{"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."}
{"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. 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 ---"}
{"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."}
{"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:\n## Active Task\nUser asked: \"Ok, mach weiter\"\n\nKonkreter noch unerfüllter nächster Schritt aus dem Verlauf: `autoshorts/content/demo_pipeline.py` und `tests/test_content_demo_pipeline.py` bauen für den deterministischen End-to-End-Flow:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram Delivery Preview\n```\n\nz. B. als API:\n\n```python\nbuild_ai_life_systems_demo_delivery()\n```\n\nHinweis aus letztem Assistentenstatus: „Need README patch.“ — Es wurde offenbar gerade mit diesem nächsten Schritt begonnen bzw. sollte README danach aktualisiert werden.\n\n## Goal\nDer Nutzer will ein kontrolliertes AutoShorts-System aufbauen, das Content-Ideen strukturiert erarbeitet, daraus reviewbare Video-Kandidaten erzeugt, Quality Gates/Human Approval erzwingt und später Telegram-Review sowie Video-Erstellung/Optimierung unterstützt — zunächst ohne echte externe Side Effects, ohne echtes Telegram-Senden und ohne unkontrolliertes Publishing.\n\nGesamtpipeline-Ziel:\n\n```text\nContent-Pillar\n→ ContentBrief\n→ ScriptDraft\n→ CandidateDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram Delivery Preview\n→ später: ShotPlan / RenderBrief / Video-Erstellung testen und optimieren\n```\n\n## Constraints & Preferences\n- Sprache/Arbeitsstil: Deutsch, direkte knappe Statusupdates, gelegentlich humorvoll/sarkastisch.\n- Vorgehen: strikt TDD-orientiert:\n  - zuerst RED-Test,\n  - dann Implementierung,\n  - dann GREEN,\n  - dann Full Suite,\n  - dann README,\n  - dann Commit.\n- Keine echten Side Effects in frühen Stufen:\n  - keine Telegram API Calls,\n  - kein echtes Senden,\n  - kein Publishing,\n  - keine externen Video-/AI-APIs.\n- Human Approval bleibt mandatory.\n- Safety/Guardrails:\n  - Review/Delivery darf keine `publish`-/`post`-Sprache enthalten.\n  - Telegram-Nachrichtenlimit: max. `4096` Zeichen.\n  - Content-Validierung gegen generische AI-Slop-Phrasen.\n  - No-Go/Policy Notes werden durch die Pipeline weitergereicht.\n- Determinismus bevorzugt: Demo-Batches und Demo-Pipelines sollen reproduzierbar sein.\n- Ziel ist erst robuste Content-Erarbeitung/Review-Struktur, dann Video-Erstellung, damit GPU-Zeit nicht an schlechte Prompts verschwendet wird.\n- Niemals API Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen übernehmen; falls vorhanden: `[REDACTED]`.\n\n## Completed Actions\n1. CREATE `autoshorts/demo/__init__.py` — Demo-Package für Sample Review Batch angelegt [tool: editor/terminal].\n2. CREATE `autoshorts/demo/sample_batch.py` — deterministischen Demo-Batch implementiert mit `build_sample_ai_life_systems_batch()` und `render_sample_review_batch()` [tool: editor/terminal].\n3. CREATE `tests/test_sample_batch.py` — Tests für Sample Review Demo angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.demo'`, GREEN danach `2 passed` [tool: pytest].\n4. TEST Full Suite nach Sample Demo — Ergebnis: `43 passed in 0.09s` [tool: terminal].\n5. UPDATE `README.md` — Dokumentation ergänzt für `autoshorts.demo.sample_batch`, Sample Review Demo und Testabdeckung [tool: editor].\n6. COMMIT Sample Demo — Commit `30159ff feat: add sample review batch demo` auf Branch `feat/core-approval-quality-gates` [tool: git].\n7. CREATE `autoshorts/cli/__init__.py` — CLI-Package angelegt [tool: editor/terminal].\n8. CREATE `autoshorts/cli/demo_review_batch.py` — CLI Entry Point für `python -m autoshorts.cli.demo_review_batch` implementiert; druckt Sample Review Batch auf stdout [tool: editor/terminal].\n9. CREATE `tests/test_demo_review_batch_cli.py` — Test für Review-Batch-CLI angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.cli'`, GREEN danach `1 passed` [tool: pytest].\n10. TEST Full Suite nach Review-Batch-CLI — Ergebnis: `44 passed in 0.08s` [tool: terminal].\n11. VERIFY CLI manuell — `python -m autoshorts.cli.demo_review_batch`; zusätzlich Output nach `/tmp/autoshorts-demo-review.txt`, Datei hatte `39` Zeilen; erster Output:\n    ```text\n    ## Review Batch: sample-ai-life-systems\n    Ready: 1\n    Blocked: 1\n    Invalid: 1\n    ```\n    [tool: terminal].\n12. UPDATE `README.md` — CLI-Ausführung und `autoshorts.cli.demo_review_batch` dokumentiert [tool: editor].\n13. COMMIT Review-Batch-CLI — Commit `dc1c59c feat: add demo review batch cli` [tool: git].\n14. CREATE `autoshorts/telegram/__init__.py` — Telegram-Package angelegt [tool: editor/terminal].\n15. CREATE `autoshorts/telegram/review_sender.py` — side-effect-free Telegram Delivery Preparation implementiert mit `prepare_review_delivery(review_text: str, *, topic: str = \"review\") -> ReviewDelivery` [tool: editor/terminal].\n16. CREATE `tests/test_telegram_review_sender.py` — Tests für Telegram Delivery Preparation angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.telegram'`, GREEN danach `4 passed` [tool: pytest].\n17. IMPLEMENT Guardrails in `autoshorts/telegram/review_sender.py` — validiert nicht-leeren Text, max. `4096` Zeichen, keine `publish`-/`post`-Sprache, `requires_human_approval=True`, `side_effects=()` [tool: editor].\n18. TEST Full Suite nach Telegram Delivery Preparation — Ergebnis: `48 passed in 0.09s` [tool: terminal].\n19. VERIFY Telegram Delivery manuell — Sample Review Batch durch `prepare_review_delivery()` geschickt; Output:\n    ```text\n    telegram_review\n    review\n    True\n    ()\n    1000\n    ```\n    [tool: terminal].\n20. UPDATE `README.md` — `autoshorts.telegram.review_sender`, Delivery Preparation, Telegram-Limit, keine Side Effects, Human Approval dokumentiert [tool: editor].\n21. COMMIT Telegram Delivery Preparation — Commit `54863dd feat: add telegram review delivery preparation` [tool: git].\n22. CREATE `autoshorts/cli/demo_telegram_delivery.py` — CLI für Delivery Preview implementiert; ausführbar via `python -m autoshorts.cli.demo_telegram_delivery`; Pipeline: Sample Review Batch → `prepare_review_delivery()` → Metadata + Review Text auf stdout [tool: editor/terminal].\n23. CREATE `tests/test_demo_telegram_delivery_cli.py` — Test für Demo Telegram Delivery CLI angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.cli.demo_telegram_delivery'`, GREEN danach `1 passed` [tool: pytest].\n24. TEST Full Suite nach Demo Telegram Delivery CLI — Ergebnis: `49 passed in 0.08s` [tool: terminal].\n25. VERIFY Demo Telegram Delivery CLI manuell — Command:\n    ```bash\n    python -m autoshorts.cli.demo_telegram_delivery >/tmp/autoshorts-demo-delivery.txt\n    ```\n    Ergebnis:\n    ```text\n    45 /tmp/autoshorts-demo-delivery.txt\n    Delivery type: telegram_review\n    Topic: review\n    Requires human approval: yes\n    Side effects: none\n    Text length: 1000\n    ```\n    [tool: terminal].\n26. UPDATE `README.md` — `autoshorts.cli.demo_telegram_delivery`, Preview der vorbereiteten Telegram Delivery und „kein echtes Senden“ dokumentiert [tool: editor].\n27. COMMIT Demo Telegram Delivery CLI — Commit `53b45ca feat: add demo telegram delivery cli` [tool: git].\n28. CREATE `autoshorts/content/__init__.py` — Content-Package angelegt [tool: editor/terminal].\n29. CREATE `autoshorts/content/brief.py` — `ContentBrief`-Core implementiert inkl. `create_content_brief()` und `validate_content_brief()` [tool: editor/terminal].\n30. CREATE `tests/test_content_brief.py` — Tests für ContentBrief angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content'`, GREEN danach `4 passed` [tool: pytest].\n31. IMPLEMENT `create_content_brief()` — erzeugt strategischen Brief mit `brief_id`, Pillar, Topic, Audience Problem, Payoff, Positioning, Tone, Visual Rules, No-Go Topics, Zielplattformen und Ziel-Dauer [tool: editor].\n32. VERIFY ContentBrief Beispiel — Input:\n    ```python\n    create_content_brief(\n        pillar_id=\"ai_life_systems\",\n        topic=\"AI weekly review routine\",\n        audience_problem=\"I use AI tools but still lose control of priorities.\",\n        payoff=\"a 10-minute Sunday review that keeps AI as assistant, not boss\",\n    )\n    ```\n    erzeugte verifizierte `brief_id`:\n    ```text\n    ai_life_systems-ai-weekly-review-routine\n    ```\n    [tool: terminal/pytest].\n33. IMPLEMENT `validate_content_brief()` Guardrails — rejects fehlendes Topic, fehlendes Audience Problem, fehlenden Payoff, unsupported Platform, Duration außerhalb `10..180` [tool: editor].\n34. CREATE `autoshorts/content/script_draft.py` — `ScriptDraft`-Core implementiert inkl. `create_script_draft()` und `validate_script_draft()` [tool: editor/terminal].\n35. CREATE `tests/test_script_draft.py` — Tests für ScriptDraft angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content.script_draft'`, GREEN danach `5 passed` [tool: pytest].\n36. IMPLEMENT `create_script_draft(brief)` — erzeugt Title, Hook, Script Outline, 3–5 Retention Beats, Visual Concept, Caption, CTA, Estimated Duration [tool: editor].\n37. VERIFY ScriptDraft Beispiel — Hook:\n    ```text\n    If AI runs your week, you have already lost control.\n    ```\n    Retention Beats: `4`; Validation: `True` [tool: terminal/pytest].\n38. IMPLEMENT `validate_script_draft()` Guardrails — rejects fehlenden Hook, fehlendes Script Outline, fehlendes Visual Concept, fehlende Caption/CTA, weniger als 3 oder mehr als 5 Retention Beats, Duration außerhalb `10..180`, AI-Slop-Phrasen:\n    ```text\n    in this video\n    unlock your full potential\n    game changer\n    revolutionize your life\n    boost productivity\n    ```\n    [tool: editor].\n39. TEST Full Suite nach ContentBrief + ScriptDraft — Ergebnis: `58 passed in 0.10s` [tool: terminal].\n40. UPDATE `README.md` — `autoshorts.content.brief`, `autoshorts.content.script_draft`, Content Development Flow und Validation Guardrails dokumentiert [tool: editor].\n41. COMMIT ContentBrief + ScriptDraft — Commit `9c125f9 feat: add content brief and script draft models` [tool: git].\n42. CREATE `autoshorts/content/to_candidate.py` — Bridge von `ContentBrief + ScriptDraft` zu `CandidateDraft`/`VideoCandidate` implementiert [tool: editor/terminal].\n43. CREATE `tests/test_content_to_candidate.py` — Tests für Content-to-Candidate-Bridge angelegt; RED war `ModuleNotFoundError: No module named 'autoshorts.content.to_candidate'`, GREEN danach `4 passed` [tool: pytest].\n44. MODIFY `autoshorts/content/script_draft.py` — leere Overrides wie `hook=\"\"` werden nun wirklich als leer validiert statt durch Default-Hook ersetzt [tool: editor].\n45. IMPLEMENT `convert_script_draft_to_candidate_draft(brief, script)` — übernimmt Pillar, Title, Platforms, Duration, Hook, Script Outline, Visual Concept, Caption, CTA, Policy Notes aus Brief, Hypothesis und konservativen Initial Quality Score [tool: editor].\n46. IMPLEMENT Initial Quality Score für CandidateDraft — Werte:\n    ```text\n    hook_strength: 8\n    novelty: 8\n    clarity: 8\n    retention: 8\n    ai_slop_risk: 3\n    policy_risk: low\n    ```\n    [tool: editor].\n47. IMPLEMENT `convert_script_draft_to_candidate(brief, script)` — nutzt bestehende Candidate Factory `create_candidate()` und erzeugt echten `VideoCandidate` mit stabiler Candidate ID, Default Hashtags, Duration Class, Pillar/Platform Validation und Candidate Validation [tool: editor].\n48. VERIFY End-to-End-Pfad manuell — Pfad:\n    ```text\n    ContentBrief\n    → ScriptDraft\n    → VideoCandidate\n    → build_candidate_review_item()\n    ```\n    Output:\n    ```text\n    ai_life_systems-ai-weekly-review-routine-35s\n    ('#AI', '#LifeSystems', '#Productivity')\n    8.0\n    ready_for_review\n    ```\n    [tool: terminal].\n49. IMPLEMENT Bridge Guardrails — rejects invalid ScriptDrafts, z. B. leerer Hook; rejects mismatched Brief/Script Paare; nutzt Candidate Factory/Pillar Validation für weitere Fehler [tool: editor].\n50. ADD Policy Notes in Bridge — enthalten u. a.:\n    ```text\n    Source brief: ai_life_systems-ai-weekly-review-routine\n    Human approval remains explicit\n    No-go inherited: fake earnings\n    ```\n    [tool: editor].\n51. TEST Full Suite nach Content-to-Candidate-Bridge — Ergebnis: `62 passed in 0.09s` [tool: terminal].\n52. UPDATE `README.md` — `autoshorts.content.to_candidate`, ContentBrief + ScriptDraft → CandidateDraft und CandidateDraft → VideoCandidate via bestehender Factory dokumentiert [tool: editor].\n53. COMMIT Content-to-Candidate-Bridge — Commit `72dc887 feat: convert content drafts to video candidates` [tool: git].\n\n## Active State\n- Arbeitsverzeichnis: AutoShorts-Projekt; genauer Pfad nicht im kompaktierten Verlauf enthalten.\n- Branch:\n  ```text\n  feat/core-approval-quality-gates\n  ```\n- Letzter bekannter Commit:\n  ```text\n  72dc887 feat: convert content drafts to video candidates\n  ```\n- Bekannter Commit-Verlauf auf Branch:\n  ```text\n  72dc887 feat: convert content drafts to video candidates\n  9c125f9 feat: add content brief and script draft models\n  53b45ca feat: add demo telegram delivery cli\n  54863dd feat: add telegram review delivery preparation\n  dc1c59c feat: add demo review batch cli\n  30159ff feat: add sample review batch demo\n  b7b5270 feat: add review batch renderer\n  2155728 feat: add review workflow buckets\n  deea24f feat: add candidate factory\n  ae6a4f6 feat: add content pillar registry\n  ```\n- Letzter bekannter Full-Suite-Status:\n  ```text\n  62 passed in 0.09s\n  ```\n- Keine laufenden Server oder Prozesse bekannt.\n- Keine echten externen Integrationen aktiv.\n- Keine Telegram API Calls.\n- Keine Video-/AI-API Calls.\n- Der letzte Assistenten-Zwischenstatus nach User „Ok, mach weiter“ war:\n  ```text\n  Need README patch.\n  ```\n  Das deutet darauf hin, dass der nächste Demo-Pipeline-Schritt begonnen werden sollte oder bereits Code teilweise erstellt wurde, README aber noch fehlt. Aktuellen Dateistatus per `git status` prüfen.\n\n## In Progress\n- Aufbau eines deterministischen Demo-End-to-End-Flows:\n  ```text\n  ContentBrief\n  → ScriptDraft\n  → VideoCandidate\n  → TestBatch\n  → ReviewBatch\n  → Telegram Delivery Preview\n  ```\n- Geplante Dateien:\n  ```text\n  autoshorts/content/demo_pipeline.py\n  tests/test_content_demo_pipeline.py\n  ```\n- Geplante API:\n  ```python\n  build_ai_life_systems_demo_delivery()\n  ```\n- README-Patch steht wahrscheinlich noch aus oder wurde gerade als nötig erkannt.\n\n## Blocked\nKeine bekannten Blocker.\n\nBekannte frühere RED-Fehler wurden alle behoben:\n```text\nModuleNotFoundError: No module named 'autoshorts.demo'\nModuleNotFoundError: No module named 'autoshorts.cli'\nModuleNotFoundError: No module named 'autoshorts.telegram'\nModuleNotFoundError: No module named 'autoshorts.cli.demo_telegram_delivery'\nModuleNotFoundError: No module named 'autoshorts.content'\nModuleNotFoundError: No module named 'autoshorts.content.script_draft'\nModuleNotFoundError: No module named 'autoshorts.content.to_candidate'\n```\n\n## Key Decisions\n- Review/Telegram-Delivery bleibt zunächst vollständig side-effect-free, damit keine versehentliche Veröffentlichung oder Telegram-Auslieferung passiert.\n- Telegram Delivery Preparation erzeugt nur ein Delivery-Objekt mit Metadaten, `requires_human_approval=True` und `side_effects=()`.\n- Telegram-Limit von `4096` Zeichen wird bereits im Core validiert.\n- „publish“/„post“-Sprache ist in Review Deliveries verboten, um konzeptionell klar zwischen Review und Veröffentlichung zu trennen.\n- Content-Erarbeitung wurde bewusst vor Video-Erstellung eingeführt:\n  - Erst Brief und Script strukturiert validieren.\n  - Dann Candidate erzeugen.\n  - Dann Review.\n  - Erst später ShotPlan/RenderBrief/Video-Generation.\n- ContentBrief und ScriptDraft sind deterministisch modelliert, um Tests und Optimierung reproduzierbar zu machen.\n- AI-Slop-Phrasen werden hart rejectet, um generische Shorts-Sprache früh zu vermeiden.\n- `ScriptDraft` darf leere Overrides nicht stillschweigend durch Defaults reparieren; leere Eingaben müssen validierbar fehlschlagen.\n- Candidate Bridge nutzt bestehende Candidate Factory, damit ID, Hashtags, Duration Class und Validierungen konsistent bleiben.\n- Initialer Quality Score für aus Content erzeugte Kandidaten ist konservativ, aber reviewfähig:\n  ```text\n  hook_strength: 8\n  novelty: 8\n  clarity: 8\n  retention: 8\n  ai_slop_risk: 3\n  policy_risk: low\n  ```\n\n## Resolved Questions\n- Nutzer wollte nach Telegram Delivery Preview mit Content-Erarbeitung/Videos testen und optimieren beginnen.\n  - Antwort/Umsetzung: Es wurde zuerst der kontrollierte Core für ContentBrief und ScriptDraft gebaut, noch ohne Video-Erzeugung oder externe APIs.\n- Nutzer wollte „mach weiter“ nach ContentBrief/ScriptDraft.\n  - Antwort/Umsetzung: Es wurde die Bridge `ContentBrief + ScriptDraft → CandidateDraft → VideoCandidate` gebaut.\n- Nutzer wollte weiter nach der Bridge.\n  - Geplanter nächster Schritt: Demo-Pipeline von Content-Erarbeitung bis Telegram Delivery Preview als ein deterministischer Pipeline-Call.\n\n## Pending User Asks\n- Noch nicht erfüllt: User sagte zuletzt „Ok, mach weiter“.\n- Daraus folgt noch ausstehend:\n  - `autoshorts/content/demo_pipeline.py` erstellen.\n  - `tests/test_content_demo_pipeline.py` erstellen.\n  - API wie `build_ai_life_systems_demo_delivery()` implementieren.\n  - Pipeline testen:\n    ```text\n    ContentBrief\n    → ScriptDraft\n    → VideoCandidate\n    → TestBatch\n    → ReviewBatch\n    → Telegram Delivery Preview\n    ```\n  - README aktualisieren.\n  - Full Suite laufen lassen.\n  - Commit erstellen.\n\n## Relevant Files\n- `autoshorts/demo/__init__.py` — Demo-Package.\n- `autoshorts/demo/sample_batch.py` — deterministischer Sample Review Batch mit `build_sample_ai_life_systems_batch()` und `render_sample_review_batch()`.\n- `tests/test_sample_batch.py` — Tests für Sample Review Demo.\n- `autoshorts/cli/__init__.py` — CLI-Package.\n- `autoshorts/cli/demo_review_batch.py` — CLI für Sample Review Batch.\n- `tests/test_demo_review_batch_cli.py` — Tests für Review Batch CLI.\n- `autoshorts/telegram/__init__.py` — Telegram-Package.\n- `autoshorts/telegram/review_sender.py` — `ReviewDelivery` und `prepare_review_delivery()`.\n- `tests/test_telegram_review_sender.py` — Tests für Telegram Delivery Preparation.\n- `autoshorts/cli/demo_telegram_delivery.py` — CLI für Telegram Delivery Preview.\n- `tests/test_demo_telegram_delivery_cli.py` — Tests für Telegram Delivery CLI.\n- `autoshorts/content/__init__.py` — Content-Package.\n- `autoshorts/content/brief.py` — `ContentBrief`, `create_content_brief()`, `validate_content_brief()`.\n- `tests/test_content_brief.py` — Tests für ContentBrief.\n- `autoshorts/content/script_draft.py` — `ScriptDraft`, `create_script_draft()`, `validate_script_draft()`, inkl. Fix für leere Overrides.\n- `tests/test_script_draft.py` — Tests für ScriptDraft.\n- `autoshorts/content/to_candidate.py` — `convert_script_draft_to_candidate_draft()` und `convert_script_draft_to_candidate()`.\n- `tests/test_content_to_candidate.py` — Tests für Content-to-Candidate-Bridge.\n- `README.md` — mehrfach aktualisiert für Demo, CLI, Telegram Delivery, Content Flow und Candidate Bridge.\n- Geplant/noch zu prüfen:\n  - `autoshorts/content/demo_pipeline.py`\n  - `tests/test_content_demo_pipeline.py`\n\n## Remaining Work\n- Aktuellen Git-Status prüfen, da letzter Zwischenstatus „Need README patch.“ war.\n- Falls Demo-Pipeline-Dateien noch nicht existieren:\n  1. RED-Test für `autoshorts.content.demo_pipeline` schreiben.\n  2. `build_ai_life_systems_demo_delivery()` implementieren.\n  3. Pipeline sollte deterministisch:\n     ```text\n     create_content_brief()\n     → create_script_draft()\n     → convert_script_draft_to_candidate()\n     → TestBatch\n     → build_review_batch()\n     → render_review_batch_summary()\n     → prepare_review_delivery()\n     ```\n     ausführen.\n  4. Tests für:\n     - Delivery type `telegram_review`,\n     - Topic `review`,\n     - `requires_human_approval is True`,\n     - `side_effects == ()`,\n     - Review-Text enthält Batch-Header und Ready Count,\n     - Candidate aus Content ist enthalten.\n  5. README dokumentieren.\n  6. Full Suite laufen lassen.\n  7. Commit erstellen.\n- Danach nächster größerer Block:\n  ```text\n  ScriptDraft\n  → ShotPlan\n  → RenderBrief\n  → Visual/Video generation test\n  ```\n  um „Videos erstellen, testen und optimieren“ strukturiert anzugehen.\n\n## Critical Context\n- Sample Review Batch erzeugt exakt:\n  ```text\n  sample-ready   → ready for review\n  sample-blocked → valid, aber Quality Gate blockiert\n  sample-invalid → invalid, weil Hook fehlt\n  ```\n- Sample Review Output beginnt:\n  ```text\n  ## Review Batch: sample-ai-life-systems\n  Ready: 1\n  Blocked: 1\n  Invalid: 1\n  ```\n- Demo Telegram Delivery CLI Output beginnt:\n  ```text\n  Delivery type: telegram_review\n  Topic: review\n  Requires human approval: yes\n  Side effects: none\n  Text length: 1000\n\n  ## Review Batch: sample-ai-life-systems\n  Ready: 1\n  ```\n- `prepare_review_delivery()` erzeugt:\n  ```python\n  ReviewDelivery(\n      text=...,\n      topic=\"review\",\n      delivery_type=\"telegram_review\",\n      requires_human_approval=True,\n      side_effects=(),\n  )\n  ```\n- Telegram Delivery Guardrails:\n  - Text nicht leer.\n  - Max. `4096` Zeichen.\n  - Keine `publish`-/`post`-Sprache.\n  - Keine Side Effects.\n  - Human Approval mandatory.\n- Beispiel-ContentBrief:\n  ```python\n  create_content_brief(\n      pillar_id=\"ai_life_systems\",\n      topic=\"AI weekly review routine\",\n      audience_problem=\"I use AI tools but still lose control of priorities.\",\n      payoff=\"a 10-minute Sunday review that keeps AI as assistant, not boss\",\n  )\n  ```\n  erzeugt:\n  ```text\n  ai_life_systems-ai-weekly-review-routine\n  ```\n- Beispiel-ScriptDraft-Hook:\n  ```text\n  If AI runs your week, you have already lost control.\n  ```\n- Beispiel-ScriptDraft:\n  - Retention Beats: `4`\n  - Validation: `True`\n- AI-Slop-Blacklist:\n  ```text\n  in this video\n  unlock your full potential\n  game changer\n  revolutionize your life\n  boost productivity\n  ```\n- Content-to-Candidate manuell verifizierter Output:\n  ```text\n  ai_life_systems-ai-weekly-review-routine-35s\n  ('#AI', '#LifeSystems', '#Productivity')\n  8.0\n  ready_for_review\n  ```\n- Policy Notes aus Bridge enthalten:\n  ```text\n  Source brief: ai_life_systems-ai-weekly-review-routine\n  Human approval remains explicit\n  No-go inherited: fake earnings\n  ```\n- Letzter sicherer Teststand:\n  ```text\n  62 passed in 0.09s\n  ```\n- Keine Secrets/Credentials im Summary erhalten; falls im entfernten Verlauf vorhanden, wurden sie nicht übernommen und wären als `[REDACTED]` zu behandeln.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Content Demo Review Pipeline\n\nNeue Dateien:\n\n```text\nautoshorts/content/demo_pipeline.py\ntests/test_content_demo_pipeline.py\n```\n\n## Neuer End-to-End-Demo-Call\n\n```python\nbuild_ai_life_systems_demo_delivery()\n```\n\nDer läuft deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ TestBatch\n→ ReviewBatch\n→ Telegram ReviewDelivery\n```\n\nUnd gibt ein inspectable Result zurück:\n\n```python\nContentDemoPipelineResult(\n    brief=...,\n    script=...,\n    candidate=...,\n    testbatch=...,\n    review_batch=...,\n    delivery=...,\n)\n```\n\nAlso nicht nur finalen Text, sondern jeden Zwischenzustand. Sehr nützlich zum Debuggen und Optimieren, ohne in Rendering- oder Telegram-Nebenwirkungen zu rutschen.\n\n## Verifizierter Output\n\nManuell geprüft:\n\n```text\nai_life_systems-ai-weekly-review-routine\nai_life_systems-ai-weekly-review-routine-35s\n1 0 0\ntelegram_review review () 1294\n```\n\nBedeutung:\n\n- Brief ID: `ai_life_systems-ai-weekly-review-routine`\n- Candidate ID: `ai_life_systems-ai-weekly-review-routine-35s`\n- Review Buckets:\n  - Ready: `1`\n  - Blocked: `0`\n  - Invalid: `0`\n- Delivery:\n  - Type: `telegram_review`\n  - Topic: `review`\n  - Side effects: `()`\n  - Text length: `1294`\n\n## Safety\n\nWeiterhin:\n\n- kein echtes Telegram-Senden\n- kein Publishing\n- keine externen API Calls\n- keine generierten Medien\n- keine Side Effects\n- Human approval bleibt Pflicht\n\nEine Pipeline, die tatsächlich nichts Gefährliches tut. Man möchte fast misstrauisch werden.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.content.demo_pipeline'\n```\n\nGREEN:\n\n```text\n2 passed\n```\n\nFull suite:\n\n```text\n64 passed in 0.10s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.content.demo_pipeline`\n- End-to-End-Flow\n- alle Zwischenartefakte\n- keine send/publish side effects\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n6e6569a feat: add content demo review pipeline\n```\n\nAktueller Verlauf:\n\n```text\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n9c125f9 feat: add content brief and script draft models\n53b45ca feat: add demo telegram delivery cli\n54863dd feat: add telegram review delivery preparation\ndc1c59c feat: add demo review batch cli\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir mit dem eigentlichen Video-Erstellungs-Vorlauf beginnen:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderBrief\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/rendering/shot_plan.py\ntests/test_shot_plan.py\n```\n\nZiel:\n\n- aus `ScriptDraft` einen 4–5 Scene ShotPlan erzeugen\n- pro Scene:\n  - Start/End time\n  - Voiceover beat\n  - Visual action\n  - Motion type\n  - Caption beat\n  - Asset mode, z. B.:\n    - `mechanism_animation`\n    - `screen_recording_simulation`\n    - `cinematic_ai_video`\n    - `kinetic_text_overlay`\n- harte Guardrails:\n  - keine static slideshow\n  - keine baked-in fake UI text\n  - captions/labels renderer-owned\n  - Szenenlängen plausibel\n  - erster Frame hat Hook/Pattern Interrupt\n\nDas ist der notwendige Zwischenschritt, bevor wir Qwen-Image/Wan/ComfyUI sinnvoll anwerfen. Sonst generieren wir hübschen Nebel mit Stromrechnung."}
