +43,13 @@\\n external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n \\n \\n-def build_motion_render_plan(final_plan: FinalVideoPlan, *, output_dir: str | Path, preview_scale: float = 1.0) -> MotionRenderPlan:\\n+def build_motion_render_plan(\\n+ final_plan: FinalVideoPlan,\\n+ *,\\n+ output_dir: str | Path,\\n+ preview_scale: float = 1.0,\\n+ background_path: str | Path | None = None,\\n+) -> MotionRenderPlan:\\n validation = validate_final_video_plan(final_plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_uUTK7lHykHIjNGN62I32eN52", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_3Uk1Pd2ejCKfUaL8WgL9ctmA", "call_id": "call_3Uk1Pd2ejCKfUaL8WgL9ctmA", "response_item_id": "fc_0dc9f371a6d55e3f016a13743618a08191891003d6f2b0ebff", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" height=height,\\n fps=fps,\\n background_path=str(background_path) if background_path is not None else None,\\n )\",\"old_string\":\" height=height,\\n fps=fps,\\n )\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -68,6 +68,7 @@\\n width=width,\\n height=height,\\n fps=fps,\\n+ background_path=str(background_path) if background_path is not None else None,\\n )\\n validation = validate_motion_render_plan(plan)\\n if not validation.is_valid:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_3Uk1Pd2ejCKfUaL8WgL9ctmA", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_nNu6eNT0AhfdGm7QUIa2GS4A", "call_id": "call_nNu6eNT0AhfdGm7QUIa2GS4A", "response_item_id": "fc_0dc9f371a6d55e3f016a13743ca0508191a30b233ae9114334", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float, *, background: Image.Image | None = None) -> Image.Image:\\n w, h = plan.width, plan.height\\n if background is None:\\n image = Image.new(\\\"RGB\\\", (w, h), (6, 12, 24))\\n else:\\n image = background.copy()\\n overlay = Image.new(\\\"RGBA\\\", (w, h), (3, 7, 18, 178))\\n image = Image.alpha_composite(image.convert(\\\"RGBA\\\"), overlay).convert(\\\"RGB\\\")\\n draw = ImageDraw.Draw(image)\",\"old_string\":\"def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float) -> Image.Image:\\n w, h = plan.width, plan.height\\n image = Image.new(\\\"RGB\\\", (w, h), (6, 12, 24))\\n draw = ImageDraw.Draw(image)\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -151,9 +151,14 @@\\n _draw_text_block(draw, (x1 + 28, y1 + 86), body, font=body_font, fill=(51, 65, 85), max_width=x2 - x1 - 56, spacing=8)\\n \\n \\n-def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float) -> Image.Image:\\n+def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float, *, background: Image.Image | None = None) -> Image.Image:\\n w, h = plan.width, plan.height\\n- image = Image.new(\\\"RGB\\\", (w, h), (6, 12, 24))\\n+ if background is None:\\n+ image = Image.new(\\\"RGB\\\", (w, h), (6, 12, 24))\\n+ else:\\n+ image = background.copy()\\n+ overlay = Image.new(\\\"RGBA\\\", (w, h), (3, 7, 18, 178))\\n+ image = Image.alpha_composite(image.convert(\\\"RGBA\\\"), overlay).convert(\\\"RGB\\\")\\n draw = ImageDraw.Draw(image)\\n accent = (34, 211, 238)\\n green = (52, 211, 153)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_nNu6eNT0AhfdGm7QUIa2GS4A", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_MNuX3FGknJBjzB9HDV1z1iZQ", "call_id": "call_MNuX3FGknJBjzB9HDV1z1iZQ", "response_item_id": "fc_0dc9f371a6d55e3f016a13744620d88191bdc4c4e435306363", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" frame_paths: list[str] = []\\n background = None\\n if plan.background_path:\\n background = Image.open(plan.background_path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n frame_index = 0\",\"old_string\":\" frame_paths: list[str] = []\\n frame_index = 0\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -242,6 +242,9 @@\\n frame_dir.mkdir(parents=True, exist_ok=True)\\n Path(plan.output_dir).mkdir(parents=True, exist_ok=True)\\n frame_paths: list[str] = []\\n+ background = None\\n+ if plan.background_path:\\n+ background = Image.open(plan.background_path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n frame_index = 0\\n for scene in plan.final_plan.scenes:\\n scene_duration = (scene.end_ms - scene.start_ms) / 1000\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_MNuX3FGknJBjzB9HDV1z1iZQ", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_YyLcnziDdofWjQMTn32B1x33", "call_id": "call_YyLcnziDdofWjQMTn32B1x33", "response_item_id": "fc_0dc9f371a6d55e3f016a13744d40b08191beeaa8b5c355bb5b", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" image = _render_scene(plan, scene, progress, background=background)\",\"old_string\":\" image = _render_scene(plan, scene, progress)\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -253,7 +253,7 @@\\n count = min(count, max_frames_per_scene)\\n for local_index in range(count):\\n progress = local_index / max(1, count - 1)\\n- image = _render_scene(plan, scene, progress)\\n+ image = _render_scene(plan, scene, progress, background=background)\\n path = frame_dir / f\\\"frame_{frame_index:05d}.png\\\"\\n image.save(path, format=\\\"PNG\\\")\\n frame_paths.append(str(path))\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_YyLcnziDdofWjQMTn32B1x33", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_ozbKoDK4bEFZXnUJV8VcpdSV", "call_id": "call_ozbKoDK4bEFZXnUJV8VcpdSV", "response_item_id": "fc_0dc9f371a6d55e3f016a13745577a8819185cd09c1bed15266", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_motion_renderer.py tests/test_final_video_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \".... [100%]\\n4 passed in 0.31s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ozbKoDK4bEFZXnUJV8VcpdSV", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Generating final preview**\n\nI need to generate an actual render and think about running it fully later. First, I’ll create a motion preview with a background and see if I can reuse existing audio. I should use the current function and mux in the captions. For the final preview, it’ll be important to include subtitles, and I might need to trim it to the audio. I think I need to prepare for generating around 888 frames, but I might target a shorter duration. Let's get started!", "finish_reason": "tool_calls", "reasoning_content": "**Generating final preview**\n\nI need to generate an actual render and think about running it fully later. First, I’ll create a motion preview with a background and see if I can reuse existing audio. I should use the current function and mux in the captions. For the final preview, it’ll be important to include subtitles, and I might need to trim it to the audio. I think I need to prepare for generating around 888 frames, but I might target a shorter duration. Let's get started!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3Rdb_9k3ZInzz7FtyONAZ2P3zuazk5T4i_ElbhKiZ-oIvWAHHrVPYFQDGs3t0Zi4drCSOrVRBqRZ4Ci0vjU0Yd9J5UFBFoihzs5eePLWbw9UZWtXtHhxA6RRzkYMP5e1f_oFaQwBeIDQx9kRdLj9_hBs9oDDjJJRsFHR-qzYfeQa8sTcjRad6f_ArW5TInbXLaQ-ACCCu8QoTuh03f6pPwK2HYA52TsccwDm0hSvFWjO6hJL-JKLcUbL8FD655xd2N0ky_5zkKxq-9mxVjThjL0zIe0nj7ya5oz6INzuZUEwb1PBZnN46XAiLZLHjPt8Jmsx23qcu2ZldKKlbTPPVzl6zLiMrS-wmMc2EUiAcbpo09hJnB6IrUMrI2IQkdzSuP9-be1y4Q0hAXI1UtWJFAkyHsCbMqMyOKLNPi7nLDkj4L2hSFj6rdWkp4mT2dFYyisxp96j2ILqJdPGjNw-fHMLn99UwC8WW5V7ZiPzC4WLuVQtkSzQT71TiMWWPSYNCz8cmIZjWaInS4bC3ycZvDM974gxFyrYWw6Q2kvwMZVsjKQz5oVr_ccsEmEFw5xqkDZ4cfRWMafGXDrqBIX5u_G7PC0S_NfJxVB9aFRUcO7pv7RC56bfZJMHevrF5dNLkYZJVxwXIEN-H6zOB6vsK-bOUx0omaM1Z743hPB2Zy6D1A_q9hy5pqVqKdMpkTGfIsECd8bHP-NRXdx_jMdKdgfaM9h0akfJV7yTdBXpd9Zu8VfuB2HLMUMGx4Te3z89LjcRCAUgyIDVwajn_0CIXYVUhBx-s6Eu-D5tNFkomhiRCI2n3FNTIly2RYSUXRHKF_IKDEY5-dvQGJD0sH18GeUmZxeuW-XQ4axE7cZTDtOavyN76-S-j3aWTDmmG54dQPnqlNgqHgCVfn9SbiKK2IxWH87U2ToMNM5KKuxExKjG2Qp2LGga0gc8AS8W1-62NxVadmu1SOnUYJBQuH8alRcjW-5t5MEuOjJTHp_6jK_HHOKwViqc-LWmLfcI52z3NSNaWhX3fk-imjHjIvGpqRBg1qUvh4oiNed4PwbVFSu71L_6irYs45DTbQpFUw3P6yBjpb2mPbib0-sodhd0iTOQV7dekB1AEV936D6ywfsMbdqhqzlyD9zTB0VpIE7cQwckcwNsBw68J3hExOUyI7eLQlHM5kDwDhpa1zQBNnPqOmtVdlB9ARBk7E1ePl4WEPrNzBjHnFneZ_ksdypXp_YWq6YOM-TgH9ld4ebLjD6DFYGW2TTxTqDYDf95fnXqUZlu9SJFQGpk2nhQte2hzZrxnXo_thjWlElFXO_-jW4GOtlMoxyhF1-5GW0Z-jN8NfXjPqGhNvrlWMHusX7-GiRXp92Jy5lo9LMUbOPsF5Ou1qZSFlLW_sm4i6oB4H4gXwZ", "id": "rs_0dc9f371a6d55e3f016a13745ab57481919abaf55376eb82c3", "summary": [{"type": "summary_text", "text": "**Generating final preview**\n\nI need to generate an actual render and think about running it fully later. First, I’ll create a motion preview with a background and see if I can reuse existing audio. I should use the current function and mux in the captions. For the final preview, it’ll be important to include subtitles, and I might need to trim it to the audio. I think I need to prepare for generating around 888 frames, but I might target a shorter duration. Let's get started!"}]}], "tool_calls": [{"id": "call_x9bWY7V2GCgfHYcXa1LLMCRM", "call_id": "call_x9bWY7V2GCgfHYcXa1LLMCRM", "response_item_id": "fc_0dc9f371a6d55e3f016a13745dae848191b86aaf99652e3f53", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview\\n\\ncandidate_id = 'truetrace-ai-automation-quiet-rot-38s'\\noutput_dir = Path('data/previews/truetrace-final-motion-v1') / candidate_id\\nbackground = Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png')\\nvoiceover = build_truetrace_final_voiceover(candidate_id)\\nfinal_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\nmotion_plan = build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_path=background)\\nmotion = render_motion_preview(motion_plan)\\nif motion.returncode != 0:\\n raise SystemExit(motion.stderr)\\nvoice_path = output_dir / 'voice.mp3'\\nvoice = synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(voice_path)))\\naudio_duration = probe_media_duration_seconds(voice.output_path)\\nsubtitles = build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal_preview_plan = build_audio_caption_preview_plan(\\n video_path=motion.output_path,\\n audio_path=voice.output_path,\\n subtitle_track=subtitles,\\n output_path=output_dir / 'final-motion-audio-caption-preview.mp4',\\n duration_mode='trim_to_audio',\\n)\\nfinal = render_audio_caption_preview(final_preview_plan)\\nif final.returncode != 0:\\n raise SystemExit(final.stderr)\\nprint('background', background)\\nprint('motion', motion.output_path)\\nprint('frames', len(motion.frame_paths))\\nprint('audio_duration', f'{audio_duration:.3f}')\\nprint('subtitle_duration', subtitles.cues[-1].end_ms / 1000)\\nprint('final', final.output_path)\\nPY\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"background /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png\\nmotion data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/motion-preview.mp4\\nframes 887\\naudio_duration 36.864\\nsubtitle_duration 37.0\\nfinal data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\n9f7be649bda32aa0c3c5460ac94cdb7a40ed7e6e57267843aa863fa459112edd data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_x9bWY7V2GCgfHYcXa1LLMCRM", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Considering video extraction methods**\n\nI need to inspect some screenshots or frames in a video. I’m thinking about using ffmpeg to extract the middle frames, which could be a good way to analyze the content. I’ll have to use the terminal for this. It's a little technical, but it seems like the right tool for the job. I might also want to research how to get the exact frames I need. Let's see!", "finish_reason": "tool_calls", "reasoning_content": "**Considering video extraction methods**\n\nI need to inspect some screenshots or frames in a video. I’m thinking about using ffmpeg to extract the middle frames, which could be a good way to analyze the content. I’ll have to use the terminal for this. It's a little technical, but it seems like the right tool for the job. I might also want to research how to get the exact frames I need. Let's see!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3TWVhENnTUb3fWp_gLRTS2hAXlFMg1bBlEcneB1RJSheeAX62wT4R5UUJMqik-MYTsQFUb9PNmslnSjXABrum7M2yCJsWhOku5Z03xc2K3VKJEHpSvfA-rvY8XhT1jinhAT4HlD04hoVKgka_xmQJs4BcIIeZ6R6JZlEDeOAFda_kEL7DU4ey-cngsAxTtFZQOC2AdyaIJpxkSoD4c-H5t4qnb1K_RmGIpKWX31QfK0DCOWmAx6HChm_40Va5vuZbuAfi0xK12tI4esqpBAtSxp-cpBIHicJj2awlKzH3q4zXNkqeuCMzgtHQc85XdvatNJvSaTLw72Dx33GZc3_dltoofh8dq1gOlh7Hk2Ny7s-6tCAGFEJ17e6QZb7cHQSmcrlwOUyVtAJOVf8EpEdLKx1wHpKtDzwbUBkvEfkPCbFSM1LN6qlrOdKeXNnS4YdHizNIWD0zToYHhF1xAWUMozUWxmDO_9HpbLC7KenS6Y1_HIyjcFjCXf7ZeewEPen0ZnFW7aMHVntJcjyfeskgEutty6EjF_QT-wT8g0e7mPZIsVwq81o7gIQ2WlkJGdHb7vNjaUSBPJqfq0Vgjmn6sR5xy6ZBgQ-VKcjjYwMmXRt2kxuTGFSKKiTFcmauSB_xI_KqYii7izaHQ8otvlbZnyf8wy20EdFbpUCjsL5aG-SOMgHc6fF0E8sq_rEvvawLRwKeyGTVJN2NeVcZFQZ_dI0MMw3QEBn3fAXoJ4UmMGaaFGUALR-F51QsYq0oJ66j1DtoE6w_e6Ej9jOdq2dfk5EFnUG9nwPxMKp4iQYM5FrkBgkwor5BLmZ25rhfgJyTj4Ub54648UPUiaCt-NlZDUofqTOoYvXoK7BeBXrYrYhBGsZEd-K6glbLfI2-Hbn1X7Z9RhkEZwE28kOTg7-JFuncwTzM2uoLCLFDjycsj9U8L461_dlTlKRkJA2bvaBOHrffl1E-wuBPdQbAm0C21B2X-xaMoq-0fBIkXE23-BeNPrg63j1YJrHEDha4iRorgK6gGncpmX4h42043cUSdOhw==", "id": "rs_0dc9f371a6d55e3f016a1374d52ea481919770ab5e4c4fcf16", "summary": [{"type": "summary_text", "text": "**Considering video extraction methods**\n\nI need to inspect some screenshots or frames in a video. I’m thinking about using ffmpeg to extract the middle frames, which could be a good way to analyze the content. I’ll have to use the terminal for this. It's a little technical, but it seems like the right tool for the job. I might also want to research how to get the exact frames I need. Let's see!"}]}], "tool_calls": [{"id": "call_Z51xexttiCm2PNlnGsYlc9b8", "call_id": "call_Z51xexttiCm2PNlnGsYlc9b8", "response_item_id": "fc_0dc9f371a6d55e3f016a1374d6ce9081918b0e2005c400a861", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"mkdir -p /tmp/truetrace_review && ffmpeg -y -i data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf \\\"select='eq(n,0)+eq(n,120)+eq(n,300)+eq(n,520)+eq(n,760)'\\\" -vsync 0 /tmp/truetrace_review/frame_%02d.png >/dev/null 2>&1 && printf '%s\\\\n' /tmp/truetrace_review/frame_*.png\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"/tmp/truetrace_review/frame_01.png\\n/tmp/truetrace_review/frame_02.png\\n/tmp/truetrace_review/frame_03.png\\n/tmp/truetrace_review/frame_04.png\\n/tmp/truetrace_review/frame_05.png\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Z51xexttiCm2PNlnGsYlc9b8", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_B9pUia5ODOv3Dx1y7HEdH7NE", "call_id": "call_B9pUia5ODOv3Dx1y7HEdH7NE", "response_item_id": "fc_0dc9f371a6d55e3f016a1374dc98f88191ae91d6af7c769f59", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review/frame_03.png\",\"question\":\"Review this mid-video frame for mobile readability, whether it looks like a mechanism reveal rather than static cards, and whether any text appears clipped.\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review this mid-video frame for mobile readability, whether it looks like a mechanism reveal rather than static cards, and whether any text appears clipped."}, {"type": "image_url", "image_url": {"url": 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"tool_call_id": "call_B9pUia5ODOv3Dx1y7HEdH7NE", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Inspecting subtitle issues**\n\nI'm thinking about whether to note that the subtitle might need to be clipped or shown only partially at the bottom. It seems like having the bottom text continue onto the next line could be messy. I should probably inspect it further, maybe specifically looking at frame_04. I'm curious to see if that will help resolve the subtitle display issue! Let's see what adjustments can be made to improve clarity.", "finish_reason": "tool_calls", "reasoning_content": "**Inspecting subtitle issues**\n\nI'm thinking about whether to note that the subtitle might need to be clipped or shown only partially at the bottom. It seems like having the bottom text continue onto the next line could be messy. I should probably inspect it further, maybe specifically looking at frame_04. I'm curious to see if that will help resolve the subtitle display issue! Let's see what adjustments can be made to improve clarity.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3TlCyG97PvqjEHFEW2ReHU76jqcwG7UtND2mg5_CK2TcV3tTlh_Rms_0G2cKULgVAgQKwsF-ZMQoKn83k68rvuZmlpNl6RqzVXybaXHqHVF11l80euYxnAfDmW95QOWnhdiigC3Dje2Vx5FvDiO5jXf9uKgGPEZcaTCFvLGzfcR8DhECmyz05MmHqCYwTllU7cMnfNuKmHngnh1x8KBMUOOwoy-x6CpIoy4yUFDIs9YXGHTF0mST2fELy6J7Ulc9ucxMeLxlmSrmDo363rCkqfpZUvJdYC-LwaH7FH8pa26oFzidfwdRi0TU0wpHvnQ496aGFPqtben7Hhfo3M0C2TkL5pNmsqSDI7VLyMA9ZWoD0kqNw38vt3pfPehiFw15fBs8JCcuOb-PtgIwKbRsDbaJgOg5L6iZiABI1iyyBtbOi8DXqXqdH84V09EsnqMwiK6IpLyexFPhKganITy-kZOprMqoh_PXRCwFdHXQxwVM5R3oQRLO79g4ZCW503mYmrskvFOwk3pXnQBhZiKInbnMERGqqqw7Q4v7KKniFL_Nbr_l6gWjtvJ92_65zIoZV4-BM1G6-7jMLxCYZCfidYs3_Bp51ghz7ohK1468xGfcbfEL1mxnrI1uHSGkNB4_IoNGgc8oaTghupvZC1RKb0wdtkYsjl93guzlqMrU-63WIkcFXzpTOexo9gHP3ouQP3pJHT9yNfMcfdwjhHofuMDMcjz129Axr9VSgSvbJiWUyB3B5EINTpi4KUr7uXutAGk-_ZgKcblOKShry1ii6rMrLpJY7Jys6b-Kyj-G9oiFpp7E1LljwSE0BYJp3jVTXDxdTRvgvAJk7iEbwF3Mw0Cxo62w-Z4iTMKBjjPhlv4SuAZAzhWLKuGXF95Zb5a1CfrwvzBz0KDGAmbuj-5xqCCoW3SynutPua5QC7xEuUjKvDPqewl_kJTCBmY9hMna1_smzXyI9x8AhZD4NdAu7vC_CJUvtApMT_Uni1SyPvM44aJz4Im9ueQGFh3nvzVHvslHwpdFD_Xvfb8d1SuH27aWFTnPd4kRXpyoscVlWBXH1TuZPYv6CnDAZtf5oMMt9WkRb03RSFNtIOt4nV9bLr83Q==", "id": "rs_0dc9f371a6d55e3f016a1374e33f288191b46c030b3631b8a5", "summary": [{"type": "summary_text", "text": "**Inspecting subtitle issues**\n\nI'm thinking about whether to note that the subtitle might need to be clipped or shown only partially at the bottom. It seems like having the bottom text continue onto the next line could be messy. I should probably inspect it further, maybe specifically looking at frame_04. I'm curious to see if that will help resolve the subtitle display issue! Let's see what adjustments can be made to improve clarity."}]}], "tool_calls": [{"id": "call_Fuv5HiNqexzu5YxJuckNvs0W", "call_id": "call_Fuv5HiNqexzu5YxJuckNvs0W", "response_item_id": "fc_0dc9f371a6d55e3f016a1374e58688819194db1211468f7fcc", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review/frame_04.png\",\"question\":\"Review this three-way split frame for readability and clipping. Is the mechanism clear?\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review this three-way split frame for readability and clipping. Is the mechanism clear?"}, {"type": "image_url", "image_url": {"url": 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"}}], "tool_call_id": "call_Fuv5HiNqexzu5YxJuckNvs0W", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Fixing clipping issues in Frame 4**\n\nI see that Frame 4 has some clipping and overlap at the bottom, so I need to address that. I’m contemplating whether to reduce the subtitle font or adjust the safe zone because the final preview shows the subtitles overlapping the diagram. The scene layout might need a change, like lifting boxes or repositioning the captions to avoid overlap. I’m thinking a smaller font might help, and I need to ensure everything looks visually correct without affecting the diagram too much. Let's patch this up!", "finish_reason": "tool_calls", "reasoning_content": "**Fixing clipping issues in Frame 4**\n\nI see that Frame 4 has some clipping and overlap at the bottom, so I need to address that. I’m contemplating whether to reduce the subtitle font or adjust the safe zone because the final preview shows the subtitles overlapping the diagram. The scene layout might need a change, like lifting boxes or repositioning the captions to avoid overlap. I’m thinking a smaller font might help, and I need to ensure everything looks visually correct without affecting the diagram too much. Let's patch this up!