{"role": "user", "content": "[M G] ok, mach das."}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Retention Shot Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/__init__.py\nautoshorts/rendering/shot_plan.py\ntests/test_shot_plan.py\n```\n\n## Neue API\n\n```python\ncreate_shot_plan(script: ScriptDraft) -> ShotPlan\n```\n\nErzeugt aus einem validierten `ScriptDraft` einen deterministischen 5-Scene ShotPlan:\n\n```text\nfirst_frame_hook\n→ pattern_interrupt\n→ core_explanation\n→ screen_or_card_visual\n→ cta\n```\n\n## ShotPlan-Struktur\n\n```python\nShotPlan(\n    script_id=...,\n    title=...,\n    estimated_duration_seconds=...,\n    scenes=(...)\n)\n```\n\nJede Szene enthält:\n\n```python\nShotScene(\n    purpose=...,\n    start_seconds=...,\n    end_seconds=...,\n    voiceover_beat=...,\n    visual_action=...,\n    asset_mode=...,\n    caption_beat=...,\n    retention_goal=...,\n    labels_renderer_owned=True,\n)\n```\n\n## Asset Modes\n\nAktueller Default-Plan:\n\n```text\nfirst_frame_hook        → kinetic_text_overlay\npattern_interrupt       → mechanism_animation\ncore_explanation        → screen_recording_simulation\nscreen_or_card_visual   → mechanism_animation\ncta                     → kinetic_text_overlay\n```\n\nDamit vermeiden wir bewusst:\n\n```text\nstatic slideshow\n```\n\nWeil “AI slideshow mit Musik” nicht Content ist, sondern ein sehr langsamer Hilferuf.\n\n## Guardrails\n\n`validate_shot_plan()` rejected:\n\n- `static_slideshow`\n- baked-in fake UI text\n- generated/unreadable UI text\n- Labels/Captions, die nicht renderer-owned sind\n- schlechte Scene Timings\n- fehlende Pflichtfelder\n- Plan ohne `first_frame_hook` als erste Szene\n- ungültige Duration\n\n## Verifizierter Output\n\nManuell geprüft:\n\n```text\nai_life_systems-ai-weekly-review-routine\n5\n['first_frame_hook', 'pattern_interrupt', 'core_explanation', 'screen_or_card_visual', 'cta']\n['kinetic_text_overlay', 'mechanism_animation', 'screen_recording_simulation', 'mechanism_animation', 'kinetic_text_overlay']\nTrue\n```\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n69 passed in 0.10s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.shot_plan`\n- 5-Scene Retention Flow\n- Scene-Felder\n- Guardrails gegen Slideshow/Fake-UI/Baked Text\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\nd118a76 feat: add retention shot planning\n```\n\nAktueller Verlauf:\n\n```text\nd118a76 feat: add retention shot planning\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n9c125f9 feat: add content brief and script draft models\n53b45ca feat: add demo telegram delivery cli\n54863dd feat: add telegram review delivery preparation\ndc1c59c feat: add demo review batch cli\n30159ff feat: add sample review batch demo\nb7b5270 feat: add review batch renderer\n2155728 feat: add review workflow buckets\ndeea24f feat: add candidate factory\nae6a4f6 feat: add content pillar registry\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt der nächste wichtige Zwischenlayer:\n\n```text\nShotPlan\n→ RenderManifest\n```\n\nIch würde bauen:\n\n```text\nautoshorts/rendering/manifest.py\ntests/test_render_manifest.py\n```\n\nZiel:\n\n- ShotPlan in maschinenlesbaren RenderManifest übersetzen\n- pro Timeline Item:\n  - start/end\n  - asset mode\n  - motion instruction\n  - caption layer\n  - renderer-owned label layer\n  - placeholder asset path\n- globale Render Settings:\n  - vertical `1080x1920`\n  - fps `30`\n  - no generated text inside assets\n  - captions renderer-owned\n  - static_card_limit = `0`\n\nDanach können wir erst lokal einen Preview-Renderer bauen — und danach Qwen-Image/Wan/ComfyUI gezielt anbinden, statt blind GPU-Roulette zu spielen."}
{"role": "assistant", "content": "", "reasoning": "**Considering coding actions**\n\nI need to take action now! I want to load the relevant skill, which seems to be related to software development—maybe something about test-driven development (TDD). The user is asking me to continue coding, so I need to inspect what’s at hand carefully. It’s important to make sure I’m proceeding correctly to meet their needs. Let’s keep this efficient and effective!", "finish_reason": "tool_calls", "reasoning_content": "**Considering coding actions**\n\nI need to take action now! I want to load the relevant skill, which seems to be related to software development—maybe something about test-driven development (TDD). The user is asking me to continue coding, so I need to inspect what’s at hand carefully. It’s important to make sure I’m proceeding correctly to meet their needs. Let’s keep this efficient and effective!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEudMpspI8K1XG9ab4ssGc8b2D9_6kSAo3rOzROby08R_eHadUe0VtcqXttyNAU_zuofFqTA6Fi-GxVcc1e2GquV9gKA3G-KG-nYblJlWgcAbx-O0ImIZLUaV_EvrN899eeNs3A4pdf-7AoX5rqHGAcxLVdNRpwjP8tPZ8DwmQ1Y8LLbMEuGCnVxPlsfrxMRhN8fnZ6z1Zp6KvkCaCRSrfHCmG3PA8f1i7zL3o5_zd0Uxd-mPxAZCeDFs8dBUis8xTyloZtMp2z7UO97Au5QBqxdtc6nWDTdkisuB4ePHet__DnZpGAnmkNtpdnKSOl8oZz3qa6J_Dgo5CUcNqYAqMtgEqbbrthgHumsUlZsqSnQJedOc6ZzL8lkEym1Mkb7fTAD4Wwj7Hujqs57J0rPNNYSCDcdpGDfWEAZQXCJDh0a_UpEglqVEOlJ_henvy3zO88OdOrJCmUrOSSgieRMs-hSGYxA2QxPxCZSDe6Ycuqpfx-TQgHgk59hjXSuy3SMT_45AnWGjghIY4xptEZtveMgcwbtJKR2fff1fVcQjhjEiiTqgrmLOJQFcCp7vSVZwICgCaA1ar7YF6ZvmhsyxH0RRti3Yg97WxLj1HeT7IRwVBkhRHZAoKvE9TTqhc27QHHEIxX12t8G789M-kbNBdDXsfDBh4g_wQh9yoQtZ2vpyMC43xFhyS5FyyYenLUVFseMUr10PMk-vtIWKvM9VNgxZnQoF1o5Ni5HCkdF9Nv49XFrUMa0BB3awxSDREZr1P0hu4LfcKluSJVu_H2snosSDcLU0CpAKAVNQFHlQ_3ue-0fruOPSBQFxzK2tXmUxPMrxSwhn020g0MoOxr0DHWRbCVTLRUfR5iIaYm-YYBzl_67YPbCJ30xoLyV7Bdij5zVooiIXtAtOwZ0dFUv3P68_wIZRv8htREa-sQj1hXm7tYj6MY9MVCzAP1m-torM3T6ei8R8XeTA2Nu2zb5xElioQgqlMkIt_x-oadpJMjvBN5ZTMOie8RvXGSIASQRUqSeb1ZhtAZN05J5u2GlUCRkCl2-jsidx-GbGsXPCQqlqmplPO-c0CUdMoCBXcrhu-Dbp", "id": "rs_0e7758d08a185fa1016a12e74af48c8191ac4d3faf7a7deb8c", "summary": [{"type": "summary_text", "text": "**Considering coding actions**\n\nI need to take action now! I want to load the relevant skill, which seems to be related to software development—maybe something about test-driven development (TDD). The user is asking me to continue coding, so I need to inspect what’s at hand carefully. It’s important to make sure I’m proceeding correctly to meet their needs. Let’s keep this efficient and effective!"}]}], "tool_calls": [{"id": "call_KjKHG63MgE3GFlBPCpscU5q4", "call_id": "call_KjKHG63MgE3GFlBPCpscU5q4", "response_item_id": "fc_0e7758d08a185fa1016a12e74ca3548191b6f8b50cbbbfc8b0", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
{"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## 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_KjKHG63MgE3GFlBPCpscU5q4", "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"feat/core-approval-quality-gates\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Svy13qIIldEJ9I5b2CaTDfKp", "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "tool", "name": "read_file", "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_8NtFrM6uZcHaadpDoRVpl01v", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "assistant", "content": "", "reasoning": "**Considering implementation details**\n\nI need to implement the manifest. It’s crucial to inspect the test patterns for TestBatch and ReviewBatch to make sure everything is working correctly. I should check the README as well; it often contains valuable information that can guide me in this process. There’s a lot to juggle here, but I’m focusing on making sure I cover all the necessary parts effectively. Let's get started!", "finish_reason": "tool_calls", "reasoning_content": "**Considering implementation details**\n\nI need to implement the manifest. It’s crucial to inspect the test patterns for TestBatch and ReviewBatch to make sure everything is working correctly. I should check the README as well; it often contains valuable information that can guide me in this process. There’s a lot to juggle here, but I’m focusing on making sure I cover all the necessary parts effectively. Let's get started!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEudv4cH4nh30b9cEkDLvOGK4Uc7bg9XXHPdzabLx77NRElkMXXvm1jErEbBaSS0bMDOZRmYXrC051WDxvedJPAkWWZicOwD6k6NXlCDWBavlGZ7gvKONGPBGJQJxVQI5d7vM9MX6Evu0PrVkkeV909RLSzYs3emajZj7qu7EjUTzpFosHDv4UO-ts7zt9jD-aTWqDDpLvFfnWCleE_x2Tce-smfXOgKTCKSGrSw1I0gtg3q_gaPRtJ1-fHvfPlYjIG6ZoTm9Yqw2dvgnCzcacoIvj78dkfxP0Ze0x86GzMMHZcFPeaBtmkwiBq5XS5WgW9W6fF_tHuFnANCIXmOG72NI06eiRJAb_95LHz4KygSRo0L4hjgkQvYca8awr2YY-6FBE6T_D98Wo6iGE1ZDA5AQ1hoNFlt3yDCazt67X7hxr9u0uaN7wCMnkpnAs9tbgx5ZnhHv2erKo2Lw-BAu7Fb6PZEZMXxn5jOKyXqTkAIEF3ri4f4XQWrjxicSnoQYIZ54XIlnnyzsmIbHxP94YxI10xQH3S53prL6yP4tsGQ8gyvnJxVUzqPaAuwjOTJcanb2S1wBTCM2FsMYHUXjjU7_2kSge6yp5gD6PZTi7Zz2yZsATkAchDXeDwex4P6VPS4mDZxENDsDMj040B_NrB2VuVfc7bM9h-I0ejUoY7ewgJ1tQ33AvZ_RyP9o4N2tuK_6PmSlduGiznW1KqT9_7GpOxOVAGZQqqLfirAfFTXX7XcAMk0xbSWo07oHOBR5uTplmznzjDrA6ozHSoWa0uBFrEZwd1w0Xu91D0pKoI-pwk8uI0ZdvB6TvGglAANvl_gBOMoV2tZri4QKxho3fl82XNCCkS6M6GEtGmkyKA8vkIwB62tDvBfJNRu_63BeGiwFPh7Zt1JY-bl5EpRaSwxrJE_mZOyiHSYynBXYO8o-e6qIjU0sz2pURMk1_fHyj3XnHB3RSQ5sQmZruorcHrti4Pbx9OQjGAsf3Aew2pXyMHncW_-qkGpAweh3slbY0u-QIyrfe1kFeMVuiVmo8QPtvA==", "id": "rs_0158e9be84c9b9cf016a12e76cc80c819194ee55ab60a164cc", "summary": [{"type": "summary_text", "text": "**Considering implementation details**\n\nI need to implement the manifest. It’s crucial to inspect the test patterns for TestBatch and ReviewBatch to make sure everything is working correctly. I should check the README as well; it often contains valuable information that can guide me in this process. There’s a lot to juggle here, but I’m focusing on making sure I cover all the necessary parts effectively. Let's get started!"}]}], "tool_calls": [{"id": "call_o7Kn127cJBNXA2Vww85vWDXD", "call_id": "call_o7Kn127cJBNXA2Vww85vWDXD", "response_item_id": "fc_0158e9be84c9b9cf016a12e76fb8608191bfcc317af822b9d4", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py\",\"limit\":300}"}}, {"id": "call_qToHPqoBe7VE0TogpP71eSTO", "call_id": "call_qToHPqoBe7VE0TogpP71eSTO", "response_item_id": "fc_0158e9be84c9b9cf016a12e76fb878819183ea707b80950b7d", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"limit\":500}"}}, {"id": "call_lQrR195rFv8QAzNS5s7pKYLb", "call_id": "call_lQrR195rFv8QAzNS5s7pKYLb", "response_item_id": "fc_0158e9be84c9b9cf016a12e76fb8808191874edf3480ea366a", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/demo_pipeline.py\",\"limit\":250}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "tool", "name": "read_file", "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_o7Kn127cJBNXA2Vww85vWDXD", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "tool", "name": "read_file", "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_review_batch` — local CLI entry point that prints the deterministic review demo.\\n    12|- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n    13|- `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n    14|- `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n    15|- `autoshorts.content.demo_pipeline` — deterministic content-to-Telegram-review demo pipeline.\\n    16|- `autoshorts.content.script_draft` — structured script drafts with retention beats, visual concept, caption, CTA, and AI-slop validation.\\n    17|- `autoshorts.content.to_candidate` — converts content briefs and script drafts into candidate drafts and validated video candidates.\\n    18|- `autoshorts.demo.sample_batch` — deterministic AI-life-systems review demo with ready, blocked, and invalid candidates.\\n    19|- `autoshorts.ideas.candidate` — structured video candidate model and validation.\\n    20|- `autoshorts.ideas.factory` — draft-to-candidate factory with stable ids, default hashtags, and duration classes.\\n    21|- `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n    22|- `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n    23|- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n    24|- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n    25|- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n    26|- `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n    27|- `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n    28|- `autoshorts.scripting.quality` — conservative pre-render quality gate.\\n    29|- 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    30|\\n    31|## Content development\\n    32|\\n    33|`autoshorts.content.brief.create_content_brief()` turns a pillar, topic, audience problem, and payoff into a deterministic `ContentBrief`. The brief carries positioning, tone, visual rules, no-go topics, target platforms, and target duration before any script or renderer exists.\\n    34|\\n    35|`validate_content_brief()` rejects missing strategy fields, unsupported platforms, and durations outside `10..180` seconds.\\n    36|\\n    37|`autoshorts.content.script_draft.create_script_draft()` turns a `ContentBrief` into a structured `ScriptDraft` with:\\n    38|\\n    39|- exact hook;\\n    40|- script outline;\\n    41|- 3–5 retention beats;\\n    42|- visual concept;\\n    43|- caption and CTA;\\n    44|- estimated duration.\\n    45|\\n    46|`validate_script_draft()` rejects missing core fields, invalid beat counts, invalid durations, and generic AI-slop phrases such as `in this video` or `unlock your full potential`.\\n    47|\\n    48|`autoshorts.content.to_candidate.convert_script_draft_to_candidate_draft()` turns a validated `ContentBrief` + `ScriptDraft` pair into the existing `CandidateDraft` type. It preserves platform strategy, hook, script outline, visual concept, caption, CTA, no-go policy notes, and an initial conservative quality score.\\n    49|\\n    50|`convert_script_draft_to_candidate()` then uses the existing candidate factory to create a validated `VideoCandidate`, including stable candidate id generation, default hashtags, duration classification, and pillar/platform validation.\\n    51|\\n    52|`autoshorts.content.demo_pipeline.build_ai_life_systems_demo_delivery()` runs the deterministic demo path end-to-end:\\n    53|\\n    54|```text\\n    55|ContentBrief\\n    56|→ ScriptDraft\\n    57|→ VideoCandidate\\n    58|→ TestBatch\\n    59|→ ReviewBatch\\n    60|→ Telegram ReviewDelivery\\n    61|```\\n    62|\\n    63|The result object exposes every intermediate artifact for inspection and still has no send/publish side effects.\\n    64|\\n    65|## Render planning\\n    66|\\n    67|`autoshorts.rendering.shot_plan.create_shot_plan()` turns a validated `ScriptDraft` into a deterministic five-scene retention plan:\\n    68|\\n    69|```text\\n    70|first_frame_hook\\n    71|→ pattern_interrupt\\n    72|→ core_explanation\\n    73|→ screen_or_card_visual\\n    74|→ cta\\n    75|```\\n    76|\\n    77|Each `ShotScene` carries timing, voiceover beat, visual action, asset mode, caption beat, retention goal, and whether labels/captions remain renderer-owned.\\n    78|\\n    79|`validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n    80|\\n    81|## Human approval commands\\n    82|\\n    83|Supported review responses:\\n    84|\\n    85|- `Freigabe alle` — approve for YouTube, TikTok, and Instagram.\\n    86|- `Freigabe YouTube`\\n    87|- `Freigabe TikTok`\\n    88|- `Freigabe Instagram`\\n    89|- `Ablehnen`\\n    90|- `Ändern: <requested change>`\\n    91|\\n    92|Ambiguous messages such as `passt schon` intentionally return `needs_clarification` and must not trigger posting.\\n    93|\\n    94|## Quality gate\\n    95|\\n    96|A script candidate only proceeds to rendering when:\\n    97|\\n    98|- average of hook strength, novelty, clarity, and retention is at least `8.0`;\\n    99|- AI-slop risk is not above `5`;\\n   100|- policy risk is not `high`.\\n   101|\\n   102|This keeps rendering capacity for candidates that might actually deserve to exist. A rare mercy.\\n   103|\\n   104|## Review package\\n   105|\\n   106|`autoshorts.review.package.render_telegram_review_package()` produces the human approval message for one candidate. It includes:\\n   107|\\n   108|- candidate id, title, pillar, target platforms, and duration;\\n   109|- hook, script summary, visual concept, caption, and hashtags;\\n   110|- average quality score, AI-slop risk, policy notes, and render-gate status;\\n   111|- optional preview path;\\n   112|- only the relevant platform-specific approval commands.\\n   113|\\n   114|A blocked quality gate is rendered as `Render gate: BLOCKED — nicht automatisch rendern`.\\n   115|\\n   116|## Review workflow\\n   117|\\n   118|`build_candidate_review_item()` classifies one candidate as:\\n   119|\\n   120|- `ready_for_review` — valid and renderable; includes a review package;\\n   121|- `blocked` — valid but blocked by quality gate; still includes a review package with blocked gate status;\\n   122|- `invalid` — fails candidate validation; does not create a review package.\\n   123|\\n   124|`build_review_batch()` processes a `TestBatch` into `ready_for_review`, `blocked`, and `invalid` buckets while preserving input order inside each bucket. Optional preview paths are attached by candidate id.\\n   125|\\n   126|`render_review_batch_summary()` turns the complete `ReviewBatch` into one Telegram-safe text block with summary counts, full ready-for-review packages, and compact blocked/invalid reason lists. It deliberately avoids publish/post language; approval and publishing remain separate later stages.\\n   127|\\n   128|## Sample review demo\\n   129|\\n   130|`autoshorts.demo.sample_batch.build_sample_ai_life_systems_batch()` creates a deterministic AI-life-systems sample with:\\n   131|\\n   132|- one ready candidate;\\n   133|- one blocked candidate that fails the quality gate;\\n   134|- one invalid candidate missing its hook.\\n   135|\\n   136|`render_sample_review_batch()` builds that sample, runs it through the review workflow, and returns the Telegram-safe batch summary. This gives the later Telegram sender one boring, repeatable fixture to wire against.\\n   137|\\n   138|Run the same fixture locally with:\\n   139|\\n   140|```bash\\n   141|python -m autoshorts.cli.demo_review_batch\\n   142|```\\n   143|\\n   144|## Telegram review delivery preparation\\n   145|\\n   146|`autoshorts.telegram.review_sender.prepare_review_delivery()` wraps rendered review text in a side-effect-free `ReviewDelivery` object. It does not call Telegram. It validates that:\\n   147|\\n   148|- review text is not blank;\\n   149|- text fits Telegram's 4096-character message limit;\\n   150|- review deliveries do not contain publish/post language;\\n   151|- the delivery still requires human approval.\\n   152|\\n   153|Actual Telegram I/O remains a later explicit boundary.\\n   154|\\n   155|Preview the prepared delivery locally with:\\n   156|\\n   157|```bash\\n   158|python -m autoshorts.cli.demo_telegram_delivery\\n   159|```\\n   160|\\n   161|It prints delivery metadata first, then the review text. It still sends nothing.\\n   162|\\n   163|## Idea candidates and testbatches\\n   164|\\n   165|`VideoCandidate` is the structured unit that moves through the early pipeline. It carries:\\n   166|\\n   167|- candidate id, pillar, title, target platforms, duration, and hypothesis;\\n   168|- hook, script outline, visual concept, caption, CTA, and hashtags;\\n   169|- quality score and policy notes.\\n   170|\\n   171|`validate_candidate()` rejects missing strategic fields, unsupported platforms, and durations outside `10..180` seconds.\\n   172|\\n   173|`TestBatch` groups candidates for one experiment. `validate_testbatch()` rejects empty batches and duplicate ids. `renderable_candidates()` returns only candidates that are both valid and allowed by the quality gate.\\n   174|\\n   175|## Candidate factory\\n   176|\\n   177|`CandidateDraft` represents a raw idea before it becomes a validated `VideoCandidate`.\\n   178|\\n   179|`create_candidate()` turns a draft into a candidate by:\\n   180|\\n   181|- generating a stable id from pillar, title, and duration;\\n   182|- adding default pillar hashtags when no hashtags are supplied;\\n   183|- classifying duration as `growth_short`, `monetization_candidate`, or `experiment`;\\n   184|- appending the duration class to policy notes;\\n   185|- enforcing candidate validation and pillar/platform fit validation.\\n   186|\\n   187|Duration classes:\\n   188|\\n   189|- `growth_short`: 20–45 seconds;\\n   190|- `monetization_candidate`: 60–75 seconds;\\n   191|- `experiment`: all other valid durations.\\n   192|\\n   193|Missing hypothesis, unknown pillar, or unfit platform raises `ValueError`.\\n   194|\\n   195|## Content pillars\\n   196|\\n   197|`ContentPillar` keeps strategy separate from rendering. The default registry currently contains:\\n   198|\\n   199|- `ai_life_systems` — AI as a servant for ordinary life systems, never as values or authority.\\n   200|\\n   201|A pillar defines audience, positioning, tone, hook patterns, visual rules, no-go topics, platform fit, and monetization angle.\\n   202|\\n   203|`validate_pillar_registry()` rejects duplicate pillar ids and incomplete pillar definitions. `validate_candidate_against_pillars()` rejects candidates with unknown pillars or platforms that are not configured for that pillar.\\n   204|\\n   205|## Development\\n   206|\\n   207|Run tests:\\n   208|\\n   209|```bash\\n   210|python -m pytest tests -q\\n   211|```\\n   212|\\n   213|## Planned architecture\\n   214|\\n   215|```text\\n   216|autoshorts/\\n   217|  config/       # niches, platforms, workflow settings\\n   218|  ideas/        # idea generation and scoring\\n   219|  scripting/    # script drafts, quality gates, retention checks\\n   220|  rendering/    # local/AI-assisted preview rendering\\n   221|  review/       # Telegram review package and approval parser\\n   222|  publishing/   # platform-specific publishing metadata and later APIs\\n   223|  analytics/    # performance imports and feedback loop\\n   224|```\\n   225|\\n   226|## Non-negotiables\\n   227|\\n   228|- No credentials in git.\\n   229|- No generated media in git unless explicitly intended.\\n   230|- No automatic publishing without explicit approval.\\n   231|- Quality over speed; avoid generic AI-slop.\\n   232|\", \"total_lines\": 231, \"file_size\": 10992, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_qToHPqoBe7VE0TogpP71eSTO", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Deterministic content-to-review demo pipeline.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|from dataclasses import dataclass\\n     6|\\n     7|from autoshorts.content.brief import ContentBrief, create_content_brief\\n     8|from autoshorts.content.script_draft import ScriptDraft, create_script_draft\\n     9|from autoshorts.content.to_candidate import convert_script_draft_to_candidate\\n    10|from autoshorts.ideas.candidate import VideoCandidate\\n    11|from autoshorts.ideas.testbatch import TestBatch\\n    12|from autoshorts.review.render import render_review_batch_summary\\n    13|from autoshorts.review.workflow import ReviewBatch, build_review_batch\\n    14|from autoshorts.telegram.review_sender import ReviewDelivery, prepare_review_delivery\\n    15|\\n    16|\\n    17|@dataclass(frozen=True)\\n    18|class ContentDemoPipelineResult:\\n    19|    brief: ContentBrief\\n    20|    script: ScriptDraft\\n    21|    candidate: VideoCandidate\\n    22|    testbatch: TestBatch\\n    23|    review_batch: ReviewBatch\\n    24|    delivery: ReviewDelivery\\n    25|\\n    26|\\n    27|def build_ai_life_systems_demo_delivery() -> ContentDemoPipelineResult:\\n    28|    \\\"\\\"\\\"Run the deterministic AI-life-systems content demo through review prep.\\\"\\\"\\\"\\n    29|\\n    30|    brief = create_content_brief(\\n    31|        pillar_id=\\\"ai_life_systems\\\",\\n    32|        topic=\\\"AI weekly review routine\\\",\\n    33|        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n    34|        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    35|    )\\n    36|    script = create_script_draft(brief)\\n    37|    candidate = convert_script_draft_to_candidate(brief, script)\\n    38|    testbatch = TestBatch(\\n    39|        batch_id=\\\"content-demo-ai-life-systems\\\",\\n    40|        candidates=(candidate,),\\n    41|    )\\n    42|    review_batch = build_review_batch(testbatch)\\n    43|    review_text = render_review_batch_summary(review_batch)\\n    44|    delivery = prepare_review_delivery(review_text, topic=\\\"review\\\")\\n    45|\\n    46|    return ContentDemoPipelineResult(\\n    47|        brief=brief,\\n    48|        script=script,\\n    49|        candidate=candidate,\\n    50|        testbatch=testbatch,\\n    51|        review_batch=review_batch,\\n    52|        delivery=delivery,\\n    53|    )\\n    54|\", \"total_lines\": 53, \"file_size\": 1942, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_lQrR195rFv8QAzNS5s7pKYLb", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "assistant", "content": "", "reasoning": "**Planning test implementation**\n\nI need to start by writing tests and figuring out the validation style. The next step is to implement RenderManifest and design the necessary data classes. For the tests, I’ll ensure that creating a shot plan leads to the correct render manifest by checking settings like width, height, fps, and item count. I also need to validate that it rejects any invalid shot plans. Once done, I’ll use write_file and prepare to run the initial tests.", "finish_reason": "tool_calls", "reasoning_content": "**Planning test implementation**\n\nI need to start by writing tests and figuring out the validation style. The next step is to implement RenderManifest and design the necessary data classes. For the tests, I’ll ensure that creating a shot plan leads to the correct render manifest by checking settings like width, height, fps, and item count. I also need to validate that it rejects any invalid shot plans. Once done, I’ll use write_file and prepare to run the initial tests.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEud2eN2BAEPeQeQtReiB4QuUNp_deCvzx5Q9IxCFizGwBpmqvgNVgbRmspSzWKMJRwxwDwslO7sS0OEZvIu630GehXvnIRvb6CxetrxKCFQcI9Hvk1EvGVRIuWhonCeLDlq5nUvuefl1aEcwnIHU6dn_taNZu40P_76rPuBIZKhE3zwf40vK-iYb9Q6ngnf2fmsZOoENHTLG_pYq261TELDxpkICA4SHeIDGaWZxRPyfyZy9RdZdnAKVfgzV5Tzo_FdJdPfnq9Cp7v51Rzym0TDUSVvzu-eBTiloSmsczTGvi6a2TCFD1rEbrXRiYg3w6eV6SvewP-8bOTNHCDvlX-GCHi7rp-bWiEgsaMwVpewJtSGnyRx3F_uGJzVUurjMGWHc6ZPvizDdyT6gzi_MeAoY88eQ5q27pre7oZLxI5j-crkYOlj7gdjBtqNB79OBmx17iG0X1U_f2SSt73ICE8adfZlaUA2iYeoN-wMpes9PTAv526A4MTSND36jx1FIDjjSlN3VPHNqnc2GRo2KaYbC6OelmDgBtXJzpMNsTJ3jxTZit-V1vXOqMloBZ0bnU1-uOzoToe6ZNp4znHWP1MC3yysbNTLnAzOQfywD_xdTOGOppwbn7rUBO0hscoAiEt7ehon6Cz09qudD2DpXGBUiG97-KBCYu4JGYrByp1KrenjsS9QXehP1V-PpSMDVbpzC5qeiInhsIswEVmMuph6rWkIap5BnFrJ8qbWvX34xB-tBI5S5uUY9XSkzEHMOK6ffXEm4_s8XDutDp7Kd1N4rA9g2UsPIRZUZZUucbypXo35Y9nXbGdo58KYTmVXBL5G_cq39XftCIcDSb2TIMsY9Mpaac9qpMe5WsgsKJwTqcHjS4a0wAeNdAWYjWaXkouNmR61nCmhbKrI9FSx7nE5Inh3c78cKTpPIAgZmD9rKaLEFFSuG93hnX-vDSdNLB3QfA9AlYfKpgyrqi210fasxVsobjmPItkBnk6o1NojayqfX1-WznHQ6G4I0lS7PoeH-_fnin5Xz_DqA4A3bn7svQHDvEPxgKdwqr9fo2rnemUGmhxoV9E-J0jCi50WMHiAL412UBRV3ptJ2DHb8DW2Iy6fQ_6WenjsOOM9Jw79w1X8ty7yY-wZfp_DD9XFchgFeeuzfzA4c_x1ceSsr1zMA1rEmfybdX9bIdPfwf-FyR-j1vyciiv1ghtIZeQF6l-GexpaGO57puNnFiNHqTixuq-1PvqSLLtCekv4YMZZRYgp5qcjVg-vpLdDXkp4aiFKgcDICviWaOjWQb5ZkZPjlCC0V_ZwBrbA74GQrJPJbh7gqCzhxF96OkvRn06vsHgLC_CYTILKzcoYECMRkL3EHUysXYe966bFZ6mbHm9KuewTeBoxAZRd6glMKvjdF-QngJmVsnOYccCQ8hjEHTcr-1u2F2o_bjeYBwNefg6RPWBV2SFdGGMxpTJEbHEOB5UOCPUPT9c5oErWbCvGCfBLnXXPPh_EICHPbGrleG-2Tpba7UZ-2hJoLVKC8fndNf_GGSC0yvocwZfOfnNo3DRCeCgMfXgCo560UHVooX5I3ciUP2HexnXGwEsjWffPfBOwnItKm-yxrj5m03oeE29BDF2n9zo4qJ_zf2RuA8JbnCXU5hsifjlGi6siRcF1NEpeI", "id": "rs_0e72662c973527ee016a12e772407881918603fec261aaebc6", "summary": [{"type": "summary_text", "text": "**Planning test implementation**\n\nI need to start by writing tests and figuring out the validation style. The next step is to implement RenderManifest and design the necessary data classes. For the tests, I’ll ensure that creating a shot plan leads to the correct render manifest by checking settings like width, height, fps, and item count. I also need to validate that it rejects any invalid shot plans. Once done, I’ll use write_file and prepare to run the initial tests."}]}], "tool_calls": [{"id": "call_uBlMZSe3t0DOuCq4ErwnLrt9", "call_id": "call_uBlMZSe3t0DOuCq4ErwnLrt9", "response_item_id": "fc_0e72662c973527ee016a12e776d40481919e395a67776016bb", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_manifest.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.manifest import (\\n    RenderManifest,\\n    RenderSettings,\\n    build_render_manifest,\\n    validate_render_manifest,\\n)\\nfrom autoshorts.rendering.shot_plan import ShotPlan, ShotScene, create_shot_plan\\n\\n\\ndef _shot_plan():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    return create_shot_plan(script)\\n\\n\\ndef test_build_render_manifest_turns_shot_plan_into_vertical_timeline():\\n    plan = _shot_plan()\\n\\n    manifest = build_render_manifest(plan)\\n\\n    assert isinstance(manifest, RenderManifest)\\n    assert manifest.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert manifest.title == \\\"AI weekly review routine\\\"\\n    assert manifest.settings == RenderSettings(\\n        width=1080,\\n        height=1920,\\n        fps=30,\\n        no_generated_text_inside_assets=True,\\n        captions_renderer_owned=True,\\n        static_card_limit=0,\\n    )\\n    assert len(manifest.timeline) == len(plan.scenes)\\n    assert [item.scene_purpose for item in manifest.timeline] == [scene.purpose for scene in plan.scenes]\\n    assert manifest.timeline[0].start_seconds == 0\\n    assert manifest.timeline[-1].end_seconds == 35\\n    assert manifest.timeline[0].placeholder_asset_path == (\\n        \\\"placeholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\\\"\\n    )\\n    assert all(item.caption_layer.renderer_owned for item in manifest.timeline)\\n    assert all(item.label_layer.renderer_owned for item in manifest.timeline)\\n    assert all(\\\"static\\\" not in item.motion_instruction.casefold() for item in manifest.timeline)\\n\\n\\ndef test_validate_render_manifest_accepts_default_manifest():\\n    result = validate_render_manifest(build_render_manifest(_shot_plan()))\\n\\n    assert result.is_valid is True\\n    assert result.errors == ()\\n\\n\\ndef test_build_render_manifest_rejects_invalid_shot_plan():\\n    invalid_plan = ShotPlan(\\n        script_id=\\\"script\\\",\\n        title=\\\"Title\\\",\\n        estimated_duration_seconds=35,\\n        scenes=(\\n            ShotScene(\\n                purpose=\\\"first_frame_hook\\\",\\n                start_seconds=0,\\n                end_seconds=3,\\n                voiceover_beat=\\\"Hook\\\",\\n                visual_action=\\\"Static card\\\",\\n                asset_mode=\\\"static_slideshow\\\",\\n                caption_beat=\\\"Hook\\\",\\n                retention_goal=\\\"Stop scroll\\\",\\n            ),\\n        ),\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"static slideshow asset mode is not allowed\\\"):\\n        build_render_manifest(invalid_plan)\\n\\n\\ndef test_validate_render_manifest_rejects_unsafe_render_settings():\\n    manifest = build_render_manifest(_shot_plan())\\n    unsafe = RenderManifest(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        settings=RenderSettings(\\n            width=1920,\\n            height=1080,\\n            fps=24,\\n            no_generated_text_inside_assets=False,\\n            captions_renderer_owned=False,\\n            static_card_limit=1,\\n        ),\\n        timeline=manifest.timeline,\\n    )\\n\\n    result = validate_render_manifest(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"render size must be vertical 1080x1920\\\" in result.errors\\n    assert \\\"fps must be 30\\\" in result.errors\\n    assert \\\"generated text inside assets is not allowed\\\" in result.errors\\n    assert \\\"captions must be renderer-owned\\\" in result.errors\\n    assert \\\"static_card_limit must be 0\\\" in result.errors\\n\\n\\ndef test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers():\\n    manifest = build_render_manifest(_shot_plan())\\n    first = manifest.timeline[0]\\n    bad_item = type(first)(\\n        scene_purpose=first.scene_purpose,\\n        start_seconds=first.start_seconds,\\n        end_seconds=first.end_seconds,\\n        asset_mode=first.asset_mode,\\n        motion_instruction=first.motion_instruction,\\n        placeholder_asset_path=first.placeholder_asset_path,\\n        caption_layer=type(first.caption_layer)(text=first.caption_layer.text, renderer_owned=False),\\n        label_layer=type(first.label_layer)(text=first.label_layer.text, renderer_owned=False),\\n    )\\n    unsafe = RenderManifest(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        settings=manifest.settings,\\n        timeline=(bad_item,) + manifest.timeline[1:],\\n    )\\n\\n    result = validate_render_manifest(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"timeline item 1 caption layer must be renderer-owned\\\" in result.errors\\n    assert \\\"timeline item 1 label layer must be renderer-owned\\\" in result.errors\\n\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 4888, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_manifest.py\\\">\\nERROR [5:6] Import \\\"autoshorts.rendering.manifest\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_uBlMZSe3t0DOuCq4ErwnLrt9", "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_render_manifest.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_manifest.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_render_manifest.py:5: in <module>\\n    from autoshorts.rendering.manifest import (\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.manifest'\\n=========================== short test summary info ============================\\nERROR tests/test_render_manifest.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_MmwzmqUcwWrPu0hfDIbV9WUW", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "assistant", "content": "", "reasoning": "**Implementing validation logic**\n\nAlright, I'm diving into this implementation. I’ll need to import ValidationResult and consider using dataclasses. For the motion instruction, I should probably avoid static from visual_action. However, the test expects no static involved, which is tricky with the \"static card.\" I’ll make it so that if visual_action has Static, it defaults to no. It’s all about using functions efficiently! Let’s keep that in mind while I work through the code.", "finish_reason": "tool_calls", "reasoning_content": "**Implementing validation logic**\n\nAlright, I'm diving into this implementation. I’ll need to import ValidationResult and consider using dataclasses. For the motion instruction, I should probably avoid static from visual_action. However, the test expects no static involved, which is tricky with the \"static card.\" I’ll make it so that if visual_action has Static, it defaults to no. It’s all about using functions efficiently! Let’s keep that in mind while I work through the code.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEueZXHUE39uqPuNzsHzbvkmjligObWYS3r_IMsocb6xQ0SeO08jeYej1ewkSGTMYEO7uLDeD6t7w5tqrBhPjbLj8tXILI5R-ohfLrw08ThGqUgv7Jint1gj9W39dUw3in-jRtVFr1YnwXy8oLtugGWu59-N0FwOG0HX-2xglnUxK3WuG8XjrUrJdpAqw4FL39q9Zuefk0MQO3-o8n6zD-h-uqAJjMlcfefHzky5bijuBrlbe4vat0iRUNBjJYKdvmNsNqX5LKn18KEjdtWaECoIoO6ECtDVxS5fVs9Qgo-6QnT6ZA2TmtUDM7pu1-NRwmClNk9szrY-WwWvBSyt9euT6p-_9N1fMfOUxBaoi3hnPHvmNav98wGJCxFtw4KSfqugY7dFaUaosEiHJTYFIK8v6dKnwbHxrD8l9nYnepxe4BjyVv3Nnt4VdGPX2KWEqxamp35PNcwB1c8UZp3zvF8oTU93GkfAM_KRML3YEO0w8rryAs_KMikNvTzHKI1MKlGkANIhHGhFSPcLYB-hdVQGnWukfBvomVqaibT2QrgXXBC6RcJl1-DLgkQUk4OUAYgh2HwPRHBBxvOnRSPV1eMmmXfutGKQyK4kqUUhTeeQc-v_7Q6l-LJwbwcr4lCaEJDS18nqSS_7B6m3AECWfVlo0woDJTY5tz-TPDmDsF-N0Kb5qLBI3sVu3EuIfuQBIREB9ZMJQUWvnoMIwHCkIA_dnx894qQwy690_VGKSvpw7HoDXqBpD7Em_iF-1Gp49e3OTK6EZv7eG3gj1mWDwVUw91lSfB7bhjJgFNDeBaA0IF2xzLefGymh3FSPUXEb-_3rc-82nZi_uSzVAsnsWYnBOmWvdyeW9Q9JDF6x86e9nm3g2EfxREg1EQAqae3d9zzNmBCtxYK7x6HjKaf2uYU2_0Ghhg5YXBPtQqGG9uN3aoevJIyFakDNCqWsPxsmjtcMfh9G7Me6wEcCPqd0h5F0UdFZozMzJkWghOfEL2SP3DCtyRUxMRPwR0mtSF59kTj-_fZe15J33kc7yJJhh2PNOjsMmDcXqAcWinL5iwhJPcPG1wy2PrTnjxHfSvJXFXlGQsBWlXehn7-1HmbPQvx9GkA5K9gulOiH2sfCj_kBk5p72ek-zYNrriSssn-spozuamcc-zgThjHhzwuh_ZOJU2WGyA4RqPyK3pY9R_aZA6Q89GxKjcpIF-ZV1t5-azFqZQikREEudC0IdEgUs4VpLaNKv1J1cAF9JDkoaPDexsdF1sB_aPsOhDnKfs9OWgy26gw9Co2twwNddK9NStBjWdw==", "id": "rs_0f5d7f158116c8e3016a12e797403c8191becf3e9a32927901", "summary": [{"type": "summary_text", "text": "**Implementing validation logic**\n\nAlright, I'm diving into this implementation. I’ll need to import ValidationResult and consider using dataclasses. For the motion instruction, I should probably avoid static from visual_action. However, the test expects no static involved, which is tricky with the \"static card.\" I’ll make it so that if visual_action has Static, it defaults to no. It’s all about using functions efficiently! Let’s keep that in mind while I work through the code."}]}], "tool_calls": [{"id": "call_2pnQ4x2vjiPjKcbyGeqs9syr", "call_id": "call_2pnQ4x2vjiPjKcbyGeqs9syr", "response_item_id": "fc_0f5d7f158116c8e3016a12e799f6c48191b8f01a51bf696ada", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/manifest.py\",\"content\":\"\\\"\\\"\\\"Side-effect-free render manifest generation from shot plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.shot_plan import ShotPlan, validate_shot_plan\\n\\n\\n@dataclass(frozen=True)\\nclass RenderSettings:\\n    width: int = 1080\\n    height: int = 1920\\n    fps: int = 30\\n    no_generated_text_inside_assets: bool = True\\n    captions_renderer_owned: bool = True\\n    static_card_limit: int = 0\\n\\n\\n@dataclass(frozen=True)\\nclass RenderLayer:\\n    text: str\\n    renderer_owned: bool = True\\n\\n\\n@dataclass(frozen=True)\\nclass RenderTimelineItem:\\n    scene_purpose: str\\n    start_seconds: int\\n    end_seconds: int\\n    asset_mode: str\\n    motion_instruction: str\\n    placeholder_asset_path: str\\n    caption_layer: RenderLayer\\n    label_layer: RenderLayer\\n\\n\\n@dataclass(frozen=True)\\nclass RenderManifest:\\n    script_id: str\\n    title: str\\n    settings: RenderSettings\\n    timeline: tuple[RenderTimelineItem, ...]\\n\\n\\ndef _is_blank(value: str) -> bool:\\n    return not (value or \\\"\\\").strip()\\n\\n\\ndef _placeholder_path(script_id: str, index: int, purpose: str) -> str:\\n    return f\\\"placeholders/{script_id}/{index:02d}-{purpose}.png\\\"\\n\\n\\ndef build_render_manifest(plan: ShotPlan) -> RenderManifest:\\n    \\\"\\\"\\\"Turn a validated shot plan into a machine-readable render manifest.\\\"\\\"\\\"\\n\\n    validation = validate_shot_plan(plan)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n    timeline = tuple(\\n        RenderTimelineItem(\\n            scene_purpose=scene.purpose,\\n            start_seconds=scene.start_seconds,\\n            end_seconds=scene.end_seconds,\\n            asset_mode=scene.asset_mode,\\n            motion_instruction=scene.visual_action,\\n            placeholder_asset_path=_placeholder_path(plan.script_id, index, scene.purpose),\\n            caption_layer=RenderLayer(text=scene.caption_beat, renderer_owned=True),\\n            label_layer=RenderLayer(text=scene.purpose.replace(\\\"_\\\", \\\" \\\"), renderer_owned=True),\\n        )\\n        for index, scene in enumerate(plan.scenes, start=1)\\n    )\\n    return RenderManifest(\\n        script_id=plan.script_id,\\n        title=plan.title,\\n        settings=RenderSettings(),\\n        timeline=timeline,\\n    )\\n\\n\\ndef validate_render_manifest(manifest: RenderManifest) -> ValidationResult:\\n    errors: list[str] = []\\n    if _is_blank(manifest.script_id):\\n        errors.append(\\\"script_id is required\\\")\\n    if _is_blank(manifest.title):\\n        errors.append(\\\"title is required\\\")\\n\\n    settings = manifest.settings\\n    if (settings.width, settings.height) != (1080, 1920):\\n        errors.append(\\\"render size must be vertical 1080x1920\\\")\\n    if settings.fps != 30:\\n        errors.append(\\\"fps must be 30\\\")\\n    if not settings.no_generated_text_inside_assets:\\n        errors.append(\\\"generated text inside assets is not allowed\\\")\\n    if not settings.captions_renderer_owned:\\n        errors.append(\\\"captions must be renderer-owned\\\")\\n    if settings.static_card_limit != 0:\\n        errors.append(\\\"static_card_limit must be 0\\\")\\n\\n    if not manifest.timeline:\\n        errors.append(\\\"at least one timeline item is required\\\")\\n\\n    previous_end: int | None = None\\n    for index, item in enumerate(manifest.timeline, start=1):\\n        if _is_blank(item.scene_purpose):\\n            errors.append(f\\\"timeline item {index} scene_purpose is required\\\")\\n        if item.end_seconds <= item.start_seconds:\\n            errors.append(f\\\"timeline item {index} end_seconds must be greater than start_seconds\\\")\\n        if previous_end is not None and item.start_seconds < previous_end:\\n            errors.append(f\\\"timeline item {index} start_seconds overlaps previous item\\\")\\n        previous_end = item.end_seconds\\n\\n        for field_name, value in {\\n            \\\"asset_mode\\\": item.asset_mode,\\n            \\\"motion_instruction\\\": item.motion_instruction,\\n            \\\"placeholder_asset_path\\\": item.placeholder_asset_path,\\n            \\\"caption_layer text\\\": item.caption_layer.text,\\n            \\\"label_layer text\\\": item.label_layer.text,\\n        }.items():\\n            if _is_blank(value):\\n                errors.append(f\\\"timeline item {index} {field_name} is required\\\")\\n\\n        if item.asset_mode == \\\"static_slideshow\\\":\\n            errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n        if \\\"baked-in\\\" in item.motion_instruction.casefold():\\n            errors.append(\\\"baked-in text in render assets is not allowed\\\")\\n        if not item.caption_layer.renderer_owned:\\n            errors.append(f\\\"timeline item {index} caption layer must be renderer-owned\\\")\\n        if not item.label_layer.renderer_owned:\\n            errors.append(f\\\"timeline item {index} label layer must be renderer-owned\\\")\\n\\n    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
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{"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@@ -20,6 +20,7 @@\\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 - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_otbJx1Y7bSx8gwQHxhA2pc87", "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_4ysRup1g0SbJerSUlF1UFDhE", "call_id": "call_4ysRup1g0SbJerSUlF1UFDhE", "response_item_id": "fc_00ee909a45132f73016a12e7c563fc8191bf622b1ee8861c6a", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"`validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n\\n## Human approval commands\",\"new_string\":\"`validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n\\n`autoshorts.rendering.manifest.build_render_manifest()` turns a validated `ShotPlan` into a machine-readable `RenderManifest` with:\\n\\n- vertical `1080x1920` render settings;\\n- `30` fps;\\n- one timeline item per shot scene;\\n- motion instructions inherited from the shot plan;\\n- deterministic placeholder asset paths;\\n- renderer-owned caption and label layers;\\n- `no_generated_text_inside_assets=True`;\\n- `static_card_limit=0`.\\n\\n`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n\\n## Human approval commands\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
{"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@@ -79,6 +79,19 @@\\n \\n `validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n \\n+`autoshorts.rendering.manifest.build_render_manifest()` turns a validated `ShotPlan` into a machine-readable `RenderManifest` with:\\n+\\n+- vertical `1080x1920` render settings;\\n+- `30` fps;\\n+- one timeline item per shot scene;\\n+- motion instructions inherited from the shot plan;\\n+- deterministic placeholder asset paths;\\n+- renderer-owned caption and label layers;\\n+- `no_generated_text_inside_assets=True`;\\n+- `static_card_limit=0`.\\n+\\n+`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_4ysRup1g0SbJerSUlF1UFDhE", "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "assistant", "content": "", "reasoning": "**Checking commit status**\n\nI need to commit my changes, so first, I should check the status to see what’s been modified. I’ll also want to look at the differences in the files to ensure everything is correct before proceeding. It’s important to be thorough here, so I can avoid any surprises later. Let’s take a moment to go through the status and see what’s been updated. I'll ensure my work is ready for a clean commit!", "finish_reason": "tool_calls", "reasoning_content": "**Checking commit status**\n\nI need to commit my changes, so first, I should check the status to see what’s been modified. I’ll also want to look at the differences in the files to ensure everything is correct before proceeding. It’s important to be thorough here, so I can avoid any surprises later. Let’s take a moment to go through the status and see what’s been updated. I'll ensure my work is ready for a clean commit!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEufXmlIgeZhOgVUnLGPsGv9MzNBeIi52FPQofl9ZlRK5xOqYPE1lsXwSEzomcFXl7MDMvR0OVHasPpLsZBSTBMoah7w1WobLV5Ky6NBu-C2I9f5DwmjkOvrmul7G3_0JUy5YZeG5mcsEP2zknqtLQOmzvOrCoKBD5xqFjpT8S97UpQ61Evazh2xGIyJJn2qtq674t5a8PxG5PTUIf0Vr5QviOCVcr3RuVjOb2UCVIz5WmxDDV4iGyb8w7pzOqZk748iQN_Jlw73i4OLHxInBwbucuV3DXTU4z2rcBRvPNvRQAfmvYodssUAwVyRrSnddE3ZGpQO32geQVfW3Yq4Xph7o0vgRai5CDVC3eFr7RvlC0XQpm7X6NhuK7mQ50Cxdk7ZlWkXgPdfROLVNkXP6YsiEuHLXPUyNFJb3Sqs5q7mPwUoYSAJBInYxxBtB4M46ULXloaJ4iQkyHj_NRRsiS7jAGM6Z8VXMEkmFov1ojHxMmUgfS6H9r9_lx-xozcRr3k2HTMWkzBFh0g8EKlnK_uCodwqM2fL9RwKGA0X4nOKKQdDslXbUNZ-LtKXMqmcwNWokRHkl235GOtkP7t8MJgKZzYbaX90vYitAWtV_3VkN0njs3pCd3QHEjMhp_-_MvFQwYhl6BW-7tXf37T5LjXHyfjR72ANg4DwHJY0loj0fcEV9BFOPaq09my-tO029w6_MS_Z85PngOQHh9FPVL-s5EiY2jCLU1gau0Ch-tN5qr9-bfZCV26QBfUTVrn06UPCLsrswxAMz0goiNpa05RBDTBNGlyi9v21Tsy-2H1SsJDS6IjXbkd_l44fewQzjTwzTVEbvRWeAJ6XT4LR5nn2CmWCz8YceNOKfbI84l-VdNHZhwRGxxENrT0c25IHBLYysHkxC0U607ShFChwVd28dfb8tgPVV3MTZWmpS5rLxwKxQvXgOb4BWjcfwsPdnf23S", "id": "rs_0c2b81bc1f330601016a12e7d65db08191879a72ccf5d18197", "summary": [{"type": "summary_text", "text": "**Checking commit status**\n\nI need to commit my changes, so first, I should check the status to see what’s been modified. I’ll also want to look at the differences in the files to ensure everything is correct before proceeding. It’s important to be thorough here, so I can avoid any surprises later. Let’s take a moment to go through the status and see what’s been updated. I'll ensure my work is ready for a clean commit!"}]}], "tool_calls": [{"id": "call_6oqLjMdqraNzrYt5PjjUQnor", "call_id": "call_6oqLjMdqraNzrYt5PjjUQnor", "response_item_id": "fc_0c2b81bc1f330601016a12e7d7b8a08191b785ac24166b9fbc", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git diff -- README.md autoshorts/rendering/manifest.py tests/test_render_manifest.py | sed -n '1,260p'\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/manifest.py\\n?? tests/test_render_manifest.py\\ndiff --git a/README.md b/README.md\\nindex d3493bd..3fce936 100644\\n--- a/README.md\\n+++ b/README.md\\n@@ -20,6 +20,7 @@ Implemented foundation:\\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 - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n@@ -78,6 +79,19 @@ Each `ShotScene` carries timing, voiceover beat, visual action, asset mode, capt\\n \\n `validate_shot_plan()` rejects static slideshow asset modes, baked-in fake UI text, non-renderer-owned labels/captions, bad timing, missing fields, and plans whose first scene is not the first-frame hook.\\n \\n+`autoshorts.rendering.manifest.build_render_manifest()` turns a validated `ShotPlan` into a machine-readable `RenderManifest` with:\\n+\\n+- vertical `1080x1920` render settings;\\n+- `30` fps;\\n+- one timeline item per shot scene;\\n+- motion instructions inherited from the shot plan;\\n+- deterministic placeholder asset paths;\\n+- renderer-owned caption and label layers;\\n+- `no_generated_text_inside_assets=True`;\\n+- `static_card_limit=0`.\\n+\\n+`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_6oqLjMdqraNzrYt5PjjUQnor", "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "assistant", "content": "", "reasoning": "**Finalizing German text**\n\nI realize I need to wrap up the German text in a concise manner. But I need to verify that it's clean and correctly formatted before finalizing it. It’s important for me to ensure everything looks good, so I'll double-check for any errors or awkward phrasing. I want to provide something polished and accurate. Let's make sure it's ready to go!", "finish_reason": "tool_calls", "reasoning_content": "**Finalizing German text**\n\nI realize I need to wrap up the German text in a concise manner. But I need to verify that it's clean and correctly formatted before finalizing it. It’s important for me to ensure everything looks good, so I'll double-check for any errors or awkward phrasing. I want to provide something polished and accurate. Let's make sure it's ready to go!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEufjLv83Yh8Y9cGerE2fiuN0S58dxWe612Iy_kD2RIkROr00UKaZ-btFVv4x5Fr9D4BAP-obuScb0eB0DTuugyhip9mXeMJm9RL-Btx6A32HTJ7O3V1rgpgBGfPY6zy4bQQzTpJ4EjQrpFPD7LMxjU45eCb3kEURpMMMIrqZOEueKDFJPYxLsh_D7rw-8ct5chTSUeBjuKQRivcAI4WMthwwzSZ8x17aalnUC3AhaqtesFlkXIda7GA1oLy9nYCFK35xIsb61l0UmCLDBZeLo5UHoM7n2nOp6LW8ThQO9-quESW8q7IjFpHn-1U3F1-gYBr4xM3jbyRIAp2XFnBfASgZ9FLgzDX-BPxgfSBKVlEqnsHDdBteTWUQgR6wRuSk-x_D2y9Da0IqvyphE9sWDRWPIR97Lkb8xfVesJeqp2R1V3Egs5mPuEWtD43PUdnIYp8R4ZaakQ2-Qt-dbszI5A12-fCfXiOP0reDox-pD0ZX8Dj4WkX03_Fw9LsxPPWp12r5SiODmo2j8EqOZ8Vif3faRMyL59z_se9iL69q50aZHJ2K_g56-WovAmZpXz0bAsq9Z90eaU01h06Vrig1SKfY_Skc7vKumrPIclOi4YjgmlFfT1afhzc7tUGMkRJtImoKnTQPyTqTMjcTfoXXC5Bp_I17ByQ92mqm_A8BteUrJ1nmhJDAnDo1ZUJty1sUnkMxlXGtkGndfZuwXbPOqSIJOW_G-7B0rIuddJ1ot78GFDker7QWjnrEp3fMjibWF9ah70hN-t0vaAjd1KeljqsXT7uZ5-sYxPgRTjGUcyhUnTgZDLj3kZ8vJVv9MCfGbUphZaM5bKI6N9T5OCf1O6f5iL5CC6tKDPTncQce7u-Jq6VHTyyYDmvZhCmPMMzShRp27ISjCihzcB0ymzs_b4nj2nKm-8dNxM64A87lrKvooH1gxIqYVoCVpdeN2MglOColSKeo_yPjf4OaEV0piuuuxQ==", "id": "rs_0d3a01064bd31468016a12e7e1ea788191bd462d42511e8ff9", "summary": [{"type": "summary_text", "text": "**Finalizing German text**\n\nI realize I need to wrap up the German text in a concise manner. But I need to verify that it's clean and correctly formatted before finalizing it. It’s important for me to ensure everything looks good, so I'll double-check for any errors or awkward phrasing. I want to provide something polished and accurate. Let's make sure it's ready to go!"}]}], "tool_calls": [{"id": "call_421FwF5xqeJObIaYj7idUsq7", "call_id": "call_421FwF5xqeJObIaYj7idUsq7", "response_item_id": "fc_0d3a01064bd31468016a12e7e329b881918cb601be6bda9dff", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T13:58:45.361796"}
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{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Render Manifest Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/manifest.py\ntests/test_render_manifest.py\n```\n\n## Neue API\n\n```python\nbuild_render_manifest(plan: ShotPlan) -> RenderManifest\nvalidate_render_manifest(manifest: RenderManifest) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderManifest\n```\n\n## RenderManifest-Struktur\n\n```python\nRenderManifest(\n    script_id=...,\n    title=...,\n    settings=RenderSettings(...),\n    timeline=(...)\n)\n```\n\n### Render Settings\n\nAktuell hart und bewusst konservativ:\n\n```python\nRenderSettings(\n    width=1080,\n    height=1920,\n    fps=30,\n    no_generated_text_inside_assets=True,\n    captions_renderer_owned=True,\n    static_card_limit=0,\n)\n```\n\nAlso vertikal, Shorts-tauglich, captions/labels im Renderer — nicht in generierten Assets eingebrannt. Genau richtig, weil AI-Modelle bei Text gern aussehen, als hätte ein Drucker Dämonenbeschwörung gelernt.\n\n## Timeline Items\n\nPro ShotScene entsteht ein `RenderTimelineItem` mit:\n\n```text\nscene_purpose\nstart_seconds\nend_seconds\nasset_mode\nmotion_instruction\nplaceholder_asset_path\ncaption_layer\nlabel_layer\n```\n\nBeispiel verifiziert:\n\n```text\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\n```\n\n## Guardrails\n\n`validate_render_manifest()` rejected:\n\n- falsches Format statt `1080x1920`\n- falsche FPS statt `30`\n- generated text inside assets\n- nicht renderer-owned captions\n- `static_card_limit != 0`\n- leere Timeline\n- schlechte Timeline-Timings\n- `static_slideshow`\n- nicht renderer-owned caption/label layers\n- fehlende Pflichtfelder\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.manifest'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n74 passed in 0.11s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n(1080, 1920, 30)\n5\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\nTrue\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.manifest`\n- RenderManifest\n- Timeline Items\n- Vertical Settings\n- Safety Constraints\n- keine Render-/API-Side-Effects\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\ne258635 feat: add render manifest planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir einen lokalen Preview-Renderer bauen — weiterhin ohne ComfyUI/Wan/Qwen/API:\n\n```text\nRenderManifest\n→ Local Preview Frames / HTML Preview\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/rendering/preview.py\ntests/test_render_preview.py\n```\n\nZiel:\n\n- aus `RenderManifest` eine side-effect-free oder temp-dir-basierte Preview erzeugen\n- erstmal als HTML/SVG Timeline oder simple frame manifest\n- keine echten AI Assets\n- keine Videos\n- keine externen Calls\n- aber sichtbar genug, um Timing, Captions, Scene Flow und Motion Instructions zu prüfen\n\nDanach erst Qwen-Image/Wan/ComfyUI anbinden. Erst die Regie, dann die GPU. Sonst macht die Maschine teure Kunst mit null dramaturgischem Rückgrat.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Render Manifest Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/manifest.py\ntests/test_render_manifest.py\n```\n\n## Neue API\n\n```python\nbuild_render_manifest(plan: ShotPlan) -> RenderManifest\nvalidate_render_manifest(manifest: RenderManifest) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderManifest\n```\n\n## RenderManifest-Struktur\n\n```python\nRenderManifest(\n    script_id=...,\n    title=...,\n    settings=RenderSettings(...),\n    timeline=(...)\n)\n```\n\n### Render Settings\n\nAktuell hart und bewusst konservativ:\n\n```python\nRenderSettings(\n    width=1080,\n    height=1920,\n    fps=30,\n    no_generated_text_inside_assets=True,\n    captions_renderer_owned=True,\n    static_card_limit=0,\n)\n```\n\nAlso vertikal, Shorts-tauglich, captions/labels im Renderer — nicht in generierten Assets eingebrannt. Genau richtig, weil AI-Modelle bei Text gern aussehen, als hätte ein Drucker Dämonenbeschwörung gelernt.\n\n## Timeline Items\n\nPro ShotScene entsteht ein `RenderTimelineItem` mit:\n\n```text\nscene_purpose\nstart_seconds\nend_seconds\nasset_mode\nmotion_instruction\nplaceholder_asset_path\ncaption_layer\nlabel_layer\n```\n\nBeispiel verifiziert:\n\n```text\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\n```\n\n## Guardrails\n\n`validate_render_manifest()` rejected:\n\n- falsches Format statt `1080x1920`\n- falsche FPS statt `30`\n- generated text inside assets\n- nicht renderer-owned captions\n- `static_card_limit != 0`\n- leere Timeline\n- schlechte Timeline-Timings\n- `static_slideshow`\n- nicht renderer-owned caption/label layers\n- fehlende Pflichtfelder\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.manifest'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n74 passed in 0.11s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n(1080, 1920, 30)\n5\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\nTrue\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.manifest`\n- RenderManifest\n- Timeline Items\n- Vertical Settings\n- Safety Constraints\n- keine Render-/API-Side-Effects\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\ne258635 feat: add render manifest planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir einen lokalen Preview-Renderer bauen — weiterhin ohne ComfyUI/Wan/Qwen/API:\n\n```text\nRenderManifest\n→ Local Preview Frames / HTML Preview\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/rendering/preview.py\ntests/test_render_preview.py\n```\n\nZiel:\n\n- aus `RenderManifest` eine side-effect-free oder temp-dir-basierte Preview erzeugen\n- erstmal als HTML/SVG Timeline oder simple frame manifest\n- keine echten AI Assets\n- keine Videos\n- keine externen Calls\n- aber sichtbar genug, um Timing, Captions, Scene Flow und Motion Instructions zu prüfen\n\nDanach erst Qwen-Image/Wan/ComfyUI anbinden. Erst die Regie, dann die GPU. Sonst macht die Maschine teure Kunst mit null dramaturgischem Rückgrat."}], "id": "msg_060035eb87c40e14016a12e7e594b881918f4e77533a44eeeb", "phase": "final_answer"}], "timestamp": "2026-05-24T13:58:45.361796"}