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3TvWZd0XmHcaw1yrmmfwf0b9mwcJd2QROxXF_fGTcsJ6j4Yw5OWi3kExUDGGYhK9qmIjcZAAnIoBFJWXxpBHiBi05VPzWKUSaHftHn9kRHlXlCi8n60veDmcfA2XPAbYsC9B2ScrMOWotNwP1fouFv4J9P4_w-eZcDrZLpHGd24Uh-UAkL4mSwueT7H3kDhZgWJ8eTfAlQ4qWzRIvNxdqN4lZqm5BBFTfDwYRjuFTMBoFyqibdwU1SW7kKPZ9sE0SQpNfboHb1aaA6WkLwOqh0GbkgxwMu_8pka04obLBtZ3QpIMq75ubl4DVbokIyitZs57WzgogMtVxK3AICzfrGKGo6SF4-AR1trqx0Mkc71-QuMdIxXs3nkKXqXbJD0kn6VRYEQYDZOcGbkVg6oGiOO32wLBBuM0YItE2HpKgVuJ_zUk4OmZHgKQQPatTLcm-mNZ2iQ0jpZOES851KzlCwtZqa6yepnebj_GEyHpw0ReHVfUfAJuS6rKxMn0KEcsYKAPBvIjpzEnCCb9Axs_cbYg8AzDMXktTclCAGbQlBXbpkaqDADHbnEa1awU2XDtzNNkVerHjp41hDvT3xDhZ2PMyy27cBV_uvmn4v_PzkHo80i7-oatOnqAyT5FYJS98kSbtp5347v8lJGvBA962wbLLNAQRGN4PqdQM9TZsbLWBWerwMFOA9ZCerRwbdBAGKk-uAfS-kVDYCW-c5VHjSb28AN08RSqarWLlFRjtfpizBZVq89VginhftDrsTfp8jHirm-dUHEWlzLyp0UpQU1OrYasz6qWOwitfvzq20ehV_drcLYCU4ezQOqaw9K_4To1VFBe3LPgIYgYGg6aw5vFz5Qs5k9T3c90S4-nGrpGJIBM74s5N6v4-gAJ5d7w688Q3_1-lb93io4l4_OoTGKqluxFa9m-om1Q4CdizNMWwGIy41OOHy99020XbU2HwTIGroV_0p_sB75dU5GO8Sa0ly4sJoqDEHHt8no8qxfBQR2fxlFEWcmhFyKMZ8jPtesigQoGBEOScD68KD8Je1duglGJrS7CQgGHokn5Vn_GClASKXrS-BpIxgesg1zmSG7D_-yVWuAwXSys8FtBopOY6dS6nn5YfAYIzB48NxhjrsC3FSpTk6lgBaw2HuASK2mwrcO5PrwRjIf3H7baR1UBgMc4SnrWeMwSTjTEMfdgO_Y1mfuJC9YuyIBhoizyldiyCSHUIIJXo7v-bi1cFhkzndE1vhnHHVv5EFARP99ZhzMAt1zQrJVUPhiFTHoqeddP-6UfNvM0WidOWewgkBRQf3vDzYruP5Gpc46JnsOh5YbonPDJBWNYaG5BBTfOuVe-vzrBN1GSUbZG5Hlf1Hm6ggpsMcsEuCJkO0VL-jzUUBufojLcULQ9U_EcUZ0zw9KEiIvkl5tywrtT9DKiSBIzfMRInl95GeaV5_7hwXesD_-o8XT9JNMBx9fGdd8V8J65_vdp7QKnRO2RQ2rDe-k3tsoMu_wa-HzqUEXiPE3VTjQhd12PhmSfyn2YMY3w3ZNrPAFg2zawcrmzupjMGU3d98Kga02BU24RjKBQ51aHzdPapeE0a8DqFABSk4k8b9eLD5cMGM_nTppRfDvNHgWyQTKz6dM9ZIq793IRTuM-ymPxKvCxp5kcJKOHF2WUhboEXqwpUoe9fgKaYxGDv4KpKw_DplUILstCqXs9JQONv2fOnUgxwuorAkXs6JLnEWw9m8TaRvkrWAIAh6yyv9CmADYG8EFGP7tVxfL2RSymjaGVhDn4fGBVehAasuttqnjbDfCj858UU9EXNynWPt38Q==", "id": "rs_0dc9f371a6d55e3f016a1374e863a48191a4db125ec5a75a5e", "summary": [{"type": "summary_text", "text": "**Fixing clipping issues in Frame 4**\n\nI see that Frame 4 has some clipping and overlap at the bottom, so I need to address that. I’m contemplating whether to reduce the subtitle font or adjust the safe zone because the final preview shows the subtitles overlapping the diagram. The scene layout might need a change, like lifting boxes or repositioning the captions to avoid overlap. I’m thinking a smaller font might help, and I need to ensure everything looks visually correct without affecting the diagram too much. Let's patch this up!"}]}], "tool_calls": [{"id": "call_d3XU5ey5pInGT9Bl6m8OJtkA", "call_id": "call_d3XU5ey5pInGT9Bl6m8OJtkA", "response_item_id": "fc_0dc9f371a6d55e3f016a1374efb5b88191b5b61e5bbd848b96", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" by = int(h * (0.42 + idx * 0.12))\",\"old_string\":\" by = int(h * (0.48 + idx * 0.14))\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -213,7 +213,7 @@\\n for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n bx = margin\\n- by = int(h * (0.48 + idx * 0.14))\\n+ by = int(h * (0.42 + idx * 0.12))\\n ex = int(bx + (w - 2 * margin) * reveal)\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_d3XU5ey5pInGT9Bl6m8OJtkA", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3T3CtqmI39DZwkw5MG1SAQI4GXwRYUqc86iu8viz_9D7tSCeuiR0x2AL_S9W_B-JVWPiWimLKbDdaO8bUmlNXMJ-6CbW5YAPYWx2Zh5YtJ2VDqaKP6HIlADTyKoSW3oxvauNiSe4yM7PGJ-9uJug5l5erWJv9VLkAUcKXc6bWX3UxdMApGD86Usgg_eCrvE19RrTfyCctO8m4_6ANm7oVT3XjFVwgh9nQFkKnLq2bhkCpi14vmbH_Bw1gLGyyVv5pcP1PYtT1mdDu03k3XLYxNOcR2StKIKReKuj6JcmZcVgVvrdpLoLGebRw2JFHJs8JQMNWVIT4u_Ez6Q7kKqbjstlCpC6cCfYOz0QvI_lMP1sB9DYo49XBHcKiBTJN55oZZFIkcdV4aaWZpX64E90muZOp-cB5ytNgDGXScIl4jlTl9uONOqGI6q6RBcO1mMvPV_e4i7q4vdmXz3qhPhfElSQa07VZeYaOdVZNfnXXUWp84NAODE1gZCQtexxma0yKaQXii1TBMJ2zdfxJvH4wZNMso6h96OeN973F5fNSvXTqNB5HOyI8JCsO1KyOP3wyxWoJJZnqLpEAiRgB4GaFbG7ENjxGKM3fdSDZLuqbJufZxjCaCBYjRdy3Bj_rMCb0yfJx3BBPfnwS6zhWEb67xQ5UZdXMVbNEj6jfyow5DjPbEjCqTXPIm9R9kgm_eQ2GbKOn_CXVGyoJeXeLJyX5XLDad9k5boruKOxr_rCUHvSWi2ZAv8W3QtRlbGAnhtO3a8hd851v9GmlNn_kOkFY02BpiCkr0eOtJV-Kyh4gjbMLoudca-i63rtvAt5VZDHyWvsrGJmAXSh0xv3A22_L4bsrYcR4ORGQ3Vu8NHbqEtFbrx_Acl6S2p5asY50srtJRYyJtppe1pSJqDtsYGMZgFYVv-tB3dPacitk_g1DfF8AYIU7uCVJtdfkdlLVj8U1aHP8zx1dyZ0qbd0gfemQuoPw==", "id": "rs_0dc9f371a6d55e3f016a1374f6244881919c462d32e884b337", "summary": []}], "tool_calls": [{"id": "call_8HOthZHRhr1Qsub7ForUxvup", "call_id": "call_8HOthZHRhr1Qsub7ForUxvup", "response_item_id": "fc_0dc9f371a6d55e3f016a1374f721948191869e3b2ae636a90c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview\\n\\ncandidate_id = 'truetrace-ai-automation-quiet-rot-38s'\\noutput_dir = Path('data/previews/truetrace-final-motion-v2') / candidate_id\\nbackground = Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png')\\nvoiceover = build_truetrace_final_voiceover(candidate_id)\\nfinal_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\nmotion = render_motion_preview(build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_path=background))\\nif motion.returncode != 0:\\n raise SystemExit(motion.stderr)\\nvoice_path = output_dir / 'voice.mp3'\\nvoice = synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(voice_path)))\\naudio_duration = probe_media_duration_seconds(voice.output_path)\\nsubtitles = build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal = render_audio_caption_preview(build_audio_caption_preview_plan(\\n video_path=motion.output_path,\\n audio_path=voice.output_path,\\n subtitle_track=subtitles,\\n output_path=output_dir / 'final-motion-audio-caption-preview.mp4',\\n duration_mode='trim_to_audio',\\n))\\nif final.returncode != 0:\\n raise SystemExit(final.stderr)\\nprint('final', final.output_path)\\nprint('frames', len(motion.frame_paths))\\nprint('audio_duration', f'{audio_duration:.3f}')\\nPY\\nrm -rf /tmp/truetrace_review_v2 && mkdir -p /tmp/truetrace_review_v2 && ffmpeg -y -i data/previews/truetrace-final-motion-v2/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf \\\"select='eq(n,520)'\\\" -vsync 0 /tmp/truetrace_review_v2/frame_04.png >/dev/null 2>&1\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v2/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-final-motion-v2/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"final data/previews/truetrace-final-motion-v2/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nframes 887\\naudio_duration 36.864\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\nac6c1c4a637d09051de3fa71fcc3a56546136787d88b3aaaf3990ffce7ec9b0e data/previews/truetrace-final-motion-v2/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_8HOthZHRhr1Qsub7ForUxvup", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_xOXDJA4VaTAt0SBTkhKe5Y2u", "call_id": "call_xOXDJA4VaTAt0SBTkhKe5Y2u", "response_item_id": "fc_0dc9f371a6d55e3f016a1375711174819184f848fbd244a1d2", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review_v2/frame_04.png\",\"question\":\"Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved?\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved?"}, {"type": "image_url", "image_url": {"url": 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"}}], "tool_call_id": "call_xOXDJA4VaTAt0SBTkhKe5Y2u", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Adjusting layout elements**\n\nI’m noticing some overlap with the bottom part of the design, where the subtitle overlaps the third box. I think I might need to move the subtitles lower. The bottom text is at 740, and the third section ends at 727, so it’s close. To fix this, I could move the diagram further up by adjusting it. That sounds like a good plan! Maybe I should also consider making the subtitle shorter.", "finish_reason": "tool_calls", "reasoning_content": "**Adjusting layout elements**\n\nI’m noticing some overlap with the bottom part of the design, where the subtitle overlaps the third box. I think I might need to move the subtitles lower. The bottom text is at 740, and the third section ends at 727, so it’s close. To fix this, I could move the diagram further up by adjusting it. That sounds like a good plan! Maybe I should also consider making the subtitle shorter.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3V832wWkID_49u8YxkuMWwwQOVLM4slVG_O7U2kCSZhM8NGFtKesqSL6P8r_UKuvxhCft4l713oz1coKgitqQb_nru8z8aLrc2WhkUsOxLQB8aBOnJFVkf3o-SkvAe0BMHjGaI4wZVq-whG3K63lB5v3cbpCCAUeU18Z49BGLnk2XyDgrR69M_NfDRvD-JvlZGwv0Tjizv8OvIY1LWTQskHkB2C-uP-9FJnZ8pSORgrgRJGoiryFDPJlu4kFbFbSMwL5yoOZRHowK74xFDlOK7-bczMnzC-7J8j1CPQgkrrJKpWr5q7XgRXXXKLiDxNgWswqtnpRiwerPAw-P7ZyEL-qil_91lsBkzHxxSBQ-ZCICXeOOPbQf3N2X8e3_yuew6IQB5JS2yrVK2tdOju30GP4NqmoTo3irTf43mmNR7QfKIJYao6YKno5h_7cD5ZEQ3zEz4UiIs-WG1Im2x-j5oTA2o19QS2XaE_JyNtBeI9lji5jUafqFg5JVwYGFwhrNbTMYKWcrWhCcGHbZOOJd2tV20iFsy12Vqmaj5fkLJ5nabJi_rKAqp-xgpgzU9BkNCvkFPC8EjIdpIw1M_ui7ZADDcndvar6KF1pMCCTmML4Ww5q6JFSZoVsAFRREp8ruN6zZje2wGP2460KJFECOik0Mdo3S78QPlE6bGghFMwHRfis8_i7rB-zQM_ZdgPys2m7oNZFonUqOGdgBxnG5aS7QF9HIxurWcJm7ak9OQ02BshqwdEWPF32LmY5mHVwxRd3ML5mPMVvTQ51i-zSn9UenxX5mYrg2eqPSOE7ULpFoycsxpTP-VhxlbCMTMhZ1IFuq3NzRAqgIDjkjP_-5jCRR-flh6TXN-IFV08JK6C96nVWCvzeOltN7lthJ7-Dl8kxIGLdXDmvl9aq-tpRgtttYe3AHQ4IH_bTErdTM_NUTMPyTjQpeI_agmMvGQgv7AK8WDqdGNwwuPACmaOURu0MWHZ0kKj0oOkm0rRUMqMGVpkzoB1UY8B_O8PUOwTTR97c36cnHLbhwEmSANujMtjOgUFyZaNw6nsc1x8S0hIYTnNhXJgOIK7fc1k0DW09G8ARepaSU-ULYDd3M2dMtK3ilqVuwwxT_O1hweHDQuy3SR9IuB4nJUfO5u2k6AbET3camrMKToojSsp2nOxKrkbW45-rwsQdTla4m85kjQmiblBwOaOU0XS0QrVg0AwAaamS-YDfTsRDGg1vtAFDibAN8aPHdbVyFEVY6WGOki2Zn-9BcZf9HNp-AxkNj0T5TZxFGO3guKq-9jBckVuzRHpow==", "id": "rs_0dc9f371a6d55e3f016a137578dd2881918bab2c4431493c09", "summary": [{"type": "summary_text", "text": "**Adjusting layout elements**\n\nI’m noticing some overlap with the bottom part of the design, where the subtitle overlaps the third box. I think I might need to move the subtitles lower. The bottom text is at 740, and the third section ends at 727, so it’s close. To fix this, I could move the diagram further up by adjusting it. That sounds like a good plan! Maybe I should also consider making the subtitle shorter."}]}], "tool_calls": [{"id": "call_ZQ8rGO5STcqwpRbyX9Z3xtBG", "call_id": "call_ZQ8rGO5STcqwpRbyX9Z3xtBG", "response_item_id": "fc_0dc9f371a6d55e3f016a13757c2bc481918184935b07cb1f67", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" by = int(h * (0.37 + idx * 0.105))\",\"old_string\":\" by = int(h * (0.42 + idx * 0.12))\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -213,7 +213,7 @@\\n for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n bx = margin\\n- by = int(h * (0.42 + idx * 0.12))\\n+ by = int(h * (0.37 + idx * 0.105))\\n ex = int(bx + (w - 2 * margin) * reveal)\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ZQ8rGO5STcqwpRbyX9Z3xtBG", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_giHFHMksCVO45Z8MMhSYs2zh", "call_id": "call_giHFHMksCVO45Z8MMhSYs2zh", "response_item_id": "fc_0dc9f371a6d55e3f016a137581c0cc81918db2d07d38cf2c74", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview\\ncandidate_id='truetrace-ai-automation-quiet-rot-38s'\\noutput_dir=Path('data/previews/truetrace-final-motion-v3')/candidate_id\\nbackground=Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png')\\nvoiceover=build_truetrace_final_voiceover(candidate_id)\\nfinal_plan=build_truetrace_weird_email_final_video_plan(voiceover)\\nmotion=render_motion_preview(build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_path=background))\\nif motion.returncode: raise SystemExit(motion.stderr)\\nvoice=synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(output_dir/'voice.mp3')))\\naudio_duration=probe_media_duration_seconds(voice.output_path)\\nsubtitles=build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal=render_audio_caption_preview(build_audio_caption_preview_plan(video_path=motion.output_path,audio_path=voice.output_path,subtitle_track=subtitles,output_path=output_dir/'final-motion-audio-caption-preview.mp4',duration_mode='trim_to_audio'))\\nif final.returncode: raise SystemExit(final.stderr)\\nprint(final.output_path)\\nPY\\nrm -rf /tmp/truetrace_review_v3 && mkdir -p /tmp/truetrace_review_v3 && ffmpeg -y -i data/previews/truetrace-final-motion-v3/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf \\\"select='eq(n,520)'\\\" -vsync 0 /tmp/truetrace_review_v3/frame_04.png >/dev/null 2>&1\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v3/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-final-motion-v3/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"data/previews/truetrace-final-motion-v3/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\ne703b2b9fe5574f866a7bdf415de508f357cbc972fd6ebc5b4bb79360b9e51a6 data/previews/truetrace-final-motion-v3/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_giHFHMksCVO45Z8MMhSYs2zh", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_UkC6YiSZTCPilOdFxbaHI4Ej", "call_id": "call_UkC6YiSZTCPilOdFxbaHI4Ej", "response_item_id": "fc_0dc9f371a6d55e3f016a1375fc8964819180bc865638804cd0", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review_v3/frame_04.png\",\"question\":\"Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved enough for a Telegram review prototype?\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved enough for a Telegram review prototype?"}, {"type": "image_url", "image_url": {"url": 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"}}], 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"id": "rs_0dc9f371a6d55e3f016a13760259e08191b21bccdd9356b885", "summary": []}], "tool_calls": [{"id": "call_3EldqKOUxivXLirVcarsQUWq", "call_id": "call_3EldqKOUxivXLirVcarsQUWq", "response_item_id": "fc_0dc9f371a6d55e3f016a1376063af08191a0d8801ca93b6d91", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" positions = []\\n for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n by = int(h * (0.37 + idx * 0.105))\\n ex = int(margin + (w - 2 * margin) * reveal)\\n positions.append((top, bottom, color, by, ex))\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n for top, bottom, color, by, _ex in positions:\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n draw.text((margin + 28, by + 18), f\\\"{top} → {bottom}\\\", font=mid_font, fill=(248, 250, 252))\",\"old_string\":\" for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n bx = margin\\n by = int(h * (0.37 + idx * 0.105))\\n ex = int(bx + (w - 2 * margin) * reveal)\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n draw.text((margin + 28, by + 18), f\\\"{top} → {bottom}\\\", font=mid_font, fill=(248, 250, 252))\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -210,12 +210,14 @@\\n draw.rounded_rectangle((start[0] - 120, start[1] - 48, start[0] + 120, start[1] + 48), radius=20, fill=(30, 41, 59), outline=accent, width=3)\\n draw.text((start[0] - 72, start[1] - 16), \\\"Input\\\", font=mid_font, fill=(226, 232, 240))\\n labels = [(\\\"Normal\\\", \\\"Go through\\\", green), (\\\"Unclear\\\", \\\"Review\\\", amber), (\\\"Weird/Risky\\\", \\\"Stop\\\", red)]\\n+ positions = []\\n for idx, (top, bottom, color) in enumerate(labels):\\n reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))\\n- bx = margin\\n by = int(h * (0.37 + idx * 0.105))\\n- ex = int(bx + (w - 2 * margin) * reveal)\\n+ ex = int(margin + (w - 2 * margin) * reveal)\\n+ positions.append((top, bottom, color, by, ex))\\n draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)\\n+ for top, bottom, color, by, _ex in positions:\\n draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)\\n draw.text((margin + 28, by + 18), f\\\"{top} → {bottom}\\\", font=mid_font, fill=(248, 250, 252))\\n else:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_3EldqKOUxivXLirVcarsQUWq", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": 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"id": "rs_0dc9f371a6d55e3f016a137617a01c8191aa96faf1d00362af", "summary": []}], "tool_calls": [{"id": "call_BulFa7EBQ5UHSkEeZKQqGyIo", "call_id": "call_BulFa7EBQ5UHSkEeZKQqGyIo", "response_item_id": "fc_0dc9f371a6d55e3f016a1376184c1c81918049603636b92c7a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview\\ncandidate_id='truetrace-ai-automation-quiet-rot-38s'\\noutput_dir=Path('data/previews/truetrace-final-motion-v4')/candidate_id\\nbackground=Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png')\\nvoiceover=build_truetrace_final_voiceover(candidate_id)\\nfinal_plan=build_truetrace_weird_email_final_video_plan(voiceover)\\nmotion=render_motion_preview(build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_path=background))\\nif motion.returncode: raise SystemExit(motion.stderr)\\nvoice=synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(output_dir/'voice.mp3')))\\naudio_duration=probe_media_duration_seconds(voice.output_path)\\nsubtitles=build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal=render_audio_caption_preview(build_audio_caption_preview_plan(video_path=motion.output_path,audio_path=voice.output_path,subtitle_track=subtitles,output_path=output_dir/'final-motion-audio-caption-preview.mp4',duration_mode='trim_to_audio'))\\nif final.returncode: raise SystemExit(final.stderr)\\nprint(final.output_path)\\nPY\\nrm -rf /tmp/truetrace_review_v4 && mkdir -p /tmp/truetrace_review_v4 && ffmpeg -y -i data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf \\\"select='eq(n,520)'\\\" -vsync 0 /tmp/truetrace_review_v4/frame_04.png >/dev/null 2>&1\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\n3893b94eabf353e6388d1ca8b1fc06bae19cb216cc98f6b44956739e58853051 data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_BulFa7EBQ5UHSkEeZKQqGyIo", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_BjoFHAf6OhZmh4LADx8crAhL", "call_id": "call_BjoFHAf6OhZmh4LADx8crAhL", "response_item_id": "fc_0dc9f371a6d55e3f016a13768dabd88191b657d96e6d6f911a", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review_v4/frame_04.png\",\"question\":\"Final check: is the three-way split readable, arrows behind labels, and no critical clipping/overlap for a review prototype?\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Final check: is the three-way split readable, arrows behind labels, and no critical clipping/overlap for a review prototype?"}, {"type": "image_url", "image_url": {"url": 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"}}], "tool_call_id": "call_BjoFHAf6OhZmh4LADx8crAhL", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": "**Considering documentation updates**\n\nI need to update the README and documentation, and then ensure that all tests are complete. But first, I might generate the image asset for the final video. It could be a good idea to mention ChatGPT Image through the Codex, as it relates. After that, I’ll commit everything and add the documentation. This is feeling like a productive plan! I want to make sure everything is in order.", "finish_reason": "tool_calls", "reasoning_content": "**Considering documentation updates**\n\nI need to update the README and documentation, and then ensure that all tests are complete. But first, I might generate the image asset for the final video. It could be a good idea to mention ChatGPT Image through the Codex, as it relates. After that, I’ll commit everything and add the documentation. This is feeling like a productive plan! I want to make sure everything is in order.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3aUZ5mZWmeckfhCXx8OGLR9tgNMn6sroa-F0-UJ39jNRS5UBCPHKV13_OyBbdduffw8GIN1zQvIizMGcwOTlcH03US9De0iReyMcm6dHtZv5OalQy4pHabH6MVinlIJ2AmBXybDsVUbyIwk2Ei3aO-11FZEV2OV1mhUPqo41Tybed9tLfe2L-zPEEZ9Ff5QYQSq2G6U4-yuhtjHzuPv_78SvgAhFAOdsrWkOHJ-Qb0zanRPyyDlsMpYlDkQgm_6uRUeydmwdixuWAfZHff153vpNxDKHN204phxu3uPwinQQ8oxuqcR8NYfgxzu44_6XJvZDNrrwDosxevPrmN2L3QvCyTBxk2gHeaiv4IJy0tSko-G-6n-Rc6QxKT8JGKlcIR80QDrQ_kcg9V2cBn6QwpOZl7s78M42JHGsxtj38MzLV7mwPd_q5IanA3I2oE5ZSOvFWNj1ZZzqU4nBhesAgKWf2hI0Qiw41X1Yvs6sXwu_xdBUMYZucoeBhQk-lIu3Jhl_K5zoyqkvNQYmMU1_0rml4OjOaQ5FT0N3iovn2NCZJSATrZ29cO2XABbr_kLtpowXuTuH5UTBxJUgSiaxEK1inbA08kZP8G9z2apxfHKWGGoZ38CX8ZQwEXOvXJ5DxNs_lbbrq8LT8WRu_qFgRdq4R2aLGgSKBBRQ0Z_JjCkvr4KF-kIEoRcW4hI9qh32XA2wATyg8dzUiQ9f9ORdEY1to0bQ8VqwP4m7Ej6qoRyrZ_s6azRtTvDTrxCOrt6eVoINXNc78pVrVkuxuoV5SFprVTcU4HHNViBeqOdupVaPqoPrkaDelLboK9D0kesZaHk8v2q-UT6a1nL-mWNPYjflorWPLHCqVXs5_b43XSzhPe-U2Ifl93J7rFE-T-C6kU8Bsv4voamM1W5eNOiuiOv5DVNqQRhNMZkfnGKbCDeUCWLfFYxdzY7E0opnqaXb8JvncQtM44uGawSZy4--0oFZuB3-YrMEYosA5q-W5cEjruirka_pY9qL425gJSkxE-z96Aoz9pZEGoB5uCQIl_xEAn5aRBspRpeaK9rESJCNhP_mi_zv-04EFUulUGaYtoC3E-oaHcC6yhQcFjffn2Fd0oEcyQnRLmXonx3sBfkK22mrxQXquv_QBNTIMaLU0ovUmFjWcuBooBJ_5gPa5Bc_Q==", "id": "rs_0dc9f371a6d55e3f016a137692b3348191b51b58da2b4b0821", "summary": [{"type": "summary_text", "text": "**Considering documentation updates**\n\nI need to update the README and documentation, and then ensure that all tests are complete. But first, I might generate the image asset for the final video. It could be a good idea to mention ChatGPT Image through the Codex, as it relates. After that, I’ll commit everything and add the documentation. This is feeling like a productive plan! I want to make sure everything is in order."}]}], "tool_calls": [{"id": "call_TSPkNK7JtPeNQB0TqPKPN836", "call_id": "call_TSPkNK7JtPeNQB0TqPKPN836", "response_item_id": "fc_0dc9f371a6d55e3f016a137694c5a48191b9e0e22b1df5ad84", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.\\n- `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.\\n- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\",\"old_string\":\"- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -35,6 +35,8 @@\\n - `autoshorts.strategy.brand` — TrueTraceShorts brand profile, default descriptions/bios/tags, and brand-fit validation.\\n - `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n - `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.\\n+- `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.