{"role": "assistant", "content": "", "reasoning": "**Considering skill loading**\n\nI think we need to continue by loading the relevant skill for Test-Driven Development (TDD). Each turn, I should scan for skills, but maybe it's unnecessary to load again if they're already there. I need to assess whether to implement a preview and inspect what tools are needed. So, it feels essential to identify any relevant skills before proceeding, but I’m wondering if I might be overcomplicating things!", "finish_reason": "tool_calls", "reasoning_content": "**Considering skill loading**\n\nI think we need to continue by loading the relevant skill for Test-Driven Development (TDD). Each turn, I should scan for skills, but maybe it's unnecessary to load again if they're already there. I need to assess whether to implement a preview and inspect what tools are needed. So, it feels essential to identify any relevant skills before proceeding, but I’m wondering if I might be overcomplicating things!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEug2C0d2Nka81XuDfK7JAdtpehGSaRKmGJi5UyLlq1UBvKDv4_drZDItzPWZx8x5BKLepv7xd-XO5osRppYYjNHzNXManUuS78_S6aHCQ_V0yjN8Zn-7u3xPEPoJAy73CxAbjxFvN3p9vALvyUvHqt1xZ95ZH3ooJmtR-eMmBApZoYUZZfekZtuB50RDQlcp4BCVbdXHVzvDc18WIWApS3MVWmWaBSHFselvv2W6unRRkEHnYwVFUJtTqjtDSm4rqUGMXupAceLXsewcrgzH1JICCFJBMLbggB96dDsCj3__A-FzkXKpf9kCTA9vSNtT87Plf1dfX_Gz816Vts7RKltnncHd8Dq67bHpTPI4JElQSvBMqjQSfbj7QnM8EqoD2jl2UoatE5hT_49aYNlJrTbZMiVA--9LX_mcihbbCLp-VNcFPPqLFSnUf5d9qiO4DOF_3QapJdjMLzTKYn5RVyKAy9h05juYZfIUtAcfZj-fNfMFaeGkplLsq0Kb-gxo0-yMAs4ClPc4qIv0AQ1VyyglwM0V5RsPMaIhPyxovyYhw3ms2Aph40YRniV9E5f_Fnl9pTmTMZRoZQpC8OCOlYHEvLgE2Zy0v1YyYChODc7mO_xxCOjtGNZBixANJ_kNGstQKzq_Iv2Bqxzj990jDByHmWHK_F5aGgzJX7pbIxNENZ1RnKfUrw3l_zR_9dt5JR-Oef4jku3X6QT3kFEfTTzP9OawsGZwgwpLTktg6nKCvXgrEKQD0g4zt2FOHINCPxP7VfPiqaCpb6Vh4LeN896ubPKlUcoALNvRFG0zC3OUIz5qY5W6W9p1_g2SQjNgHPqedOfmp4aze7rdbiZgSWtUzIyNCPw8IJLypwbXJ3B1ZemKd4b0uD-WikfkURNbSlF5XUHwvSNwLJ-Brf6k_haf1IxDCyboISOHTNWbGf9aFQ66npNhR72yA7G82Zbx0fpnPgfPtqsIrcsWcBNuQQgCgNvL59SUXdBXNvJYvFnpX4nBXCAaX1JXjwoxs5GHXqlJ-Wla5p7VpKa2ug6VrrlJiCls7hSs7GOHhrNA2X3rr3xcVPHCdg_xMsZzCN7YPpqNEkj4bRWlefHDYed25KNaIjXFAojKyFPux5EQk8UAR0QVqa7HZmxAm3GGjyYv-DEgqkaGTdFPcQHmqmi_b9QkK3Ip2mulDW5hvmgSh68nU0LcfN6gMgAzJvzmGxOAyS6j", "id": "rs_060035eb87c40e14016a12e833c5048191a3ac28fc31b2a6d6", "summary": [{"type": "summary_text", "text": "**Considering skill loading**\n\nI think we need to continue by loading the relevant skill for Test-Driven Development (TDD). Each turn, I should scan for skills, but maybe it's unnecessary to load again if they're already there. I need to assess whether to implement a preview and inspect what tools are needed. So, it feels essential to identify any relevant skills before proceeding, but I’m wondering if I might be overcomplicating things!"}]}], "tool_calls": [{"id": "call_5WZFUg4pHPVlka3CGpnAJaKY", "call_id": "call_5WZFUg4pHPVlka3CGpnAJaKY", "response_item_id": "fc_060035eb87c40e14016a12e8364f348191a2fcf938e4fc83e0", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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## 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_5WZFUg4pHPVlka3CGpnAJaKY", "timestamp": "2026-05-24T14:01:56.422229"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Side-effect-free render manifest generation from shot plans.\\\"\\\"\\\"\\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|from autoshorts.rendering.shot_plan import ShotPlan, validate_shot_plan\\n     9|\\n    10|\\n    11|@dataclass(frozen=True)\\n    12|class RenderSettings:\\n    13|    width: int = 1080\\n    14|    height: int = 1920\\n    15|    fps: int = 30\\n    16|    no_generated_text_inside_assets: bool = True\\n    17|    captions_renderer_owned: bool = True\\n    18|    static_card_limit: int = 0\\n    19|\\n    20|\\n    21|@dataclass(frozen=True)\\n    22|class RenderLayer:\\n    23|    text: str\\n    24|    renderer_owned: bool = True\\n    25|\\n    26|\\n    27|@dataclass(frozen=True)\\n    28|class RenderTimelineItem:\\n    29|    scene_purpose: str\\n    30|    start_seconds: int\\n    31|    end_seconds: int\\n    32|    asset_mode: str\\n    33|    motion_instruction: str\\n    34|    placeholder_asset_path: str\\n    35|    caption_layer: RenderLayer\\n    36|    label_layer: RenderLayer\\n    37|\\n    38|\\n    39|@dataclass(frozen=True)\\n    40|class RenderManifest:\\n    41|    script_id: str\\n    42|    title: str\\n    43|    settings: RenderSettings\\n    44|    timeline: tuple[RenderTimelineItem, ...]\\n    45|\\n    46|\\n    47|def _is_blank(value: str) -> bool:\\n    48|    return not (value or \\\"\\\").strip()\\n    49|\\n    50|\\n    51|def _placeholder_path(script_id: str, index: int, purpose: str) -> str:\\n    52|    return f\\\"placeholders/{script_id}/{index:02d}-{purpose}.png\\\"\\n    53|\\n    54|\\n    55|def build_render_manifest(plan: ShotPlan) -> RenderManifest:\\n    56|    \\\"\\\"\\\"Turn a validated shot plan into a machine-readable render manifest.\\\"\\\"\\\"\\n    57|\\n    58|    validation = validate_shot_plan(plan)\\n    59|    if not validation.is_valid:\\n    60|        raise ValueError(\\\"; \\\".join(validation.errors))\\n    61|\\n    62|    timeline = tuple(\\n    63|        RenderTimelineItem(\\n    64|            scene_purpose=scene.purpose,\\n    65|            start_seconds=scene.start_seconds,\\n    66|            end_seconds=scene.end_seconds,\\n    67|            asset_mode=scene.asset_mode,\\n    68|            motion_instruction=scene.visual_action,\\n    69|            placeholder_asset_path=_placeholder_path(plan.script_id, index, scene.purpose),\\n    70|            caption_layer=RenderLayer(text=scene.caption_beat, renderer_owned=True),\\n    71|            label_layer=RenderLayer(text=scene.purpose.replace(\\\"_\\\", \\\" \\\"), renderer_owned=True),\\n    72|        )\\n    73|        for index, scene in enumerate(plan.scenes, start=1)\\n    74|    )\\n    75|    return RenderManifest(\\n    76|        script_id=plan.script_id,\\n    77|        title=plan.title,\\n    78|        settings=RenderSettings(),\\n    79|        timeline=timeline,\\n    80|    )\\n    81|\\n    82|\\n    83|def validate_render_manifest(manifest: RenderManifest) -> ValidationResult:\\n    84|    errors: list[str] = []\\n    85|    if _is_blank(manifest.script_id):\\n    86|        errors.append(\\\"script_id is required\\\")\\n    87|    if _is_blank(manifest.title):\\n    88|        errors.append(\\\"title is required\\\")\\n    89|\\n    90|    settings = manifest.settings\\n    91|    if (settings.width, settings.height) != (1080, 1920):\\n    92|        errors.append(\\\"render size must be vertical 1080x1920\\\")\\n    93|    if settings.fps != 30:\\n    94|        errors.append(\\\"fps must be 30\\\")\\n    95|    if not settings.no_generated_text_inside_assets:\\n    96|        errors.append(\\\"generated text inside assets is not allowed\\\")\\n    97|    if not settings.captions_renderer_owned:\\n    98|        errors.append(\\\"captions must be renderer-owned\\\")\\n    99|    if settings.static_card_limit != 0:\\n   100|        errors.append(\\\"static_card_limit must be 0\\\")\\n   101|\\n   102|    if not manifest.timeline:\\n   103|        errors.append(\\\"at least one timeline item is required\\\")\\n   104|\\n   105|    previous_end: int | None = None\\n   106|    for index, item in enumerate(manifest.timeline, start=1):\\n   107|        if _is_blank(item.scene_purpose):\\n   108|            errors.append(f\\\"timeline item {index} scene_purpose is required\\\")\\n   109|        if item.end_seconds <= item.start_seconds:\\n   110|            errors.append(f\\\"timeline item {index} end_seconds must be greater than start_seconds\\\")\\n   111|        if previous_end is not None and item.start_seconds < previous_end:\\n   112|            errors.append(f\\\"timeline item {index} start_seconds overlaps previous item\\\")\\n   113|        previous_end = item.end_seconds\\n   114|\\n   115|        for field_name, value in {\\n   116|            \\\"asset_mode\\\": item.asset_mode,\\n   117|            \\\"motion_instruction\\\": item.motion_instruction,\\n   118|            \\\"placeholder_asset_path\\\": item.placeholder_asset_path,\\n   119|            \\\"caption_layer text\\\": item.caption_layer.text,\\n   120|            \\\"label_layer text\\\": item.label_layer.text,\\n   121|        }.items():\\n   122|            if _is_blank(value):\\n   123|                errors.append(f\\\"timeline item {index} {field_name} is required\\\")\\n   124|\\n   125|        if item.asset_mode == \\\"static_slideshow\\\":\\n   126|            errors.append(\\\"static slideshow asset mode is not allowed\\\")\\n   127|        if \\\"baked-in\\\" in item.motion_instruction.casefold():\\n   128|            errors.append(\\\"baked-in text in render assets is not allowed\\\")\\n   129|        if not item.caption_layer.renderer_owned:\\n   130|            errors.append(f\\\"timeline item {index} caption layer must be renderer-owned\\\")\\n   131|        if not item.label_layer.renderer_owned:\\n   132|            errors.append(f\\\"timeline item {index} label layer must be renderer-owned\\\")\\n   133|\\n   134|    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n   135|\", \"total_lines\": 134, \"file_size\": 4800, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_xN2J7CYTVkSHgqFsMzb1QHsu", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "tool", "name": "read_file", "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.manifest import (\\n     6|    RenderManifest,\\n     7|    RenderSettings,\\n     8|    build_render_manifest,\\n     9|    validate_render_manifest,\\n    10|)\\n    11|from autoshorts.rendering.shot_plan import ShotPlan, ShotScene, create_shot_plan\\n    12|\\n    13|\\n    14|def _shot_plan():\\n    15|    brief = create_content_brief(\\n    16|        pillar_id=\\\"ai_life_systems\\\",\\n    17|        topic=\\\"AI weekly review routine\\\",\\n    18|        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n    19|        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    20|    )\\n    21|    script = create_script_draft(brief)\\n    22|    return create_shot_plan(script)\\n    23|\\n    24|\\n    25|def test_build_render_manifest_turns_shot_plan_into_vertical_timeline():\\n    26|    plan = _shot_plan()\\n    27|\\n    28|    manifest = build_render_manifest(plan)\\n    29|\\n    30|    assert isinstance(manifest, RenderManifest)\\n    31|    assert manifest.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    32|    assert manifest.title == \\\"AI weekly review routine\\\"\\n    33|    assert manifest.settings == RenderSettings(\\n    34|        width=1080,\\n    35|        height=1920,\\n    36|        fps=30,\\n    37|        no_generated_text_inside_assets=True,\\n    38|        captions_renderer_owned=True,\\n    39|        static_card_limit=0,\\n    40|    )\\n    41|    assert len(manifest.timeline) == len(plan.scenes)\\n    42|    assert [item.scene_purpose for item in manifest.timeline] == [scene.purpose for scene in plan.scenes]\\n    43|    assert manifest.timeline[0].start_seconds == 0\\n    44|    assert manifest.timeline[-1].end_seconds == 35\\n    45|    assert manifest.timeline[0].placeholder_asset_path == (\\n    46|        \\\"placeholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\\\"\\n    47|    )\\n    48|    assert all(item.caption_layer.renderer_owned for item in manifest.timeline)\\n    49|    assert all(item.label_layer.renderer_owned for item in manifest.timeline)\\n    50|    assert all(\\\"static\\\" not in item.motion_instruction.casefold() for item in manifest.timeline)\\n    51|\\n    52|\\n    53|def test_validate_render_manifest_accepts_default_manifest():\\n    54|    result = validate_render_manifest(build_render_manifest(_shot_plan()))\\n    55|\\n    56|    assert result.is_valid is True\\n    57|    assert result.errors == ()\\n    58|\\n    59|\\n    60|def test_build_render_manifest_rejects_invalid_shot_plan():\\n    61|    invalid_plan = ShotPlan(\\n    62|        script_id=\\\"script\\\",\\n    63|        title=\\\"Title\\\",\\n    64|        estimated_duration_seconds=35,\\n    65|        scenes=(\\n    66|            ShotScene(\\n    67|                purpose=\\\"first_frame_hook\\\",\\n    68|                start_seconds=0,\\n    69|                end_seconds=3,\\n    70|                voiceover_beat=\\\"Hook\\\",\\n    71|                visual_action=\\\"Static card\\\",\\n    72|                asset_mode=\\\"static_slideshow\\\",\\n    73|                caption_beat=\\\"Hook\\\",\\n    74|                retention_goal=\\\"Stop scroll\\\",\\n    75|            ),\\n    76|        ),\\n    77|    )\\n    78|\\n    79|    with pytest.raises(ValueError, match=\\\"static slideshow asset mode is not allowed\\\"):\\n    80|        build_render_manifest(invalid_plan)\\n    81|\\n    82|\\n    83|def test_validate_render_manifest_rejects_unsafe_render_settings():\\n    84|    manifest = build_render_manifest(_shot_plan())\\n    85|    unsafe = RenderManifest(\\n    86|        script_id=manifest.script_id,\\n    87|        title=manifest.title,\\n    88|        settings=RenderSettings(\\n    89|            width=1920,\\n    90|            height=1080,\\n    91|            fps=24,\\n    92|            no_generated_text_inside_assets=False,\\n    93|            captions_renderer_owned=False,\\n    94|            static_card_limit=1,\\n    95|        ),\\n    96|        timeline=manifest.timeline,\\n    97|    )\\n    98|\\n    99|    result = validate_render_manifest(unsafe)\\n   100|\\n   101|    assert result.is_valid is False\\n   102|    assert \\\"render size must be vertical 1080x1920\\\" in result.errors\\n   103|    assert \\\"fps must be 30\\\" in result.errors\\n   104|    assert \\\"generated text inside assets is not allowed\\\" in result.errors\\n   105|    assert \\\"captions must be renderer-owned\\\" in result.errors\\n   106|    assert \\\"static_card_limit must be 0\\\" in result.errors\\n   107|\\n   108|\\n   109|def test_validate_render_manifest_rejects_timeline_items_without_renderer_owned_layers():\\n   110|    manifest = build_render_manifest(_shot_plan())\\n   111|    first = manifest.timeline[0]\\n   112|    bad_item = type(first)(\\n   113|        scene_purpose=first.scene_purpose,\\n   114|        start_seconds=first.start_seconds,\\n   115|        end_seconds=first.end_seconds,\\n   116|        asset_mode=first.asset_mode,\\n   117|        motion_instruction=first.motion_instruction,\\n   118|        placeholder_asset_path=first.placeholder_asset_path,\\n   119|        caption_layer=type(first.caption_layer)(text=first.caption_layer.text, renderer_owned=False),\\n   120|        label_layer=type(first.label_layer)(text=first.label_layer.text, renderer_owned=False),\\n   121|    )\\n   122|    unsafe = RenderManifest(\\n   123|        script_id=manifest.script_id,\\n   124|        title=manifest.title,\\n   125|        settings=manifest.settings,\\n   126|        timeline=(bad_item,) + manifest.timeline[1:],\\n   127|    )\\n   128|\\n   129|    result = validate_render_manifest(unsafe)\\n   130|\\n   131|    assert result.is_valid is False\\n   132|    assert \\\"timeline item 1 caption layer must be renderer-owned\\\" in result.errors\\n   133|    assert \\\"timeline item 1 label layer must be renderer-owned\\\" in result.errors\\n   134|\", \"total_lines\": 133, \"file_size\": 4888, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_kw19fOoBbmsgf7CV5sgzMwp8", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": "**Planning HTML preview tests**\n\nI need to start by writing tests for the preview. There’s some design involved—maybe I’ll work with PreviewArtifact as an HTML string with specific side effects and timeline rows to validate the preview. The user mentioned a temporary directory or a preference for side-effect-free operations, so I’ll focus on creating a side-effect-free HTML string preview. I'll use a function to build an HTML preview from the manifest and ensure the manifest's validation is included. It seems testing will be necessary, and I should avoid publish or post options and lean toward test-driven development (TDD).", "finish_reason": "tool_calls", "reasoning_content": "**Planning HTML preview tests**\n\nI need to start by writing tests for the preview. There’s some design involved—maybe I’ll work with PreviewArtifact as an HTML string with specific side effects and timeline rows to validate the preview. The user mentioned a temporary directory or a preference for side-effect-free operations, so I’ll focus on creating a side-effect-free HTML string preview. I'll use a function to build an HTML preview from the manifest and ensure the manifest's validation is included. It seems testing will be necessary, and I should avoid publish or post options and lean toward test-driven development (TDD).", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEuhMcVgACnB68mRisxxS8-AZ8x5l13vDz4lJmoUI_x5Bk41o3DBxpYI7BT8dp6vMy092v_jmdUmVGpH1S4UFgGUsZmGbf62H67RRGAEFAgGdn7o4hYdfr6EwgrRleFXhIl_Bvq136BXQOrsAPt4ObhxPYMDJwgkpPyLuNtfFJgI6jHQDgjQ1_NMcv5uaeUA1Uub2dG7CwUaV63GVW9rcCcse_xxomgaN9ll8f1T4FL8Mo2rC_dCw0HNgnTlapLD7rL0_LoRUvz53TciAYpjkTCSCVAk1bZLyL674R0E6mkseL6JZ2OIAPenmm1DqnPyVKyG-BPw_WZCScJ31GClVi5b3lTrNY_bzM297nkMdnlvRvh1P-E2ukZWI_SXyHDj4TM1Iifhfy1VUaOtfnFTiswlnZ48JtJ3zOvCvHIYaeICPMhbYzJrZ9O5rHDgl4MJa4NDhM5uHbEgQJtNv2xQGadQSYHhjlbIwk5v0qJxGC8Hv6pJz3NlqQf1Z-KqTyqP8klreVpeCc43Jtwx2yEL13fHwAKIOq5ZvDXZug1GzMX5-Ta0Is7AQlRaUk2_0hjAK8OWeKVC3jAPqB714y9s9Zgse3EVTkz0AEXhW_A75te_WxQGB01w_F7Ck91QfQ4JjgA0JIqp-LH4PcA9wi7Tt7cwWbFkJUPirFpNOkdsPXxBMil9AR_YaqamL_7GnnNnieHwgaFk_-E4HpdxxZCkIKbEfhWMM7mRiYrk7KOZwyHkKjcV3wKr2KPykPlbiD130mSo027rKMhyCr97_YgL04_PSrK72_3xD_oVLU3VA37CGznADbBQ6T6tVtOCVaM8_c74LxfaH0nEXYahMW4zon2qmRrYF-nA3W2hBhc13XDmpk9Eohus4LQ8Yz2-wBbiWBVp5Z9YeTMjd3lzSwa42yto9_GDzm3THQcZUnsB3YbFjXrVwUHFO_oxkNGtIHv0nBLuHKrQ3I1KtkkVUEGklVwY3rKf2NmWdycGrMrUnjnS9BWR6ozuZR-saR5Wu0qIDcv4fjQrq2mUUavIfjHAyx0Ji2C7LEP28eehoBHoMP0O4hNN13fweJksPjaQKYJoo8BddQha9s5i6jTmphHZmK5333qgrpkpXU8b68hbYWEtl3VfjzFoPcQEQ2pzr-fazxOjlCGaega8Vml-8tPnOohmVwX1BEnR5CXLM25nnk52MUBCMguK7cTBM_LVz1QZkmbNBYpMP-k9woJ60CLhoWyXnR9WwSuWlJuauKwCb1ABi02A2OG_gdEEgUJkYcfNeO1nEvGar-Nr71wui2CMQt8sYH4n6gy7Fg4DYSmnlODtgX2rg0w78K03Jlf_lK7SXBQi2-TuY-GieZs0Wo33WI4SCVD7e14BeWe30EVc_xLUEaEvc57iUpgoMTR_e3H00rLQDMxU1BnkuZ1pUqvVK1o3fD6zXl9RxnQi6CzW5uX0MRBh0yEkzVInVT_rZ-SL4R53hclJwd0R_WAFdin7RJXpLBGYLOdxmqcPkNM-GTrecZ9iK7u65bw99pAtyHG45murKOqEzsA6dp_0yATUn7fn59g==", "id": "rs_060035eb87c40e14016a12e84838408191853e8355f92573e5", "summary": [{"type": "summary_text", "text": "**Planning HTML preview tests**\n\nI need to start by writing tests for the preview. There’s some design involved—maybe I’ll work with PreviewArtifact as an HTML string with specific side effects and timeline rows to validate the preview. The user mentioned a temporary directory or a preference for side-effect-free operations, so I’ll focus on creating a side-effect-free HTML string preview. I'll use a function to build an HTML preview from the manifest and ensure the manifest's validation is included. It seems testing will be necessary, and I should avoid publish or post options and lean toward test-driven development (TDD)."}]}], "tool_calls": [{"id": "call_VeIvamIVIAdQjD024US8zKv0", "call_id": "call_VeIvamIVIAdQjD024US8zKv0", "response_item_id": "fc_060035eb87c40e14016a12e84ce9388191983d9716019b5317", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_preview.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.manifest import RenderManifest, RenderSettings, build_render_manifest\\nfrom autoshorts.rendering.preview import RenderPreview, build_html_preview, validate_render_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _manifest():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    return build_render_manifest(create_shot_plan(script))\\n\\n\\ndef test_build_html_preview_creates_side_effect_free_timeline_preview():\\n    manifest = _manifest()\\n\\n    preview = build_html_preview(manifest)\\n\\n    assert isinstance(preview, RenderPreview)\\n    assert preview.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert preview.preview_type == \\\"html_timeline\\\"\\n    assert preview.side_effects == ()\\n    assert preview.external_calls == ()\\n    assert \\\"<html\\\" in preview.html\\n    assert \\\"AI weekly review routine\\\" in preview.html\\n    assert \\\"1080x1920\\\" in preview.html\\n    assert \\\"30fps\\\" in preview.html\\n    assert \\\"first_frame_hook\\\" in preview.html\\n    assert \\\"0–3s\\\" in preview.html\\n    assert \\\"placeholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\\\" in preview.html\\n    assert \\\"If AI runs your week, you have already lost control.\\\" in preview.html\\n\\n\\ndef test_validate_render_preview_accepts_default_preview():\\n    result = validate_render_preview(build_html_preview(_manifest()))\\n\\n    assert result.is_valid is True\\n    assert result.errors == ()\\n\\n\\ndef test_build_html_preview_rejects_invalid_manifest():\\n    manifest = _manifest()\\n    invalid = RenderManifest(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        settings=RenderSettings(width=1920, height=1080),\\n        timeline=manifest.timeline,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"render size must be vertical 1080x1920\\\"):\\n        build_html_preview(invalid)\\n\\n\\ndef test_validate_render_preview_rejects_side_effects_and_external_calls():\\n    preview = build_html_preview(_manifest())\\n    unsafe = RenderPreview(\\n        script_id=preview.script_id,\\n        title=preview.title,\\n        preview_type=preview.preview_type,\\n        html=preview.html,\\n        side_effects=(\\\"write_file\\\",),\\n        external_calls=(\\\"comfyui\\\",),\\n    )\\n\\n    result = validate_render_preview(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"preview must not have side effects\\\" in result.errors\\n    assert \\\"preview must not call external renderers\\\" in result.errors\\n\\n\\ndef test_build_html_preview_escapes_timeline_text():\\n    manifest = _manifest()\\n    first = manifest.timeline[0]\\n    dangerous_item = type(first)(\\n        scene_purpose=first.scene_purpose,\\n        start_seconds=first.start_seconds,\\n        end_seconds=first.end_seconds,\\n        asset_mode=first.asset_mode,\\n        motion_instruction=\\\"Move <script>alert('x')</script>\\\",\\n        placeholder_asset_path=first.placeholder_asset_path,\\n        caption_layer=type(first.caption_layer)(text=\\\"Human < approval\\\", renderer_owned=True),\\n        label_layer=first.label_layer,\\n    )\\n    dangerous = RenderManifest(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        settings=manifest.settings,\\n        timeline=(dangerous_item,) + manifest.timeline[1:],\\n    )\\n\\n    preview = build_html_preview(dangerous)\\n\\n    assert \\\"<script>\\\" not in preview.html\\n    assert \\\"&lt;script&gt;\\\" in preview.html\\n    assert \\\"Human &lt; approval\\\" in preview.html\\n\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3772, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_preview.py\\\">\\nERROR [6:6] Import \\\"autoshorts.rendering.preview\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_VeIvamIVIAdQjD024US8zKv0", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_Wsr5dMTNactYqtKNP9TwCr3A", "call_id": "call_Wsr5dMTNactYqtKNP9TwCr3A", "response_item_id": "fc_060035eb87c40e14016a12e85f74ac8191a9d8f20f5abb185a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_render_preview.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_render_preview.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_render_preview.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_render_preview.py:6: in <module>\\n    from autoshorts.rendering.preview import RenderPreview, build_html_preview, validate_render_preview\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.preview'\\n=========================== short test summary info ============================\\nERROR tests/test_render_preview.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_Wsr5dMTNactYqtKNP9TwCr3A", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": "**Considering implementation details**\n\nI’m thinking about how to implement the manifest validation process. It will only reject baked-in strings, so that’s something to consider. I might need an external_calls type tuple for this. Additionally, I should think about HTML escaping to ensure everything is safe and functional. It feels like I’m making progress, but it’s also a bit tricky ensuring everything fits together, you know?", "finish_reason": "tool_calls", "reasoning_content": "**Considering implementation details**\n\nI’m thinking about how to implement the manifest validation process. It will only reject baked-in strings, so that’s something to consider. I might need an external_calls type tuple for this. Additionally, I should think about HTML escaping to ensure everything is safe and functional. It feels like I’m making progress, but it’s also a bit tricky ensuring everything fits together, you know?", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEuhloZvTgqtCcHclHRWFflKkfsBtm5fpDgjhSkgJc4hIs4jsyzIltou1RopNQ0W006wXHFGXtFY5reziPg-bKdn6StUpHnLQwVwPa52MyoPXI4I7fzHviFrkogQyoVK-nMI6CArWrk5zKeQr_BPQ4ajHikDSQS8I18pkCb-rAYsTP99i8wxc0UzPXVTSakQJ7oX0b9mN_wYH8Id2IT9pBHqkZgWinHFyC0CfAUeEQ3mBLt_FVbEnxeupSRQQjJxXmYXWs8dhaHTtAE7CWoZimOwA-1TKnb9LbCwXHNWhQffjIfqpgCC-QbVPCPZZbAihZ1dwGxDLuOS_HkNztaIC5CDeDxxhkFZRM-YTYol5IAIi8ZskxOJ3e2xq4kiyIn3-J4E5hB-x-IWqmakfZporYN3NrrDGMBXHaiv-Ce78LbvbdXJkUOCw0X7G9qZUVBV_0X4u-lZCbrR0cexfp4ikK7b-5JiZifRuEKFZfZIfkmyZZ74EXrn_7RCBnmVj2ceH2V4e5nYkMN7wi_sqrn5gq5KYJcr2R1Sak9auYCVjiK-1WtS3WFfl766nfw-1Znt0C2cnsAvadS_VxsUJr9yOXjXA596RMznpSmGgUX6mpa534_3Ip-KHw1ApwWQ56O9kcteot4MjYQxjFaNy0WF57gqfVNm7l7YORsYHMxh7Mi5ZRbF1wsMkKSXdthABGKPIQt5lHAELHc2Oy9-JxFlmuTXfS0fPvhPjIi__72cB0yoIh-4uUfvHmnDnEzRFW_2uCDbpW7fYQwMf2GtxYVob5dZ-WJZOH5t4yhYnWovJc9eitvl6nJnptImSt0ZmrdEZ6ggWIFQsYtRc1KnfI3fa1WbPniPGyu0m5jZYjx_cfV3t9B4fOzjCY0t9ULlEIzn2KdwirDPZsqX43JNcGG-vCMluCrE0lIB9BbNTuwMFDIx0VLMZJUl7BC2UFkNQLRK5KlqKl6aTVCAGjfYHU1T-OGwkVjE4p74y1-r5uJvcOrB3MKctKkLws3cBkCMMr_uEvgrUoz4vqlta30URJMigJIVjXQ4u53b31Ot17bkh3NrIJ7-nh9Vc7csWsCzIZ1Us5rvQOcVJ6z6krJEKIVDTGRboSDRJNW3VQ1ELLJ1qp9R4LoY=", "id": "rs_060035eb87c40e14016a12e8633ef08191915fc038dc49dfdb", "summary": [{"type": "summary_text", "text": "**Considering implementation details**\n\nI’m thinking about how to implement the manifest validation process. It will only reject baked-in strings, so that’s something to consider. I might need an external_calls type tuple for this. Additionally, I should think about HTML escaping to ensure everything is safe and functional. It feels like I’m making progress, but it’s also a bit tricky ensuring everything fits together, you know?"}]}], "tool_calls": [{"id": "call_UefFmDUxTNKo6hp1Eujfg7f3", "call_id": "call_UefFmDUxTNKo6hp1Eujfg7f3", "response_item_id": "fc_060035eb87c40e14016a12e865276c819181f78db567bb3519", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Side-effect-free HTML preview generation for render manifests.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom html import escape\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.manifest import RenderManifest, validate_render_manifest\\n\\n\\n@dataclass(frozen=True)\\nclass RenderPreview:\\n    script_id: str\\n    title: str\\n    preview_type: str\\n    html: str\\n    side_effects: tuple[str, ...] = ()\\n    external_calls: tuple[str, ...] = ()\\n\\n\\ndef _is_blank(value: str) -> bool:\\n    return not (value or \\\"\\\").strip()\\n\\n\\ndef _timeline_row(index: int, item) -> str:\\n    time_range = f\\\"{item.start_seconds}–{item.end_seconds}s\\\"\\n    return \\\"\\\".join(\\n        (\\n            '<section class=\\\"scene\\\">',\\n            f\\\"<h2>{index:02d}. {escape(item.scene_purpose)}</h2>\\\",\\n            f\\\"<p><strong>Time:</strong> {escape(time_range)}</p>\\\",\\n            f\\\"<p><strong>Asset mode:</strong> {escape(item.asset_mode)}</p>\\\",\\n            f\\\"<p><strong>Motion:</strong> {escape(item.motion_instruction)}</p>\\\",\\n            f\\\"<p><strong>Caption:</strong> {escape(item.caption_layer.text)}</p>\\\",\\n            f\\\"<p><strong>Label:</strong> {escape(item.label_layer.text)}</p>\\\",\\n            f\\\"<p><strong>Placeholder:</strong> {escape(item.placeholder_asset_path)}</p>\\\",\\n            \\\"</section>\\\",\\n        )\\n    )\\n\\n\\ndef build_html_preview(manifest: RenderManifest) -> RenderPreview:\\n    \\\"\\\"\\\"Build an inspectable HTML timeline preview without rendering media.\\\"\\\"\\\"\\n\\n    validation = validate_render_manifest(manifest)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n    settings = manifest.settings\\n    rows = \\\"\\\\n\\\".join(_timeline_row(index, item) for index, item in enumerate(manifest.timeline, start=1))\\n    html = f\\\"\\\"\\\"<!doctype html>\\n<html lang=\\\"en\\\">\\n<head>\\n  <meta charset=\\\"utf-8\\\">\\n  <title>{escape(manifest.title)} — render preview</title>\\n  <style>\\n    body {{ font-family: system-ui, sans-serif; background: #111827; color: #f9fafb; margin: 2rem; }}\\n    .meta, .scene {{ border: 1px solid #374151; border-radius: 12px; padding: 1rem; margin: 1rem 0; }}\\n    .scene {{ background: #1f2937; }}\\n    strong {{ color: #93c5fd; }}\\n  </style>\\n</head>\\n<body>\\n  <main>\\n    <h1>{escape(manifest.title)}</h1>\\n    <section class=\\\"meta\\\">\\n      <p><strong>Script:</strong> {escape(manifest.script_id)}</p>\\n      <p><strong>Format:</strong> {settings.width}x{settings.height}, {settings.fps}fps</p>\\n      <p><strong>Text policy:</strong> captions and labels are renderer-owned; no generated text inside assets.</p>\\n    </section>\\n    {rows}\\n  </main>\\n</body>\\n</html>\\\"\\\"\\\"\\n    return RenderPreview(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        preview_type=\\\"html_timeline\\\",\\n        html=html,\\n        side_effects=(),\\n        external_calls=(),\\n    )\\n\\n\\ndef validate_render_preview(preview: RenderPreview) -> ValidationResult:\\n    errors: list[str] = []\\n    if _is_blank(preview.script_id):\\n        errors.append(\\\"script_id is required\\\")\\n    if _is_blank(preview.title):\\n        errors.append(\\\"title is required\\\")\\n    if preview.preview_type != \\\"html_timeline\\\":\\n        errors.append(\\\"preview_type must be html_timeline\\\")\\n    if _is_blank(preview.html):\\n        errors.append(\\\"html is required\\\")\\n    if \\\"<html\\\" not in preview.html.casefold():\\n        errors.append(\\\"html preview must contain an html document\\\")\\n    if preview.side_effects:\\n        errors.append(\\\"preview must not have side effects\\\")\\n    if preview.external_calls:\\n        errors.append(\\\"preview must not call external renderers\\\")\\n    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/preview.py\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
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{"role": "assistant", "content": "", "reasoning": "**Updating README and verifying**\n\nI need to update the README to ensure it reflects the latest changes. After that, I should probably double-check everything manually to make sure nothing’s overlooked before committing. It's important to be thorough here! I want to avoid any mistakes that could cause confusion later on. A clear README helps everyone understand the project better, so I’ll take my time with this step. Once it’s all good, I’ll go ahead and commit the updates.", "finish_reason": "tool_calls", "reasoning_content": "**Updating README and verifying**\n\nI need to update the README to ensure it reflects the latest changes. After that, I should probably double-check everything manually to make sure nothing’s overlooked before committing. It's important to be thorough here! I want to avoid any mistakes that could cause confusion later on. A clear README helps everyone understand the project better, so I’ll take my time with this step. Once it’s all good, I’ll go ahead and commit the updates.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEuiE9EtCHEdagF6MtEJ5elIThqDV1jolYVQ-vjTkO3flk8e25f8VeUHwn1Ae3dQa3I_e8qvEXgpDVcxCji465NcGXuCEb-KQkWPX7Mfr-oZazXclNx-rd6oqr4QtdSQ0ue-lAx4cbJ0rVdZb_p6VVC-I4nZnpCenMmKnOuDufncrdDdkTscBSrxN9dDgquzAskdtvDNWBMgl_7cKqzJ2BH8nICPUO5pRKmWJG3IWmMd9OIhAwvPJ_prSe8PEYJcpOMVGjz4lmE40Tb1sSzq9O71s178gDPFiEylqmZgQlc5VWZ96rjTKM3AwH6w1lMkGOV5bpZDc8cduveRs3_twj3jwl5LD2I4SObDCWzWPTuzf4CXRnQMMVB0prECxr8lRVbveDn-AbWLfEh00J2uK6T2q98w1ySnqz5FRgYifA-HopYOagZ-hVPhB5XfYsYT_CX-ZadPvJZQ_WpSOuucHnmGHMPaj3cCvO32qIPiB4r6JF-iklX_hsUEENBz0heZHriCtIFYupj4Dx21jLVb0OFsC3j3ZP9shzFz8HLXlcT8-6U6l00EEQ8r_be8pvO5llG8Qu06W7nVevglPaJI04GeHxykiqUATiJl64Vi0feP7bzYW5HN-6uQT9nEYiEw4Heo4jNizSNnoShcysdeR_duu4_p_RE43GCzzjnUoKtoggy0SSZ5bowyFiWxxEd_usHQg4vYCdmJ8Tba9rQu3VSzX0OlSQflFEOfwxvO9NRI2l3VounERI6sKSNqFdWRBt7mcJMMeUZJ5WSkYbvXFzdYVKjOUECrRYMfRodDLGQwB2cj0kStUxg6lpbqnXPdoIdB16gFZk8VMWXqfp0BFeIuwjo2KO8hL0lIU1m5Kl2fKRuI0lk5LqZ8WEkcbPGj59oeJdMUQ5ODGbt5qJy6vawiF3dJbnP5gF3vFnTC5iycdIb9UAC6pgM890aFbkcHPfb9IkfRiPXpZqhvmN8VGMSGgI--YuPP36QSJf0ifcV4kkqI=", "id": "rs_060035eb87c40e14016a12e88315fc8191bef9ffbeba1140d0", "summary": [{"type": "summary_text", "text": "**Updating README and verifying**\n\nI need to update the README to ensure it reflects the latest changes. After that, I should probably double-check everything manually to make sure nothing’s overlooked before committing. It's important to be thorough here! I want to avoid any mistakes that could cause confusion later on. A clear README helps everyone understand the project better, so I’ll take my time with this step. Once it’s all good, I’ll go ahead and commit the updates."}]}], "tool_calls": [{"id": "call_J6YSXP9WDxtcYFL6XZdFXe07", "call_id": "call_J6YSXP9WDxtcYFL6XZdFXe07", "response_item_id": "fc_060035eb87c40e14016a12e88486bc8191b36c0fc92f967a4e", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\",\"new_string\":\"- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"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@@ -21,6 +21,7 @@\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n+- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_J6YSXP9WDxtcYFL6XZdFXe07", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_JfKIJv20B6sRjvZidRWDpCHs", "call_id": "call_JfKIJv20B6sRjvZidRWDpCHs", "response_item_id": "fc_060035eb87c40e14016a12e88a5400819184f9b4fa001a086f", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n\\n`autoshorts.rendering.preview.build_html_preview()` turns a validated `RenderManifest` into a side-effect-free `RenderPreview` HTML document. It exposes script metadata, vertical format, fps, every timeline item, placeholder asset paths, motion instructions, and renderer-owned caption/label text for local inspection before any media generation.\\n\\n`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n\\n## Human approval commands\",\"old_string\":\"`validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"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@@ -93,6 +93,10 @@\\n \\n `validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n \\n+`autoshorts.rendering.preview.build_html_preview()` turns a validated `RenderManifest` into a side-effect-free `RenderPreview` HTML document. It exposes script metadata, vertical format, fps, every timeline item, placeholder asset paths, motion instructions, and renderer-owned caption/label text for local inspection before any media generation.\\n+\\n+`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_JfKIJv20B6sRjvZidRWDpCHs", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_vxLzTthrQZZTIG1xPRNQkD8O", "call_id": "call_vxLzTthrQZZTIG1xPRNQkD8O", "response_item_id": "fc_060035eb87c40e14016a12e892d4848191890acfc939329772", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview, validate_render_preview\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\nmanifest = build_render_manifest(create_shot_plan(script))\\npreview = build_html_preview(manifest)\\nprint(preview.script_id)\\nprint(preview.preview_type)\\nprint(preview.side_effects, preview.external_calls)\\nprint(len(preview.html))\\nprint('first_frame_hook' in preview.html)\\nprint(validate_render_preview(preview).is_valid)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_afDHdDVwerUHi1uByaDKBCcb", "call_id": "call_afDHdDVwerUHi1uByaDKBCcb", "response_item_id": "fc_060035eb87c40e14016a12e89b9fd88191973d9eca6b92685e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git diff -- README.md autoshorts/rendering/preview.py tests/test_render_preview.py | sed -n '1,260p'\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/preview.py\\n?? tests/test_render_preview.py\\ndiff --git a/README.md b/README.md\\nindex 3fce936..0a53748 100644\\n--- a/README.md\\n+++ b/README.md\\n@@ -21,6 +21,7 @@ Implemented foundation:\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n+- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n@@ -92,6 +93,10 @@ Each `ShotScene` carries timing, voiceover beat, visual action, asset mode, capt\\n \\n `validate_render_manifest()` rejects unsafe render settings, generated text inside assets, non-renderer-owned captions/labels, static slideshow timeline items, missing timeline fields, and bad timeline timing. The manifest is still a preparation artifact only; it renders nothing and calls no external API.\\n \\n+`autoshorts.rendering.preview.build_html_preview()` turns a validated `RenderManifest` into a side-effect-free `RenderPreview` HTML document. It exposes script metadata, vertical format, fps, every timeline item, placeholder asset paths, motion instructions, and renderer-owned caption/label text for local inspection before any media generation.\\n+\\n+`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_afDHdDVwerUHi1uByaDKBCcb", "timestamp": "2026-05-24T14:01:56.422229"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"[feat/core-approval-quality-gates 42ac32f] feat: add side-effect-free render preview\\n 3 files changed, 210 insertions(+)\\n create mode 100644 autoshorts/rendering/preview.py\\n create mode 100644 tests/test_render_preview.py\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\\nd118a76 feat: add retention shot planning\\n6e6569a feat: add content demo review pipeline\\n72dc887 feat: convert content drafts to video candidates\\n9c125f9 feat: add content brief and script draft models\\n53b45ca feat: add demo telegram delivery cli\\n54863dd feat: add telegram review delivery preparation\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_H6XeDjhbNPVjAaxlzI3zMQqw", "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Side-effect-free Render Preview\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/preview.py\ntests/test_render_preview.py\n```\n\n## Neue API\n\n```python\nbuild_html_preview(manifest: RenderManifest) -> RenderPreview\nvalidate_render_preview(preview: RenderPreview) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML RenderPreview\n```\n\n## RenderPreview\n\n```python\nRenderPreview(\n    script_id=...,\n    title=...,\n    preview_type=\"html_timeline\",\n    html=...,\n    side_effects=(),\n    external_calls=(),\n)\n```\n\nWichtig: Das Ding rendert **noch kein Video**, ruft **kein ComfyUI**, schreibt **keine Dateien**, schickt **nichts an Telegram**. Es ist nur eine inspectable HTML-Timeline.\n\nLangweilig? Ja. Genau deshalb überlebt es den Kontakt mit Realität.\n\n## Inhalt der HTML Preview\n\nDie Preview zeigt:\n\n- Script ID\n- Titel\n- Format `1080x1920`\n- `30fps`\n- Text Policy:\n  - captions/labels renderer-owned\n  - no generated text inside assets\n- pro Timeline Item:\n  - Scene Purpose\n  - Zeitfenster, z. B. `0–3s`\n  - Asset Mode\n  - Motion Instruction\n  - Caption\n  - Label\n  - Placeholder Asset Path\n\nBeispiel-Pfad verifiziert:\n\n```text\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\n```\n\n## Safety / Guardrails\n\n`validate_render_preview()` rejected:\n\n- blank previews\n- falscher Preview Type\n- fehlendes HTML-Dokument\n- Side Effects\n- externe Renderer Calls\n\nZusätzlich ist HTML-Escaping getestet:\n\n```text\n<script> → &lt;script&gt;\n```\n\nAlso kein fröhliches Self-XSS in unserer eigenen Preview. Kleine Dinge, grosse Wirkung.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.preview'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n79 passed in 0.12s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\nhtml_timeline\n() ()\n3296\nTrue\nTrue\n```\n\nBedeutung:\n\n- richtige Script ID\n- Preview Type `html_timeline`\n- keine Side Effects\n- keine External Calls\n- HTML Länge: `3296`\n- enthält `first_frame_hook`\n- Preview validiert erfolgreich\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.preview`\n- HTML Timeline Preview\n- side-effect-free Boundary\n- keine ComfyUI-/Video-/Telegram-Calls\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n42ac32f feat: add side-effect-free render preview\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt fehlt der praktische Demo-Entry für die komplette Render-Planung:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/cli/demo_render_preview.py\ntests/test_demo_render_preview_cli.py\n```\n\nZiel:\n\n- `python -m autoshorts.cli.demo_render_preview`\n- druckt Preview-Metadaten\n- druckt danach HTML auf stdout\n- weiterhin:\n  - keine Datei schreiben\n  - kein Video rendern\n  - kein ComfyUI\n  - keine externen Calls\n\nDann haben wir einen lokalen, reproduzierbaren Preview-Command, den wir später entweder in eine Datei umleiten oder als Telegram/Browser-Artifact anzeigen können. Danach erst kommt der erste echte Renderer-Adapter.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Side-effect-free Render Preview\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/preview.py\ntests/test_render_preview.py\n```\n\n## Neue API\n\n```python\nbuild_html_preview(manifest: RenderManifest) -> RenderPreview\nvalidate_render_preview(preview: RenderPreview) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML RenderPreview\n```\n\n## RenderPreview\n\n```python\nRenderPreview(\n    script_id=...,\n    title=...,\n    preview_type=\"html_timeline\",\n    html=...,\n    side_effects=(),\n    external_calls=(),\n)\n```\n\nWichtig: Das Ding rendert **noch kein Video**, ruft **kein ComfyUI**, schreibt **keine Dateien**, schickt **nichts an Telegram**. Es ist nur eine inspectable HTML-Timeline.\n\nLangweilig? Ja. Genau deshalb überlebt es den Kontakt mit Realität.\n\n## Inhalt der HTML Preview\n\nDie Preview zeigt:\n\n- Script ID\n- Titel\n- Format `1080x1920`\n- `30fps`\n- Text Policy:\n  - captions/labels renderer-owned\n  - no generated text inside assets\n- pro Timeline Item:\n  - Scene Purpose\n  - Zeitfenster, z. B. `0–3s`\n  - Asset Mode\n  - Motion Instruction\n  - Caption\n  - Label\n  - Placeholder Asset Path\n\nBeispiel-Pfad verifiziert:\n\n```text\nplaceholders/ai_life_systems-ai-weekly-review-routine/01-first_frame_hook.png\n```\n\n## Safety / Guardrails\n\n`validate_render_preview()` rejected:\n\n- blank previews\n- falscher Preview Type\n- fehlendes HTML-Dokument\n- Side Effects\n- externe Renderer Calls\n\nZusätzlich ist HTML-Escaping getestet:\n\n```text\n<script> → &lt;script&gt;\n```\n\nAlso kein fröhliches Self-XSS in unserer eigenen Preview. Kleine Dinge, grosse Wirkung.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.preview'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n79 passed in 0.12s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\nhtml_timeline\n() ()\n3296\nTrue\nTrue\n```\n\nBedeutung:\n\n- richtige Script ID\n- Preview Type `html_timeline`\n- keine Side Effects\n- keine External Calls\n- HTML Länge: `3296`\n- enthält `first_frame_hook`\n- Preview validiert erfolgreich\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.preview`\n- HTML Timeline Preview\n- side-effect-free Boundary\n- keine ComfyUI-/Video-/Telegram-Calls\n\n## Commit\n\nBranch:\n\n```text\nfeat/core-approval-quality-gates\n```\n\nNeuer Commit:\n\n```text\n42ac32f feat: add side-effect-free render preview\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt fehlt der praktische Demo-Entry für die komplette Render-Planung:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n```\n\nIch würde als nächstes bauen:\n\n```text\nautoshorts/cli/demo_render_preview.py\ntests/test_demo_render_preview_cli.py\n```\n\nZiel:\n\n- `python -m autoshorts.cli.demo_render_preview`\n- druckt Preview-Metadaten\n- druckt danach HTML auf stdout\n- weiterhin:\n  - keine Datei schreiben\n  - kein Video rendern\n  - kein ComfyUI\n  - keine externen Calls\n\nDann haben wir einen lokalen, reproduzierbaren Preview-Command, den wir später entweder in eine Datei umleiten oder als Telegram/Browser-Artifact anzeigen können. Danach erst kommt der erste echte Renderer-Adapter."}], "id": "msg_060035eb87c40e14016a12e8a4ca8c819181255867008c30a6", "phase": "final_answer"}], "timestamp": "2026-05-24T14:01:56.422229"}
{"role": "assistant", "content": "", "reasoning": "**Planning for implementation**\n\nI’m thinking we need to continue implementing the CLI for demo_render_preview and also consider the roadmap ahead. I want to load relevant skills, like Test-Driven Development (TDD) and potentially social media shorts to prepare for the user’s questions about next steps. It seems that social media automation might also be relevant, and I’m considering how to make it test-driven too. It’s all connected!", "finish_reason": "tool_calls", "reasoning_content": "**Planning for implementation**\n\nI’m thinking we need to continue implementing the CLI for demo_render_preview and also consider the roadmap ahead. I want to load relevant skills, like Test-Driven Development (TDD) and potentially social media shorts to prepare for the user’s questions about next steps. It seems that social media automation might also be relevant, and I’m considering how to make it test-driven too. It’s all connected!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEu1GZ1Ie8MPWRpltGzugSU43NjkFFg4IqmadROUkmHk0DWFDD6O0IuKz_moa3vvo4fvogWHZD8nJ7quvUmKUSaHFMEk2-Uf4GzFZ3-m1kQfGOVp-To6--7YNIvx1FtVXwIyXN6nv1zQ92J2NEA6Xa7QIbbVxxsI0YvAa0MFWMvtQY4It_vJ3bOnfqYi3CugyT4eZR9sF57dv6J1sgRRX0l4CYtXl15hbqipemvMGoA2rcQf6mUyAwR3EKBzYURwF6tS8Bet2xq-zERZevrXo-ZWTGgei8UwmzBwEXATJSyMg926OGfm_HGbn29eufzVhoeBwycBb04HOCxxPK2drKrCSkLUGrud8rZV4VbMqKMZtHRL5UQ9MH-slMRbUZDe4-TdiNBGEdaa3DCV6mZjJ3w39ZBI_drGVN_v7K2gF5rkFbFYzeXsN0NWUlfepprKgvG-e6Z4xNxHk_T-pPlCRxAk4CdrfWLn7uRYqQ1CxpebeDLqbpqM-QNEfDy6He1cx0kb2iVicOi0Gi3FTR3CFbD2dRuDyZjQAHhqfvLcXjjzXJ85vyCC_VnFIA9HgBju42NOrPY22XC4EgS_d-pH-DI-mtC4Wnaqbtnkmpa6GOXgqRwG7smxzawJ8nCAVoDTF7arTOzw4pbQ9vZlYhoFOctpsYVjUhfmoll8yG683qYk1ugNd4ajSnuLfl7oKy4astyjVCum7yKfYrAfuJYBRGiQFkP24e799XcJ9Yxgx1MlnFR9PQIDzhfxB8-qMeIyk0gcqyTNpd3NI4B2PzSkvX6yhkYXblOctWS8FCl834nTuVJ3ZXS3D1DFzVF3djW-uKL5vJqJwE4UbR2gHs_lg8mHgSd4qKp6I2dAfohDz3aDjxeXDv0B1_rUEat2IUgUFfbppVtYcjEqQlIMuSKoR8BtXkfrNsI95KyvAubRMYG6X3YcL82Y5UEtC-kH0tmOwL91j5UG1bW7VRuYfjsDBzBa_5ucybFYSArur38zX1vnLNIsCdzr7nN5nNbEK-7LZLZrT45BKFL8jYx56f127veIwRvKPAH3RLko86evM7ybuetdtQgCFTL_Uyg_lbdMpaENIdisLQBKcz_dLm5bsAUhz4V2eQjjHbSnaZ2Dz_B8yEDOkcCRAMzspIoKwnJRpu6FvbZ9prYu6HhzUyHtkSmuq60CXLXKdvXpFQW5SvfyQaAkiDJnnQYIOQxKDzzW9IwGSW0xwUUgXVJNroF8GW1LAmCJN9f1ZDox7dbTANCJzLPLQ_OYqKNiM5bQlAH6f4KXS", "id": "rs_060035eb87c40e14016a12ed3b1fb08191972d6b167e4036e8", "summary": [{"type": "summary_text", "text": "**Planning for implementation**\n\nI’m thinking we need to continue implementing the CLI for demo_render_preview and also consider the roadmap ahead. I want to load relevant skills, like Test-Driven Development (TDD) and potentially social media shorts to prepare for the user’s questions about next steps. It seems that social media automation might also be relevant, and I’m considering how to make it test-driven too. It’s all connected!"}]}], "tool_calls": [{"id": "call_o2VIfUFGwi7axAVdnfYzobD9", "call_id": "call_o2VIfUFGwi7axAVdnfYzobD9", "response_item_id": "fc_060035eb87c40e14016a12ed4601f08191b37eb2ecdbade6bb", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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## 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_o2VIfUFGwi7axAVdnfYzobD9", "timestamp": "2026-05-24T14:23:07.826636"}
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{"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\\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: render vertical scene/card frames from manifest fields, assemble them with FFmpeg, then use the MP4 for timing/approval review. 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/<slug>/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. **Human approval loop before posting**\\n   - Send preview to the user through the requested channel, usually Telegram.\\n   - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.\\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\\n7. **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/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/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 over-optimize renderer quality before testing whether the content format earns retention.\\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/approval-first-retention-studio-core.md\", \"references/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-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/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_UrbNUa7sO6MTXR8DX57WWLgD", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEu1PxdG9lIP3q6_Ll67LO_eALcz_puiWc6wv2LjKwS_Me7qEUB7Bs6eqiAvvxCEze7h4dAvbKwl9_IY14rYRXTjZzSC8wPc9f0thuStWoJht4Jz5eBkEzg8_04-GvSCloebOtWKn5qc_vJ3AABDkaSRRMtTtEs5ZcnoRTRd06Yk8IFiE-jb6LxFczmvfkFSdunvqwoO_2hL38Iw2wh_PaowsDDDUWzGg_aF-J7kNsPgp44xEdpsjPtbxKQFY1OH3ztDXL5_51Mjpu4J1q2tAwl5MLOnV0ZsBpFyMgDuNzcdJi67aH5KYGFDT9f2HDEVba5K13TzV8tZ2mIsHTKCy84eFhBGFYie_pBX-hkXLyYrkv6HwJCewSnZGPw0X9OCbGDCCcRx1pI3s1V3l_wuj6tgl6_UnQbEdqKDeP4N9RDKNqgZOxzGo4eMEG0VKnn1KGKU7r879cAkebAQU-WwEvCr--mJqm5j4dP3RR21KzYMhhYbvionA-O7zjSH_2CPAZFWIEPZ1gIhdOezUhz2LwX6-zgCz8kYgcmZOFPP1CHgXbiQu2JLw7ernxhNhpMVqKKf4C_-6lAPZe-WJNSC8d-_XTjD295MRmXusvmKIKHgjDFnoQUKpCL_UDc9xWeufhGs-4kykbjdcexmA5tJ5sIRjls30YXLh7WMc5SFHs2SA11zM_Mty_d-oCdCu0hAv3Q6osRVZ-HVJ2dHodaRzTaUn15QUKSka85hrhDZc-8lPXvCTY2eZ4hoUlUIW0AkE4Hudt9hKAtPlf2Od7qae0FCI8VgxN7lBQmQ60Llp-C4MYhgkP9tJJ1j-8O9qAhMyabJn2C_hQyuNmNhQiMYtC3d_vqlu0Xeez92hENL7zN1AmhKtsBP4DoYwHXx-qyboKyG2adiCbOkG1XInLkNrw5ZwQhRNReJo4YpmYKKDY88t0HixVEetNnK1aFbOX9C0dw9MQJkTReiZxX1FA9iuNOmBdceMzNVxaQyvrNIAQnpG8r_nTEhGWvidBJopCLvHWYei", "id": "rs_060035eb87c40e14016a12ed4bb8248191bd0477b3ce925f9c", "summary": []}], "tool_calls": [{"id": "call_fDkrvuxIvfkaBDGazel1sZmQ", "call_id": "call_fDkrvuxIvfkaBDGazel1sZmQ", "response_item_id": "fc_060035eb87c40e14016a12ed4f00fc81919658c5ba45a78597", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git branch --show-current\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}, {"id": "call_SQ8S9ACBDzrYKcNlx6oC1JM2", "call_id": "call_SQ8S9ACBDzrYKcNlx6oC1JM2", "response_item_id": "fc_060035eb87c40e14016a12ed4f01108191a429be903c3d3773", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":220,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_telegram_delivery.py\"}"}}, {"id": "call_TofPv8UiCPOgLXBD9hQs8buF", "call_id": "call_TofPv8UiCPOgLXBD9hQs8buF", "response_item_id": "fc_060035eb87c40e14016a12ed4f011c819186532326fa82682d", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":220,\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_telegram_delivery_cli.py\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"feat/core-approval-quality-gates\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_fDkrvuxIvfkaBDGazel1sZmQ", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Print the deterministic sample Telegram review delivery.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|import sys\\n     6|from collections.abc import Sequence\\n     7|from typing import TextIO\\n     8|\\n     9|from autoshorts.demo.sample_batch import render_sample_review_batch\\n    10|from autoshorts.telegram.review_sender import prepare_review_delivery\\n    11|\\n    12|\\n    13|def main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int:\\n    14|    \\\"\\\"\\\"Print delivery metadata and review text without sending anything.