\\n+- `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.\\n - `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n - `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n - `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}, \"_warning\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md was modified by sibling subagent '20260524_204754_10d508' but this agent never read it. Read the file before writing to avoid overwriting the sibling's changes.\"}", "tool_call_id": "call_TSPkNK7JtPeNQB0TqPKPN836", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_dTZmBVrK5J1fECIrk0xQbkep", "call_id": "call_dTZmBVrK5J1fECIrk0xQbkep", "response_item_id": "fc_0dc9f371a6d55e3f016a13769ec31481919dccf2fe7b361ecc", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n\\nThe first final-near motion prototype uses a hybrid model: ChatGPT Image/Codex can provide text-free background texture, while `autoshorts.rendering.motion_renderer` owns the actual inbox cards, arrows, labels, captions, and FFmpeg assembly. This keeps generated imagery useful without trusting it to spell or explain anything.\\n\\n## Strategy scoring\",\"old_string\":\"The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n\\n## Strategy scoring\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -86,6 +86,8 @@\\n \\n The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.\\n \\n+The first final-near motion prototype uses a hybrid model: ChatGPT Image/Codex can provide text-free background texture, while `autoshorts.rendering.motion_renderer` owns the actual inbox cards, arrows, labels, captions, and FFmpeg assembly. This keeps generated imagery useful without trusting it to spell or explain anything.\\n+\\n ## Strategy scoring\\n \\n `autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_dTZmBVrK5J1fECIrk0xQbkep", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_6ZERpAIZOCEsMn9iPKgE16Rf", "call_id": "call_6ZERpAIZOCEsMn9iPKgE16Rf", "response_item_id": "fc_0dc9f371a6d55e3f016a1376a85f60819194b097f2787619ed", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"# Final Motion Prototype Notes\\n\\n## Decision\\n\\nFor the first real TrueTrace video prototype, use a hybrid rendering stack:\\n\\n```text\\nChatGPT Image / Codex image generation → text-free cinematic background texture\\nProcedural motion renderer → all cards, labels, arrows, diagrams, and mechanism text\\nEdgeTTS → voiceover\\nASS/FFmpeg → deterministic subtitle burn-in and final assembly\\n```\\n\\n## Why\\n\\nThe pilot is a mechanism-reveal short, not a cinematic story scene. AI image/video can provide mood and texture, but it must not own:\\n\\n- email text;\\n- path labels;\\n- arrows;\\n- captions;\\n- factual claims;\\n- the three-way workflow mechanism.\\n\\nThose layers stay renderer-owned so the final video remains readable on mobile and does not contain fake/generated UI text.\\n\\n## Current pilot\\n\\nCandidate:\\n\\n```text\\ntruetrace-ai-automation-quiet-rot-38s\\n```\\n\\nFrame sequence:\\n\\n```text\\nclean-demo\\n→ weird-email\\n→ wrong-routing\\n→ three-way-split\\n→ final-rule\\n```\\n\\nCore visual mechanism:\\n\\n```text\\nNormal → Go through\\nUnclear → Review\\nWeird/Risky → Stop\\n```\\n\\n## Generated prototype artifact\\n\\nThe current review artifact is local-only and ignored by Git:\\n\\n```text\\ndata/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\n```\\n\\nIt uses one ChatGPT Image/Codex-generated text-free background plus deterministic renderer overlays.\\n\\n## Quality notes\\n\\n- The three-way split scene has been visually checked so arrows sit behind labels and subtitles do not critically overlap the mechanism.\\n- The prototype is still a review prototype, not a final public export.\\n- Next polish should improve subtitle phrasing/timing around the three-way split and optionally replace the single static background with per-scene generated backgrounds or short I2V texture clips.\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/final_motion_prototype.md\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1808, \"dirs_created\": true, \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_6ZERpAIZOCEsMn9iPKgE16Rf", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_WsozFl5TkuI0lPIHX5KP9GSW", "call_id": "call_WsozFl5TkuI0lPIHX5KP9GSW", "response_item_id": "fc_0dc9f371a6d55e3f016a1376b605c08191baa2a5bf150648dc", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 43%]\\n........................................................................ [ 86%]\\n...................... [100%]\\n166 passed in 33.16s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_WsozFl5TkuI0lPIHX5KP9GSW", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_jPjsO9W4nvyisIXV61wcHYmo", "call_id": "call_jPjsO9W4nvyisIXV61wcHYmo", "response_item_id": "fc_0dc9f371a6d55e3f016a1376de56d08191b9d458b557aacf5f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/rendering/final_video_plan.py autoshorts/rendering/motion_renderer.py tests/test_final_video_plan.py tests/test_motion_renderer.py README.md docs/final_motion_prototype.md && git commit -m \\\"feat: add TrueTrace final motion prototype\\\" && git status --short && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/final_video_plan.py\\n?? autoshorts/rendering/motion_renderer.py\\n?? docs/final_motion_prototype.md\\n?? tests/test_final_video_plan.py\\n?? tests/test_motion_renderer.py\\n[feat/core-approval-quality-gates 14310f9] feat: add TrueTrace final motion prototype\\n 6 files changed, 665 insertions(+)\\n create mode 100644 autoshorts/rendering/final_video_plan.py\\n create mode 100644 autoshorts/rendering/motion_renderer.py\\n create mode 100644 docs/final_motion_prototype.md\\n create mode 100644 tests/test_final_video_plan.py\\n create mode 100644 tests/test_motion_renderer.py\\n14310f9 feat: add TrueTrace final motion prototype\\n7375311 feat: make TrueTrace pilot beginner friendly\\nb9b6423 feat: align TrueTrace voiceover previews\\n3f68001 feat: add EdgeTTS audio caption preview\\n066201d feat: attach audio plans to TrueTrace previews\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_jPjsO9W4nvyisIXV61wcHYmo", "timestamp": "2026-05-25T00:08:59.857036"} {"role": "assistant", "content": "Erledigt, Sir — ich habe losgelegt und den ersten **richtigen Motion-Prototypen** gebaut, mit ChatGPT Image/Codex als Hintergrund-Quelle statt lokalem Qwen Image.\n\n## Gebaut\n\nNeue Module:\n\n```text\nautoshorts/rendering/final_video_plan.py\nautoshorts/rendering/motion_renderer.py\n```\n\nNeue Tests:\n\n```text\ntests/test_final_video_plan.py\ntests/test_motion_renderer.py\n```\n\nNeue Doku:\n\n```text\ndocs/final_motion_prototype.md\n```\n\nCommit:\n\n```text\n14310f9 feat: add TrueTrace final motion prototype\n```\n\n## Was jetzt neu ist\n\n### 1. FinalVideoPlan\n\nNeues Artefakt:\n\n```python\nFinalVideoPlan\n```\n\nEs definiert die echten Produktionsszenen:\n\n```text\nclean-demo\n→ weird-email\n→ wrong-routing\n→ three-way-split\n→ final-rule\n```\n\nMit:\n\n```text\n1080x1920\n30fps\n37s duration\nrenderer-owned text\nno AI-generated text allowed\n```\n\nWichtig: AI darf nur Hintergrund/Textur liefern. Der Renderer besitzt weiterhin:\n\n```text\nemail text\nlabels\narrows\nworkflow paths\nsubtitles\ncaptions\n```\n\nAlso keine „AI hat Review als Reviwe geschrieben“-Katastrophe. Fortschritt, mit Helm.\n\n---\n\n### 2. ChatGPT Image/Codex als Background-Quelle genutzt\n\nIch habe über `image_generate` / OpenAI Codex ein textfreies vertikales Hintergrundbild erzeugt:\n\n```text\n/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png\n```\n\nPrompt-Kern:\n\n```text\nmodern AI automation short background,\nclean laptop/workspace,\ndark productivity dashboard glow,\nblue/green accents,\nno readable text,\nno logos,\nempty central space for renderer-owned overlays\n```\n\nDas Bild wird im Motion Renderer als visuelle Textur verwendet. Alle erklärenden Elemente liegen darüber als deterministic overlays.\n\n---\n\n### 3. Procedural Motion Renderer\n\nNeuer Renderer:\n\n```python\nbuild_motion_render_plan()\nrender_motion_preview()\n```\n\nEr erzeugt lokal:\n\n```text\nanimated inbox cards\nweird email card\nwrong-routing warning\nthree-way split diagram\nfinal rule card\n```\n\nMit FFmpeg-Ausgabe:\n\n```text\nmotion-preview.mp4\n```\n\nDann wird wie bisher ergänzt:\n\n```text\nEdgeTTS voiceover\nASS subtitles\nFFmpeg audio+caption burn-in\n```\n\n---\n\n## Neuer Video-Prototyp\n\nFinaler Review Clip:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\n\n## Verifiziert\n\n```text\nVideo: h264\nSize: 540x960 review preview\nDuration: 36.833s\nAudio: aac\nAudio duration: 36.864s\n```\n\nSHA256:\n\n```text\n3893b94eabf353e6388d1ca8b1fc06bae19cb216cc98f6b44956739e58853051\n```\n\nFrames gerendert:\n\n```text\n887\n```\n\n## Visuelle QA\n\nIch habe die kritische `three-way-split` Szene extrahiert und visuell geprüft.\n\nFixes gemacht:\n\n- Diagramm weiter nach oben geschoben.\n- Arrows hinter die Labels gelegt.\n- Subtitle-Overlap reduziert.\n- `Normal → Go through`, `Unclear → Review`, `Weird/Risky → Stop` bleibt lesbar.\n\nDie Szene ist jetzt für einen Review-Prototyp brauchbar. Nicht Cannes. Aber klarer als die vorherigen Karten — also bereits ein zivilisatorischer Sprung.\n\n## Tests\n\nFull Suite:\n\n```text\n166 passed in 33.16s\n```\n\n## Architektur jetzt\n\n```text\nFinalVoiceoverScript\n→ FinalVideoPlan\n→ ChatGPT Image text-free background\n→ MotionRenderer procedural overlays\n→ EdgeTTS voiceover\n→ ASS subtitles\n→ FFmpeg final preview\n```\n\n## Wichtig zur Richtung\n\nDas ist jetzt der richtige Ansatz:\n\n```text\nChatGPT Image / Codex:\nMood, background, cinematic texture\n\nRenderer:\nTruth, labels, arrows, captions, mechanism\n\nFFmpeg:\nAssembly, audio, subtitle burn-in\n```\n\nNicht:\n\n```text\nAI soll bitte alles generieren und korrekt buchstabieren\n```\n\nDenn das wäre wieder GPU-Roulette mit Bildungsauftrag.\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich den Clip **qualitativ polieren**, nicht nochmal Architektur umbauen:\n\n```text\n1. Subtitle phrasing bei der 3-way-split Szene kürzen\n2. Scene-specific ChatGPT Image backgrounds erzeugen:\n - clean demo inbox\n - weird email moment\n - abstract AI route\n - mechanism background\n3. Motion etwas lebendiger machen:\n - subtle zoom\n - card slide timing\n - warning pulse\n - progress/rhythm beats\n4. Danach 1080x1920 final export bauen\n```\n\nDer aktuelle Clip ist der erste echte „richtige Video“-Prototyp: Voice, Captions, Motion, Mechanism und ChatGPT-Image-Textur in einer Pipeline.", "reasoning": "**Reviewing README changes**\n\nI’m noticing that the README file might not be included in my last commit. The commit indicates 6 files changed, suggesting the README wasn't staged, which is a bit puzzling. I need to run `git add README`. However, it looks like it’s not listed in the creations. I want to ensure the final version is concise, includes media, and mentions the ChatGPT image used. I should also refer to the generated background path and test 166.", "finish_reason": "stop", "reasoning_content": "**Reviewing README changes**\n\nI’m noticing that the README file might not be included in my last commit. The commit indicates 6 files changed, suggesting the README wasn't staged, which is a bit puzzling. I need to run `git add README`. However, it looks like it’s not listed in the creations. I want to ensure the final version is concise, includes media, and mentions the ChatGPT image used. I should also refer to the generated background path and test 166.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3bov_NKIXIiQIa0_ncXB-5rE6xF5QC13IkTWiSIZalPUBOTgumiUvgxA4_PJQ-XU9Oe6KsDqK6qwutbg4ijdQqN5v3NjJP8z1oWu0bbBheYzPJMxwh3avIGLU7TCkGgn59yAGz3biBqq02DeTzPFA2w33cw0rah2gX6UbMf5etruqWuQ4KhnMbdmdOygTrBFwafaDvoSbSq66kolY1-o9VEmtGKhfS6nzoQmfyw4HiFbYBnfIVPNa6dSbbiYfRkqgm_nxlPjFqtHdFljB6O5G6wgYS8AbhqpHTYMrzuoxb6lrHtjdVu9VMOWB8eJuBJsm7rB-iE5UzD6Mt3pggj1PlaU51Od97kucYNtrxjPUnP5s509oUuc1phsKm1iPC88jEt9lTXlRlu3_fF5zklfmC7RxM65NgQ6j9XFcU_wINQk8lIDUEymzrgSmDU2x8CJrfRUCv5wFgkAcGUiiEgJWVJ5t_k9535IiaCOLCNsCbhfqa0KFuEqNkOziGTGzfEp6lPYrVFdAeocrq2myiqaQpG5T_fEOSL5jPqI9ay3ZHAiiKqGd01XA81hT1igRlKkBH6C0SAfVv73Li91KcHXsuWoUzi4CQjCnSz3t26WKPbJM9bKxlozCfku5Vvs314kGJvf7gO9pQmLkx8PNmQiK9QWvFE36ntqUyIf1_BpU4Rs_EbO0IUywxrOqI13omhz4uT8LmP4IAKygMmDLJGDa7jX7oncX-1T9malbj569gqTdfNNTrcLJOI56VT0G1fj-NvLzQGPih3VNyHDyzH2y5MuAd3yoL2JhBGjYqT6o43Nz_h95m8MUiOG6ig875NqF6QGlAOesnJEZxfdyIfWtdq1SngQkwCemL-nCVdRdZl8Bldus5lj5rKBlDUQYNblNWK6brgvC-MNX6uIkaRRbK117YHYTMJSalg1tD0J5nKuFXj6v1-wkgdcDCq2a7mQ8E4Hb8DRtxnSy55A28FOJuJJD36qftpVKikIDpwkCKEHtSMrLd3i5L_17fM7O5C75-HnQkAtCKTVPWyiiJXzcd29oyhLUCIK0Zp2BqJA9-QzLJ0Sxy7TAbDbEVJj3GO2TSYAGXMsE6whFLfeZHUOclgMZpHgPHipE7y6Ooc02Ap1Zvh3pyhjdez_qVQdG1ifldNWy73e9C9DXh8S6jbtss-rGtEmS3wDBA8Hq-3sRPey9KY1dBIl8wTPWfj_iVBPdEQuycgf_NoBruM9v_E4C9QB_xMed3SjyAetc7eVj0qL6mNtjh0ydEja0jcbDRGjm_vMKYzxm52tMCJzIBi8gKbFwlwn1gq6MDL3qEqfeazFYZ9693dHbRzfI_vhhIC1RVsn3wi7HN3RhjMBS7y40dl0xN1Yqbca-Qn2NDcWRxK5D8=", "id": "rs_0dc9f371a6d55e3f016a1376e4d75c8191af0c41339a7fc632", "summary": [{"type": "summary_text", "text": "**Reviewing README changes**\n\nI’m noticing that the README file might not be included in my last commit. The commit indicates 6 files changed, suggesting the README wasn't staged, which is a bit puzzling. I need to run `git add README`. However, it looks like it’s not listed in the creations. I want to ensure the final version is concise, includes media, and mentions the ChatGPT image used. I should also refer to the generated background path and test 166."}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir — ich habe losgelegt und den ersten **richtigen Motion-Prototypen** gebaut, mit ChatGPT Image/Codex als Hintergrund-Quelle statt lokalem Qwen Image.\n\n## Gebaut\n\nNeue Module:\n\n```text\nautoshorts/rendering/final_video_plan.py\nautoshorts/rendering/motion_renderer.py\n```\n\nNeue Tests:\n\n```text\ntests/test_final_video_plan.py\ntests/test_motion_renderer.py\n```\n\nNeue Doku:\n\n```text\ndocs/final_motion_prototype.md\n```\n\nCommit:\n\n```text\n14310f9 feat: add TrueTrace final motion prototype\n```\n\n## Was jetzt neu ist\n\n### 1. FinalVideoPlan\n\nNeues Artefakt:\n\n```python\nFinalVideoPlan\n```\n\nEs definiert die echten Produktionsszenen:\n\n```text\nclean-demo\n→ weird-email\n→ wrong-routing\n→ three-way-split\n→ final-rule\n```\n\nMit:\n\n```text\n1080x1920\n30fps\n37s duration\nrenderer-owned text\nno AI-generated text allowed\n```\n\nWichtig: AI darf nur Hintergrund/Textur liefern. Der Renderer besitzt weiterhin:\n\n```text\nemail text\nlabels\narrows\nworkflow paths\nsubtitles\ncaptions\n```\n\nAlso keine „AI hat Review als Reviwe geschrieben“-Katastrophe. Fortschritt, mit Helm.\n\n---\n\n### 2. ChatGPT Image/Codex als Background-Quelle genutzt\n\nIch habe über `image_generate` / OpenAI Codex ein textfreies vertikales Hintergrundbild erzeugt:\n\n```text\n/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png\n```\n\nPrompt-Kern:\n\n```text\nmodern AI automation short background,\nclean laptop/workspace,\ndark productivity dashboard glow,\nblue/green accents,\nno readable text,\nno logos,\nempty central space for renderer-owned overlays\n```\n\nDas Bild wird im Motion Renderer als visuelle Textur verwendet. Alle erklärenden Elemente liegen darüber als deterministic overlays.\n\n---\n\n### 3. Procedural Motion Renderer\n\nNeuer Renderer:\n\n```python\nbuild_motion_render_plan()\nrender_motion_preview()\n```\n\nEr erzeugt lokal:\n\n```text\nanimated inbox cards\nweird email card\nwrong-routing warning\nthree-way split diagram\nfinal rule card\n```\n\nMit FFmpeg-Ausgabe:\n\n```text\nmotion-preview.mp4\n```\n\nDann wird wie bisher ergänzt:\n\n```text\nEdgeTTS voiceover\nASS subtitles\nFFmpeg audio+caption burn-in\n```\n\n---\n\n## Neuer Video-Prototyp\n\nFinaler Review Clip:\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\n\n## Verifiziert\n\n```text\nVideo: h264\nSize: 540x960 review preview\nDuration: 36.833s\nAudio: aac\nAudio duration: 36.864s\n```\n\nSHA256:\n\n```text\n3893b94eabf353e6388d1ca8b1fc06bae19cb216cc98f6b44956739e58853051\n```\n\nFrames gerendert:\n\n```text\n887\n```\n\n## Visuelle QA\n\nIch habe die kritische `three-way-split` Szene extrahiert und visuell geprüft.\n\nFixes gemacht:\n\n- Diagramm weiter nach oben geschoben.\n- Arrows hinter die Labels gelegt.\n- Subtitle-Overlap reduziert.\n- `Normal → Go through`, `Unclear → Review`, `Weird/Risky → Stop` bleibt lesbar.\n\nDie Szene ist jetzt für einen Review-Prototyp brauchbar. Nicht Cannes. Aber klarer als die vorherigen Karten — also bereits ein zivilisatorischer Sprung.\n\n## Tests\n\nFull Suite:\n\n```text\n166 passed in 33.16s\n```\n\n## Architektur jetzt\n\n```text\nFinalVoiceoverScript\n→ FinalVideoPlan\n→ ChatGPT Image text-free background\n→ MotionRenderer procedural overlays\n→ EdgeTTS voiceover\n→ ASS subtitles\n→ FFmpeg final preview\n```\n\n## Wichtig zur Richtung\n\nDas ist jetzt der richtige Ansatz:\n\n```text\nChatGPT Image / Codex:\nMood, background, cinematic texture\n\nRenderer:\nTruth, labels, arrows, captions, mechanism\n\nFFmpeg:\nAssembly, audio, subtitle burn-in\n```\n\nNicht:\n\n```text\nAI soll bitte alles generieren und korrekt buchstabieren\n```\n\nDenn das wäre wieder GPU-Roulette mit Bildungsauftrag.\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich den Clip **qualitativ polieren**, nicht nochmal Architektur umbauen:\n\n```text\n1. Subtitle phrasing bei der 3-way-split Szene kürzen\n2. Scene-specific ChatGPT Image backgrounds erzeugen:\n - clean demo inbox\n - weird email moment\n - abstract AI route\n - mechanism background\n3. Motion etwas lebendiger machen:\n - subtle zoom\n - card slide timing\n - warning pulse\n - progress/rhythm beats\n4. Danach 1080x1920 final export bauen\n```\n\nDer aktuelle Clip ist der erste echte „richtige Video“-Prototyp: Voice, Captions, Motion, Mechanism und ChatGPT-Image-Textur in einer Pipeline."}], "id": "msg_0dc9f371a6d55e3f016a1376e861c881918cb0246db5e68243", "phase": "final_answer"}], "timestamp": "2026-05-25T00:08:59.857036"} {"role": "user", "content": "[M G] Ok, mach weiter.", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": "**Continuing with skills**\n\nI need to keep going and focus on the relevant skills like TDD, social aspects, and image handling. It might be time to act and polish the subtitle phrasing. I should build tests and inspect the subtitles closely, maybe looking at the final preview to ensure they have the right style. I'll be sure to use the necessary tools in the process to make everything come together.", "finish_reason": "tool_calls", "reasoning_content": "**Continuing with skills**\n\nI need to keep going and focus on the relevant skills like TDD, social aspects, and image handling. It might be time to act and polish the subtitle phrasing. I should build tests and inspect the subtitles closely, maybe looking at the final preview to ensure they have the right style. I'll be sure to use the necessary tools in the process to make everything come together.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3k_bAJx9AS1gxLgikM5L_5UrsrMoQU5w4icQwWL6Q8KMC_qjpqDrlCi8y8u8qoy1lTFoBWUtj_jUbC2GgP4hFittvY1nGdKE5EOaD5FMyHoC0V0ATkMKUswIwUvivwd_fEGEtIWcA63hUjKMDTHLcKFlmxxP3IBnb2HyP_VIoPCn3BEEU0VylGGyjBriAeGrtj9Q9Z0ulv_VNw_ir5GV3LPr-H0nf_U8Cc0iF7bGyItpS16nDfBMl94fERScT8Pa8ujdSe9_P5Am5z8ndhE1s-OW2_YCQ0IXNgrdjS1-PNL39FO3680l-z8s5PiOcAZKywUCv89rdbhacD6n77hD1694f2eAGAiagOi4VD8JL_kWLs1AWgR4-42d9CP0C_L3YINRDGPU1zzTkdMT_tlYy_zCN7rjgo33UYY65xqgBN-FGvHTUzR7BwnKs1Lh-iLe9aA4aY-QiU3y4z-ZZbXZoHzg6fFzjwy3FEM00LfIAzr4ZRhOwV9SiOeLV9ZaDE3kkqcBouIGq6XFjmD2yV3_YGsRfZor5mGyR13jzgbE2ksDBJ7Ytt1YP24vweSIxX7olW7YRE7C9r_OYTAJF665L6US0pEoNUdZEcUiF9sqX8CdyXJlsRmNreY3wLHOzHkjKtYWBMTVShQU1ZdNuqVF4XLc1b-Z927mHEIGXTMsCntvlmJPTfGLATwwRN-1tAcC08lpV646q8awAFlP13xrwtkm4qmq2vogwIUwI-GEBaMiRnoWQd-Y1-AMg-3vM-_8N1f5qDaWLt1qUsgR1InACBku74Dpj9LXi8QUE4P8ayuEyEDr_B6TxmQKdZlregOpU9y2Jtj9zuumEsReMuTyoKNQ8yzqCgOElAlzplWdU52qO2W1eO3YRoA5inYPtjYdL0lJhOlLtvzUt5JHtIPUdMPRi4LfFU8QDLidYbW94-JGvRvJGuHN5lFN7z6l8JWRgknOV3_XQVH6945KgqfYOZVYSfl0i2qvsKMsUFL2DEOyx3UEk62BsIkOSWrt-cUAB5oqOPTFHuifX6E_l5ndH5vcF6QBiZetrMRN4sSKqRRKz17HppLDCm2G6GBSTJGxvs2gSARUqjp4DK6rWhYCg4StIUMnjjPn2Yfn0KLmi2E0PEb4SLoaImJKpbYtViT1ur9_dNj1Mygh1tpyVnaQpdjijhcDiDhgCkC9iBjjXwTDh4=", "id": "rs_0dc9f371a6d55e3f016a13793c86688191ad62a47d75940d8d", "summary": [{"type": "summary_text", "text": "**Continuing with skills**\n\nI need to keep going and focus on the relevant skills like TDD, social aspects, and image handling. It might be time to act and polish the subtitle phrasing. I should build tests and inspect the subtitles closely, maybe looking at the final preview to ensure they have the right style. I'll be sure to use the necessary tools in the process to make everything come together."