\\\"\\\"\\\"\\n    15|\\n    16|    _ = argv or ()\\n    17|    output = stdout or sys.stdout\\n    18|    delivery = prepare_review_delivery(render_sample_review_batch(), topic=\\\"review\\\")\\n    19|    side_effects = \\\", \\\".join(delivery.side_effects) if delivery.side_effects else \\\"none\\\"\\n    20|    approval = \\\"yes\\\" if delivery.requires_human_approval else \\\"no\\\"\\n    21|\\n    22|    print(f\\\"Delivery type: {delivery.delivery_type}\\\", file=output)\\n    23|    print(f\\\"Topic: {delivery.topic}\\\", file=output)\\n    24|    print(f\\\"Requires human approval: {approval}\\\", file=output)\\n    25|    print(f\\\"Side effects: {side_effects}\\\", file=output)\\n    26|    print(f\\\"Text length: {len(delivery.text)}\\\", file=output)\\n    27|    print(\\\"\\\", file=output)\\n    28|    print(delivery.text, file=output)\\n    29|    return 0\\n    30|\\n    31|\\n    32|if __name__ == \\\"__main__\\\":\\n    33|    raise SystemExit(main())\\n    34|\", \"total_lines\": 33, \"file_size\": 1206, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_SQ8S9ACBDzrYKcNlx6oC1JM2", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|from autoshorts.cli.demo_telegram_delivery import main\\n     2|\\n     3|\\n     4|def test_demo_telegram_delivery_cli_prints_metadata_and_review_text(capsys):\\n     5|    exit_code = main([])\\n     6|\\n     7|    captured = capsys.readouterr()\\n     8|\\n     9|    assert exit_code == 0\\n    10|    assert \\\"Delivery type: telegram_review\\\" in captured.out\\n    11|    assert \\\"Topic: review\\\" in captured.out\\n    12|    assert \\\"Requires human approval: yes\\\" in captured.out\\n    13|    assert \\\"Side effects: none\\\" in captured.out\\n    14|    assert \\\"Text length:\\\" in captured.out\\n    15|    assert \\\"## Review Batch: sample-ai-life-systems\\\" in captured.out\\n    16|    assert \\\"Ready: 1\\\" in captured.out\\n    17|    assert \\\"Blocked: 1\\\" in captured.out\\n    18|    assert \\\"Invalid: 1\\\" in captured.out\\n    19|    assert captured.err == \\\"\\\"\\n    20|    assert \\\"publish\\\" not in captured.out.casefold()\\n    21|    assert \\\"post\\\" not in captured.out.casefold()\\n    22|\", \"total_lines\": 21, \"file_size\": 791, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_TofPv8UiCPOgLXBD9hQs8buF", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_x3mEt2hNvl84cV8LqTXh4nNs", "call_id": "call_x3mEt2hNvl84cV8LqTXh4nNs", "response_item_id": "fc_060035eb87c40e14016a12ed5662a48191b93d48a5c2246a8c", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_render_preview_cli.py\",\"content\":\"from autoshorts.cli.demo_render_preview import main\\n\\n\\ndef test_demo_render_preview_cli_prints_metadata_and_html_preview(capsys):\\n    exit_code = main([])\\n\\n    captured = capsys.readouterr()\\n\\n    assert exit_code == 0\\n    assert \\\"Preview type: html_timeline\\\" in captured.out\\n    assert \\\"Script: ai_life_systems-ai-weekly-review-routine\\\" in captured.out\\n    assert \\\"Side effects: none\\\" in captured.out\\n    assert \\\"External calls: none\\\" in captured.out\\n    assert \\\"HTML length:\\\" in captured.out\\n    assert \\\"<!doctype html>\\\" in captured.out\\n    assert \\\"AI weekly review routine\\\" in captured.out\\n    assert \\\"first_frame_hook\\\" in captured.out\\n    assert \\\"1080x1920\\\" in captured.out\\n    assert \\\"30fps\\\" in captured.out\\n    assert captured.err == \\\"\\\"\\n    assert \\\"comfyui\\\" not in captured.out.casefold()\\n    assert \\\"publish\\\" not in captured.out.casefold()\\n    assert \\\"post\\\" not in captured.out.casefold()\\n\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 894, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_render_preview_cli.py\\\">\\nERROR [1:6] Import \\\"autoshorts.cli.demo_render_preview\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_x3mEt2hNvl84cV8LqTXh4nNs", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_mMWS9YNAOPBEM4qarCXuv0Gr", "call_id": "call_mMWS9YNAOPBEM4qarCXuv0Gr", "response_item_id": "fc_060035eb87c40e14016a12ed60e68c81918c053486d6e1d633", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_render_preview_cli.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n____________ ERROR collecting tests/test_demo_render_preview_cli.py ____________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_render_preview_cli.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_demo_render_preview_cli.py:1: in <module>\\n    from autoshorts.cli.demo_render_preview import main\\nE   ModuleNotFoundError: No module named 'autoshorts.cli.demo_render_preview'\\n=========================== short test summary info ============================\\nERROR tests/test_demo_render_preview_cli.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.11s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_mMWS9YNAOPBEM4qarCXuv0Gr", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_aBrjZi2VP4rCNbJKLLZUvcpx", "call_id": "call_aBrjZi2VP4rCNbJKLLZUvcpx", "response_item_id": "fc_060035eb87c40e14016a12ed6488b88191991e12b2841226ba", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Print the deterministic HTML render preview without rendering media.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport sys\\nfrom collections.abc import Sequence\\nfrom typing import TextIO\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _build_demo_preview():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    shot_plan = create_shot_plan(script)\\n    manifest = build_render_manifest(shot_plan)\\n    return build_html_preview(manifest)\\n\\n\\ndef main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int:\\n    \\\"\\\"\\\"Print preview metadata and HTML without file writes or renderer calls.\\\"\\\"\\\"\\n\\n    _ = argv or ()\\n    output = stdout or sys.stdout\\n    preview = _build_demo_preview()\\n    side_effects = \\\", \\\".join(preview.side_effects) if preview.side_effects else \\\"none\\\"\\n    external_calls = \\\", \\\".join(preview.external_calls) if preview.external_calls else \\\"none\\\"\\n\\n    print(f\\\"Preview type: {preview.preview_type}\\\", file=output)\\n    print(f\\\"Script: {preview.script_id}\\\", file=output)\\n    print(f\\\"Side effects: {side_effects}\\\", file=output)\\n    print(f\\\"External calls: {external_calls}\\\", file=output)\\n    print(f\\\"HTML length: {len(preview.html)}\\\", file=output)\\n    print(\\\"\\\", file=output)\\n    print(preview.html, file=output)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_render_preview.py\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1841, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_aBrjZi2VP4rCNbJKLLZUvcpx", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_7qpNwnBS03rFxvBdBppJUEWZ", "call_id": "call_7qpNwnBS03rFxvBdBppJUEWZ", "response_item_id": "fc_060035eb87c40e14016a12ed73b17c8191bee9c51f648393bd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_render_preview_cli.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \".                                                                        [100%]\\n1 passed in 0.05s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7qpNwnBS03rFxvBdBppJUEWZ", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_zOk1ife6j3dxQnqPgidjgm4g", "call_id": "call_zOk1ife6j3dxQnqPgidjgm4g", "response_item_id": "fc_060035eb87c40e14016a12ed775be881918d6a60f3431076fe", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python -m autoshorts.cli.demo_render_preview >/tmp/autoshorts-render-preview.html && python - <<'PY'\\nfrom pathlib import Path\\np=Path('/tmp/autoshorts-render-preview.html')\\ntext=p.read_text()\\nprint(len(text))\\nprint(text.splitlines()[0])\\nprint(text.splitlines()[1])\\nprint(text.splitlines()[2])\\nprint(text.splitlines()[3])\\nprint(text.splitlines()[4])\\nprint('<!doctype html>' in text)\\nprint('first_frame_hook' in text)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 90%]\\n........                                                                 [100%]\\n80 passed in 0.12s\\n3433\\nPreview type: html_timeline\\nScript: ai_life_systems-ai-weekly-review-routine\\nSide effects: none\\nExternal calls: none\\nHTML length: 3296\\nTrue\\nTrue\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_zOk1ife6j3dxQnqPgidjgm4g", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "Need README patch commit.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need README patch commit."}], "id": "msg_060035eb87c40e14016a12ed7d022c8191a45ec2a4527b3e46", "phase": "commentary"}], "tool_calls": [{"id": "call_reMChMhfM9dqneVSLiLTutZZ", "call_id": "call_reMChMhfM9dqneVSLiLTutZZ", "response_item_id": "fc_060035eb87c40e14016a12ed7d546c8191ab731b5ce744cc6a", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\",\"new_string\":\"- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
{"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@@ -8,6 +8,7 @@\\n \\n Implemented foundation:\\n \\n+- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n - `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n - `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n - `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_reMChMhfM9dqneVSLiLTutZZ", "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_erOKOW5Yn3Muib8gGtXZwus0", "call_id": "call_erOKOW5Yn3Muib8gGtXZwus0", "response_item_id": "fc_060035eb87c40e14016a12ed83c964819193464eb1a40ad4da", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n\\nRun the same deterministic preview locally with:\\n\\n```bash\\npython -m autoshorts.cli.demo_render_preview\\n```\\n\\nIt prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n\\n## Human approval commands\",\"old_string\":\"`validate_render_preview()` rejects blank previews, wrong preview types, missing HTML documents, side effects, and external renderer calls. Preview generation still does not write files, render videos, call ComfyUI, or touch Telegram.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T14:23:07.826636"}
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{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Render Preview CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_render_preview.py\ntests/test_demo_render_preview_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_render_preview\n```\n\nDer läuft jetzt deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML RenderPreview\n→ stdout\n```\n\nEr druckt zuerst Metadaten:\n\n```text\nPreview type: html_timeline\nScript: ai_life_systems-ai-weekly-review-routine\nSide effects: none\nExternal calls: none\nHTML length: 3296\n```\n\nDann das komplette HTML.\n\n## Verifiziert\n\n```text\n80 passed in 0.12s\n```\n\nManueller CLI-Test:\n\n```text\n3433\nPreview type: html_timeline\nScript: ai_life_systems-ai-weekly-review-routine\nSide effects: none\nExternal calls: none\nHTML length: 3296\nTrue\nTrue\n```\n\nBedeutung:\n\n- CLI läuft.\n- HTML wird erzeugt.\n- enthält `<!doctype html>`.\n- enthält `first_frame_hook`.\n- keine Datei wird geschrieben.\n- keine externen Calls.\n- kein ComfyUI.\n- kein Video-Rendering.\n\nCommit:\n\n```text\nf8c5588 feat: add demo render preview cli\n```\n\nArbeitsverzeichnis sauber.\n\n---\n\n## Nächste Schritte bis zum ersten Social-Media-Freigabeentwurf\n\nWir sind jetzt hier:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n```\n\nFür den ersten echten Freigabeentwurf fehlen noch diese Bausteine:\n\n## 1. Social Media Draft Package\n\nNächster sinnvoller Core-Baustein:\n\n```text\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nZiel:\n\n```text\nVideoCandidate + RenderPreview/Manifest\n→ SocialMediaDraft\n```\n\nEnthält:\n\n- Plattformen:\n  - YouTube Shorts\n  - TikTok\n  - Instagram Reels\n- Titelvarianten\n- Caption\n- Hashtags\n- CTA\n- geplante Dauer\n- Hook\n- Risk/Quality summary\n- Preview-Hinweis\n- Status: `requires_human_approval`\n- keine API Calls\n\nDas ist der erste eigentliche “Social-Media-Beitragsentwurf”.\n\n## 2. Review Renderer für SocialMediaDraft\n\nDanach:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nZiel:\n\n```text\nSocialMediaDraft\n→ Telegram-safe approval message\n```\n\nAlso eine Nachricht wie:\n\n```text\n## Social Media Draft Review\n\nCandidate: ai_life_systems-ai-weekly-review-routine-35s\nPlatforms: YouTube Shorts, TikTok, Instagram Reels\nDuration: 35s\n\nHook:\nIf AI runs your week, you have already lost control.\n\nTitle:\nAI should not run your week\n\nCaption:\n...\n\nHashtags:\n#AI #LifeSystems #Productivity\n\nPreview:\nHTML timeline available / video preview pending\n\nApproval commands:\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nFreigabe alle\nAblehnen\nÄndern: <...>\n```\n\nWichtig: Noch kein Upload. Nur Freigabeentwurf.\n\n## 3. Draft Delivery Preparation\n\nDann:\n\n```text\nautoshorts/social/delivery.py\ntests/test_social_delivery.py\n```\n\nZiel:\n\n```text\nSocialMediaDraft Review Text\n→ Telegram Delivery Plan\n```\n\nAnalog zur bisherigen ReviewDelivery:\n\n- `requires_human_approval=True`\n- `side_effects=()`\n- Telegram-Limit validieren\n- keine Credentials\n- keine externen Calls\n- kein automatischer Plattform-Upload\n\n## 4. Demo CLI für ersten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_social_draft.py\ntests/test_demo_social_draft_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_social_draft\n```\n\nOutput:\n\n```text\nDelivery type: social_draft_review\nRequires human approval: yes\nSide effects: none\n\n## Social Media Draft Review\n...\n```\n\nDas wäre der erste komplette Social-Media-Freigabeentwurf — noch ohne Video-Datei, aber mit Content, Caption, Plattformen, Hashtags, Approval Commands und Preview-Referenz.\n\n## 5. Danach: Video Preview Artefakt\n\nErst danach würde ich den ersten visuellen Artefakt-Renderer bauen:\n\n```text\nRenderManifest\n→ local preview artifact\n```\n\nOptionen:\n\n- HTML only — haben wir schon\n- SVG frames\n- PNG scene cards\n- MP4 timing preview via local renderer/FFmpeg\n\nFür Freigabe wäre ein MP4-Preview natürlich besser, aber der Freigabeentwurf kann bereits vorher existieren.\n\n## 6. Danach: TTS / Voice Layer\n\nTTS sollten wir **nach** Social Draft und Preview-Struktur sauber designen, nicht vorher.\n\nDann kommen Fragen wie:\n\n- Stimme:\n  - deine Präferenz: `de-DE-KillianNeural`\n  - für englischen Content evtl. eigene Voice\n- Voiceover-Script finalisieren\n- Timing:\n  - Wörter pro Sekunde\n  - Scene durations\n  - Caption sync\n- Output:\n  - `.wav` oder `.mp3`\n  - später eventuell `.srt`\n- Provider:\n  - lokal/free vs Edge TTS vs andere\n- Approval:\n  - Text-to-speech preview braucht eigene Freigabe, weil schlechte Voice jedes Video sofort in die “LinkedIn Motivational NPC”-Hölle schiebt.\n\n---\n\n## Meine Empfehlung für die Reihenfolge\n\nAls nächstes baue ich:\n\n```text\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nDanach direkt:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nDann haben wir den ersten echten Social-Media-Freigabeentwurf als typisiertes Objekt + Telegram-tauglichen Text.\n\nKurz gesagt:\n\n```text\nJetzt:\nRender Preview CLI fertig.\n\nNächster Block:\nSocialMediaDraft → Review Text → Telegram Delivery Preview.\n\nDanach:\nMP4 Preview.\n\nDanach:\nTTS.\n```\n\nSauber, kontrolliert, keine magische Upload-Katastrophe mit Konfetti.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEu2ScfJf83b8IagbcnX7YKHc7ZxrCnOP_zXx_4O0k5bggKw4308b41wAs5q0qj_v_-zlYvTZhMCS6Na3PNB2Unf210sNE3pdehRdu6xTAfmTDLmuDOYrUJtbZjLR-vpgj8tRHl1lW7uetSrui5HvyOLCBC7yKxTemS18jXfIrBlgT4YkHDa2uQB4w4A9MNmEZbox2SVzBbpvFl4uoA_aovkiS4g_giUv1sixopjD_8l5qwipBpU71ibXlhwdBa8YgqCNfQlSZiCz_7ineGTkRTC2te5JGlE90Hy63f0zjUHfgvRThvoZwnXbbqWg5U9iGxnmJSzUNkGehYXNl1XGn2pj6UuisrZwnCg0AbTfIrjo71IAW3q4GFhBJpmFs_zh10Nv5gG8-6Lygsa7bSuCR7PYhISEE3Oi3MsAPG8-i5Lj9wq4e41TNVpuK9fWafoF68CI2bMhO9z3LM1-N9Dpi7oieVxSbm0Zhjpm1isujED8OMcMr2Ror2Bd5P0PbfgABrgcnXNlQVQ6hQhDetiBZ6IS_s1OaKa80BWnkXJ5G1mNJ7sfwuwEk8uAXNpWSbzmR7OO0CinO9id1vBCBV2D0frgwCJ07ClnH5y_sPNL67JCRrvXh6qbiDpn7zmJm7oTi9Qw5kJbbGDkdn-PPrg6IMmuld0x43vMPH10ALuDG-_QxYMt67CYvNsV-EP43IoJbJv7ga7AcOFpRC7stBN2MYKmytvQ_3cRfAgN3n8vl0Hk9k61WLZBH6R73IPoQ9IyYdCgMV1JJ1l9T36xnRcenqHPeCZ6LptwizSaXyFBBkwoKlSIWbwhBQFko06d-5jz5geW5eNWEq9D0UyLT0jv2xpzteE9WIsR8rfb0wYSTPr0rEGkVkjq9m5vkrS8o0dtBZu1pwDC-q57KBi3N9lYjL3b7325kH5jbb9ATnlt0vvjE14yfQAhfUCToSXxaE0AfKWkymQHGvUNk-wcEBkGj22JtUXlXgNf7jZ54Vd6AKw_l8opvAD5F0U6jhxdKkfSUunGcN0u3bEhD8Irc4_XNWX8uG3oN0AsJoQmGR2fwD5Ya4rkv6B94LiLLe0OidquaHbpgKh7Zd5VTGYF3Vm0f73t0n7N5iqPXpU1SfgO_oSRx6KFtPDtueCnlkYBlQSWyqzalmDXMFJXczTWt5-_VvjrXYdvyVCucc-UBDcZDUmuIM0lOZt5vBa8oSycrwxGhatYxH1eZSpiZcoO_z2pjq6Z3feSxmNNQ7WTuIfnHjDJXFCDzkQR0spqp5kDeSeyWha_CdUT_buk_Ra6upmSO5o5nc6OTYZ08KSbigHkJW9gAavMXyETK47a54LqsFhK__48pVprR024UUyuva-HlfOwJKvNb4vx80_O4cGKSSKxx8_sY-HvkCDW5OLFkTiduH3q", "id": "rs_060035eb87c40e14016a12ed90d6408191bddc8bfb9902496d", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Render Preview CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_render_preview.py\ntests/test_demo_render_preview_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_render_preview\n```\n\nDer läuft jetzt deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ HTML RenderPreview\n→ stdout\n```\n\nEr druckt zuerst Metadaten:\n\n```text\nPreview type: html_timeline\nScript: ai_life_systems-ai-weekly-review-routine\nSide effects: none\nExternal calls: none\nHTML length: 3296\n```\n\nDann das komplette HTML.\n\n## Verifiziert\n\n```text\n80 passed in 0.12s\n```\n\nManueller CLI-Test:\n\n```text\n3433\nPreview type: html_timeline\nScript: ai_life_systems-ai-weekly-review-routine\nSide effects: none\nExternal calls: none\nHTML length: 3296\nTrue\nTrue\n```\n\nBedeutung:\n\n- CLI läuft.\n- HTML wird erzeugt.\n- enthält `<!doctype html>`.\n- enthält `first_frame_hook`.\n- keine Datei wird geschrieben.\n- keine externen Calls.\n- kein ComfyUI.\n- kein Video-Rendering.\n\nCommit:\n\n```text\nf8c5588 feat: add demo render preview cli\n```\n\nArbeitsverzeichnis sauber.\n\n---\n\n## Nächste Schritte bis zum ersten Social-Media-Freigabeentwurf\n\nWir sind jetzt hier:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n```\n\nFür den ersten echten Freigabeentwurf fehlen noch diese Bausteine:\n\n## 1. Social Media Draft Package\n\nNächster sinnvoller Core-Baustein:\n\n```text\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nZiel:\n\n```text\nVideoCandidate + RenderPreview/Manifest\n→ SocialMediaDraft\n```\n\nEnthält:\n\n- Plattformen:\n  - YouTube Shorts\n  - TikTok\n  - Instagram Reels\n- Titelvarianten\n- Caption\n- Hashtags\n- CTA\n- geplante Dauer\n- Hook\n- Risk/Quality summary\n- Preview-Hinweis\n- Status: `requires_human_approval`\n- keine API Calls\n\nDas ist der erste eigentliche “Social-Media-Beitragsentwurf”.\n\n## 2. Review Renderer für SocialMediaDraft\n\nDanach:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nZiel:\n\n```text\nSocialMediaDraft\n→ Telegram-safe approval message\n```\n\nAlso eine Nachricht wie:\n\n```text\n## Social Media Draft Review\n\nCandidate: ai_life_systems-ai-weekly-review-routine-35s\nPlatforms: YouTube Shorts, TikTok, Instagram Reels\nDuration: 35s\n\nHook:\nIf AI runs your week, you have already lost control.\n\nTitle:\nAI should not run your week\n\nCaption:\n...\n\nHashtags:\n#AI #LifeSystems #Productivity\n\nPreview:\nHTML timeline available / video preview pending\n\nApproval commands:\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nFreigabe alle\nAblehnen\nÄndern: <...>\n```\n\nWichtig: Noch kein Upload. Nur Freigabeentwurf.\n\n## 3. Draft Delivery Preparation\n\nDann:\n\n```text\nautoshorts/social/delivery.py\ntests/test_social_delivery.py\n```\n\nZiel:\n\n```text\nSocialMediaDraft Review Text\n→ Telegram Delivery Plan\n```\n\nAnalog zur bisherigen ReviewDelivery:\n\n- `requires_human_approval=True`\n- `side_effects=()`\n- Telegram-Limit validieren\n- keine Credentials\n- keine externen Calls\n- kein automatischer Plattform-Upload\n\n## 4. Demo CLI für ersten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_social_draft.py\ntests/test_demo_social_draft_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_social_draft\n```\n\nOutput:\n\n```text\nDelivery type: social_draft_review\nRequires human approval: yes\nSide effects: none\n\n## Social Media Draft Review\n...\n```\n\nDas wäre der erste komplette Social-Media-Freigabeentwurf — noch ohne Video-Datei, aber mit Content, Caption, Plattformen, Hashtags, Approval Commands und Preview-Referenz.\n\n## 5. Danach: Video Preview Artefakt\n\nErst danach würde ich den ersten visuellen Artefakt-Renderer bauen:\n\n```text\nRenderManifest\n→ local preview artifact\n```\n\nOptionen:\n\n- HTML only — haben wir schon\n- SVG frames\n- PNG scene cards\n- MP4 timing preview via local renderer/FFmpeg\n\nFür Freigabe wäre ein MP4-Preview natürlich besser, aber der Freigabeentwurf kann bereits vorher existieren.\n\n## 6. Danach: TTS / Voice Layer\n\nTTS sollten wir **nach** Social Draft und Preview-Struktur sauber designen, nicht vorher.\n\nDann kommen Fragen wie:\n\n- Stimme:\n  - deine Präferenz: `de-DE-KillianNeural`\n  - für englischen Content evtl. eigene Voice\n- Voiceover-Script finalisieren\n- Timing:\n  - Wörter pro Sekunde\n  - Scene durations\n  - Caption sync\n- Output:\n  - `.wav` oder `.mp3`\n  - später eventuell `.srt`\n- Provider:\n  - lokal/free vs Edge TTS vs andere\n- Approval:\n  - Text-to-speech preview braucht eigene Freigabe, weil schlechte Voice jedes Video sofort in die “LinkedIn Motivational NPC”-Hölle schiebt.\n\n---\n\n## Meine Empfehlung für die Reihenfolge\n\nAls nächstes baue ich:\n\n```text\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nDanach direkt:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nDann haben wir den ersten echten Social-Media-Freigabeentwurf als typisiertes Objekt + Telegram-tauglichen Text.\n\nKurz gesagt:\n\n```text\nJetzt:\nRender Preview CLI fertig.\n\nNächster Block:\nSocialMediaDraft → Review Text → Telegram Delivery Preview.\n\nDanach:\nMP4 Preview.\n\nDanach:\nTTS.\n```\n\nSauber, kontrolliert, keine magische Upload-Katastrophe mit Konfetti."}], "id": "msg_060035eb87c40e14016a12ed9286a88191883f6d49b5c8ffb7", "phase": "final_answer"}], "timestamp": "2026-05-24T14:23:07.826636"}
{"role": "assistant", "content": "", "reasoning": "**Loading relevant skills**\n\nI need to act now, focusing on loading the necessary skills. Should I confirm if TDD is already ready? Before replying, I want to scan for anything relevant, particularly the social media shorts automation and TDD. It's possible that this was previously loaded, but with a new turn in the conversation, I should double-check. Let’s use the skill view to ensure everything is in place.", "finish_reason": "tool_calls", "reasoning_content": "**Loading relevant skills**\n\nI need to act now, focusing on loading the necessary skills. Should I confirm if TDD is already ready? Before replying, I want to scan for anything relevant, particularly the social media shorts automation and TDD. It's possible that this was previously loaded, but with a new turn in the conversation, I should double-check. Let’s use the skill view to ensure everything is in place.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvPag3GB6crmKhO9jseyHiZSDcjKUzWZ9Hb-e8AzBVayow85xKCJdqgbWUUe_RO0iQC8NDvmlpAd1MTgs_rUnp3vl57Z1ZMYBDhx4BmuOeta38Nq75m75z6gjqG_SnBjJHvbOcTarqXiCNjqWVsvNjoMKUo5GNeNNWjdKlsfa5tRFoX_pj_Vg_j1-twPTfGRiQ7ybCgWHwX97HAsjxUzepHGFH0BiCDrbUIjEAGCwlRO7nCCfhq2j7oTOWfGaIrHhX9uNNGQ9Ruc7Qz8iccevqR8SiSAgIHg4O2JB2G_CqeIVn-eto2d4vrQGf--By4Jth9I56f0tFGSHineZsU-Javrzl5HG4NcEwS9yMhIsx4h_NZER9jE0eAi4z_szzgWGWCZvbACwffPdO6UbFTl5io7NkjZ-NDc9b-5ojvrDYg9IXb13zsmsMaFKDgenw-dce4yq4Iw23XqAwL175AyrTd9CeOOCoIcXWK56q1MsnImzX8gdYAzv2ys1ZgJa0Bzu7LVoBCeYIaLvw8harPGIvOjWSd69EKbiJ3DUQZlTuLJU-mpu_G-QksNUe3E-J9B7WBclWCEPXtG17PubkDdVAw7cgX9c7BXHqUXXpI84xS1XfmrcAXui40nIoKSE5I23J1UuGQJQbc74ay4OD2h0AGKiGjY6IgGEIRGbJ1TF_QDJLHaYhczmXVlNWHuL8nbGUQ_PHTtwis1PFjI8PKkGL7tXUMpRvwLSCMrhFkzJiRl7HAdOgbpPJCFY-v32877gudzOE6MZcU3PVvA0Fh1ZvofrF8Mkoh7szJNplFMb3UKq5J-aXRWVWmRpef3ZMmrHK0AQYC4cZqRv5mX56eauJHsCthyxAcw7xGJXo_Ng9IHkNGL3-5AZABP2gwVL9XoHQ3n5m7ltDtCnt7_f2Mq54yy7_cZPCcyTqfcmhpTOTnD_TpcgkS0yo8yr8qGB-d8xpx7R3qMkwCtM1QzVmde06-870gm3rImJNhKwj3z4LKJ7nvgjvJnsyZZ-VZ8sfOhs22LElojGa0-nRmlEunMxuBPFU747mcd2QZttg3bm3RDC5cS8NGpPV9Hfk-8cjrxzg5Cj74kLs8ZHbzC3wSpx2nzR6nVhcwpvZctoTWUmblDNA8e5eFKXdd1PEPYThwcp5uSsAj7pw82dUSd6g-TfFvHqox6PP2LCZa50z5jQOgHrIg=", "id": "rs_060035eb87c40e14016a12f3d7f4d081918fbc417098e551f1", "summary": [{"type": "summary_text", "text": "**Loading relevant skills**\n\nI need to act now, focusing on loading the necessary skills. Should I confirm if TDD is already ready? Before replying, I want to scan for anything relevant, particularly the social media shorts automation and TDD. It's possible that this was previously loaded, but with a new turn in the conversation, I should double-check. Let’s use the skill view to ensure everything is in place."}]}], "tool_calls": [{"id": "call_eXuQzIB5O14ABYvOcmybohOy", "call_id": "call_eXuQzIB5O14ABYvOcmybohOy", "response_item_id": "fc_060035eb87c40e14016a12f3da43048191a023abc1dda14bca", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_e5KL5jOfq0NSyT9L4Gp8mXuP", "call_id": "call_e5KL5jOfq0NSyT9L4Gp8mXuP", "response_item_id": "fc_060035eb87c40e14016a12f3da43188191b32787cc02d201df", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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## 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_eXuQzIB5O14ABYvOcmybohOy", "timestamp": "2026-05-24T15:11:18.976797"}
{"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\\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: render vertical scene/card frames from manifest fields, assemble them with FFmpeg, then use the MP4 for timing/approval review. 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/<slug>/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 approval package 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   - Add a local demo CLI that prints metadata first, then the draft review text; it must not write files, call renderers, call Telegram, or access credentials.\\n   - See `references/social-draft-approval-package.md` for the concrete artifact sequence and guardrails.\\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   - 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/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/social-draft-approval-package.md` for the typed first-post approval package to build before TTS or platform APIs. 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 over-optimize renderer quality before testing whether the content format earns retention.\\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/manifest-preview-renderer.md\", \"references/content-development-core.md\", \"references/ai-video-asset-planning.md\", \"references/approval-first-content-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/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_e5KL5jOfq0NSyT9L4Gp8mXuP", "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/social-draft-approval-package.md\", \"content\": \"# Social Draft Approval Package Pattern\\n\\nUse this when moving from render planning toward the first user-facing social-media post draft for approval.\\n\\n## Goal\\n\\nProduce a complete approval draft before TTS, video rendering, or platform APIs:\\n\\n```text\\nVideoCandidate + RenderManifest/Preview\\n→ SocialMediaDraft\\n→ SocialMediaDraft review text\\n→ side-effect-free delivery plan\\n→ demo CLI output\\n```\\n\\nThis gives the user a concrete post candidate to approve or revise while still avoiding accidental publishing, external calls, and wasted GPU/TTS work.\\n\\n## Recommended typed artifacts\\n\\n1. `SocialMediaDraft`\\n   - candidate id\\n   - target platforms: YouTube Shorts, TikTok, Instagram Reels\\n   - title variants\\n   - caption\\n   - hashtags\\n   - CTA\\n   - hook\\n   - planned duration\\n   - quality/risk summary\\n   - preview reference, e.g. HTML timeline available or MP4 pending\\n   - `requires_human_approval=True`\\n   - `side_effects=()`\\n\\n2. Social review renderer\\n   - Telegram-safe text block\\n   - includes candidate, platforms, hook, titles, caption, hashtags, preview note, quality/risk summary\\n   - includes approval commands: `Freigabe alle`, platform-specific approvals, `Ablehnen`, `Ändern: <...>`\\n   - must not imply automatic upload or publishing\\n\\n3. Draft delivery preparation\\n   - validates message length and safety language\\n   - returns typed delivery plan with `requires_human_approval=True` and `side_effects=()`\\n   - no Telegram API call yet\\n\\n4. Demo CLI\\n   - e.g. `python -m autoshorts.cli.demo_social_draft`\\n   - prints delivery metadata first, then the review text\\n   - no file writes, platform calls, renderer calls, or credential access\\n\\n## Sequence before TTS\\n\\nDo not start with TTS. First get approval on the content package:\\n\\n```text\\nContentBrief\\n→ ScriptDraft\\n→ VideoCandidate\\n→ ShotPlan\\n→ RenderManifest\\n→ Preview\\n→ SocialMediaDraft\\n→ Review text\\n→ Approval delivery preview\\n```\\n\\nOnly after the draft is structurally approved should TTS design start:\\n\\n- voice selection\\n- final voiceover script\\n- words-per-second / timing\\n- caption sync\\n- audio format\\n- provider choice\\n- separate voice approval if needed\\n\\n## Guardrails\\n\\n- No credentials in draft output.\\n- No platform API calls.\\n- No publish/post semantics except inside explicit approval-command documentation if the codebase already uses that language carefully.\\n- Preview references are allowed; generated media paths should be placeholders unless a later renderer created real artifacts.\\n- Keep captions, numbers, labels, arrows, UI text, claims, and platform metadata renderer-owned / system-owned, never baked into generated AI assets.\\n\", \"file_type\": \".md\"}", "tool_call_id": "call_aEyNyK05wK65dmt8vIt7FNKZ", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Planning test implementation**\n\nI’m thinking about implementing tests for the social/draft.py file. First, I need to inspect the candidate classes within that file to understand what I’m dealing with. It’s essential to know the structure before diving into testing. Once I have a grasp of the classes, I can outline the tests I want to create, ensuring they cover all necessary cases. Let’s keep moving forward!", "finish_reason": "tool_calls", "reasoning_content": "**Planning test implementation**\n\nI’m thinking about implementing tests for the social/draft.py file. First, I need to inspect the candidate classes within that file to understand what I’m dealing with. It’s essential to know the structure before diving into testing. Once I have a grasp of the classes, I can outline the tests I want to create, ensuring they cover all necessary cases. Let’s keep moving forward!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvPnZzYO3KcsJ7MgTHpl74N6ZwRwissZUvkdtOKX657gUHnnqNsZGLD8p1NHYP2DaICPcCY1BDnSlhW3WUcdiO888q8iD26jolSQ50z4znVjoymhZE3ar5CcdUos_t7x2D7VUVKOYyjJRvA4Qjd-NDVg0t8FNhd1W_XSEDCNWoEwx4HFQGlkEa0SpfS8H6KtGHX4L3NaeSB1IzWm258Ny0FG7rmzKwVyA5OH_NlKanpkXeAM62vFV4bAdDpqDwhZz55wekmeFu8trEp_MGGa__bUxY4Plh1vaWg8sHnHWpP2y3VWxij2bNI6PFygHGDPg19YSMF_ADEa4qbkfRz-Ll40DmtFXAjaai77vihbq1EQDEsKRAnk9AY8VcSvaCEx50sisLAS1W_qcdqd5l2a_sPkKS6jYpsldJxlMFcnMTOVU3mN4Jf8dNaM1iLUkCpk7laqAjModxt39vs2BLzOCIKJ1_B-RDInGYifiqvsrRhh3L_F0VLDv2iRLzB96TyXb6Hb6XEmLmEkK0c1mQq8uMvHtzoviQScz3jr4TfVNCabBXFwt4BJsziWETBiJz9LZQQNrbDwoLl3ttbGKdrIftgf-fZ41ugQl55hfi_QqlRqXXqRcIdlt1VGtRqUQakaQZ0x7yFZVQTEz82rtx7XbXKXmrE2prQ51VgJbFjiRRVMSBSdKWO3n6vyUfRUqYSnEz7Lw0pwINSVycNEorSC7XMTDLqiENOcP_-ns9efAxJXyIGZ7uDwhTfUFzAvpeFh_KCBVpfhgjFBDq04bhiK35gflxXYLrqefO6-vFCeO4CLXA769V5Eq_OOpmo7FjD4qWB2ni8ybRp07jrQqH_M2NABDFFM2Cm1V6SiIWNcNDBPJIwInQP-5UlvCTcF6Pi28vc9makDFLkH_8YH6jZ6788a4C42JGESY7jBeBsB4H3QtkbxoTikFU-TG-eLOAq-1t1xXQ4eY_aqIX3sMtIeehJygvs_sAVO5uTCEz9j55ZDP2k=", "id": "rs_060035eb87c40e14016a12f3e121b481918758b7ae23df1ea8", "summary": [{"type": "summary_text", "text": "**Planning test implementation**\n\nI’m thinking about implementing tests for the social/draft.py file. First, I need to inspect the candidate classes within that file to understand what I’m dealing with. It’s essential to know the structure before diving into testing. Once I have a grasp of the classes, I can outline the tests I want to create, ensuring they cover all necessary cases. Let’s keep moving forward!"}]}], "tool_calls": [{"id": "call_2ifLhhUUgmxwFFnYAVcnS8rY", "call_id": "call_2ifLhhUUgmxwFFnYAVcnS8rY", "response_item_id": "fc_060035eb87c40e14016a12f3e771848191908d0378403beece", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/ideas/candidate.py\",\"limit\":260}"}}, {"id": "call_p7mHYIEEtFFO0vvLdVs5NbX2", "call_id": "call_p7mHYIEEtFFO0vvLdVs5NbX2", "response_item_id": "fc_060035eb87c40e14016a12f3e771988191bf0cb0458a147379", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/to_candidate.py\",\"limit\":220}"}}, {"id": "call_rIvktuLAX7u6ctaukFkuFPGX", "call_id": "call_rIvktuLAX7u6ctaukFkuFPGX", "response_item_id": "fc_060035eb87c40e14016a12f3e771a48191873ed03ba7dba3f4", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/preview.py\",\"limit\":220}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Video candidate domain model and validation.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|from dataclasses import dataclass\\n     6|\\n     7|from autoshorts.review.package import ReviewPackage\\n     8|from autoshorts.scripting.quality import ScriptQualityScore\\n     9|\\n    10|SUPPORTED_PLATFORMS = (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n    11|\\n    12|\\n    13|@dataclass(frozen=True)\\n    14|class ValidationResult:\\n    15|    is_valid: bool\\n    16|    errors: tuple[str, ...] = ()\\n    17|\\n    18|\\n    19|@dataclass(frozen=True)\\n    20|class VideoCandidate:\\n    21|    candidate_id: str\\n    22|    pillar: str\\n    23|    title: str\\n    24|    platforms: tuple[str, ...]\\n    25|    duration_seconds: int\\n    26|    hook: str\\n    27|    script_outline: str\\n    28|    visual_concept: str\\n    29|    caption: str\\n    30|    cta: str\\n    31|    hashtags: tuple[str, ...]\\n    32|    quality_score: ScriptQualityScore\\n    33|    policy_notes: tuple[str, ...]\\n    34|    hypothesis: str\\n    35|\\n    36|    def to_review_package(self, preview_path: str | None = None) -> ReviewPackage:\\n    37|        return ReviewPackage(\\n    38|            candidate_id=self.candidate_id,\\n    39|            title=self.title,\\n    40|            pillar=self.pillar,\\n    41|            duration_seconds=self.duration_seconds,\\n    42|            hook=self.hook,\\n    43|            script_summary=self.script_outline,\\n    44|            visual_concept=self.visual_concept,\\n    45|            caption=self.caption,\\n    46|            hashtags=self.hashtags,\\n    47|            platforms=self.platforms,\\n    48|            quality_score=self.quality_score,\\n    49|            policy_notes=self.policy_notes,\\n    50|            preview_path=preview_path,\\n    51|        )\\n    52|\\n    53|\\n    54|def _is_blank(value: str) -> bool:\\n    55|    return not (value or \\\"\\\").strip()\\n    56|\\n    57|\\n    58|def validate_candidate(candidate: VideoCandidate) -> ValidationResult:\\n    59|    errors: list[str] = []\\n    60|\\n    61|    required_fields = {\\n    62|        \\\"candidate_id\\\": candidate.candidate_id,\\n    63|        \\\"pillar\\\": candidate.pillar,\\n    64|        \\\"title\\\": candidate.title,\\n    65|        \\\"hook\\\": candidate.hook,\\n    66|        \\\"script_outline\\\": candidate.script_outline,\\n    67|        \\\"visual_concept\\\": candidate.visual_concept,\\n    68|        \\\"caption\\\": candidate.caption,\\n    69|        \\\"cta\\\": candidate.cta,\\n    70|        \\\"hypothesis\\\": candidate.hypothesis,\\n    71|    }\\n    72|    for field_name, value in required_fields.items():\\n    73|        if _is_blank(value):\\n    74|            errors.append(f\\\"{field_name} is required\\\")\\n    75|\\n    76|    if not 10 <= candidate.duration_seconds <= 180:\\n    77|        errors.append(\\\"duration_seconds must be between 10 and 180\\\")\\n    78|\\n    79|    if not candidate.platforms:\\n    80|        errors.append(\\\"at least one platform is required\\\")\\n    81|    for platform in candidate.platforms:\\n    82|        if platform not in SUPPORTED_PLATFORMS:\\n    83|            errors.append(f\\\"unsupported platform: {platform}\\\")\\n    84|\\n    85|    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n    86|\", \"total_lines\": 85, \"file_size\": 2557, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_2ifLhhUUgmxwFFnYAVcnS8rY", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "read_file", "content": "{\"status\": \"unchanged\", \"message\": \"File unchanged since last read. The content from the earlier read_file result in this conversation is still current — refer to that instead of re-reading.\", \"path\": \"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/content/to_candidate.py\", \"dedup\": true, \"content_returned\": false}", "tool_call_id": "call_p7mHYIEEtFFO0vvLdVs5NbX2", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Side-effect-free HTML preview generation for render manifests.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|from dataclasses import dataclass\\n     6|from html import escape\\n     7|\\n     8|from autoshorts.ideas.candidate import ValidationResult\\n     9|from autoshorts.rendering.manifest import RenderManifest, validate_render_manifest\\n    10|\\n    11|\\n    12|@dataclass(frozen=True)\\n    13|class RenderPreview:\\n    14|    script_id: str\\n    15|    title: str\\n    16|    preview_type: str\\n    17|    html: str\\n    18|    side_effects: tuple[str, ...] = ()\\n    19|    external_calls: tuple[str, ...] = ()\\n    20|\\n    21|\\n    22|def _is_blank(value: str) -> bool:\\n    23|    return not (value or \\\"\\\").strip()\\n    24|\\n    25|\\n    26|def _timeline_row(index: int, item) -> str:\\n    27|    time_range = f\\\"{item.start_seconds}–{item.end_seconds}s\\\"\\n    28|    return \\\"\\\".join(\\n    29|        (\\n    30|            '<section class=\\\"scene\\\">',\\n    31|            f\\\"<h2>{index:02d}. {escape(item.scene_purpose)}</h2>\\\",\\n    32|            f\\\"<p><strong>Time:</strong> {escape(time_range)}</p>\\\",\\n    33|            f\\\"<p><strong>Asset mode:</strong> {escape(item.asset_mode)}</p>\\\",\\n    34|            f\\\"<p><strong>Motion:</strong> {escape(item.motion_instruction)}</p>\\\",\\n    35|            f\\\"<p><strong>Caption:</strong> {escape(item.caption_layer.text)}</p>\\\",\\n    36|            f\\\"<p><strong>Label:</strong> {escape(item.label_layer.text)}</p>\\\",\\n    37|            f\\\"<p><strong>Placeholder:</strong> {escape(item.placeholder_asset_path)}</p>\\\",\\n    38|            \\\"</section>\\\",\\n    39|        )\\n    40|    )\\n    41|\\n    42|\\n    43|def build_html_preview(manifest: RenderManifest) -> RenderPreview:\\n    44|    \\\"\\\"\\\"Build an inspectable HTML timeline preview without rendering media.\\\"\\\"\\\"\\n    45|\\n    46|    validation = validate_render_manifest(manifest)\\n    47|    if not validation.is_valid:\\n    48|        raise ValueError(\\\"; \\\".join(validation.errors))\\n    49|\\n    50|    settings = manifest.settings\\n    51|    rows = \\\"\\\\n\\\".join(_timeline_row(index, item) for index, item in enumerate(manifest.timeline, start=1))\\n    52|    html = f\\\"\\\"\\\"<!doctype html>\\n    53|<html lang=\\\"en\\\">\\n    54|<head>\\n    55|  <meta charset=\\\"utf-8\\\">\\n    56|  <title>{escape(manifest.title)} — render preview</title>\\n    57|  <style>\\n    58|    body {{ font-family: system-ui, sans-serif; background: #111827; color: #f9fafb; margin: 2rem; }}\\n    59|    .meta, .scene {{ border: 1px solid #374151; border-radius: 12px; padding: 1rem; margin: 1rem 0; }}\\n    60|    .scene {{ background: #1f2937; }}\\n    61|    strong {{ color: #93c5fd; }}\\n    62|  </style>\\n    63|</head>\\n    64|<body>\\n    65|  <main>\\n    66|    <h1>{escape(manifest.title)}</h1>\\n    67|    <section class=\\\"meta\\\">\\n    68|      <p><strong>Script:</strong> {escape(manifest.script_id)}</p>\\n    69|      <p><strong>Format:</strong> {settings.width}x{settings.height}, {settings.fps}fps</p>\\n    70|      <p><strong>Text policy:</strong> captions and labels are renderer-owned; no generated text inside assets.</p>\\n    71|    </section>\\n    72|    {rows}\\n    73|  </main>\\n    74|</body>\\n    75|</html>\\\"\\\"\\\"\\n    76|    return RenderPreview(\\n    77|        script_id=manifest.script_id,\\n    78|        title=manifest.title,\\n    79|        preview_type=\\\"html_timeline\\\",\\n    80|        html=html,\\n    81|        side_effects=(),\\n    82|        external_calls=(),\\n    83|    )\\n    84|\\n    85|\\n    86|def validate_render_preview(preview: RenderPreview) -> ValidationResult:\\n    87|    errors: list[str] = []\\n    88|    if _is_blank(preview.script_id):\\n    89|        errors.append(\\\"script_id is required\\\")\\n    90|    if _is_blank(preview.title):\\n    91|        errors.append(\\\"title is required\\\")\\n    92|    if preview.preview_type != \\\"html_timeline\\\":\\n    93|        errors.append(\\\"preview_type must be html_timeline\\\")\\n    94|    if _is_blank(preview.html):\\n    95|        errors.append(\\\"html is required\\\")\\n    96|    if \\\"<html\\\" not in preview.html.casefold():\\n    97|        errors.append(\\\"html preview must contain an html document\\\")\\n    98|    if preview.side_effects:\\n    99|        errors.append(\\\"preview must not have side effects\\\")\\n   100|    if preview.external_calls:\\n   101|        errors.append(\\\"preview must not call external renderers\\\")\\n   102|    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n   103|\", \"total_lines\": 102, \"file_size\": 3651, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_rIvktuLAX7u6ctaukFkuFPGX", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Planning for tests**\n\nI see that I need to write tests for the social draft, which is a key part of the process. There’s also the need to build a fixture. Alright, so I'm thinking about how to approach this. I want to ensure that the tests are comprehensive enough to cover all the necessary aspects to make sure everything works smoothly! I'll take it step by step as I work through it.", "finish_reason": "tool_calls", "reasoning_content": "**Planning for tests**\n\nI see that I need to write tests for the social draft, which is a key part of the process. There’s also the need to build a fixture. Alright, so I'm thinking about how to approach this. I want to ensure that the tests are comprehensive enough to cover all the necessary aspects to make sure everything works smoothly! I'll take it step by step as I work through it.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvPuygr8q1vBZ-xBxiiyMppk7lxSuMpHAlfVP6alb9hKlzmPuCashlWGLnP6nuqpG0O2faCUi39FhSrh8947mN39gTBSF5d31oTbfZQ9VW5_ky5AQbXquAcQotVsnolq7EvJYaDXDI1ua2Q6DDdMolRg46M7N0emTgBlhj-QfrUQMqeAI5sHZT0mLYZITYaL5NG2SkJpuxt9dLdl13VQMcHi4QdbMzVzFv2SoHNCP_kqsb9Qes2-mDgjoNiOq4gt1kwkBQpzF-N9ntuy1iz99hZm0YK80qOvMSvJXQtvHQwu2yfP1sL9PtaVWi_49F5GXYWjJDEKGxv-LKh3HWw4V-nsyrVznXImizb0Usg71xqE0nTaGxQ8zfsYZOGEKH7YGd6j3QWeL1WEt25sZ1Katb1Ar6VCNWcV-zkZJeipzMp7SXQpwqZuxj_k9LVaWqdRWj53rd3S04md4CVM_YYeV1drqte7iYn65mM2kyfVEDQLTxe8ttxByv6AjOBh0QSFF1ZrVOad96pauX3ubx8nIjxqVtbb8oSuPeR891QUPcVQVSUdGY3jnkV5vA_N7DoFEcXXDj9Uru7yxnzg6XtYsau0L20g2O934iwHzZREepyeKfWA7KB9d88W00jau4Pl0k578RCfmQErrQcnGgM7N_Rr9D1NAkQ1lpPQL1AjYKIrQYhxx09WJa9athHPzyCQD8H3wJLYgWB7TA74oQWkHPgQP4IGohhP49ZwNuKrda3zJTk-IZ-_iULqeA9TytzSpei-2pxavj0At3z-tB7GH4wYkMZMRFoZvmhKUGnwG-adrDJAc-JfpDlN2GRoMpNZYfRLOvJ4EWjJkpKLSj0n-klPjZIyxsWALGIF65nEMkgZCa071PElNHkDWnxcUOPM0WAmeRWLtcnLRNzIikW3GomsKKtqfc_5yaOgWcgT2e0Dh9sQ_IBBcXL7A3R8myP03NclHUru-qG9999b-lq_B2h6Mi9bW-1JRit-QRk2nXibml0=", "id": "rs_060035eb87c40e14016a12f3ec782c8191b030271f5f78ab4f", "summary": [{"type": "summary_text", "text": "**Planning for tests**\n\nI see that I need to write tests for the social draft, which is a key part of the process. There’s also the need to build a fixture. Alright, so I'm thinking about how to approach this. I want to ensure that the tests are comprehensive enough to cover all the necessary aspects to make sure everything works smoothly! I'll take it step by step as I work through it."}]}], "tool_calls": [{"id": "call_fccztMi8X1cuQGSRjDnNeaXU", "call_id": "call_fccztMi8X1cuQGSRjDnNeaXU", "response_item_id": "fc_060035eb87c40e14016a12f3eec7648191a095140da37d55ae", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_draft.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.social.draft import (\\n    SocialMediaDraft,\\n    build_social_media_draft,\\n    validate_social_media_draft,\\n)\\n\\n\\ndef _candidate_and_preview():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    candidate = convert_script_draft_to_candidate(brief, script)\\n    preview = build_html_preview(build_render_manifest(create_shot_plan(script)))\\n    return candidate, preview\\n\\n\\ndef test_build_social_media_draft_creates_approval_first_post_draft():\\n    candidate, preview = _candidate_and_preview()\\n\\n    draft = build_social_media_draft(candidate, preview)\\n\\n    assert isinstance(draft, SocialMediaDraft)\\n    assert draft.candidate_id == \\\"ai_life_systems-ai-weekly-review-routine-35s\\\"\\n    assert draft.platforms == (\\\"youtube\\\", \\\"tiktok\\\", \\\"instagram\\\")\\n    assert draft.platform_labels == (\\\"YouTube Shorts\\\", \\\"TikTok\\\", \\\"Instagram Reels\\\")\\n    assert draft.title_variants == (\\n        \\\"AI weekly review routine\\\",\\n        \\\"If AI runs your week, you have already lost control.\\\",\\n    )\\n    assert draft.caption == candidate.caption\\n    assert draft.hashtags == (\\\"#AI\\\", \\\"#LifeSystems\\\", \\\"#Productivity\\\")\\n    assert draft.cta == candidate.cta\\n    assert draft.hook == candidate.hook\\n    assert draft.planned_duration_seconds == 35\\n    assert draft.preview_reference == \\\"HTML timeline preview available\\\"\\n    assert draft.preview_type == \\\"html_timeline\\\"\\n    assert draft.requires_human_approval is True\\n    assert draft.side_effects == ()\\n    assert \\\"Quality average: 8.0\\\" in draft.quality_risk_summary\\n    assert \\\"AI-slop risk: 3\\\" in draft.quality_risk_summary\\n    assert \\\"Policy risk: low\\\" in draft.quality_risk_summary\\n\\n\\ndef test_validate_social_media_draft_accepts_default_draft():\\n    candidate, preview = _candidate_and_preview()\\n\\n    result = validate_social_media_draft(build_social_media_draft(candidate, preview))\\n\\n    assert result.is_valid is True\\n    assert result.errors == ()\\n\\n\\ndef test_build_social_media_draft_rejects_preview_for_different_script():\\n    candidate, preview = _candidate_and_preview()\\n    mismatched = type(preview)(\\n        script_id=\\\"other-script\\\",\\n        title=preview.title,\\n        preview_type=preview.preview_type,\\n        html=preview.html,\\n        side_effects=preview.side_effects,\\n        external_calls=preview.external_calls,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"preview script_id must match candidate source brief\\\"):\\n        build_social_media_draft(candidate, mismatched)\\n\\n\\ndef test_validate_social_media_draft_rejects_missing_approval_and_side_effects():\\n    candidate, preview = _candidate_and_preview()\\n    draft = build_social_media_draft(candidate, preview)\\n    unsafe = SocialMediaDraft(\\n        candidate_id=draft.candidate_id,\\n        platforms=draft.platforms,\\n        platform_labels=draft.platform_labels,\\n        title_variants=draft.title_variants,\\n        caption=draft.caption,\\n        hashtags=draft.hashtags,\\n        cta=draft.cta,\\n        hook=draft.hook,\\n        planned_duration_seconds=draft.planned_duration_seconds,\\n        quality_risk_summary=draft.quality_risk_summary,\\n        preview_reference=draft.preview_reference,\\n        preview_type=draft.preview_type,\\n        requires_human_approval=False,\\n        side_effects=(\\\"platform_upload\\\",),\\n    )\\n\\n    result = validate_social_media_draft(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"human approval is required\\\" in result.errors\\n    assert \\\"social media drafts must not have side effects\\\" in result.errors\\n\\n\\ndef test_validate_social_media_draft_rejects_unsupported_platform_and_blank_fields():\\n    draft = SocialMediaDraft(\\n        candidate_id=\\\"\\\",\\n        platforms=(\\\"linkedin\\\",),\\n        platform_labels=(\\\"LinkedIn\\\",),\\n        title_variants=(),\\n        caption=\\\"\\\",\\n        hashtags=(),\\n        cta=\\\"\\\",\\n        hook=\\\"\\\",\\n        planned_duration_seconds=9,\\n        quality_risk_summary=\\\"\\\",\\n        preview_reference=\\\"\\\",\\n        preview_type=\\\"\\\",\\n        requires_human_approval=True,\\n        side_effects=(),\\n    )\\n\\n    result = validate_social_media_draft(draft)\\n\\n    assert result.is_valid is False\\n    assert \\\"candidate_id is required\\\" in result.errors\\n    assert \\\"unsupported platform: linkedin\\\" in result.errors\\n    assert \\\"at least one title variant is required\\\" in result.errors\\n    assert \\\"caption is required\\\" in result.errors\\n    assert \\\"at least one hashtag is required\\\" in result.errors\\n    assert \\\"cta is required\\\" in result.errors\\n    assert \\\"hook is required\\\" in result.errors\\n    assert \\\"planned_duration_seconds must be between 10 and 180\\\" in result.errors\\n    assert \\\"quality_risk_summary is required\\\" in result.errors\\n    assert \\\"preview_reference is required\\\" in result.errors\\n    assert \\\"preview_type is required\\\" in result.errors\\n\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5367, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_draft.py\\\">\\nERROR [9:6] Import \\\"autoshorts.social.draft\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_fccztMi8X1cuQGSRjDnNeaXU", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_5HzwQOynSyAH6d8uROojAZqR", "call_id": "call_5HzwQOynSyAH6d8uROojAZqR", "response_item_id": "fc_060035eb87c40e14016a12f791c52c81919374b5206a0e8eb8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_social_draft.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_social_draft.py __________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_draft.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_social_draft.py:9: in <module>\\n    from autoshorts.social.draft import (\\nE   ModuleNotFoundError: No module named 'autoshorts.social'\\n=========================== short test summary info ============================\\nERROR tests/test_social_draft.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_5HzwQOynSyAH6d8uROojAZqR", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Determining ScriptQualityScore fields**\n\nI need to figure out which fields are involved in the ScriptQualityScore. It’s crucial to identify the specific requirements for this part before I can move forward. I should explore the documentation or guidelines that might outline what exactly those fields are and how they contribute to the overall score. This will help ensure I have all the necessary components to implement it correctly. So, let's clarify those details!", "finish_reason": "tool_calls", "reasoning_content": "**Determining ScriptQualityScore fields**\n\nI need to figure out which fields are involved in the ScriptQualityScore. It’s crucial to identify the specific requirements for this part before I can move forward. I should explore the documentation or guidelines that might outline what exactly those fields are and how they contribute to the overall score. This will help ensure I have all the necessary components to implement it correctly. So, let's clarify those details!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvfDBh2ebo1dEPLyf7tpe2is-ii3xpQGR42I45zkaSFcW_sRugtrhRrfP27eAV3Lj_p5W3NVblVNZKQfuRK8IAqsprQcijb-nSa1TQ2GZ27_wugtDcBexhApjnsrddC40pwD_lLOZ4XGztk_mWv76L5UpZCqKgoD-c9Ney3QwbMvBazsFzod8PJUS-6GZ42zZ3Li2W0iYx1Bfr5RCtAIbumEYrUuPdHqf3p9sMMl1KTgPiD9LXWQMqTBCJQrRQwZrY6vEkbpH6qXfiCi0tKgoBiSuwP_hSQk4pUGet028q0Q-2hCEtJdsQhq92ccoFI8FA6L5NEqo9wUxse6eBaLaDBL3CkQW6ikUoZ9c34BDKRHkM7lL2LaI-7DomDyVnJJNdDPuCRuQpCwWUvqRdsk-oDkklEdrOHFeKoVAKxZ_tR1CGDPOK9cKwl7mW3k4Bt249JdKjtR8FkgTtr8eYbLXiCVWouEuOnwP-GXbkL3Iv0ilg_OyuIegZz540aZ7zTv5KI3dUTzAoMCgNyspSSnY9iRTVAVBbq7LNvKIyWmzSh66pqXjs3h5ds27KtdHzRwpzKRZJ5lJ95iKKqG2uclKiKc-Rv4eaBDNQquPhePlvJf5Gh6xQzaeGiZr-KK445Frfxo8IpNiGaJPkzLmGh-Vz5brGRMP6tXWh6Vo2nLKvrIzDr1lp6VasO8bcCh09rt2p3SWb8CvpynWikWvNVA_gYYUfugBlE17i4WRoT5NPXuom3PxYpCIDxUu5ZpcR3npcBzv4k1xJ84YTUSwsPx8Ew6IC8iEpIi1YQPPRnt0kzUuMCCLm2Ssuei21I5Mf4jj_sGZlqOfEO0AdcHHp86zaKUViQ1QWA4leCMpEu2Uc7cYVBOcBemrHxoN1dR34XVqesxm4nI7jvj9etaFzjVyZEUE_ab3DW_GMtAOVBQTb2P-aCXl7AosfqU14BWSVaCSrCsn7M5LtYkAKnrMrzdr0TY_A==", "id": "rs_060035eb87c40e14016a12f7c199b0819192603c4ddeef4440", "summary": [{"type": "summary_text", "text": "**Determining ScriptQualityScore fields**\n\nI need to figure out which fields are involved in the ScriptQualityScore. It’s crucial to identify the specific requirements for this part before I can move forward. I should explore the documentation or guidelines that might outline what exactly those fields are and how they contribute to the overall score. This will help ensure I have all the necessary components to implement it correctly. So, let's clarify those details!"}]}], "tool_calls": [{"id": "call_QWl602OpTCF4lvYz1ufM04uu", "call_id": "call_QWl602OpTCF4lvYz1ufM04uu", "response_item_id": "fc_060035eb87c40e14016a12f7c33a3481918e7945b98b0bd3e5", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/scripting/quality.py\",\"limit\":200}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Deterministic pre-render quality gate for script candidates.\\\"\\\"\\\"\\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|PolicyRisk = Literal[\\\"low\\\", \\\"medium\\\", \\\"high\\\"]\\n     9|\\n    10|\\n    11|@dataclass(frozen=True)\\n    12|class ScriptQualityScore:\\n    13|    \\\"\\\"\\\"Human/LLM-reviewable scores for one short-form script candidate.\\n    14|\\n    15|    Numeric dimensions use a 0-10 scale. ``ai_slop_risk`` is inverse: higher\\n    16|    means more generic, uncanny, or trust-damaging output risk.\\n    17|    \\\"\\\"\\\"\\n    18|\\n    19|    hook_strength: int\\n    20|    novelty: int\\n    21|    clarity: int\\n    22|    retention: int\\n    23|    ai_slop_risk: int\\n    24|    policy_risk: PolicyRisk = \\\"low\\\"\\n    25|\\n    26|    @property\\n    27|    def average_quality(self) -> float:\\n    28|        return (self.hook_strength + self.novelty + self.clarity + self.retention) / 4\\n    29|\\n    30|\\n    31|def should_render(\\n    32|    score: ScriptQualityScore,\\n    33|    *,\\n    34|    minimum_average: float = 8.0,\\n    35|    maximum_ai_slop_risk: int = 5,\\n    36|) -> bool:\\n    37|    \\\"\\\"\\\"Return whether a script should proceed to rendering.