}]}], "tool_calls": [{"id": "call_25BOfggRR3MiUmrkU08Ye9E6", "call_id": "call_25BOfggRR3MiUmrkU08Ye9E6", "response_item_id": "fc_0dc9f371a6d55e3f016a13793f4b608191b936cd93834b7890", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_EyhGK2DnRjnY8nuBkVGgTrz9", "call_id": "call_EyhGK2DnRjnY8nuBkVGgTrz9", "response_item_id": "fc_0dc9f371a6d55e3f016a13793f4b7881919e7a35b037db8ec7", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}, {"id": "call_uplnI2qLcfBFjzRMbrFlukOc", "call_id": "call_uplnI2qLcfBFjzRMbrFlukOc", "response_item_id": "fc_0dc9f371a6d55e3f016a13793f4b8481918df1903076ca1476", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/subtitles.py\",\"limit\":220}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"test-driven-development\", \"description\": \"TDD: enforce RED-GREEN-REFACTOR, tests before code.\", \"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"], \"content\": \"---\\nname: test-driven-development\\ndescription: \\\"TDD: enforce RED-GREEN-REFACTOR, tests before code.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent (adapted from obra/superpowers)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [testing, tdd, development, quality, red-green-refactor]\\n related_skills: [systematic-debugging, writing-plans, subagent-driven-development]\\n---\\n\\n# Test-Driven Development (TDD)\\n\\n## Overview\\n\\nWrite the test first. Watch it fail. Write minimal code to pass.\\n\\n**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.\\n\\n**Violating the letter of the rules is violating the spirit of the rules.**\\n\\n## When to Use\\n\\n**Always:**\\n- New features\\n- Bug fixes\\n- Refactoring\\n- Behavior changes\\n\\n**Exceptions (ask the user first):**\\n- Throwaway prototypes\\n- Generated code\\n- Configuration files\\n\\nThinking \\\"skip TDD just this once\\\"? Stop. That's rationalization.\\n\\n## The Iron Law\\n\\n```\\nNO PRODUCTION CODE WITHOUT A FAILING TEST FIRST\\n```\\n\\nWrite code before the test? Delete it. Start over.\\n\\n**No exceptions:**\\n- Don't keep it as \\\"reference\\\"\\n- Don't \\\"adapt\\\" it while writing tests\\n- Don't look at it\\n- Delete means delete\\n\\nImplement fresh from tests. Period.\\n\\n## Red-Green-Refactor Cycle\\n\\n### RED — Write Failing Test\\n\\nWrite one minimal test showing what should happen.\\n\\n**Good test:**\\n```python\\ndef test_retries_failed_operations_3_times():\\n attempts = 0\\n def operation():\\n nonlocal attempts\\n attempts += 1\\n if attempts < 3:\\n raise Exception('fail')\\n return 'success'\\n\\n result = retry_operation(operation)\\n\\n assert result == 'success'\\n assert attempts == 3\\n```\\nClear name, tests real behavior, one thing.\\n\\n**Bad test:**\\n```python\\ndef test_retry_works():\\n mock = MagicMock()\\n mock.side_effect = [Exception(), Exception(), 'success']\\n result = retry_operation(mock)\\n assert result == 'success' # What about retry count? Timing?\\n```\\nVague name, tests mock not real code.\\n\\n**Requirements:**\\n- One behavior per test\\n- Clear descriptive name (\\\"and\\\" in name? Split it)\\n- Real code, not mocks (unless truly unavoidable)\\n- Name describes behavior, not implementation\\n\\n### Verify RED — Watch It Fail\\n\\n**MANDATORY. Never skip.**\\n\\n```bash\\n# Use terminal tool to run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n```\\n\\nConfirm:\\n- Test fails (not errors from typos)\\n- Failure message is expected\\n- Fails because the feature is missing\\n\\n**Test passes immediately?** You're testing existing behavior. Fix the test.\\n\\n**Test errors?** Fix the error, re-run until it fails correctly.\\n\\n### GREEN — Minimal Code\\n\\nWrite the simplest code to pass the test. Nothing more.\\n\\n**Good:**\\n```python\\ndef add(a, b):\\n return a + b # Nothing extra\\n```\\n\\n**Bad:**\\n```python\\ndef add(a, b):\\n result = a + b\\n logging.info(f\\\"Adding {a} + {b} = {result}\\\") # Extra!\\n return result\\n```\\n\\nDon't add features, refactor other code, or \\\"improve\\\" beyond the test.\\n\\n**Cheating is OK in GREEN:**\\n- Hardcode return values\\n- Copy-paste\\n- Duplicate code\\n- Skip edge cases\\n\\nWe'll fix it in REFACTOR.\\n\\n### Verify GREEN — Watch It Pass\\n\\n**MANDATORY.**\\n\\n```bash\\n# Run the specific test\\npytest tests/test_feature.py::test_specific_behavior -v\\n\\n# Then run ALL tests to check for regressions\\npytest tests/ -q\\n```\\n\\nConfirm:\\n- Test passes\\n- Other tests still pass\\n- Output pristine (no errors, warnings)\\n\\n**Test fails?** Fix the code, not the test.\\n\\n**Other tests fail?** Fix regressions now.\\n\\n### REFACTOR — Clean Up\\n\\nAfter green only:\\n- Remove duplication\\n- Improve names\\n- Extract helpers\\n- Simplify expressions\\n\\nKeep tests green throughout. Don't add behavior.\\n\\n**If tests fail during refactor:** Undo immediately. Take smaller steps.\\n\\n### Repeat\\n\\nNext failing test for next behavior. One cycle at a time.\\n\\n## Why Order Matters\\n\\n**\\\"I'll write tests after to verify it works\\\"**\\n\\nTests written after code pass immediately. Passing immediately proves nothing:\\n- Might test the wrong thing\\n- Might test implementation, not behavior\\n- Might miss edge cases you forgot\\n- You never saw it catch the bug\\n\\nTest-first forces you to see the test fail, proving it actually tests something.\\n\\n**\\\"I already manually tested all the edge cases\\\"**\\n\\nManual testing is ad-hoc. You think you tested everything but:\\n- No record of what you tested\\n- Can't re-run when code changes\\n- Easy to forget cases under pressure\\n- \\\"It worked when I tried it\\\" ≠ comprehensive\\n\\nAutomated tests are systematic. They run the same way every time.\\n\\n**\\\"Deleting X hours of work is wasteful\\\"**\\n\\nSunk cost fallacy. The time is already gone. Your choice now:\\n- Delete and rewrite with TDD (high confidence)\\n- Keep it and add tests after (low confidence, likely bugs)\\n\\nThe \\\"waste\\\" is keeping code you can't trust.\\n\\n**\\\"TDD is dogmatic, being pragmatic means adapting\\\"**\\n\\nTDD IS pragmatic:\\n- Finds bugs before commit (faster than debugging after)\\n- Prevents regressions (tests catch breaks immediately)\\n- Documents behavior (tests show how to use code)\\n- Enables refactoring (change freely, tests catch breaks)\\n\\n\\\"Pragmatic\\\" shortcuts = debugging in production = slower.\\n\\n**\\\"Tests after achieve the same goals — it's spirit not ritual\\\"**\\n\\nNo. Tests-after answer \\\"What does this do?\\\" Tests-first answer \\\"What should this do?\\\"\\n\\nTests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.\\n\\n## Common Rationalizations\\n\\n| Excuse | Reality |\\n|--------|---------|\\n| \\\"Too simple to test\\\" | Simple code breaks. Test takes 30 seconds. |\\n| \\\"I'll test after\\\" | Tests passing immediately prove nothing. |\\n| \\\"Tests after achieve same goals\\\" | Tests-after = \\\"what does this do?\\\" Tests-first = \\\"what should this do?\\\" |\\n| \\\"Already manually tested\\\" | Ad-hoc ≠ systematic. No record, can't re-run. |\\n| \\\"Deleting X hours is wasteful\\\" | Sunk cost fallacy. Keeping unverified code is technical debt. |\\n| \\\"Keep as reference, write tests first\\\" | You'll adapt it. That's testing after. Delete means delete. |\\n| \\\"Need to explore first\\\" | Fine. Throw away exploration, start with TDD. |\\n| \\\"Test hard = design unclear\\\" | Listen to the test. Hard to test = hard to use. |\\n| \\\"TDD will slow me down\\\" | TDD faster than debugging. Pragmatic = test-first. |\\n| \\\"Manual test faster\\\" | Manual doesn't prove edge cases. You'll re-test every change. |\\n| \\\"Existing code has no tests\\\" | You're improving it. Add tests for the code you touch. |\\n\\n## Red Flags — STOP and Start Over\\n\\nIf you catch yourself doing any of these, delete the code and restart with TDD:\\n\\n- Code before test\\n- Test after implementation\\n- Test passes immediately on first run\\n- Can't explain why test failed\\n- Tests added \\\"later\\\"\\n- Rationalizing \\\"just this once\\\"\\n- \\\"I already manually tested it\\\"\\n- \\\"Tests after achieve the same purpose\\\"\\n- \\\"Keep as reference\\\" or \\\"adapt existing code\\\"\\n- \\\"Already spent X hours, deleting is wasteful\\\"\\n- \\\"TDD is dogmatic, I'm being pragmatic\\\"\\n- \\\"This is different because...\\\"\\n\\n**All of these mean: Delete code. Start over with TDD.**\\n\\n## Verification Checklist\\n\\nBefore marking work complete:\\n\\n- [ ] Every new function/method has a test\\n- [ ] Watched each test fail before implementing\\n- [ ] Each test failed for expected reason (feature missing, not typo)\\n- [ ] Wrote minimal code to pass each test\\n- [ ] All tests pass\\n- [ ] Output pristine (no errors, warnings)\\n- [ ] Tests use real code (mocks only if unavoidable)\\n- [ ] Edge cases and errors covered\\n\\nCan't check all boxes? You skipped TDD. Start over.\\n\\n## When Stuck\\n\\n| Problem | Solution |\\n|---------|----------|\\n| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |\\n| Test too complicated | Design too complicated. Simplify the interface. |\\n| Must mock everything | Code too coupled. Use dependency injection. |\\n| Test setup huge | Extract helpers. Still complex? Simplify the design. |\\n\\n## Hermes Agent Integration\\n\\n### Running Tests\\n\\nUse the `terminal` tool to run tests at each step:\\n\\n```python\\n# RED — verify failure\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# GREEN — verify pass\\nterminal(\\\"pytest tests/test_feature.py::test_name -v\\\")\\n\\n# Full suite — verify no regressions\\nterminal(\\\"pytest tests/ -q\\\")\\n```\\n\\n### CLI Entry Points\\n\\nFor new `python -m package.cli.command` style entry points, keep the TDD loop boring and side-effect free:\\n\\n1. Write a test that imports `main` from the missing CLI module and calls `main([])`.\\n2. Capture stdout/stderr with the project test framework (`capsys` in pytest) or inject a `stdout` file-like object if supported.\\n3. RED should usually be an import failure for the missing CLI module/package, not a vague end-to-end shell failure.\\n4. Implement `main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int` so tests can call it without spawning a subprocess.\\n5. Add the `if __name__ == \\\"__main__\\\": raise SystemExit(main())` guard only after the callable path is green.\\n6. Verify both the unit test and the real module invocation, e.g. `python -m package.cli.command`, before committing.\\n\\nThis catches command wiring while avoiding accidental network calls, publishing actions, or other theatrical nonsense masquerading as a CLI test.\\n\\n### With delegate_task\\n\\nWhen dispatching subagents for implementation, enforce TDD in the goal:\\n\\n```python\\ndelegate_task(\\n goal=\\\"Implement [feature] using strict TDD\\\",\\n context=\\\"\\\"\\\"\\n Follow test-driven-development skill:\\n 1. Write failing test FIRST\\n 2. Run test to verify it fails\\n 3. Write minimal code to pass\\n 4. Run test to verify it passes\\n 5. Refactor if needed\\n 6. Commit\\n\\n Project test command: pytest tests/ -q\\n Project structure: [describe relevant files]\\n \\\"\\\"\\\",\\n toolsets=['terminal', 'file']\\n)\\n```\\n\\n### With systematic-debugging\\n\\nBug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.\\n\\nNever fix bugs without a test.\\n\\n## Demo / Fixture Features\\n\\nWhen adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.\\n\\n## Side-Effect-Free Integration Boundaries\\n\\nWhen building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:\\n\\n1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.\\n2. RED should be a missing module/API or missing validation failure, not a real network/send failure.\\n3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.\\n4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.\\n5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.\\n6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.\\n\\nThis pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.\\n\\n### Media / Rendering Pipeline Boundaries\\n\\nWhen building toward image/video generation or renderer integration, add deterministic planning artifacts before any GPU/API work:\\n\\n1. Write tests for the wished-for pure API first, e.g. `create_shot_plan(script)`, `build_render_manifest(plan)`, `build_local_preview_plan(manifest)`, or `build_ffmpeg_concat_plan(plan, frames, output_path)`.\\n2. RED should be a missing module/API or missing validation behavior — never a failed ComfyUI/Wan/Qwen/API invocation.\\n3. Keep each stage inspectable and side-effect-free until the explicit adapter layer: `ScriptDraft -> ShotPlan -> RenderManifest -> PreviewPlan/PreviewArtifact -> renderer adapter`.\\n4. Assert stable IDs/order, scene counts, timing continuity, vertical settings (`1080x1920`, `30fps` when building Shorts/Reels/TikTok), reduced preview settings (`540x960`, `24fps` when building fast local timing previews), and placeholder/runtime paths before generating media.\\n5. Add guardrail tests that reject static slideshows, baked-in/generated text inside assets, non-renderer-owned captions/labels, unsafe render settings, missing fields, bad timings, non-PNG frame paths, non-MP4 output paths, and mismatched frame counts.\\n6. Split artifact-writing/rendering into explicit boundaries after the pure plans are green: frame writer returns `side_effects=(\\\"write_png\\\",)`, concat planning remains side-effect-free, and the FFmpeg renderer returns `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` plus `external_calls=(\\\"ffmpeg\\\",)`.\\n7. Only after the manifest/preview boundary is green should a later adapter call local GPU tools or external services. Planning first; GPU roulette later.\\n\\nThis prevents wasting render time on unclear direction and keeps expensive media generation behind a small, audited boundary.\\n\\n### Approval-First Media/Social Pipelines\\n\\nWhen a feature crosses from content planning toward media/social delivery, prefer many small RED-GREEN-README-COMMIT slices instead of one large integration jump:\\n\\n1. typed artifact/model (`SocialMediaDraft`, `RenderManifest`, etc.)\\n2. renderer/formatter for human review text\\n3. side-effect-free delivery/preparation boundary\\n4. demo CLI that prints metadata first, then payload\\n\\nFor each slice, write the missing import/API test first, verify the expected import/API failure, implement only that slice, run the focused test, run the full suite, update docs, and commit. Keep invariant tests explicit: `requires_human_approval=True`, `side_effects=()`, no external calls, no credential access, no file writes unless that adapter is the explicit feature, and no accidental action language such as `publish`/`post` in review/delivery text.\\n\\nThis staged approach keeps approval semantics auditable and prevents a later renderer, Telegram sender, or platform adapter from being smuggled into formatting code.\\n\\n## Testing Anti-Patterns\\n\\n- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test\\n- **Testing implementation details** — test behavior/results, not internal method calls\\n- **Happy path only** — always test edge cases, errors, and boundaries\\n- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them\\n- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.\\n- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.\\n- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`\\\"\\\"`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.\\n\\n## Final Rule\\n\\n```\\nProduction code → test exists and failed first\\nOtherwise → not TDD\\n```\\n\\nNo exceptions without the user's explicit permission.\\n\", \"path\": \"software-development/test-driven-development/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/software-development/test-driven-development\", \"linked_files\": {\"references\": [\"references/deterministic-demo-fixtures.md\", \"references/side-effect-free-delivery-boundaries.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_25BOfggRR3MiUmrkU08Ye9E6", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"description\": \"Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\", \"tags\": [\"shorts\", \"tiktok\", \"youtube-shorts\", \"reels\", \"creator-monetization\", \"content-automation\", \"social-media\"], \"related_skills\": [], \"content\": \"---\\nname: social-media-shorts-automation\\ndescription: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.\\nversion: 1.0.0\\ntags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]\\n---\\n\\n# Social-Media Shorts Automation\\n\\nUse this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.\\n\\n## Core principle\\n\\nTreat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.\\n\\nFor this user's AutoShorts workflows, approval previews should be German for concept comprehension, while final social-media videos should remain English unless explicitly changed. Treat preview legibility as a quality gate: readable font sizes, pixel-width-aware line wrapping, no clipped words, and no overcrowded cards.\\n\\n## Retention-Studio / Mechanism-Reveal Pipeline\\n\\nFor quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:\\n\\n```text\\nstrategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer\\n```\\n\\nWhen implementing a new repo or core pipeline, first scaffold the approval-first typed artifact chain in `references/approval-first-retention-studio-core.md`:\\n\\n```text\\nContentPillar → ContentBrief → ScriptDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery → ShotPlan\\n```\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\nBefore video generation, build an inspectable content-to-review spine: `ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate → TestBatch → ReviewBatch → Telegram ReviewDelivery`. Keep it deterministic, side-effect free, and human-approval-first so hooks, retention beats, visual concepts, policy notes, and review copy can be optimized before spending GPU/API time. See `references/content-to-review-pipeline.md` for the implementation pattern and guardrails.\\n\\nHard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.\\n\\n## Recommended workflow\\n\\n1. **Define channel position**\\n - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.\\n - Target audience and emotional problem.\\n - Language and tone.\\n - Whether the brand is faceless, AI-persona-led, or creator-led.\\n - No-go topics and theological/ethical boundaries if faith-related.\\n\\n2. **Generate test ideas before code-heavy automation**\\n - Create 20–30 candidate ideas across 2–4 content pillars.\\n - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.\\n - Select a small first batch (e.g. 3 per pillar) for real testing.\\n - Before renderers or external AI APIs, add a deterministic content-development core: `ContentPillar → ContentBrief → ScriptDraft → CandidateDraft → VideoCandidate`. See `references/content-development-core.md`.\\n\\n3. **Use hook-first, retention-led scripting**\\n - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.\\n - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.\\n - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.\\n - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.\\n - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.\\n\\n4. **Quality gate before rendering**\\n Score drafts on:\\n - hook strength\\n - novelty\\n - emotional pull\\n - clarity\\n - retention risk\\n - comment/share potential\\n - AI-slop risk\\n - platform/policy risk\\n Render only drafts above the chosen threshold, e.g. 8/10.\\n\\n5. **Visual strategy**\\n - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.\\n - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.\\n - Use pattern interrupts every 2–3 seconds for retention.\\n - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.\\n - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.\\n - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.\\n - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: first create a side-effect-free local preview plan, then write deterministic PNG scene cards, then build a side-effect-free FFmpeg concat plan, and only then call FFmpeg from an explicit renderer boundary. The boundary should report side effects such as `write_png`, `write_concat`, `call_ffmpeg`, and `write_mp4`; keep generated PNG/MP4 artifacts out of Git. See `references/manifest-preview-renderer.md`.\\n - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.\\n - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips//scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.\\n - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.\\n - When using NVIDIA Build/API Catalog for keyframes, prefer hosted `ai.api.nvidia.com` calls before considering self-hosted NIM deployment; implement a configurable model fallback chain, store the key as a runtime secret, and inspect/clean logos, emblems, license plates, and fake text before I2V. See `references/nvidia-keyframe-generation.md`.\\n\\n6. **Social-media draft and content-preview approval packages before TTS/rendering polish**\\n - Before TTS, platform APIs, or polished video generation, build a typed `SocialMediaDraft` from the approved candidate plus render manifest/preview.\\n - Include platform targets, title variants, caption, hashtags, CTA, hook, duration, quality/risk summary, and preview reference.\\n - Render this into a Telegram-safe approval message and wrap it in a side-effect-free delivery plan with `requires_human_approval=True` and `side_effects=()`.\\n - After a local MP4 timing preview exists, combine the MP4 path plus social draft review into a side-effect-free content preview approval package. This package should carry `approval_language=de`, `final_video_language=en`, explicit approval commands, no external calls, and no side effects.\\n - Add local demo CLIs that print metadata first, then review text; draft-only CLIs must not write files, call renderers, call Telegram, or access credentials. MP4 preview CLIs may write local artifacts and call FFmpeg only at explicit renderer boundaries that report side effects.\\n - See `references/social-draft-approval-package.md` and `references/content-automation-patterns.md` for concrete artifact sequence, user-specific language rules, and external workflow patterns.\\n\\n7. **Human approval loop before posting**\\n - Send preview to the user through the requested channel, usually Telegram.\\n - Include: video or preview reference, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\n - When sending multiple preview videos, verify and label distinct artifacts before delivery: candidate ID/title, absolute path, duration, and preferably checksum/hash. If several previews are visually similar timing cards, do not spam them as separate unlabeled media; send a concise indexed list first or bundle with clear labels so the user can tell them apart.\\n - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.\\n - Do not publish automatically unless the user has explicitly authorized that workflow.\\n\\n8. **TTS / voice layer after draft approval**\\n - Treat TTS as a later layer, not the starting point for a first post draft.\\n - Decide voice, final voiceover script, words-per-second, scene timing, caption sync, audio format, provider, and separate voice approval only after the social draft package is reviewable.\\n - This avoids spending TTS/rendering effort on unapproved hooks, captions, or platform positioning.\\n - For final-near previews, keep one copy source of truth: final voiceover → script draft/retention beats → shot plan → top card text → scene captions → subtitle track → TTS audio. Do not mix old approval-card text with new spoken/subtitle text.\\n - If the user says the result is too abstract/abgehoben, rewrite around a concrete beginner-friendly everyday failure example before polishing visuals. Prefer “one weird email arrives” / missing field / unclear request / wrong confident action over jargon such as `exception path`, `input boundary`, or `output standard`. See `references/beginner-friendly-audio-caption-alignment.md`.\\n\\n9. **Publish and measure**\\n Track at minimum:\\n - platform, video id/url, niche, format, hook, duration\\n - views at 2h/24h/7d\\n - likes, comments, shares, saves, follows\\n - retention metrics if available\\n Use results to generate the next batch.\\n\\n## Platform duration/monetization notes\\n\\nSee `references/preview-media-delivery-guardrails.md` for avoiding duplicate-looking/unlabeled preview-video spam in Telegram review workflows.\\n\\nSee `references/audio-caption-preview-pipeline.md` for the approval-first EdgeTTS + ASS subtitles + FFmpeg review-MP4 pipeline, including actual-audio-duration probing and the explicit `preserve_video` vs `trim_to_audio` duration-mode guardrail.\\n\\nSee `references/final-motion-hybrid-rendering.md` for the final-near hybrid renderer pattern: ChatGPT Image/Codex or other AI tools provide text-free background/keyframe texture while deterministic renderer overlays own text, arrows, labels, captions, and final assembly.\\n\\nSee `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/local-preview-frame-writer.md` for the staged `LocalPreviewPlan → PNG scene cards → FFmpeg concat plan` boundary before MP4 rendering. See `references/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. See `references/content-automation-patterns.md` for public workflow patterns, queue/status/cost tracking, versioned approval, synthetic-content disclosure, originality gates, and the user's German-preview/English-final rule. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. See `references/nvidia-keyframe-generation.md` for NVIDIA-hosted keyframe generation, model fallback, secret handling, and pre-I2V logo/plate cleanup. Re-check official docs before making business-critical decisions because platform programs change often.\\n\\n## Project/repo hygiene for automated video bots\\n\\n- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.\\n- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.\\n- Store platform credentials as runtime secrets; never print raw tokens.\\n- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.\\n- For approval-first Retention Studio implementations, use a typed side-effect-free core before real I/O: `ContentBrief`, `ScriptDraft`, `VideoCandidate`, review batches, Telegram delivery plans, then shot plans. See `references/approval-first-retention-studio-core.md`.\\n\\n## Sensitive-content positioning\\n\\nWhen AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.\\n\\n## Pitfalls\\n\\n- Do not send several generated preview videos as unlabeled media attachments just because they are distinct files. Telegram/mobile previews can make timing-card MP4s look identical. Verify uniqueness (path + title + hash/duration) and provide labels or an indexed summary; if the user complains about duplicate-looking media, stop resending, verify artifacts, and continue the pipeline.\\n- Do not let final-near preview layers drift apart: if the top field, voiceover, and subtitles do not say the same thing, rebuild the render plan from one final script source before sending another clip.\\n- Do not make broad-audience AI-system shorts sound like architecture lectures. For early-AI users, convert abstract workflow concepts into concrete everyday failures first, then reveal the principle.\\n- Do not over-optimize renderer quality before testing whether the content format earns retention.\\n- Do not let German approval previews become unreadable technical cards: use pixel-width-aware wrapping, readable fonts, limited line counts, and visual checks for clipped/overcrowded text.\\n- Do not confuse approval language with final audience language. For this user, approval previews are German by default; final social videos remain English unless explicitly changed.\\n- Do not copy public automation workflows wholesale: keep their queue/status/cost tracking and template structure, but remove blind auto-publishing until a human approval/version lock exists.\\n- Do not mass-generate repetitive shorts without originality/inauthentic-content gates; this can harm monetization/account health and create AI slop.\\n- Do not jump directly from a script idea to ComfyUI/Wan/Qwen/Image/video generation. First build and inspect the content-to-review spine so weak hooks, generic scripts, policy issues, and static visual concepts are killed before render time.\\n- Do not create a parallel candidate path for content experiments; convert content artifacts into the existing candidate factory input so IDs, hashtags, duration classes, quality gates, and pillar/platform validation stay centralized.\\n- Do not use `x or default` for optional content/script overrides in factories; explicit blank strings and zero durations must pass through to validation so tests can catch them.\\n- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.\\n- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.\\n- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.\", \"path\": \"social-media/social-media-shorts-automation/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/social-media/social-media-shorts-automation\", \"linked_files\": {\"references\": [\"references/wan-i2v-retention-studio.md\", \"references/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/social-draft-approval-package.md\", \"references/approval-first-retention-studio-core.md\", \"references/local-preview-frame-writer.md\", \"references/preview-media-delivery-guardrails.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-pipeline.md\", \"references/beginner-friendly-audio-caption-alignment.md\", \"references/audio-caption-preview-pipeline.md\", \"references/retention-studio-pipeline-pattern.md\", \"references/monetization-and-approval-notes.md\", \"references/content-to-review-pipeline.md\", \"references/retention-led-shorts.md\", \"references/final-motion-hybrid-rendering.md\", \"references/imported-ai-clip-assembly.md\", \"references/content-automation-patterns.md\", \"references/retention-render-planning.md\", \"references/nvidia-keyframe-generation.md\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\"}", "tool_call_id": "call_EyhGK2DnRjnY8nuBkVGgTrz9", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Deterministic renderer-owned subtitle cue planning.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|import re\\n 6|from dataclasses import dataclass, replace\\n 7|\\n 8|from autoshorts.ideas.candidate import ValidationResult\\n 9|\\n 10|\\n 11|_WORD_RE = re.compile(r\\\"[A-Za-z0-9]+(?:['-][A-Za-z0-9]+)?\\\")\\n 12|\\n 13|\\n 14|@dataclass(frozen=True)\\n 15|class SubtitleStyle:\\n 16| name: str\\n 17| primary_color: str\\n 18| accent_color: str\\n 19| stroke_color: str\\n 20| shadow_color: str\\n 21| placement: str\\n 22| max_lines: int\\n 23| min_words_per_cue: int\\n 24| max_words_per_cue: int\\n 25| safe_zone_bottom_percent: int\\n 26| font_weight: str\\n 27|\\n 28| @classmethod\\n 29| def hormozi_inspired(cls) -> \\\"SubtitleStyle\\\":\\n 30| \\\"\\\"\\\"Return the TrueTrace kinetic-caption baseline: inspired mechanics, original brand skin.\\\"\\\"\\\"\\n 31|\\n 32| return cls(\\n 33| name=\\\"truetrace_hormozi_inspired\\\",\\n 34| primary_color=\\\"#FFFFFF\\\",\\n 35| accent_color=\\\"#22D3EE\\\",\\n 36| stroke_color=\\\"#050816\\\",\\n 37| shadow_color=\\\"#000000\\\",\\n 38| placement=\\\"lower_middle_above_ui_safe_zone\\\",\\n 39| max_lines=2,\\n 40| min_words_per_cue=2,\\n 41| max_words_per_cue=5,\\n 42| safe_zone_bottom_percent=14,\\n 43| font_weight=\\\"800\\\",\\n 44| )\\n 45|\\n 46|\\n 47|@dataclass(frozen=True)\\n 48|class SubtitleCue:\\n 49| text: str\\n 50| start_ms: int\\n 51| end_ms: int\\n 52| active_word: str\\n 53| renderer_owned: bool = True\\n 54| max_lines: int = 2\\n 55| emphasis_color: str = \\\"#22D3EE\\\"\\n 56|\\n 57|\\n 58|@dataclass(frozen=True)\\n 59|class SubtitleTrack:\\n 60| cues: tuple[SubtitleCue, ...]\\n 61| style: SubtitleStyle\\n 62| renderer_owned: bool = True\\n 63| side_effects: tuple[str, ...] = ()\\n 64| external_calls: tuple[str, ...] = ()\\n 65|\\n 66| def with_cues(self, cues: tuple[SubtitleCue, ...]) -> \\\"SubtitleTrack\\\":\\n 67| return replace(self, cues=cues)\\n 68|\\n 69|\\n 70|def _words(text: str) -> list[str]:\\n 71| return _WORD_RE.findall(text)\\n 72|\\n 73|\\n 74|def _chunk_words(words: list[str], *, min_words: int, max_words: int) -> list[list[str]]:\\n 75| chunks: list[list[str]] = []\\n 76| index = 0\\n 77| while index < len(words):\\n 78| remaining = len(words) - index\\n 79| take = min(max_words, remaining)\\n 80| if 0 < remaining < min_words and chunks:\\n 81| chunks[-1].extend(words[index:])\\n 82| break\\n 83| if remaining == min_words + 1:\\n 84| take = min_words\\n 85| chunks.append(words[index : index + take])\\n 86| index += take\\n 87| return chunks\\n 88|\\n 89|\\n 90|def _active_word(chunk: list[str]) -> str:\\n 91| # Prefer the longest meaningful word so the highlighted token carries semantic weight.\\n 92| return max((word.upper() for word in chunk), key=lambda word: (len(word), word))\\n 93|\\n 94|\\n 95|def build_hormozi_subtitle_track(text: str, *, total_duration_seconds: int) -> SubtitleTrack:\\n 96| \\\"\\\"\\\"Create continuous 2..5-word renderer-owned subtitle cues for final burn-in.\\\"\\\"\\\"\\n 97|\\n 98| if not text.strip():\\n 99| raise ValueError(\\\"subtitle text is required\\\")\\n 100| if total_duration_seconds <= 0:\\n 101| raise ValueError(\\\"total_duration_seconds must be positive\\\")\\n 102|\\n 103| style = SubtitleStyle.hormozi_inspired()\\n 104| words = _words(text)\\n 105| if len(words) < style.min_words_per_cue:\\n 106| raise ValueError(\\\"subtitle text must contain at least two words\\\")\\n 107|\\n 108| chunks = _chunk_words(words, min_words=style.min_words_per_cue, max_words=style.max_words_per_cue)\\n 109| total_ms = total_duration_seconds * 1000\\n 110| cues: list[SubtitleCue] = []\\n 111| for index, chunk in enumerate(chunks):\\n 112| start_ms = round(index * total_ms / len(chunks))\\n 113| end_ms = round((index + 1) * total_ms / len(chunks))\\n 114| cues.append(\\n 115| SubtitleCue(\\n 116| text=\\\" \\\".join(word.upper() for word in chunk),\\n 117| start_ms=start_ms,\\n 118| end_ms=end_ms,\\n 119| active_word=_active_word(chunk),\\n 120| renderer_owned=True,\\n 121| max_lines=style.max_lines,\\n 122| emphasis_color=style.accent_color,\\n 123| )\\n 124| )\\n 125|\\n 126| track = SubtitleTrack(cues=tuple(cues), style=style)\\n 127| validation = validate_subtitle_track(track)\\n 128| if not validation.is_valid:\\n 129| raise ValueError(\\\"; \\\".join(validation.errors))\\n 130| return track\\n 131|\\n 132|\\n 133|def validate_subtitle_track(track: SubtitleTrack) -> ValidationResult:\\n 134| errors: list[str] = []\\n 135| if not track.renderer_owned:\\n 136| errors.append(\\\"subtitle track must be renderer-owned\\\")\\n 137| if track.side_effects:\\n 138| errors.append(\\\"subtitle track must not have side effects\\\")\\n 139| if track.external_calls:\\n 140| errors.append(\\\"subtitle track must not have external calls\\\")\\n 141| if not track.cues:\\n 142| errors.append(\\\"at least one subtitle cue is required\\\")\\n 143|\\n 144| previous_end: int | None = None\\n 145| for index, cue in enumerate(track.cues, start=1):\\n 146| cue_words = cue.text.split()\\n 147| if not cue.renderer_owned:\\n 148| errors.append(f\\\"subtitle cue {index} must be renderer-owned\\\")\\n 149| if not (track.style.min_words_per_cue <= len(cue_words) <= track.style.max_words_per_cue):\\n 150| errors.append(f\\\"subtitle cue {index} must contain 2..5 words\\\")\\n 151| if cue.end_ms <= cue.start_ms:\\n 152| errors.append(f\\\"subtitle cue {index} end_ms must be greater than start_ms\\\")\\n 153| if previous_end is None:\\n 154| if cue.start_ms != 0:\\n 155| errors.append(\\\"first subtitle cue must start at 0ms\\\")\\n 156| elif cue.start_ms != previous_end:\\n 157| errors.append(f\\\"subtitle cue {index} must start when the previous cue ends\\\")\\n 158| if not cue.active_word.strip():\\n 159| errors.append(f\\\"subtitle cue {index} active_word is required\\\")\\n 160| elif cue.active_word not in {word.upper() for word in cue_words}:\\n 161| errors.append(f\\\"subtitle cue {index} active_word must appear in cue text\\\")\\n 162| if cue.max_lines > track.style.max_lines:\\n 163| errors.append(f\\\"subtitle cue {index} exceeds style max lines\\\")\\n 164| previous_end = cue.end_ms\\n 165|\\n 166| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 167|\", \"total_lines\": 166, \"file_size\": 5805, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_uplnI2qLcfBFjzRMbrFlukOc", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": "**Planning scene backgrounds**\n\nI need to generate scene-specific backgrounds, maybe four images, and update the renderer to support those backgrounds for each scene. The subtitle safe zone might need adjusting upward too. So, let's create an asset plan based on that. The user wants me to continue, so I’ll focus next on scene-specific image backgrounds and render them in version 5. I think we won't need TDD for generated images, but will need tests for accepting scene background paths.", "finish_reason": "tool_calls", "reasoning_content": "**Planning scene backgrounds**\n\nI need to generate scene-specific backgrounds, maybe four images, and update the renderer to support those backgrounds for each scene. The subtitle safe zone might need adjusting upward too. So, let's create an asset plan based on that. The user wants me to continue, so I’ll focus next on scene-specific image backgrounds and render them in version 5. I think we won't need TDD for generated images, but will need tests for accepting scene background paths.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3lGcrTBeSYIKd9I8D4CqqKUqR6pBzkJ4ECtLfaz2HC-Okbmszkb9rwK1c5VHwjbKX6Vgw5aCR40iiInqYqldOoXw4-_sBsEoR75KaQKzQRAEdEm4ipPI_oZs9RehRX6bNr6T-Rab2KpyrJ8gDxSYPmBk9JPJGgIy9rlae3UKIocpsGfsx1BNcVOSUgE6TyOtto79vfF2xLcC2QcVz52oGIa1bpB-GWxhDAxcW_Ebz0fSQHTdsrvzkzRwqW7uux5hfJHZYsHUaE3y87i46fdrfvHNyPdmfpEUPonQwlgAE_scVOikkhIbcALTQO5MaIpkgjnWIXe9uCgqWpFlB5mkSkG42OzWSBEk07-Lele2trsveHKEOOxKaS_7ZfW90Va9RqUh_TBesMP92YX1nXJkAGTaWVJWAvDc308U0enjk1XNOTIkOJ-BoRo_s1adbEJV3bnZgh7oCKI9ZWJHZdyMB5RX1vJN1WqVXQY-6Cu28EAS8_1pLXhvE4psWrYDE8K3ElYtS2nUe8OIM0l0h25MIJ5oGqOCnLuMgMd5Rie1KS2pV2fBmEAt7Of3C21STz5qUyLWafK_0ZHhT-KrsB5CsIEKcZu8twwDIF3p_vaw8MRtb2yFRQNtnEIOh7VjUiUhqEMoHPJeWTxYeqr_h9_wPT_QCLsdgGl4Nlp_UlAkh-WCNJljKFRNVbPD34BrTimtXJngHRViMaTJc8GSz0tLrFw04c63T9hl-aNmxTMKEU3Cp1fQ7oLKDEdItbOH6BFLIC0_spyRd2REP_vWC-IxmCSMXi5x4DVlz_1rRdtII7no36I1voDuFGucoQ9KVVW0Y6rjwXCnkQ6UKr7Bh0YeDWwHoK1ur1s5_X50cXh9LEUh8PGQ-JUxQSm5_wxkFaxc5yzsAGh8U4r7Cd_EteolmVGtuRMeLPcp4Q2Fpg0UcH6si9REXFLx17rGWYwxWtqgYy92loMRhdH__3vgGaEDfD2zMzCbGg-yyJeogukw4yu1N3lbPypWBeNBmwFhGz3je9OSzTLatz3FQolBRbOPjbwdlYBZeMgi0K9LDMZECxVMYQGmAvBbI7iu-9T4sSYyjhqV1HrHJRAY_yJV5q97Eudik9vXkepBXUjZJKqMRnGJfI_eC6dWJF_uyQDi9XgjHmOyBnDTJyRM6Bl2iYbT7XwdDejmRVP1LKJ-W9xRrz_TEW95hGSH2StOEK3RoKMl6Wnb-iHR2gjiGaoj22V4SKPlHsf5n4AZqwynWftbGDmWTP5AD99h7oSuZPUBkyu8dHy7fmwfRIybQnVzWnVieKuV0tKdtttIAxFIUgeb1f7rHQD3SdOUplxM9i5L66a07Aqt1KUBU5N4zRZyIs7nJ2M3S0Pf_Vz_AjNALXFMI0b_4KyrQJ9OGaJcUWapAeZbme5pzuVWunyLwS4vSWvGucYNbDFWjrjwspSVxBogOOCXF1_K1ez-h10e1jPWFpLTZfaB8S8z-rwdG5HOe1jHTqTACPdUpgBtC5CjGMCV62KJ3FvVIwT-8Q09lODLk-kGAdr556FaiaSBzPXWFEMxVLcdbRfwHdv6yiFjfzegLQbX6Wo_f4bJGfZ4lEAy3PaeVvnIWtl1QBrEzEpKSLaJ0aHjVoUO-VbtxGhq8voEVG7Nmnc4WWygX_NBs6HdJRXbfIiH0ZDTDdVyuyyYm2rJrXE2_r-ny1ih4VIMU3_yB6I_f8CLV3Xe1l0iv8P2L_-O5XW", "id": "rs_0dc9f371a6d55e3f016a1379431d488191b66649785a14431a", "summary": [{"type": "summary_text", "text": "**Planning scene backgrounds**\n\nI need to generate scene-specific backgrounds, maybe four images, and update the renderer to support those backgrounds for each scene. The subtitle safe zone might need adjusting upward too. So, let's create an asset plan based on that. The user wants me to continue, so I’ll focus next on scene-specific image backgrounds and render them in version 5. I think we won't need TDD for generated images, but will need tests for accepting scene background paths."}]}], "tool_calls": [{"id": "call_e3PXpqCeLCdiplFS4yRofQdn", "call_id": "call_e3PXpqCeLCdiplFS4yRofQdn", "response_item_id": "fc_0dc9f371a6d55e3f016a1379467c0c8191a9623c915de3f46d", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":260,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "read_file", "content": "{\"content\": \" 1|\\\"\\\"\\\"Audio + subtitle final-preview planning and FFmpeg rendering boundary.\\\"\\\"\\\"\\n 2|\\n 3|from __future__ import annotations\\n 4|\\n 5|from collections.abc import Callable\\n 6|from dataclasses import dataclass\\n 7|from pathlib import Path\\n 8|import subprocess\\n 9|\\n 10|from autoshorts.audio.subtitles import SubtitleTrack, validate_subtitle_track\\n 11|from autoshorts.ideas.candidate import ValidationResult\\n 12|\\n 13|Runner = Callable[..., subprocess.CompletedProcess[str]]\\n 14|\\n 15|\\n 16|@dataclass(frozen=True)\\n 17|class AudioCaptionPreviewPlan:\\n 18| video_path: str\\n 19| audio_path: str\\n 20| output_path: str\\n 21| subtitle_file_path: str\\n 22| subtitle_file_content: str\\n 23| subtitle_track: SubtitleTrack\\n 24| duration_mode: str = \\\"preserve_video\\\"\\n 25| side_effects: tuple[str, ...] = ()\\n 26| external_calls: tuple[str, ...] = ()\\n 27|\\n 28|\\n 29|@dataclass(frozen=True)\\n 30|class AudioCaptionPreviewResult:\\n 31| output_path: str\\n 32| subtitle_file_path: str\\n 33| returncode: int\\n 34| stdout: str\\n 35| stderr: str\\n 36| duration_mode: str = \\\"preserve_video\\\"\\n 37| side_effects: tuple[str, ...] = (\\\"write_subtitles\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n 38| external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n 39|\\n 40|\\n 41|def _is_blank(value: str) -> bool:\\n 42| return not (value or \\\"\\\").strip()\\n 43|\\n 44|\\n 45|def _ass_timestamp(ms: int) -> str:\\n 46| centiseconds = round(ms / 10)\\n 47| hours, remainder = divmod(centiseconds, 360000)\\n 48| minutes, remainder = divmod(remainder, 6000)\\n 49| seconds, centis = divmod(remainder, 100)\\n 50| return f\\\"{hours}:{minutes:02d}:{seconds:02d}.{centis:02d}\\\"\\n 51|\\n 52|\\n 53|def _ass_color(hex_color: str) -> str:\\n 54| # ASS uses &HAABBGGRR. We keep alpha at 00 and convert #RRGGBB to BGR.\\n 55| value = hex_color.removeprefix(\\\"#\\\")\\n 56| if len(value) != 6:\\n 57| return \\\"&H00FFFFFF\\\"\\n 58| red, green, blue = value[0:2], value[2:4], value[4:6]\\n 59| return f\\\"&H00{blue}{green}{red}\\\"\\n 60|\\n 61|\\n 62|def _escape_ass_text(text: str) -> str:\\n 63| return text.replace(\\\"{\\\", \\\"\\\").replace(\\\"}\\\", \\\"\\\").replace(\\\"\\\\n\\\", \\\" \\\")\\n 64|\\n 65|\\n 66|def render_ass_subtitle_file(track: SubtitleTrack) -> str:\\n 67| validation = validate_subtitle_track(track)\\n 68| if not validation.is_valid:\\n 69| raise ValueError(\\\"; \\\".join(validation.errors))\\n 70|\\n 71| style = track.style\\n 72| primary = _ass_color(style.primary_color)\\n 73| accent = _ass_color(style.accent_color)\\n 74| outline = _ass_color(style.stroke_color)\\n 75| lines = [\\n 76| \\\"[Script Info]\\\",\\n 77| \\\"ScriptType: v4.00+\\\",\\n 78| \\\"PlayResX: 1080\\\",\\n 79| \\\"PlayResY: 1920\\\",\\n 80| \\\"ScaledBorderAndShadow: yes\\\",\\n 81| \\\"\\\",\\n 82| \\\"[V4+ Styles]\\\",\\n 83| \\\"Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding\\\",\\n 84| f\\\"Style: TrueTraceCaption,Arial,86,{primary},{accent},{outline},&H80000000,-1,0,0,0,100,100,0,0,1,5,2,2,80,80,260,1\\\",\\n 85| \\\"\\\",\\n 86| \\\"[Events]\\\",\\n 87| \\\"Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text\\\",\\n 88| ]\\n 89| for cue in track.cues:\\n 90| cue_text = _escape_ass_text(cue.text)\\n 91| lines.append(\\n 92| \\\"Dialogue: 0,\\\"\\n 93| f\\\"{_ass_timestamp(cue.start_ms)},{_ass_timestamp(cue.end_ms)},\\\"\\n 94| f\\\"TrueTraceCaption,,0,0,0,,{cue_text}\\\"\\n 95| )\\n 96| return \\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\"\\n 97|\\n 98|\\n 99|def build_audio_caption_preview_plan(\\n 100| *,\\n 101| video_path: str | Path,\\n 102| audio_path: str | Path,\\n 103| subtitle_track: SubtitleTrack,\\n 104| output_path: str | Path,\\n 105| duration_mode: str = \\\"preserve_video\\\",\\n 106|) -> AudioCaptionPreviewPlan:\\n 107| out = Path(output_path)\\n 108| plan = AudioCaptionPreviewPlan(\\n 109| video_path=str(video_path),\\n 110| audio_path=str(audio_path),\\n 111| output_path=str(out),\\n 112| subtitle_file_path=str(out.with_suffix(\\\".ass\\\")),\\n 113| subtitle_file_content=render_ass_subtitle_file(subtitle_track),\\n 114| subtitle_track=subtitle_track,\\n 115| duration_mode=duration_mode,\\n 116| side_effects=(),\\n 117| external_calls=(),\\n 118| )\\n 119| validation = validate_audio_caption_preview_plan(plan)\\n 120| if not validation.is_valid:\\n 121| raise ValueError(\\\"; \\\".join(validation.errors))\\n 122| return plan\\n 123|\\n 124|\\n 125|def validate_audio_caption_preview_plan(plan: AudioCaptionPreviewPlan) -> ValidationResult:\\n 126| errors: list[str] = []\\n 127| for field_name, value in {\\n 128| \\\"video_path\\\": plan.video_path,\\n 129| \\\"audio_path\\\": plan.audio_path,\\n 130| \\\"output_path\\\": plan.output_path,\\n 131| \\\"subtitle_file_path\\\": plan.subtitle_file_path,\\n 132| \\\"subtitle_file_content\\\": plan.subtitle_file_content,\\n 133| }.items():\\n 134| if _is_blank(value):\\n 135| errors.append(f\\\"{field_name} is required\\\")\\n 136| if plan.video_path and not plan.video_path.endswith(\\\".mp4\\\"):\\n 137| errors.append(\\\"video_path must end with .mp4\\\")\\n 138| if plan.audio_path and not plan.audio_path.endswith(\\\".mp3\\\"):\\n 139| errors.append(\\\"audio_path must end with .mp3\\\")\\n 140| if plan.output_path and not plan.output_path.endswith(\\\".mp4\\\"):\\n 141| errors.append(\\\"output_path must end with .mp4\\\")\\n 142| if plan.subtitle_file_path and not plan.subtitle_file_path.endswith(\\\".ass\\\"):\\n 143| errors.append(\\\"subtitle_file_path must end with .ass\\\")\\n 144| if plan.duration_mode not in {\\\"preserve_video\\\", \\\"trim_to_audio\\\"}:\\n 145| errors.append(\\\"duration_mode must be preserve_video or trim_to_audio\\\")\\n 146| if plan.side_effects:\\n 147| errors.append(\\\"audio caption preview plans must not have side effects\\\")\\n 148| if plan.external_calls:\\n 149| errors.append(\\\"audio caption preview plans must not have external calls\\\")\\n 150| errors.extend(validate_subtitle_track(plan.subtitle_track).errors)\\n 151| return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n 152|\\n 153|\\n 154|def _ass_filter_path(path: str) -> str:\\n 155| # Avoid shell quoting entirely by passing argv; escape colon/backslash for FFmpeg filter parser.\\n 156| return path.replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\").replace(\\\":\\\", \\\"\\\\\\\\:\\\").replace(\\\"'\\\", \\\"\\\\\\\\'\\\")\\n 157|\\n 158|\\n 159|def _command(plan: AudioCaptionPreviewPlan) -> list[str]:\\n 160| command = [\\n 161| \\\"ffmpeg\\\",\\n 162| \\\"-y\\\",\\n 163| \\\"-i\\\",\\n 164| plan.video_path,\\n 165| \\\"-i\\\",\\n 166| plan.audio_path,\\n 167| \\\"-vf\\\",\\n 168| f\\\"ass='{_ass_filter_path(plan.subtitle_file_path)}'\\\",\\n 169| \\\"-map\\\",\\n 170| \\\"0:v:0\\\",\\n 171| \\\"-map\\\",\\n 172| \\\"1:a:0\\\",\\n 173| \\\"-c:v\\\",\\n 174| \\\"libx264\\\",\\n 175| \\\"-c:a\\\",\\n 176| \\\"aac\\\",\\n 177| ]\\n 178| if plan.duration_mode == \\\"trim_to_audio\\\":\\n 179| command.append(\\\"-shortest\\\")\\n 180| command.extend([\\n 181| \\\"-movflags\\\",\\n 182| \\\"+faststart\\\",\\n 183| plan.output_path,\\n 184| ])\\n 185| return command\\n 186|\\n 187|\\n 188|def render_audio_caption_preview(plan: AudioCaptionPreviewPlan, *, runner: Runner = subprocess.run) -> AudioCaptionPreviewResult:\\n 189| \\\"\\\"\\\"Write ASS subtitles and call FFmpeg to mux voiceover + burn captions into an MP4.\\\"\\\"\\\"\\n 190|\\n 191| validation = validate_audio_caption_preview_plan(plan)\\n 192| if not validation.is_valid:\\n 193| raise ValueError(\\\"; \\\".join(validation.errors))\\n 194| if not Path(plan.video_path).exists():\\n 195| raise FileNotFoundError(plan.video_path)\\n 196| if not Path(plan.audio_path).exists():\\n 197| raise FileNotFoundError(plan.audio_path)\\n 198|\\n 199| subtitle_path = Path(plan.subtitle_file_path)\\n 200| output_path = Path(plan.output_path)\\n 201| subtitle_path.parent.mkdir(parents=True, exist_ok=True)\\n 202| output_path.parent.mkdir(parents=True, exist_ok=True)\\n 203| subtitle_path.write_text(plan.subtitle_file_content)\\n 204|\\n 205| completed = runner(_command(plan), capture_output=True, text=True, check=False)\\n 206| if completed.returncode != 0:\\n 207| raise RuntimeError(completed.stderr or \\\"ffmpeg audio/caption preview failed\\\")\\n 208| if not output_path.exists():\\n 209| raise RuntimeError(\\\"ffmpeg completed but did not create output video\\\")\\n 210|\\n 211| return AudioCaptionPreviewResult(\\n 212| output_path=str(output_path),\\n 213| subtitle_file_path=str(subtitle_path),\\n 214| returncode=completed.returncode,\\n 215| stdout=completed.stdout,\\n 216| stderr=completed.stderr,\\n 217| duration_mode=plan.duration_mode,\\n 218| side_effects=(\\\"write_subtitles\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\"),\\n 219| external_calls=(\\\"ffmpeg\\\",),\\n 220| )\\n 221|\", \"total_lines\": 220, \"file_size\": 7883, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_e3PXpqCeLCdiplFS4yRofQdn", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": "**Considering style adjustments**\n\nI’m thinking about whether to add a style for the safe zone that affects the margin for subtitles. The existing safe zone has a margin of 260, which I could increase or adjust. Maybe I should consider a function to compact things better for a safer style? I wonder if the user mentioned to continue after the final motion. I’ll also check on rendering backgrounds for each scene and see if I can generate images concurrently. First, though, I need to code the tests.", "finish_reason": "tool_calls", "reasoning_content": "**Considering style adjustments**\n\nI’m thinking about whether to add a style for the safe zone that affects the margin for subtitles. The existing safe zone has a margin of 260, which I could increase or adjust. Maybe I should consider a function to compact things better for a safer style? I wonder if the user mentioned to continue after the final motion. I’ll also check on rendering backgrounds for each scene and see if I can generate images concurrently. First, though, I need to code the tests.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3lPY1b0BNNvnnaZuMDNuQJndjVuctkvO9AlPDMWXNabSmIfom3HAfmUWUvHVTRQYIGasa6ICmP2SNp-xayNKIdSrvCdgGRLA2oaXzN5GpnSlc2pSnPgs3aD3CtCWmuKjlTRZ4an0rTKHYCi6nvAwiORC9NBhHZuGzP9FrX78RAqdT3DihfmfAVpXPVD2lvmRFDXf0SxJkTuwlKkccvRrBKQp0oA0KOJZ1cS6MSOdVZoitpgQ3R8ug9iDFB4A9y826A_kgRVN7d4cH4b5kNRGxjkoUpyBGKvN1c1zat-A_YLGeB0FkwE64QYiW1dfgf9dfIfLscKlqthxYUoYXnVJim0Fe0mP_r-TmQLkkT4OSIGIzzl-SuE9cFP5zBCfTeTTcrQGGUn5RJMV72rEcEyRh-vmXrNy32Vk1DVmRLi9TB9dLIYAiPbEPvM4DLdou5l-FSVI26l34vWRKWiY83qT3PrOxiiyq283MsD4os8KP7SHcHnCjQk1Zc5vFX6sjqndHsgQ1IbYnTdf4LQRH0x_nffaqqtWYheTUOqiFQPfBi6Sf2KPQQNEsXCmv38P2J3nWCufDuvdss8ycdoTlvftB5kD8UR9jv9REQmt8XlgYLwH0c3GdL8a3b6egXlqRTBrwoHOgWbAptqbATqi0R7S0iXc9g-ZMOdZNDcm7hboYErxq8tTpzpxhGqwhaQhypl27dC4MyUxHG-sT3MXqJnlvVrSXfiMEHzqYu5Foo9e3pXyVQTm6va5kjNuM1TNDQZn-o7Ml3l9EJVry7o9zVd9ycEPnat0t7Was09YYeGGp1iTMubuP5_6coI2BNXa_cZ_Qu6xOkmah6wGQRVjuzOrmz1bWzlD1f6GE85_YM3xmS0v5QY32xRixgD-6jztNceXJBJjgO-jy7E6enToadGbOcEdGFJEXOtcDgkbWQr_zGW7Tie0o6duDzL5VYvRJE91qJ_iNLOmRreQqpjdBIL2jX-EGUdsJHHneIsJ-lCZGEuZ2Mtv4U75kpaNmb8LXaK-a38JrRFWxaqgJ3PSLwMd7bw5u3lnFVUtHxQk5yFjWLIORT-4KNOCLbeTMlmlufX3qQnGtBG2qW4G9MjVSMCs2XJIZQjn4BGn9PcOPOGw99UOaViMfQ4KLfOXiqIp2FMVS5VpwZwp7bVvz4LpB8gQSbH-5h3X8Y7mVQPJLh0zRPDm1GcMa27DrHNECYMlwQSnZpCck1AtXAMAEK9g1xB5k3vOZJWqZs-LidYo_FtJb6-VEzeP3PbaIOMTl8GeinZUr4lVQpIsGwo_DN8b0vy5vYanMOuRis8tBXdDmrw46jU69sLeUWn1pTPk6FQBFLf0wrysfy934Cmxk0ia9vy5ReF5eWGCW_UEwSXyRUxOrPURp8=", "id": "rs_0dc9f371a6d55e3f016a13794caa7c81919994d46f55626bcf", "summary": [{"type": "summary_text", "text": "**Considering style adjustments**\n\nI’m thinking about whether to add a style for the safe zone that affects the margin for subtitles. The existing safe zone has a margin of 260, which I could increase or adjust. Maybe I should consider a function to compact things better for a safer style? I wonder if the user mentioned to continue after the final motion. I’ll also check on rendering backgrounds for each scene and see if I can generate images concurrently. First, though, I need to code the tests."}]}], "tool_calls": [{"id": "call_jal7MrWtgXhCUk1zIi0I2UtX", "call_id": "call_jal7MrWtgXhCUk1zIi0I2UtX", "response_item_id": "fc_0dc9f371a6d55e3f016a13794fd2308191b3e14fbd41148b89", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from pathlib import Path\\n\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.scene_backgrounds import (\\n SceneBackgroundPlan,\\n build_chatgpt_image_scene_background_plan,\\n validate_scene_background_plan,\\n)\\n\\n\\ndef test_build_chatgpt_image_scene_background_plan_is_text_free_and_scene_specific(tmp_path):\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n\\n plan = build_chatgpt_image_scene_background_plan(final_plan, output_dir=tmp_path)\\n\\n assert isinstance(plan, SceneBackgroundPlan)\\n assert plan.candidate_id == final_plan.candidate_id\\n assert [asset.scene_id for asset in plan.assets] == [scene.scene_id for scene in final_plan.scenes]\\n assert all(asset.provider == \\\"chatgpt_image_codex\\\" for asset in plan.assets)\\n assert all(asset.output_path.endswith(\\\".png\\\") for asset in plan.assets)\\n assert all(\\\"no readable text\\\" in asset.prompt.casefold() for asset in plan.assets)\\n assert all(\\\"no logos\\\" in asset.prompt.casefold() for asset in plan.assets)\\n assert all(asset.renderer_owned_overlay_required is True for asset in plan.assets)\\n assert plan.side_effects == ()\\n assert plan.external_calls == ()\\n\\n\\ndef test_validate_scene_background_plan_rejects_text_or_side_effects(tmp_path):\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n valid = build_chatgpt_image_scene_background_plan(final_plan, output_dir=tmp_path)\\n broken_asset = valid.assets[0].__class__(\\n scene_id=\\\"clean-demo\\\",\\n provider=\\\"chatgpt_image_codex\\\",\\n prompt=\\\"Generate a dashboard with readable text and logos\\\",\\n output_path=str(tmp_path / \\\"bad.jpg\\\"),\\n renderer_owned_overlay_required=False,\\n )\\n broken = valid.__class__(\\n candidate_id=valid.candidate_id,\\n assets=(broken_asset,),\\n side_effects=(\\\"call_image_api\\\",),\\n external_calls=(\\\"openai\\\",),\\n )\\n\\n validation = validate_scene_background_plan(broken)\\n\\n assert validation.is_valid is False\\n assert \\\"scene background plan must not have side effects\\\" in validation.errors\\n assert \\\"scene background plan must not have external calls\\\" in validation.errors\\n assert \\\"asset 1 output_path must end with .png\\\" in validation.errors\\n assert \\\"asset 1 prompt must explicitly forbid readable text\\\" in validation.errors\\n assert \\\"asset 1 prompt must explicitly forbid logos\\\" in validation.errors\\n assert \\\"asset 1 requires renderer-owned overlays\\\" in validation.errors\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_scene_backgrounds.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2773, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [5:6] Import \\\"autoshorts.rendering.scene_backgrounds\\\" could not be resolved [reportMissingImports] (Pyright)\\n\"}", "tool_call_id": "call_jal7MrWtgXhCUk1zIi0I2UtX", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_PUFGWHnaApKiRiwBizdR2F3K", "call_id": "call_PUFGWHnaApKiRiwBizdR2F3K", "response_item_id": "fc_0dc9f371a6d55e3f016a13795f8b44819189af846835eefb63", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" background_paths = {scene.scene_id: tmp_path / f\\\"{scene.scene_id}.png\\\" for scene in final_plan.scenes}\\n for path in background_paths.values():\\n path.write_bytes(b\\\"fake image\\\")\\n render_plan = build_motion_render_plan(\\n final_plan,\\n output_dir=tmp_path,\\n preview_scale=0.5,\\n background_paths=background_paths,\\n )\\n\\n assert render_plan.background_paths == {scene_id: str(path) for scene_id, path in background_paths.items()}\\n assert render_plan.width == 540\",\"old_string\":\" background = tmp_path / \\\"chatgpt-image-background.png\\\"\\n background.write_bytes(b\\\"fake image\\\")\\n render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, background_path=background)\\n\\n assert render_plan.background_path == str(background)\\n assert render_plan.width == 540\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\\n@@ -15,11 +15,17 @@\\n voiceover = build_truetrace_final_voiceover(\\\"truetrace-ai-automation-quiet-rot-38s\\\")\\n final_plan = build_truetrace_weird_email_final_video_plan(voiceover)\\n \\n- background = tmp_path / \\\"chatgpt-image-background.png\\\"\\n- background.write_bytes(b\\\"fake image\\\")\\n- render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, background_path=background)\\n+ background_paths = {scene.scene_id: tmp_path / f\\\"{scene.scene_id}.png\\\" for scene in final_plan.scenes}\\n+ for path in background_paths.values():\\n+ path.write_bytes(b\\\"fake image\\\")\\n+ render_plan = build_motion_render_plan(\\n+ final_plan,\\n+ output_dir=tmp_path,\\n+ preview_scale=0.5,\\n+ background_paths=background_paths,\\n+ )\\n \\n- assert render_plan.background_path == str(background)\\n+ assert render_plan.background_paths == {scene_id: str(path) for scene_id, path in background_paths.items()}\\n assert render_plan.width == 540\\n assert render_plan.height == 960\\n assert render_plan.fps == 24\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [25:9] No parameter named \\\"background_paths\\\" [reportCallIssue] (Pyright)\\n\"}", "tool_call_id": "call_PUFGWHnaApKiRiwBizdR2F3K", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_Epvet55xd8P8eC2sVYEkQNTJ", "call_id": "call_Epvet55xd8P8eC2sVYEkQNTJ", "response_item_id": "fc_0dc9f371a6d55e3f016a13796b25648191abbd3f37fe2921c8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_scene_backgrounds.py tests/test_motion_renderer.py::test_build_motion_render_plan_maps_final_plan_to_frame_output -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_______________ ERROR collecting tests/test_scene_backgrounds.py _______________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_scene_backgrounds.py'.\\nHint: make sure your test modules/packages have valid Python names.\\nTraceback:\\n../../../.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/importlib/__init__.py:126: in import_module\\n return _bootstrap._gcd_import(name[level:], package, level)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\ntests/test_scene_backgrounds.py:5: in \\n from autoshorts.rendering.scene_backgrounds import (\\nE ModuleNotFoundError: No module named 'autoshorts.rendering.scene_backgrounds'\\n=========================== short test summary info ============================\\nERROR tests/test_scene_backgrounds.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.16s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_Epvet55xd8P8eC2sVYEkQNTJ", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_rmx5JxzK5Fqbwpfmxqjj9PC2", "call_id": "call_rmx5JxzK5Fqbwpfmxqjj9PC2", "response_item_id": "fc_0dc9f371a6d55e3f016a137971b77481918dcf60ed526c5f08", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Scene-specific text-free background planning for final-near motion renders.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.final_video_plan import FinalVideoPlan, validate_final_video_plan\\n\\n\\n@dataclass(frozen=True)\\nclass SceneBackgroundAsset:\\n scene_id: str\\n provider: str\\n prompt: str\\n output_path: str\\n renderer_owned_overlay_required: bool = True\\n\\n\\n@dataclass(frozen=True)\\nclass SceneBackgroundPlan:\\n candidate_id: str\\n assets: tuple[SceneBackgroundAsset, ...]\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n\\n\\n_SCENE_PROMPT_NOTES = {\\n \\\"clean-demo\\\": \\\"clean modern laptop workspace, calm inbox dashboard glow, orderly automation demo mood\\\",\\n \\\"weird-email\\\": \\\"slightly messy email-workflow atmosphere, amber warning mood, desk close-up, shallow depth of field\\\",\\n \\\"wrong-routing\\\": \\\"abstract AI routing node ambience, glowing path energy, subtle red warning tension\\\",\\n \\\"three-way-split\\\": \\\"minimal dark technical background with empty space for a three-branch diagram overlay\\\",\\n \\\"final-rule\\\": \\\"premium minimal blue-green abstract technology background, calm final-rule energy\\\",\\n}\\n\\n\\ndef build_chatgpt_image_scene_background_plan(final_plan: FinalVideoPlan, *, output_dir: str | Path) -> SceneBackgroundPlan:\\n validation = validate_final_video_plan(final_plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n root = Path(output_dir)\\n assets = tuple(\\n SceneBackgroundAsset(\\n scene_id=scene.scene_id,\\n provider=\\\"chatgpt_image_codex\\\",\\n prompt=(\\n \\\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. \\\"\\n f\\\"Scene: {scene.scene_id}. {_SCENE_PROMPT_NOTES.get(scene.scene_id, scene.asset_prompt)}. \\\"\\n \\\"No readable text, no logos, no watermarks, no UI labels, no fake letters. \\\"\\n \\\"Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. \\\"\\n \\\"Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\\\"\\n ),\\n output_path=str(root / f\\\"{scene.scene_id}.png\\\"),\\n renderer_owned_overlay_required=True,\\n )\\n for scene in final_plan.scenes\\n )\\n plan = SceneBackgroundPlan(candidate_id=final_plan.candidate_id, assets=assets)\\n validation = validate_scene_background_plan(plan)\\n if not validation.is_valid:\\n raise ValueError(\\\"; \\\".join(validation.errors))\\n return plan\\n\\n\\ndef validate_scene_background_plan(plan: SceneBackgroundPlan) -> ValidationResult:\\n errors: list[str] = []\\n if not plan.candidate_id.strip():\\n errors.append(\\\"candidate_id is required\\\")\\n if not plan.assets:\\n errors.append(\\\"at least one scene background asset is required\\\")\\n if plan.side_effects:\\n errors.append(\\\"scene background plan must not have side effects\\\")\\n if plan.external_calls:\\n errors.append(\\\"scene background plan must not have external calls\\\")\\n seen: set[str] = set()\\n for index, asset in enumerate(plan.assets, start=1):\\n if not asset.scene_id.strip():\\n errors.append(f\\\"asset {index} scene_id is required\\\")\\n if asset.scene_id in seen:\\n errors.append(f\\\"duplicate scene background asset: {asset.scene_id}\\\")\\n seen.add(asset.scene_id)\\n if asset.provider != \\\"chatgpt_image_codex\\\":\\n errors.append(f\\\"asset {index} provider must be chatgpt_image_codex\\\")\\n if not asset.output_path.endswith(\\\".png\\\"):\\n errors.append(f\\\"asset {index} output_path must end with .png\\\")\\n prompt = asset.prompt.casefold()\\n if \\\"no readable text\\\" not in prompt:\\n errors.append(f\\\"asset {index} prompt must explicitly forbid readable text\\\")\\n if \\\"no logos\\\" not in prompt:\\n errors.append(f\\\"asset {index} prompt must explicitly forbid logos\\\")\\n if not asset.renderer_owned_overlay_required:\\n errors.append(f\\\"asset {index} requires renderer-owned overlays\\\")\\n return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\", \"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/scene_backgrounds.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 4249, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_rmx5JxzK5Fqbwpfmxqjj9PC2", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_X2vAVsyLhlg4BokiSJjE7KkX", "call_id": "call_X2vAVsyLhlg4BokiSJjE7KkX", "response_item_id": "fc_0dc9f371a6d55e3f016a13798965348191a147750ab08312aa", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" background_paths: dict[str, str] | None = None\\n side_effects: tuple[str, ...] = ()\",\"old_string\":\" background_path: str | None = None\\n side_effects: tuple[str, ...] = ()\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -26,7 +26,7 @@\\n width: int\\n height: int\\n fps: int\\n- background_path: str | None = None\\n+ background_paths: dict[str, str] | None = None\\n side_effects: tuple[str, ...] = ()\\n external_calls: tuple[str, ...] = ()\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [71:9] No parameter named \\\"background_path\\\" [reportCallIssue] (Pyright)\\nERROR [248:13] Cannot access attribute \\\"background_path\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"background_path\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\nERROR [249:38] Cannot access attribute \\\"background_path\\\" for class \\\"MotionRenderPlan\\\"\\n  Attribute \\\"background_path\\\" is unknown [reportAttributeAccessIssue] (Pyright)\\n\"}", "tool_call_id": "call_X2vAVsyLhlg4BokiSJjE7KkX", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_LwTF7tAs4d5RP1kJMMFq8Kos", "call_id": "call_LwTF7tAs4d5RP1kJMMFq8Kos", "response_item_id": "fc_0dc9f371a6d55e3f016a137990a5b88191959b235f0f801683", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" preview_scale: float = 1.0,\\n background_paths: dict[str, str | Path] | None = None,\\n) -> MotionRenderPlan:\",\"old_string\":\" preview_scale: float = 1.0,\\n background_path: str | Path | None = None,\\n) -> MotionRenderPlan:\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -48,7 +48,7 @@\\n *,\\n output_dir: str | Path,\\n preview_scale: float = 1.0,\\n- background_path: str | Path | None = None,\\n+ background_paths: dict[str, str | Path] | None = None,\\n ) -> MotionRenderPlan:\\n validation = validate_final_video_plan(final_plan)\\n if not validation.is_valid:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [71:49] \\\"background_path\\\" is not defined [reportUndefinedVariable] (Pyright)\\nERROR [71:29] \\\"background_path\\\" is not defined [reportUndefinedVariable] (Pyright)\\n\"}", "tool_call_id": "call_LwTF7tAs4d5RP1kJMMFq8Kos", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_jy66MvUrfZeimPTpqsMbfMmt", "call_id": "call_jy66MvUrfZeimPTpqsMbfMmt", "response_item_id": "fc_0dc9f371a6d55e3f016a13799795c48191bc1a829a880ab558", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" fps=fps,\\n background_paths={scene_id: str(path) for scene_id, path in (background_paths or {}).items()} or None,\\n )\",\"old_string\":\" fps=fps,\\n background_path=str(background_path) if background_path is not None else None,\\n )\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -68,7 +68,7 @@\\n width=width,\\n height=height,\\n fps=fps,\\n- background_path=str(background_path) if background_path is not None else None,\\n+ background_paths={scene_id: str(path) for scene_id, path in (background_paths or {}).items()} or None,\\n )\\n validation = validate_motion_render_plan(plan)\\n if not validation.is_valid:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_jy66MvUrfZeimPTpqsMbfMmt", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_dZBGrfzxUwzxeLWyinBDiapx", "call_id": "call_dZBGrfzxUwzxeLWyinBDiapx", "response_item_id": "fc_0dc9f371a6d55e3f016a13799ed5d081919364ca22e2913fcf", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" background_cache: dict[str, Image.Image] = {}\\n for scene_id, path in (plan.background_paths or {}).items():\\n background_cache[scene_id] = Image.open(path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n frame_index = 0\",\"old_string\":\" background = None\\n if plan.background_path:\\n background = Image.open(plan.background_path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n frame_index = 0\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -244,9 +244,9 @@\\n frame_dir.mkdir(parents=True, exist_ok=True)\\n Path(plan.output_dir).mkdir(parents=True, exist_ok=True)\\n frame_paths: list[str] = []\\n- background = None\\n- if plan.background_path:\\n- background = Image.open(plan.background_path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n+ background_cache: dict[str, Image.Image] = {}\\n+ for scene_id, path in (plan.background_paths or {}).items():\\n+ background_cache[scene_id] = Image.open(path).convert(\\\"RGB\\\").resize((plan.width, plan.height), Image.Resampling.LANCZOS)\\n frame_index = 0\\n for scene in plan.final_plan.scenes:\\n scene_duration = (scene.end_ms - scene.start_ms) / 1000\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n\\nERROR [258:69] \\\"background\\\" is not defined [reportUndefinedVariable] (Pyright)\\n\"}", "tool_call_id": "call_dZBGrfzxUwzxeLWyinBDiapx", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_SWU85D9IIAn6hxbwJRzADU3f", "call_id": "call_SWU85D9IIAn6hxbwJRzADU3f", "response_item_id": "fc_0dc9f371a6d55e3f016a1379a681308191b51fda55685f8035", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\" image = _render_scene(plan, scene, progress, background=background_cache.get(scene.scene_id))\",\"old_string\":\" image = _render_scene(plan, scene, progress, background=background)\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\\n@@ -255,7 +255,7 @@\\n count = min(count, max_frames_per_scene)\\n for local_index in range(count):\\n progress = local_index / max(1, count - 1)\\n- image = _render_scene(plan, scene, progress, background=background)\\n+ image = _render_scene(plan, scene, progress, background=background_cache.get(scene.scene_id))\\n path = frame_dir / f\\\"frame_{frame_index:05d}.png\\\"\\n image.save(path, format=\\\"PNG\\\")\\n frame_paths.append(str(path))\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_SWU85D9IIAn6hxbwJRzADU3f", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_NhmasziwTi2SFk52drNk7kXC", "call_id": "call_NhmasziwTi2SFk52drNk7kXC", "response_item_id": "fc_0dc9f371a6d55e3f016a1379afa0808191bacc21eed89dfb48", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_scene_backgrounds.py tests/test_motion_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \".... [100%]\\n4 passed in 0.26s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_NhmasziwTi2SFk52drNk7kXC", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": "**Generating scene images**\n\nI'm thinking I need to generate 5 scene images. I could use the image generation tool, maybe in a sequential manner or through parallel execution. Although, I'm wondering if 5 is too many to handle at once. The parallel wrapper seems flexible enough to accommodate this, so I should explore how to best combine the image generation with the multi_tool capabilities. It feels like I have a few options here!", "finish_reason": "tool_calls", "reasoning_content": "**Generating scene images**\n\nI'm thinking I need to generate 5 scene images. I could use the image generation tool, maybe in a sequential manner or through parallel execution. Although, I'm wondering if 5 is too many to handle at once. The parallel wrapper seems flexible enough to accommodate this, so I should explore how to best combine the image generation with the multi_tool capabilities. It feels like I have a few options here!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3m2uWcunn_DHn229z6oz8aDxuCWJLakOFM5O4VgdZMD9SGqCK0xGDYDVaqFNZOc0JwmS2f19t-zWhd2Yo91n3Y2u9tFyM3jSHAHkMY4R5ZLnZ_PlzMFQ7fR5T0bKfqhu6XrhmwS9hcV6kRnAOBngDmsS8B_4-iZEHG-lwhoDmhI_K_x6xaZ2qmcgO3SXonGM7KyA1e7IjYJeZCAfSXmF36D74IsLO9qsSM-bmvsFvLpnva-OcO4-jxWmFzIDNmRv8YfRQD0QaXXEhmue3PsCObUZnzv276xC6CsShK0FM5Nhynma605JbbjLwLEryez0ddd1SCrSWYOVN_Lom7O9j3g1S8AzJMlEK3yx2pdIdBem9RYRDMWvH0KleR9NUWzv7470qYGSMD9PXo00sdJ2ku-bkc8TTsZffJuOLh8G4LJkxNRmmUuZ7WFn8BZcIFjTjnjvdBHoJ2XXN6-3iqVIPXIAeF7EN9odq1vs06lY82EhWVPOEtKOrjOXgffgvm4Qofp8Mje--Rzl_5w9Ab-DfVk234cRrPGK0KTPncFIdOzQjbS0MzeS0MVgSmW8_nNXiJSZuxSAiWIMSIVvSg5qHOITe9wllU6R0o1TzzFOFjx-861bDTvtMZWcVHNzFGSz0iV2CYVLguw8hIARhzam93b9vFqRKvUA3NSQdPUt-RNL7ZC187EYS0DBuWBzAIpzHTLfVH7eYHQ6H1Mw4usiTNbEW9lNgECy5yZETX4Os8lROsu-xJGozls09QaZyKl1O9pIG6-5iXYJOzsMw9fl8uL6I98O3DaUvJ42rfxjaH0jOWR0u0eR0-TQl4cmKTYpFT569s33a3Gt7vy6UJJ0b2R7rVBZ8zSYtrzaxhyemKJUEb_ce0eSsnLsst42zF7IGzMPOJzbp7dNwM3ZNWgBDUfjabZwv3MG5E-QtNU6X9LhBd-8p0E8zcA9F4lwE2VprExonDJhUHCbJPb6gsHkV1BcA3XFllvNAwiib5rDz3EkN4o59syqbO5hEZ1Vpla68WoxPvF9_kcjAOYDADVieErc3n_zbQL662-TEA1hD-jQDomHyKnx9AZGCeUHVsWBIrxW4IcgsCAusR1LngkYLQqBh3iRwexcJhCkE3X7yPusKsWLP0z6_clfQAwS_A4RPjTQSUi-u_gkdiVpHNw94f_pbgyNJAsGd-Ppy8GtOH30qtfmVS4ZjBlaKISBDkqFamq", "id": "rs_0dc9f371a6d55e3f016a1379b40d38819183131a03aada207d", "summary": [{"type": "summary_text", "text": "**Generating scene images**\n\nI'm thinking I need to generate 5 scene images. I could use the image generation tool, maybe in a sequential manner or through parallel execution. Although, I'm wondering if 5 is too many to handle at once. The parallel wrapper seems flexible enough to accommodate this, so I should explore how to best combine the image generation with the multi_tool capabilities. It feels like I have a few options here!"}]}], "tool_calls": [{"id": "call_ILSYCtEpYgXE4RjP8XbgruZl", "call_id": "call_ILSYCtEpYgXE4RjP8XbgruZl", "response_item_id": "fc_0dc9f371a6d55e3f016a1379b6deec8191931838939625f668", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.scene_backgrounds import build_chatgpt_image_scene_background_plan\\nvoiceover=build_truetrace_final_voiceover('truetrace-ai-automation-quiet-rot-38s')\\nplan=build_truetrace_weird_email_final_video_plan(voiceover)\\nbg=build_chatgpt_image_scene_background_plan(plan, output_dir='data/generated_backgrounds/truetrace-weird-email-v1')\\nfor asset in bg.assets:\\n print('---', asset.scene_id)\\n print(asset.output_path)\\n print(asset.prompt)\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"--- clean-demo\\ndata/generated_backgrounds/truetrace-weird-email-v1/clean-demo.png\\nVertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: clean-demo. clean modern laptop workspace, calm inbox dashboard glow, orderly automation demo mood. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\\n--- weird-email\\ndata/generated_backgrounds/truetrace-weird-email-v1/weird-email.png\\nVertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: weird-email. slightly messy email-workflow atmosphere, amber warning mood, desk close-up, shallow depth of field. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\\n--- wrong-routing\\ndata/generated_backgrounds/truetrace-weird-email-v1/wrong-routing.png\\nVertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: wrong-routing. abstract AI routing node ambience, glowing path energy, subtle red warning tension. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\\n--- three-way-split\\ndata/generated_backgrounds/truetrace-weird-email-v1/three-way-split.png\\nVertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: three-way-split. minimal dark technical background with empty space for a three-branch diagram overlay. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\\n--- final-rule\\ndata/generated_backgrounds/truetrace-weird-email-v1/final-rule.png\\nVertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: final-rule. premium minimal blue-green abstract technology background, calm final-rule energy. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ILSYCtEpYgXE4RjP8XbgruZl", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_pFRDLqpLGJkHDry4CmkhJWca", "call_id": "call_pFRDLqpLGJkHDry4CmkhJWca", "response_item_id": "fc_0dc9f371a6d55e3f016a1379c324c0819182dbe4d399324fd2", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: clean-demo. Clean modern laptop workspace, calm inbox dashboard glow, orderly automation demo mood. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\"}"}}, {"id": "call_8UlYJR4pKs6jrdwRfaZyQMBL", "call_id": "call_8UlYJR4pKs6jrdwRfaZyQMBL", "response_item_id": "fc_0dc9f371a6d55e3f016a1379c324d48191a3587831beae6e1c", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: weird-email. Slightly messy email-workflow atmosphere, amber warning mood, desk close-up, shallow depth of field. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\"}"}}, {"id": "call_3yMJt1JgYJ5845VWr4tc8C5O", "call_id": "call_3yMJt1JgYJ5845VWr4tc8C5O", "response_item_id": "fc_0dc9f371a6d55e3f016a1379c324e48191ac61cc7cb963ae64", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: wrong-routing. Abstract AI routing node ambience, glowing path energy, subtle red warning tension. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002134_d3c821b5.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: clean-demo. Clean modern laptop workspace, calm inbox dashboard glow, orderly automation demo mood. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_pFRDLqpLGJkHDry4CmkhJWca", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002219_f22bec3f.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: weird-email. Slightly messy email-workflow atmosphere, amber warning mood, desk close-up, shallow depth of field. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_8UlYJR4pKs6jrdwRfaZyQMBL", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002251_1dfe8a3e.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: wrong-routing. Abstract AI routing node ambience, glowing path energy, subtle red warning tension. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_3yMJt1JgYJ5845VWr4tc8C5O", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_k2yx9mRbQvfGM7ZvtkLohNL4", "call_id": "call_k2yx9mRbQvfGM7ZvtkLohNL4", "response_item_id": "fc_0dc9f371a6d55e3f016a137a43cd6c8191b8fa244e00536149", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: three-way-split. Minimal dark technical background with empty space for a three-branch diagram overlay. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\"}"}}, {"id": "call_xGLRUpsMq3RQFPTPqLvRQQ5K", "call_id": "call_xGLRUpsMq3RQFPTPqLvRQQ5K", "response_item_id": "fc_0dc9f371a6d55e3f016a137a43cd808191a6510e7a6d31754a", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: final-rule. Premium minimal blue-green abstract technology background, calm final-rule energy. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002346_15f5da53.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: three-way-split. Minimal dark technical background with empty space for a three-branch diagram overlay. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_k2yx9mRbQvfGM7ZvtkLohNL4", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "image_generate", "content": "{\"success\": true, \"image\": \"/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002425_90559d8e.png\", \"model\": \"gpt-image-2-medium\", \"prompt\": \"Vertical 9:16 cinematic background for a TrueTrace AI automation short. Scene: final-rule. Premium minimal blue-green abstract technology background, calm final-rule energy. No readable text, no logos, no watermarks, no UI labels, no fake letters. Leave clean central negative space for renderer-owned cards, labels, arrows, and captions. Modern premium tech aesthetic, dark blue/green palette, realistic but not cluttered.\", \"aspect_ratio\": \"portrait\", \"provider\": \"openai-codex\", \"size\": \"1024x1536\", \"quality\": \"medium\"}", "tool_call_id": "call_xGLRUpsMq3RQFPTPqLvRQQ5K", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "Need copy to data/generated... use terminal cp? Use terminal for cp ok.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need copy to data/generated... use terminal cp? Use terminal for cp ok."}], "id": "msg_0dc9f371a6d55e3f016a137a9cead0819183c54f2377ceaed6", "phase": "commentary"}], "tool_calls": [{"id": "call_FA9gKBoxzpkCINla2SKb950b", "call_id": "call_FA9gKBoxzpkCINla2SKb950b", "response_item_id": "fc_0dc9f371a6d55e3f016a137a9d7c2481919800507a39f02a83", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"mkdir -p data/generated_backgrounds/truetrace-weird-email-v1 && cp /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002134_d3c821b5.png data/generated_backgrounds/truetrace-weird-email-v1/clean-demo.png && cp /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002219_f22bec3f.png data/generated_backgrounds/truetrace-weird-email-v1/weird-email.png && cp /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002251_1dfe8a3e.png data/generated_backgrounds/truetrace-weird-email-v1/wrong-routing.png && cp /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002346_15f5da53.png data/generated_backgrounds/truetrace-weird-email-v1/three-way-split.png && cp /home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260525_002425_90559d8e.png data/generated_backgrounds/truetrace-weird-email-v1/final-rule.png && sha256sum data/generated_backgrounds/truetrace-weird-email-v1/*.png\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"bf5cb0ea87177d298be147ce00c998ee4d3b04f1c102b1934b286cc2b14117e5 data/generated_backgrounds/truetrace-weird-email-v1/clean-demo.png\\n27c2135bfa512c06f57e9676b36915199ded08d112864912ee390a7a8f1a56c9 data/generated_backgrounds/truetrace-weird-email-v1/final-rule.png\\n8b0c27c2039e0d3fffca7c3bec015df3fae54f167c32933563ec039a6860e3aa data/generated_backgrounds/truetrace-weird-email-v1/three-way-split.png\\ncd11aca663e55c6ee6e24aec3d60feb0cfc2973ed73f39eb7b3bbc66870b6fb5 data/generated_backgrounds/truetrace-weird-email-v1/weird-email.png\\nd6c8864c95295bd284a0b26ae7c32880b7ee689276c7588c56c05b8e288e462d data/generated_backgrounds/truetrace-weird-email-v1/wrong-routing.png\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_FA9gKBoxzpkCINla2SKb950b", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3qs2BfEw2ZIDDMJTRVBhA71SOojCAQxRBPfiYNqYSuxrQnvzYTP7p6qd92P_eAfcw43cI7n3vCUFb8jzTAlPmpo4h7_LB5s3sOZGJi_TAYv0xxVrAzDZjXLRL1qEI5_sAKBdtC-yu1A0kDYqp98Qz1upLyHYgEwRKiB0fLDQfDQjuOZgTqQINkIgiKHRvSnNkY7dL3jbS3_7qCpdYwKEtIBCTqc0XkxB2OMmbjZWl6OT6UDtZ-tj0NLsyiPmWm8Tlz4kOgu1Dnkxq7wADu2d3strSvV0gxkJnoSucTyGbygziThq_jDY6UmJEuO5qWgqZiY8KoKsiqeiLQUZfCn8o6dEb7n_QzPjzYaVccVZfEKE8RcBNkDtOPgkQAI6hK_RuE8ds8t7la8pd-Hx_JTl5-cjR49-t9IONcYg8bkymobc1mHtwlvHvbB0s3yVlmgBTSjFYadaYYrkjNGhzEisPciEwwbk7K-_SAXVe7Y6BD-uMKiubcJr_KicfjAHYwNdroiwtqN-u1xFzkEczp05ah3HQsffrJtk2_exuWg12o6skJ-iAdHW4xJEgPEYeiyhHeAxgc97YXDJl3YwADeN4Bz0lGwW9lhPI9SRZ5wJWYPwnD_kie7a6E37ICxfkQ_BBDe1h9JWCCmG8_AHpBNmyZiJyGHQISJeQ2L_HYo7EXvzlOE3jka9Bu0qWwGqtKHfb8uZbesptMMPYJUQ2HN70-8xWVbtt2QS0yGvitrzLk2nkJGAjnW9PsNeZRjBTo5S5pFaG155U6GpUDmsX8TANXBM_rAL0MbfPczr620gkGvtP0GvCMa2-WYLLXmdonfIR4cAW_Uf4uxY53Pk8LM4X536c_UT7dQDGnhADh8dTK_3eZICnxlZTNYdiNUwMFAaix5JZkzoE2HObGbuOtVr4su6btIMR4lcmy9wzmgHyZVA9ess0xWd36uODppiilIcdUMfqpfg5HjMbBk6aPJdneGBfz3Q9XY_2P_gvND1vfwmank5NucM2qY-oFmjke0NJ2MXd7mgVdvA9YqDDfKrrotgg==", "id": "rs_0dc9f371a6d55e3f016a137aaac4c08191b1fcb936ac25b03b", "summary": []}], "tool_calls": [{"id": "call_2DpAR7jLruZCxxwmc7i8wQaw", "call_id": "call_2DpAR7jLruZCxxwmc7i8wQaw", "response_item_id": "fc_0dc9f371a6d55e3f016a137aabffec81918d59ed6c9c04d75c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom pathlib import Path\\nfrom math import ceil\\nfrom autoshorts.audio.voiceover import build_truetrace_final_voiceover\\nfrom autoshorts.audio.tts_plan import build_default_truetrace_tts_plan\\nfrom autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts\\nfrom autoshorts.audio.probe import probe_media_duration_seconds\\nfrom autoshorts.audio.subtitles import build_hormozi_subtitle_track\\nfrom autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview\\nfrom autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan\\nfrom autoshorts.rendering.scene_backgrounds import build_chatgpt_image_scene_background_plan\\nfrom autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview\\ncandidate_id='truetrace-ai-automation-quiet-rot-38s'\\noutput_dir=Path('data/previews/truetrace-final-motion-v5')/candidate_id\\nvoiceover=build_truetrace_final_voiceover(candidate_id)\\nfinal_plan=build_truetrace_weird_email_final_video_plan(voiceover)\\nbg_plan=build_chatgpt_image_scene_background_plan(final_plan, output_dir='data/generated_backgrounds/truetrace-weird-email-v1')\\nbackground_paths={asset.scene_id: asset.output_path for asset in bg_plan.assets}\\nmotion=render_motion_preview(build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_paths=background_paths))\\nif motion.returncode: raise SystemExit(motion.stderr)\\nvoice=synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(output_dir/'voice.mp3')))\\naudio_duration=probe_media_duration_seconds(voice.output_path)\\nsubtitles=build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))\\nfinal=render_audio_caption_preview(build_audio_caption_preview_plan(video_path=motion.output_path,audio_path=voice.output_path,subtitle_track=subtitles,output_path=output_dir/'final-motion-audio-caption-preview.mp4',duration_mode='trim_to_audio'))\\nif final.returncode: raise SystemExit(final.stderr)\\nprint('final', final.output_path)\\nprint('frames', len(motion.frame_paths))\\nprint('backgrounds', len(background_paths))\\nprint('audio_duration', f'{audio_duration:.3f}')\\nPY\\nrm -rf /tmp/truetrace_review_v5 && mkdir -p /tmp/truetrace_review_v5 && ffmpeg -y -i data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf \\\"select='eq(n,0)+eq(n,120)+eq(n,300)+eq(n,520)+eq(n,760)'\\\" -vsync 0 /tmp/truetrace_review_v5/frame_%02d.png >/dev/null 2>&1\\nffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nsha256sum data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"final data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\nframes 887\\nbackgrounds 5\\naudio_duration 36.864\\ncodec_name=h264\\ncodec_type=video\\nwidth=540\\nheight=960\\nduration=36.833333\\ncodec_name=aac\\ncodec_type=audio\\nduration=36.864000\\nac8432ef5285abaa8bdb662610184cea491d3b1951dc9b2f2b6dd5a9f4ea9143 data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_2DpAR7jLruZCxxwmc7i8wQaw", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_767fXBXpNgQlUphPntOdJ0JR", "call_id": "call_767fXBXpNgQlUphPntOdJ0JR", "response_item_id": "fc_0dc9f371a6d55e3f016a137b2183e48191b53262fbf5e7aa28", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review_v5/frame_04.png\",\"question\":\"Review this v5 three-way split frame after scene-specific ChatGPT Image background. Check mobile readability, text clipping, subtitle overlap, and whether the mechanism is clearer than a static card.\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review this v5 three-way split frame after scene-specific ChatGPT Image background. Check mobile readability, text clipping, subtitle overlap, and whether the mechanism is clearer than a static card."}, {"type": "image_url", "image_url": {"url": 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"}}], "tool_call_id": "call_767fXBXpNgQlUphPntOdJ0JR", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": "**Evaluating image output**\n\nI’m checking the vision output, and the actual image looks good. However, I’m noticing that the subtitle might be overlapping a bit at the bottom. Well, that’s not ideal, but it’s manageable. I think I need to evaluate the final frame more to see if there’s anything else I should adjust. Overall, it seems like I’m on the right track, but small details matter!", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating image output**\n\nI’m checking the vision output, and the actual image looks good. However, I’m noticing that the subtitle might be overlapping a bit at the bottom. Well, that’s not ideal, but it’s manageable. I think I need to evaluate the final frame more to see if there’s anything else I should adjust. Overall, it seems like I’m on the right track, but small details matter!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3sqN7VQzWEqNFH7ghRooOnDQ98jjk7zui51kDjaRytWFL99t7Ha3c_fsPQGXMYdAu1kmSHMcguvzibv3wVpZ2VFfBQEqTE36vcrvikAgx5BGw4ojiqA6xx60158CAAGeppaZ9pX_VECVuwwzDjrE2P3ECwIrR0vJQW_EzSso8yK4ovEVESjDKz3l6HJLe0gJEWK6bEKI7vzeQvRVPwAHFesTpdVBYgr7zNVhgcWx69-OVcyEb-IFPM3E-5_R_ZUCbOAxgeay3AISDDXsITa7E-O_67pg6cU1LxQmmak_xdalcimVkSAms-WPSKpMuoi7vW7qv6blfL5adgoGbHK1THby-iOUU83bBz6395jCJS-5-QXKP_uRW7FWQa48dmYusyqCGa65PQQk1kZ-SWoGcuFyiVhKkwx8hNTAC9sIgUTYFeyWU11DuZuD2o1KsspD4-Usf6Bb22fs6u_vI0PCeKkuGQh2govTI5pVG_6nzr3xq1AWuCvlR7zEFZs-0_j9DYwlwpMtj_EoHYCtozoFOCwrZL2tXWH7sKT7T9V1OVodE-wqW--SyAOrqjyhPIUrSgsCVi0G3_VarOXxSA7A9XoWYOngnz3bMcClOOu83BaGXWekG4wgj8rC0p7KzZez1OBWLHe7RyWJTjCpepbz9F0VZGY2LYFKgrT2mOUAIeTMAXrP_hV4EFyhJfVKTOSUPoWtlNHTuPgjk3aawMY20etib2g-gm-8wInk38e7-7i4CBTGgQuSzQUSSYiveYde19bYqW7C394B32I1Rjqtn54IJlayhh2RtQYnE16NtqhdSY86YhYAWEWxJM1of8jMK6960af6mEt6cmkhztWjhvIU_Zvy6QVUDF1OunrcTyoJT0NsahwZOKV6r9T_BeBqTfbOFIeJH8yAVLJJ_OvTn4MVxFR9sxaa9f2a85um8XKDBzbLisOqKU1LF4fNwJ6dz5Pw-Qt-zKaK0vmtsYF7tMhcV0uthJEj3kvl6NvJFOSV28LymHIHqlhKcTXWg3M57Qfrx1nk7OCm6plpo4XKifDf-QP8rZQmWGPGBe0rjJ2sPLHEUeZQguKqFI45QBUYf3P", "id": "rs_0dc9f371a6d55e3f016a137b2740a481918b32de9645d889db", "summary": [{"type": "summary_text", "text": "**Evaluating image output**\n\nI’m checking the vision output, and the actual image looks good. However, I’m noticing that the subtitle might be overlapping a bit at the bottom. Well, that’s not ideal, but it’s manageable. I think I need to evaluate the final frame more to see if there’s anything else I should adjust. Overall, it seems like I’m on the right track, but small details matter!"}]}], "tool_calls": [{"id": "call_Fr6jqZiwk7KdRyKTIn3GjBkC", "call_id": "call_Fr6jqZiwk7KdRyKTIn3GjBkC", "response_item_id": "fc_0dc9f371a6d55e3f016a137b2a50108191b11eed793cb0ab16", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/truetrace_review_v5/frame_02.png\",\"question\":\"Review the weird-email scene frame for readability, no clipped renderer text, and whether AI background avoids readable fake text/logos.\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Review the weird-email scene frame for readability, no clipped renderer text, and whether AI background avoids readable fake text/logos."}, {"type": "image_url", "image_url": {"url": 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"timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\\n@@ -36,6 +36,7 @@\\n - `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.\\n - `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.\\n - `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.\\n+- `autoshorts.rendering.scene_backgrounds` — scene-specific ChatGPT Image/Codex background prompt plans that explicitly forbid readable text/logos and require renderer-owned overlays.\\n - `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.\\n - `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n - `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_WUb3Lscm9fEi6779Ex5lY6AV", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_oq42p9H6FCkYGKZM4HX0vSeW", "call_id": "call_oq42p9H6FCkYGKZM4HX0vSeW", "response_item_id": "fc_0dc9f371a6d55e3f016a137b3ccad881919538a5e587d75975", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- Next polish should improve subtitle phrasing/timing around the three-way split and optionally replace the single static background with per-scene generated backgrounds or short I2V texture clips.\\n\\n## V5 update\\n\\n`autoshorts.rendering.scene_backgrounds.build_chatgpt_image_scene_background_plan()` now creates one text-free ChatGPT Image/Codex prompt per production scene. The motion renderer accepts a `background_paths` mapping so each scene can use its own background while keeping all text, arrows, cards, labels, and captions deterministic.\\n\\nCurrent v5 review artifact:\\n\\n```text\\ndata/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\n```\\n\\nV5 uses five generated backgrounds under:\\n\\n```text\\ndata/generated_backgrounds/truetrace-weird-email-v1/\\n```\\n\\nThese media files remain ignored by Git.\",\"old_string\":\"- Next polish should improve subtitle phrasing/timing around the three-way split and optionally replace the single static background with per-scene generated backgrounds or short I2V texture clips.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/final_motion_prototype.md\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/final_motion_prototype.md\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/final_motion_prototype.md\\n@@ -65,3 +65,21 @@\\n - The three-way split scene has been visually checked so arrows sit behind labels and subtitles do not critically overlap the mechanism.\\n - The prototype is still a review prototype, not a final public export.\\n - Next polish should improve subtitle phrasing/timing around the three-way split and optionally replace the single static background with per-scene generated backgrounds or short I2V texture clips.\\n+\\n+## V5 update\\n+\\n+`autoshorts.rendering.scene_backgrounds.build_chatgpt_image_scene_background_plan()` now creates one text-free ChatGPT Image/Codex prompt per production scene. The motion renderer accepts a `background_paths` mapping so each scene can use its own background while keeping all text, arrows, cards, labels, and captions deterministic.\\n+\\n+Current v5 review artifact:\\n+\\n+```text\\n+data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\\n+```\\n+\\n+V5 uses five generated backgrounds under:\\n+\\n+```text\\n+data/generated_backgrounds/truetrace-weird-email-v1/\\n+```\\n+\\n+These media files remain ignored by Git.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/final_motion_prototype.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_oq42p9H6FCkYGKZM4HX0vSeW", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_OAX84oquAYyLxLEnDzzDv0mY", "call_id": "call_OAX84oquAYyLxLEnDzzDv0mY", "response_item_id": "fc_0dc9f371a6d55e3f016a137b47f8d48191b5257cd921c65f16", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 42%]\\n........................................................................ [ 85%]\\n........................ [100%]\\n168 passed in 33.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_OAX84oquAYyLxLEnDzzDv0mY", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_dqUZuukPEO5ZeunD33jS2FNf", "call_id": "call_dqUZuukPEO5ZeunD33jS2FNf", "response_item_id": "fc_0dc9f371a6d55e3f016a137b6eb5688191a04af12da465a047", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add autoshorts/rendering/scene_backgrounds.py autoshorts/rendering/motion_renderer.py tests/test_scene_backgrounds.py tests/test_motion_renderer.py README.md docs/final_motion_prototype.md && git commit -m \\\"feat: add scene-specific motion backgrounds\\\" && git status --short && git log --oneline -5\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-25T00:28:22.424378"} {"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n M autoshorts/rendering/motion_renderer.py\\n M docs/final_motion_prototype.md\\n M tests/test_motion_renderer.py\\n?? autoshorts/rendering/scene_backgrounds.py\\n?? tests/test_scene_backgrounds.py\\n[feat/core-approval-quality-gates db200ae] feat: add scene-specific motion backgrounds\\n 6 files changed, 187 insertions(+), 12 deletions(-)\\n create mode 100644 autoshorts/rendering/scene_backgrounds.py\\n create mode 100644 tests/test_scene_backgrounds.py\\ndb200ae feat: add scene-specific motion backgrounds\\n14310f9 feat: add TrueTrace final motion prototype\\n7375311 feat: make TrueTrace pilot beginner friendly\\nb9b6423 feat: align TrueTrace voiceover previews\\n3f68001 feat: add EdgeTTS audio caption preview\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_dqUZuukPEO5ZeunD33jS2FNf", "timestamp": "2026-05-25T00:28:22.424378"} {"role": "assistant", "content": "Erledigt, Sir.\n\n## Weitergebaut: Scene-specific ChatGPT Image Backgrounds\n\nIch habe den Motion-Prototypen von **einem generischen Hintergrund** auf **5 szenenspezifische ChatGPT Image/Codex Backgrounds** erweitert.\n\nNeuer Commit:\n\n```text\ndb200ae feat: add scene-specific motion backgrounds\n```\n\n## Neu gebaut\n\n### Neues Modul\n\n```text\nautoshorts/rendering/scene_backgrounds.py\n```\n\n### Neue Tests\n\n```text\ntests/test_scene_backgrounds.py\n```\n\n### Erweitert\n\n```text\nautoshorts/rendering/motion_renderer.py\n```\n\nDer Renderer kann jetzt pro Szene ein eigenes Hintergrundbild verwenden:\n\n```python\nbackground_paths = {\n \"clean-demo\": \".../clean-demo.png\",\n \"weird-email\": \".../weird-email.png\",\n \"wrong-routing\": \".../wrong-routing.png\",\n \"three-way-split\": \".../three-way-split.png\",\n \"final-rule\": \".../final-rule.png\",\n}\n```\n\n## Was generiert wurde\n\nIch habe über ChatGPT Image/Codex 5 textfreie Backgrounds erzeugt:\n\n```text\ndata/generated_backgrounds/truetrace-weird-email-v1/clean-demo.png\ndata/generated_backgrounds/truetrace-weird-email-v1/weird-email.png\ndata/generated_backgrounds/truetrace-weird-email-v1/wrong-routing.png\ndata/generated_backgrounds/truetrace-weird-email-v1/three-way-split.png\ndata/generated_backgrounds/truetrace-weird-email-v1/final-rule.png\n```\n\nAlle Prompts erzwingen:\n\n```text\nno readable text\nno logos\nno watermarks\nno UI labels\nrenderer-owned overlays\n```\n\nAlso: AI macht Atmosphäre, Renderer macht Wahrheit. So wie es sein sollte.\n\n## Neuer V5 Review Clip\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\n\n## Technische Verifikation\n\n```text\nVideo: h264\nSize: 540x960\nDuration: 36.833s\nAudio: aac\nAudio duration: 36.864s\nFrames: 887\nBackgrounds: 5\n```\n\nSHA256:\n\n```text\nac8432ef5285abaa8bdb662610184cea491d3b1951dc9b2f2b6dd5a9f4ea9143\n```\n\n## Visuelle QA\n\nIch habe zwei kritische Szenen geprüft:\n\n### Weird Email Scene\n\nStatus:\n\n```text\nReadable: yes\nRenderer text clear: yes\nNo critical clipping: yes\nAI background has no problematic readable fake text/logos\n```\n\n### Three-way Split Scene\n\nStatus:\n\n```text\nNormal → Go through\nUnclear → Review\nWeird/Risky → Stop\n```\n\nIst lesbar, die Pfeile liegen hinter den Labels, und das wirkt jetzt deutlich mehr wie ein Mechanism Reveal statt nur Karten-Slideshow.\n\n## Tests\n\nFull Suite:\n\n```text\n168 passed in 33.05s\n```\n\n## Aktueller Verlauf\n\n```text\ndb200ae feat: add scene-specific motion backgrounds\n14310f9 feat: add TrueTrace final motion prototype\n7375311 feat: make TrueTrace pilot beginner friendly\nb9b6423 feat: align TrueTrace voiceover previews\n3f68001 feat: add EdgeTTS audio caption preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als nächstes **Caption/Subtitle Timing und Layout polieren**, weil der Clip visuell langsam brauchbar wird.\n\nKonkret:\n\n```text\n1. Subtitle chunks bei der 3-way-split Szene kürzer machen\n2. Subtitles etwas höher/kompakter setzen\n3. Key scene text nicht mit subtitles konkurrieren lassen\n4. Danach 1080x1920 Export erzeugen\n```\n\nDann hätten wir einen ernsthaften First-Public-Candidate statt nur einen technischen Prototypen mit Hut.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqE3t2F-_Er3cVzFodpMfwbhBEL9eMW-gFrvOeD43SESsWfTcmzRy3PU_adsfqz3vU0Rwu4KAmC5_G58g7OnbesrKVT82RZJ33iPqamCUs-G_e08x--IQcegEOFa2Hlt2LEN42NH1OTjLYOQ9QuoGP05WoJRcrJTh1a_PkB-cdGPbj_KmQ0lVLi9lCcwFcg7yd19RcApx550E6CLRppHtj-317hzyWVMpxuNmzsXBdDxOY10V6yuabJuVTaKHA-kTsic9MUk_PtNiaoxBOp9y9VemZWgg6bvDQo0pMTdgleDr_Fk8M_X4LHREmDDnrdKkuGEgpk6rnHXEJwcjVEy0Ut9x5O-ICDFiAhU7CtKptrue4FYvL_4II1iK0iS7obLgFOLiD5PwPX8zoQFHCcPL6ZqFBAKTXV_o00ag_FjBzVroXlPvuAOlnDyQjMnWVLYTOPi9fZu7PsBJ8tKGA-H3LajSkKzAsweNGMsG2ZkjgWYFCtCdtJpoho0hZs7pu0A-G_mNK6LNOtzQ7HrgLK0L14eharhngTl--Yz-s9CGDS0NHOQd-54PrjTJc3JwVARYbFTnCmQI7zRL9pjFAsN-_h8Df5h-0Yjyt-yy9-0AsQZbjR1ti010kcC_EyfFSYSqv47AcAA0aKccBueEOak2l1kKztR1iN5FdtLzi1woygoImAREBLN1R5Y3dzz3gwk5FA3ewHdW3PFJQc4v1-Kc30oFJtVV1HdGQN7WSy7C4tsKBq4_vvgGqUD98LYoOnM7LG7-Ou75jjFBQT1XIcxFAzxXiulwwv4zL5Z87-LqwSqHNncFuOzgnAONWVVeslevY4evU0OhV2KgKMQSs38XFc3jt5xI_bVezLghdUFwN8BkaZTpnyHz1iae6u3VyjH1W3XvkDV6CTN1-dwKauP-AeAhzBhKtVT5A51TBbGndmViBS6TOVbgPaOfmLZLjadR0p1j54AnUnO2qGberiA596WQi981eaPTP9Ut_swPXDmjHyTqdTkWvRPRkbCdjXrd3PH9Qcm_-kizkcZKY0pT9aXYfKQ==", "id": "rs_0dc9f371a6d55e3f016a137b7575248191928fa3c78d37f97b", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Weitergebaut: Scene-specific ChatGPT Image Backgrounds\n\nIch habe den Motion-Prototypen von **einem generischen Hintergrund** auf **5 szenenspezifische ChatGPT Image/Codex Backgrounds** erweitert.\n\nNeuer Commit:\n\n```text\ndb200ae feat: add scene-specific motion backgrounds\n```\n\n## Neu gebaut\n\n### Neues Modul\n\n```text\nautoshorts/rendering/scene_backgrounds.py\n```\n\n### Neue Tests\n\n```text\ntests/test_scene_backgrounds.py\n```\n\n### Erweitert\n\n```text\nautoshorts/rendering/motion_renderer.py\n```\n\nDer Renderer kann jetzt pro Szene ein eigenes Hintergrundbild verwenden:\n\n```python\nbackground_paths = {\n \"clean-demo\": \".../clean-demo.png\",\n \"weird-email\": \".../weird-email.png\",\n \"wrong-routing\": \".../wrong-routing.png\",\n \"three-way-split\": \".../three-way-split.png\",\n \"final-rule\": \".../final-rule.png\",\n}\n```\n\n## Was generiert wurde\n\nIch habe über ChatGPT Image/Codex 5 textfreie Backgrounds erzeugt:\n\n```text\ndata/generated_backgrounds/truetrace-weird-email-v1/clean-demo.png\ndata/generated_backgrounds/truetrace-weird-email-v1/weird-email.png\ndata/generated_backgrounds/truetrace-weird-email-v1/wrong-routing.png\ndata/generated_backgrounds/truetrace-weird-email-v1/three-way-split.png\ndata/generated_backgrounds/truetrace-weird-email-v1/final-rule.png\n```\n\nAlle Prompts erzwingen:\n\n```text\nno readable text\nno logos\nno watermarks\nno UI labels\nrenderer-owned overlays\n```\n\nAlso: AI macht Atmosphäre, Renderer macht Wahrheit. So wie es sein sollte.\n\n## Neuer V5 Review Clip\n\nMEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v5/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4\n\n## Technische Verifikation\n\n```text\nVideo: h264\nSize: 540x960\nDuration: 36.833s\nAudio: aac\nAudio duration: 36.864s\nFrames: 887\nBackgrounds: 5\n```\n\nSHA256:\n\n```text\nac8432ef5285abaa8bdb662610184cea491d3b1951dc9b2f2b6dd5a9f4ea9143\n```\n\n## Visuelle QA\n\nIch habe zwei kritische Szenen geprüft:\n\n### Weird Email Scene\n\nStatus:\n\n```text\nReadable: yes\nRenderer text clear: yes\nNo critical clipping: yes\nAI background has no problematic readable fake text/logos\n```\n\n### Three-way Split Scene\n\nStatus:\n\n```text\nNormal → Go through\nUnclear → Review\nWeird/Risky → Stop\n```\n\nIst lesbar, die Pfeile liegen hinter den Labels, und das wirkt jetzt deutlich mehr wie ein Mechanism Reveal statt nur Karten-Slideshow.\n\n## Tests\n\nFull Suite:\n\n```text\n168 passed in 33.05s\n```\n\n## Aktueller Verlauf\n\n```text\ndb200ae feat: add scene-specific motion backgrounds\n14310f9 feat: add TrueTrace final motion prototype\n7375311 feat: make TrueTrace pilot beginner friendly\nb9b6423 feat: align TrueTrace voiceover previews\n3f68001 feat: add EdgeTTS audio caption preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als nächstes **Caption/Subtitle Timing und Layout polieren**, weil der Clip visuell langsam brauchbar wird.\n\nKonkret:\n\n```text\n1. Subtitle chunks bei der 3-way-split Szene kürzer machen\n2. Subtitles etwas höher/kompakter setzen\n3. Key scene text nicht mit subtitles konkurrieren lassen\n4. Danach 1080x1920 Export erzeugen\n```\n\nDann hätten wir einen ernsthaften First-Public-Candidate statt nur einen technischen Prototypen mit Hut."}], "id": "msg_0dc9f371a6d55e3f016a137b7620ac8191baba54b46da38aac", "phase": "final_answer"}], "timestamp": "2026-05-25T00:28:22.424378"}