\\n    38|\\n    39|    The gate is intentionally conservative: content below threshold waits for\\n    40|    rewrite instead of spending render time on AI slop. A dreary but economical\\n    41|    little kindness.\\n    42|    \\\"\\\"\\\"\\n    43|\\n    44|    if score.policy_risk == \\\"high\\\":\\n    45|        return False\\n    46|    if score.ai_slop_risk > maximum_ai_slop_risk:\\n    47|        return False\\n    48|    return score.average_quality >= minimum_average\\n    49|\", \"total_lines\": 48, \"file_size\": 1331, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_QWl602OpTCF4lvYz1ufM04uu", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_8W49IQcWBiiH9mCnTEkjWeZL", "call_id": "call_8W49IQcWBiiH9mCnTEkjWeZL", "response_item_id": "fc_060035eb87c40e14016a12f7c9415881919908edd5021dea8d", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Approval-first social media draft artifacts.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import SUPPORTED_PLATFORMS, ValidationResult, VideoCandidate, validate_candidate\\nfrom autoshorts.rendering.preview import RenderPreview, validate_render_preview\\n\\n_PLATFORM_LABELS = {\\n    \\\"youtube\\\": \\\"YouTube Shorts\\\",\\n    \\\"tiktok\\\": \\\"TikTok\\\",\\n    \\\"instagram\\\": \\\"Instagram Reels\\\",\\n}\\n\\n\\n@dataclass(frozen=True)\\nclass SocialMediaDraft:\\n    candidate_id: str\\n    platforms: tuple[str, ...]\\n    platform_labels: tuple[str, ...]\\n    title_variants: tuple[str, ...]\\n    caption: str\\n    hashtags: tuple[str, ...]\\n    cta: str\\n    hook: str\\n    planned_duration_seconds: int\\n    quality_risk_summary: str\\n    preview_reference: str\\n    preview_type: str\\n    requires_human_approval: bool = True\\n    side_effects: tuple[str, ...] = ()\\n\\n\\ndef _is_blank(value: str) -> bool:\\n    return not (value or \\\"\\\").strip()\\n\\n\\ndef _quality_risk_summary(candidate: VideoCandidate) -> str:\\n    score = candidate.quality_score\\n    return (\\n        f\\\"Quality average: {score.average_quality:.1f}; \\\"\\n        f\\\"AI-slop risk: {score.ai_slop_risk}; \\\"\\n        f\\\"Policy risk: {score.policy_risk}\\\"\\n    )\\n\\n\\ndef _source_brief_id(candidate: VideoCandidate) -> str | None:\\n    prefix = \\\"Source brief: \\\"\\n    for note in candidate.policy_notes:\\n        if note.startswith(prefix):\\n            return note.removeprefix(prefix).strip()\\n    return None\\n\\n\\ndef build_social_media_draft(candidate: VideoCandidate, preview: RenderPreview) -> SocialMediaDraft:\\n    \\\"\\\"\\\"Build a side-effect-free platform draft from a candidate and preview.\\\"\\\"\\\"\\n\\n    candidate_validation = validate_candidate(candidate)\\n    if not candidate_validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(candidate_validation.errors))\\n\\n    preview_validation = validate_render_preview(preview)\\n    if not preview_validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(preview_validation.errors))\\n\\n    source_brief_id = _source_brief_id(candidate)\\n    if source_brief_id is not None and preview.script_id != source_brief_id:\\n        raise ValueError(\\\"preview script_id must match candidate source brief\\\")\\n\\n    draft = SocialMediaDraft(\\n        candidate_id=candidate.candidate_id,\\n        platforms=candidate.platforms,\\n        platform_labels=tuple(_PLATFORM_LABELS[platform] for platform in candidate.platforms),\\n        title_variants=(candidate.title, candidate.hook),\\n        caption=candidate.caption,\\n        hashtags=candidate.hashtags,\\n        cta=candidate.cta,\\n        hook=candidate.hook,\\n        planned_duration_seconds=candidate.duration_seconds,\\n        quality_risk_summary=_quality_risk_summary(candidate),\\n        preview_reference=\\\"HTML timeline preview available\\\",\\n        preview_type=preview.preview_type,\\n        requires_human_approval=True,\\n        side_effects=(),\\n    )\\n    validation = validate_social_media_draft(draft)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n    return draft\\n\\n\\ndef validate_social_media_draft(draft: SocialMediaDraft) -> ValidationResult:\\n    errors: list[str] = []\\n    for field_name, value in {\\n        \\\"candidate_id\\\": draft.candidate_id,\\n        \\\"caption\\\": draft.caption,\\n        \\\"cta\\\": draft.cta,\\n        \\\"hook\\\": draft.hook,\\n        \\\"quality_risk_summary\\\": draft.quality_risk_summary,\\n        \\\"preview_reference\\\": draft.preview_reference,\\n        \\\"preview_type\\\": draft.preview_type,\\n    }.items():\\n        if _is_blank(value):\\n            errors.append(f\\\"{field_name} is required\\\")\\n\\n    if not draft.platforms:\\n        errors.append(\\\"at least one platform is required\\\")\\n    for platform in draft.platforms:\\n        if platform not in SUPPORTED_PLATFORMS:\\n            errors.append(f\\\"unsupported platform: {platform}\\\")\\n\\n    if len(draft.platform_labels) != len(draft.platforms):\\n        errors.append(\\\"platform_labels must match platforms\\\")\\n    if not draft.title_variants:\\n        errors.append(\\\"at least one title variant is required\\\")\\n    elif any(_is_blank(title) for title in draft.title_variants):\\n        errors.append(\\\"title variants must not be blank\\\")\\n    if not draft.hashtags:\\n        errors.append(\\\"at least one hashtag is required\\\")\\n    elif any(_is_blank(hashtag) for hashtag in draft.hashtags):\\n        errors.append(\\\"hashtags must not be blank\\\")\\n    if not 10 <= draft.planned_duration_seconds <= 180:\\n        errors.append(\\\"planned_duration_seconds must be between 10 and 180\\\")\\n    if not draft.requires_human_approval:\\n        errors.append(\\\"human approval is required\\\")\\n    if draft.side_effects:\\n        errors.append(\\\"social media drafts must not have side effects\\\")\\n\\n    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/draft.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "assistant", "content": "", "reasoning": "**Updating README file**\n\nI need to make sure the README file is up to date. It’s important for anyone using it to have the latest information. I should review the current content and see what needs to be added or changed. Maybe I’ll clarify instructions or update any outdated examples. I want it to be clear and helpful, so the user can easily understand and follow along. Let's not miss any crucial details!", "finish_reason": "tool_calls", "reasoning_content": "**Updating README file**\n\nI need to make sure the README file is up to date. It’s important for anyone using it to have the latest information. I should review the current content and see what needs to be added or changed. Maybe I’ll clarify instructions or update any outdated examples. I want it to be clear and helpful, so the user can easily understand and follow along. Let's not miss any crucial details!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvf14TK7qUdiAeOqWuuL222JlPHJc2YoiozYp5h-r7Pu9LgzGBgsPyrjnd07SIaYsKNxwFTF_bj1UKLrPM158vKyPmU63luWoQp5_UhcAcQIk5u4s4JpJYlbbJHdj21opN6n95MQ3IlDelYCwjL8XPXsou_Hga76YJbcsrX-jwkPWqTmVGuB2_hwsNHpU2kB0ve6451ZMHgYlbi0Bdi0onVaGG5QHZnj7Y3vEvjHSxY58pLzPqPPM4RzRdRBO1pAVdszHJh0mFdsUNaNRGMfyYMcZM1Xj16-3rkJTtIPttOCeqHyDM96Gw_50odRUboQcVsmMK_Inl_dVQkP7ddRMnG7el2YgZwpb0bwdVnoEIIMoUVGRYdcPD-5sXWBfS4o_TKK-pksWE2a-B2j2CeWJhOsecmyTMHl4YUJm58O6HjTt4PNPg4U8DYTboTSQubY3R02TQbPMLuy2RPS4MA435dgj_yqzmPEgFp_JKa_-2QKIaJ-ej0y2QurK_nfv9Pahzk6dNmsVxR9sc9pCx_KRJe-pvaIG8AnIMhZcOoxwdQwaBB-4OKz_6sczU7-3pQf3NCqjnOmAdAbuEnTN0YanMbNOQsbbPFgafcsBggZPcarov1chatQghyM1J9UI9ZfQ07zBxbQ40UzNasVoBt7gwQXCbx4pVPDIOyjYkLAn_7AhT3LU3y0LGO5wti8lvoX2HLuyQm4cCUSfDrw3wHWapiOwkBT0F50HLiaZu5tOhA2OCEITcg2pEp9stJ_ShsEj9XELPIxlkFwGqsNRVG56Q3BUP2DYfNvZEwqwO-FpB69uy_7YmDnflrstBaFzVogsbTt2XMhnVNsllf2-YhZjFRrypCKgZCwKjIOoRDIzNKbsATfa4pcWHkuFuhhHsEWvwD9uAoULYOfybpRrX85GzD2F31mt0Q7XOD3weJXY9O1dVtomUozHmAIal3oqW2Htw45", "id": "rs_060035eb87c40e14016a12f7f351108191a77ff3319ef1ef7c", "summary": [{"type": "summary_text", "text": "**Updating README file**\n\nI need to make sure the README file is up to date. It’s important for anyone using it to have the latest information. I should review the current content and see what needs to be added or changed. Maybe I’ll clarify instructions or update any outdated examples. I want it to be clear and helpful, so the user can easily understand and follow along. Let's not miss any crucial details!"}]}], "tool_calls": [{"id": "call_BqaEr9IJBPrCvVw61jzZXzkA", "call_id": "call_BqaEr9IJBPrCvVw61jzZXzkA", "response_item_id": "fc_060035eb87c40e14016a12f7f58a188191baa9f10a1c3c4289", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\",\"new_string\":\"- `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n- `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -25,6 +25,7 @@\\n - `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n+- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n - `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n - `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_BqaEr9IJBPrCvVw61jzZXzkA", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_bMZSoK4aLpgEUbRMQcvPuNHi", "call_id": "call_bMZSoK4aLpgEUbRMQcvPuNHi", "response_item_id": "fc_060035eb87c40e14016a12f7f9def08191a8fbb06207774e27", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n\\n## Social media draft\\n\\n`autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n\\n- candidate id;\\n- target platforms and human-readable platform labels;\\n- title variants;\\n- caption, hashtags, CTA, and hook;\\n- planned duration;\\n- quality/risk summary;\\n- preview reference and preview type;\\n- `requires_human_approval=True`;\\n- `side_effects=()`.\\n\\n`validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n\\n## Human approval commands\",\"old_string\":\"It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -107,6 +107,22 @@\\n \\n It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n \\n+## Social media draft\\n+\\n+`autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n+\\n+- candidate id;\\n+- target platforms and human-readable platform labels;\\n+- title variants;\\n+- caption, hashtags, CTA, and hook;\\n+- planned duration;\\n+- quality/risk summary;\\n+- preview reference and preview type;\\n+- `requires_human_approval=True`;\\n+- `side_effects=()`.\\n+\\n+`validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_bMZSoK4aLpgEUbRMQcvPuNHi", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_86NNpMcpKyVd7XO9hhjiFqzu", "call_id": "call_86NNpMcpKyVd7XO9hhjiFqzu", "response_item_id": "fc_060035eb87c40e14016a12f803a1b881919ea3bea5d94ec2eb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.social.draft import build_social_media_draft, validate_social_media_draft\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\ncandidate = convert_script_draft_to_candidate(brief, script)\\npreview = build_html_preview(build_render_manifest(create_shot_plan(script)))\\ndraft = build_social_media_draft(candidate, preview)\\nprint(draft.candidate_id)\\nprint(draft.platform_labels)\\nprint(draft.preview_reference)\\nprint(draft.requires_human_approval, draft.side_effects)\\nprint(validate_social_media_draft(draft).is_valid)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 84%]\\n.............                                                            [100%]\\n85 passed in 0.13s\\nai_life_systems-ai-weekly-review-routine-35s\\n('YouTube Shorts', 'TikTok', 'Instagram Reels')\\nHTML timeline preview available\\nTrue ()\\nTrue\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_86NNpMcpKyVd7XO9hhjiFqzu", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_sarrBSSUmQFMPq3giLsTaqRw", "call_id": "call_sarrBSSUmQFMPq3giLsTaqRw", "response_item_id": "fc_060035eb87c40e14016a12f80ea26881918ac44671506ae1dd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/social/__init__.py autoshorts/social/draft.py tests/test_social_draft.py && git commit -m \\\"feat: add social media draft model\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/social/\\n?? tests/test_social_draft.py\\n[feat/core-approval-quality-gates 1f6aa2c] feat: add social media draft model\\n 4 files changed, 286 insertions(+)\\n create mode 100644 autoshorts/social/__init__.py\\n create mode 100644 autoshorts/social/draft.py\\n create mode 100644 tests/test_social_draft.py\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\\nd118a76 feat: add retention shot planning\\n6e6569a feat: add content demo review pipeline\\n72dc887 feat: convert content drafts to video candidates\\n9c125f9 feat: add content brief and script draft models\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_sarrBSSUmQFMPq3giLsTaqRw", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Planning the next steps**\n\nI think the user might want me to keep going with the social review in this same turn. It seems like I should keep working until everything is done, right? So, I’m planning to implement the social review next, maybe even following the phrase \"machen wir so weiter.\" I believe getting things in order is important, and I need to focus on writing tests for the social review using TDD. Let’s proceed!", "finish_reason": "tool_calls", "reasoning_content": "**Planning the next steps**\n\nI think the user might want me to keep going with the social review in this same turn. It seems like I should keep working until everything is done, right? So, I’m planning to implement the social review next, maybe even following the phrase \"machen wir so weiter.\" I believe getting things in order is important, and I need to focus on writing tests for the social review using TDD. Let’s proceed!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvgV2r3cxECFN1mfk_PRk7ZD_kSzUyO819yl3r9lhXsVKQUVCH71fw8DOFGheiuUAmRhQ3OG58i5KorzzoTSBkhNbMe0U-kkf_1BROV7M3sef9aU4gdvoMXU5tmyE6zlVDt__7pQ4z2e6rzhTl5zSAkZySMtJr0LFnBPcS7kLslT1F5WUFqhnJmGJ_58GjGyyAOl2DyyNFcG66MzaH5Q2KKAJHQ6Xdi-KA8r8Ng33dQfM_a5254f7N3I_Rw16cCy58tfwBhz_uZs0EAKN-3TtKrs3im9Sl13TwrX83CroOVPS8MxQ-khzae_9HOYN1c0zZyZurfVn2jOsYRA_FntKxXsLiqgsaI9uK6LjHWF1hlKwRSqor_FKN-HZSFA_ZQxzV717Yl_bCfq0DyEa5SZ2PSs3k6v8kWAxo8uUTHUY9tdOyWueWx_rglVOwCQD1E08-f84TlMUAsfgJxYGA9i_E_t6lZWAr3hPF4vZ-IMybzYhd9bgMqdpvT3qE2XjzQ-18xX7o2PWwVPkXfZAjB61ORnIw45TPKm3GxoRFHmIUdYuQ4ulqIPIGElG7IKEKTrGCIrZGjKUkkyBN8e6q_WJOGOURdH2dzajxDDIOsOy1rfA38MmXtF9uy49leiu1hhTp4qbdV3bL20R1Logox84IYnPJKXZ5eCjj39Fxywmzv8prQNjmUh6RYMx_caYc_eZHV9i-2WjIe0DtBClEISVkLdGf9oR0Mdckw7-PL-9lT0A3vsVJxtKp-dwAafRlVSCRATN2Tg6TL80t3sei-7UkS_QWRraVERSh3UHGd6gfQ38lVOH0GTuvinQ4sKdarjVXyCsMazviqkUVUwqhumTsEafhnnAWGvqZnGfeNNtvEk2iPZDNzFjdfMNzB4XWR2-0T0a9DBXMR84dmuPmBEFlURLD7ciHLaj7OEf7KNrJvLB4BOJgscCaVlzHXHAOSrbvcXLKbK0anXmY09rYE5kJyzVIAkl4nNCj8QEeB7PT8IE_U1_ahS5wnKtAzvj28DUogPifY_alTEJHEO_K1q1lywJdZLwulSHzqshTSW35TgnZIrBiuEHnjtU98wQn0tZ3oRT29kUJEAYTya-DwPQ1vRdJwbYDbgvLfBwkk6YXQEUJ0fHuft-nOgWSM4C8tvwnGe2AGdgF4iKf2z8jctF7DjcTOhgqOb4mhSLnMMHQH7aAlObXDAsdJoXNu_kYPiiGxPYFVMoGBX_sCrMxATeybatjXZy4juyA6hxPab82tiF-5AWRI_l2QRryFbmpVq1Q0YB12oM8TUX4W1AhHLfl7f_S2-ozzkHmNtaNEG1lLDkco=", "id": "rs_060035eb87c40e14016a12f8126d4c819183ff11ac4b74d9de", "summary": [{"type": "summary_text", "text": "**Planning the next steps**\n\nI think the user might want me to keep going with the social review in this same turn. It seems like I should keep working until everything is done, right? So, I’m planning to implement the social review next, maybe even following the phrase \"machen wir so weiter.\" I believe getting things in order is important, and I need to focus on writing tests for the social review using TDD. Let’s proceed!"}]}], "tool_calls": [{"id": "call_AM9c7XSAtuYJmEdpy5euBV5q", "call_id": "call_AM9c7XSAtuYJmEdpy5euBV5q", "response_item_id": "fc_060035eb87c40e14016a12f815a2ec8191afbc9fd702428d40", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.social.draft import SocialMediaDraft, build_social_media_draft\\nfrom autoshorts.social.review import render_social_media_draft_review\\n\\n\\ndef _draft():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    candidate = convert_script_draft_to_candidate(brief, script)\\n    preview = build_html_preview(build_render_manifest(create_shot_plan(script)))\\n    return build_social_media_draft(candidate, preview)\\n\\n\\ndef test_render_social_media_draft_review_creates_telegram_safe_approval_text():\\n    review_text = render_social_media_draft_review(_draft())\\n\\n    assert review_text.startswith(\\\"## Social Media Draft Review\\\")\\n    assert \\\"Candidate: ai_life_systems-ai-weekly-review-routine-35s\\\" in review_text\\n    assert \\\"Platforms: YouTube Shorts, TikTok, Instagram Reels\\\" in review_text\\n    assert \\\"Duration: 35s\\\" in review_text\\n    assert \\\"Hook:\\\" in review_text\\n    assert \\\"If AI runs your week, you have already lost control.\\\" in review_text\\n    assert \\\"Title variants:\\\" in review_text\\n    assert \\\"Caption:\\\" in review_text\\n    assert \\\"Hashtags: #AI #LifeSystems #Productivity\\\" in review_text\\n    assert \\\"Preview: HTML timeline preview available (html_timeline)\\\" in review_text\\n    assert \\\"Quality/Risk: Quality average: 8.0; AI-slop risk: 3; Policy risk: low\\\" in review_text\\n    assert \\\"Requires human approval: yes\\\" in review_text\\n    assert \\\"Side effects: none\\\" in review_text\\n    assert \\\"Approval commands:\\\" in review_text\\n    assert \\\"Freigabe YouTube\\\" in review_text\\n    assert \\\"Freigabe TikTok\\\" in review_text\\n    assert \\\"Freigabe Instagram\\\" in review_text\\n    assert \\\"Freigabe alle\\\" in review_text\\n    assert \\\"Ablehnen\\\" in review_text\\n    assert \\\"Ändern: <requested change>\\\" in review_text\\n    assert len(review_text) <= 4096\\n    assert \\\"publish\\\" not in review_text.casefold()\\n    assert \\\"post\\\" not in review_text.casefold()\\n\\n\\ndef test_render_social_media_draft_review_rejects_invalid_draft():\\n    draft = _draft()\\n    invalid = SocialMediaDraft(\\n        candidate_id=\\\"\\\",\\n        platforms=draft.platforms,\\n        platform_labels=draft.platform_labels,\\n        title_variants=draft.title_variants,\\n        caption=draft.caption,\\n        hashtags=draft.hashtags,\\n        cta=draft.cta,\\n        hook=draft.hook,\\n        planned_duration_seconds=draft.planned_duration_seconds,\\n        quality_risk_summary=draft.quality_risk_summary,\\n        preview_reference=draft.preview_reference,\\n        preview_type=draft.preview_type,\\n        requires_human_approval=draft.requires_human_approval,\\n        side_effects=draft.side_effects,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"candidate_id is required\\\"):\\n        render_social_media_draft_review(invalid)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_review.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3340, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_review.py\\\">\\nERROR [10:6] Import \\\"autoshorts.social.review\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_AM9c7XSAtuYJmEdpy5euBV5q", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_8WFh7BQJZKCAMxq4POWv0LO2", "call_id": "call_8WFh7BQJZKCAMxq4POWv0LO2", "response_item_id": "fc_060035eb87c40e14016a12f829b81c819184d00916ee2c0849", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_social_review.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_social_review.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_review.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_social_review.py:10: in <module>\\n    from autoshorts.social.review import render_social_media_draft_review\\nE   ModuleNotFoundError: No module named 'autoshorts.social.review'\\n=========================== short test summary info ============================\\nERROR tests/test_social_review.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_8WFh7BQJZKCAMxq4POWv0LO2", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_O6RGqdJYLRsmbDya2AbwqYWo", "call_id": "call_O6RGqdJYLRsmbDya2AbwqYWo", "response_item_id": "fc_060035eb87c40e14016a12f82daad48191bfd813f09157c219", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Telegram-safe social media draft review rendering.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom autoshorts.social.draft import SocialMediaDraft, validate_social_media_draft\\n\\n\\ndef _platform_approval_command(platform: str) -> str:\\n    if platform == \\\"youtube\\\":\\n        return \\\"Freigabe YouTube\\\"\\n    if platform == \\\"tiktok\\\":\\n        return \\\"Freigabe TikTok\\\"\\n    if platform == \\\"instagram\\\":\\n        return \\\"Freigabe Instagram\\\"\\n    return f\\\"Freigabe {platform}\\\"\\n\\n\\ndef render_social_media_draft_review(draft: SocialMediaDraft) -> str:\\n    \\\"\\\"\\\"Render a social-media draft as a Telegram-safe approval message.\\\"\\\"\\\"\\n\\n    validation = validate_social_media_draft(draft)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n    approval_commands = tuple(_platform_approval_command(platform) for platform in draft.platforms)\\n    title_lines = \\\"\\\\n\\\".join(f\\\"- {title}\\\" for title in draft.title_variants)\\n    command_lines = \\\"\\\\n\\\".join(\\n        f\\\"- {command}\\\"\\n        for command in (\\n            *approval_commands,\\n            \\\"Freigabe alle\\\",\\n            \\\"Ablehnen\\\",\\n            \\\"Ändern: <requested change>\\\",\\n        )\\n    )\\n    side_effects = \\\", \\\".join(draft.side_effects) if draft.side_effects else \\\"none\\\"\\n    approval = \\\"yes\\\" if draft.requires_human_approval else \\\"no\\\"\\n\\n    return \\\"\\\\n\\\".join(\\n        (\\n            \\\"## Social Media Draft Review\\\",\\n            \\\"\\\",\\n            f\\\"Candidate: {draft.candidate_id}\\\",\\n            f\\\"Platforms: {', '.join(draft.platform_labels)}\\\",\\n            f\\\"Duration: {draft.planned_duration_seconds}s\\\",\\n            \\\"\\\",\\n            \\\"Hook:\\\",\\n            draft.hook,\\n            \\\"\\\",\\n            \\\"Title variants:\\\",\\n            title_lines,\\n            \\\"\\\",\\n            \\\"Caption:\\\",\\n            draft.caption,\\n            \\\"\\\",\\n            f\\\"Hashtags: {' '.join(draft.hashtags)}\\\",\\n            f\\\"CTA: {draft.cta}\\\",\\n            f\\\"Preview: {draft.preview_reference} ({draft.preview_type})\\\",\\n            f\\\"Quality/Risk: {draft.quality_risk_summary}\\\",\\n            f\\\"Requires human approval: {approval}\\\",\\n            f\\\"Side effects: {side_effects}\\\",\\n            \\\"\\\",\\n            \\\"Approval commands:\\\",\\n            command_lines,\\n        )\\n    )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/review.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "assistant", "content": "", "reasoning": "**Updating README file**\n\nI need to make sure I update the README file and prepare a commit for it. This is essential for keeping documentation accurate and clear for users. I want to ensure that any changes or additions I've made are reflected in that file. So, I’m planning to review the current contents, assess what's needed, and make those updates before finalizing the commit. It's important to keep everything up-to-date!", "finish_reason": "tool_calls", "reasoning_content": "**Updating README file**\n\nI need to make sure I update the README file and prepare a commit for it. This is essential for keeping documentation accurate and clear for users. I want to ensure that any changes or additions I've made are reflected in that file. So, I’m planning to review the current contents, assess what's needed, and make those updates before finalizing the commit. It's important to keep everything up-to-date!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvhJdsRioKfQCXdBzHVpGOOs2Q4N198fqnxKnc2wzfKYO6UUTrNyqB08BKdbCnVOlPEQXNjfIf364Y9dbupHQ9d6djpCT1IHKi4bjXstTn8pGkLglblUf4WKJY8rTwAbRjEpiE_HmNmwSAlAm03cfUXA0eaR_IBEvbnXbMrfySXflj1ZOgKDXQ0DxaB_OU_eoJCViwPgvdQsgCNX6pddnXTcASNF7WiCfwIuvjYeaS-QLDGjDtnosrzyXm3rrEmGBUtvtyAPnpwKd10GTR4hn9R1Ma0OlF7m-kORVuqqmxETtw32v1eAMl-7W7yrYuIadHGC6zs_Po09gWk5DvpJnWUvCuRyLUTpnI6qziBkvvMYzuIouHeSiu2sxEUmpT0gXPU_abfpO-lzTigZfrtUkVVLGeod0mnRw8QNXbg3mixDnIe3m8Tc60BpQ00mYBXwW8JdsGyApKGR2LmFJ5n5BLSiCKI2o2IR454vCS_AgypwVSPZbC1nHR83W6HDdapCPf9iF0hSoEv1DDtAOfLFlXG6ZqqRJd1keipteNHq8g-JNUHm5mkhs1lqrcmSV9Fh4kYNSH0NnDdBxhmQXUlMvXy8UdPXOUKzU-gJPnnx69VTgU8FpoHUKOVMZEgCQEvKkjOdY2P-CCsFHmilJ4lgSN-ceI_roUgSvjUOJ-Tc7I75-SFaoO6u6UoDlhbPI116Xy-2xm2IKMJOpzXvu2YC_9HcBccx70xmORaD8y4tjDeOwvpgPU45s-uSxFbJJ23cn3urNtMPUyFqhPr9jGwAvaKKTKULqBGxo6IGoUBE8RVBmITKwTMzlDMZj6Le3x9m9yqvaars-HtY3nfSlJsy8cfb_0TQ6V6L9tOMUVA1ObsP1sQ5TwviH5NMVEFYE7Pp2s2xcCbbiiynPrxuvtgeLHtkoSXXrRoIQGRstenuFDgc9uyobbDGnuLKfdiWsCfWv_Eo", "id": "rs_060035eb87c40e14016a12f8476dec8191b4ac990e789a453c", "summary": [{"type": "summary_text", "text": "**Updating README file**\n\nI need to make sure I update the README file and prepare a commit for it. This is essential for keeping documentation accurate and clear for users. I want to ensure that any changes or additions I've made are reflected in that file. So, I’m planning to review the current contents, assess what's needed, and make those updates before finalizing the commit. It's important to keep everything up-to-date!"}]}], "tool_calls": [{"id": "call_QM9YWp1abxjPr88URkL8eWks", "call_id": "call_QM9YWp1abxjPr88URkL8eWks", "response_item_id": "fc_060035eb87c40e14016a12f8490cb481918fb37cb34fb6fa70", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\",\"old_string\":\"- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n- `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -26,6 +26,7 @@\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n - `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n+- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n - `autoshorts.review.workflow` — splits candidates into ready, blocked, and invalid review buckets.\\n - `autoshorts.telegram.review_sender` — validates Telegram review text and prepares side-effect-free delivery plans.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_QM9YWp1abxjPr88URkL8eWks", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_SZ3p81FxLktVbBnOdetKhgWO", "call_id": "call_SZ3p81FxLktVbBnOdetKhgWO", "response_item_id": "fc_060035eb87c40e14016a12f84f30b0819190aaaad18f22f539", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n\\n`autoshorts.social.review.render_social_media_draft_review()` turns a valid `SocialMediaDraft` into a Telegram-safe approval message with candidate id, platforms, duration, hook, title variants, caption, hashtags, CTA, preview reference, quality/risk summary, explicit human-approval status, and approval commands:\\n\\n- `Freigabe YouTube`\\n- `Freigabe TikTok`\\n- `Freigabe Instagram`\\n- `Freigabe alle`\\n- `Ablehnen`\\n- `Ändern: <requested change>`\\n\\nThe renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n\\n## Human approval commands\",\"old_string\":\"`validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -124,6 +124,17 @@\\n \\n `validate_social_media_draft()` rejects unsupported platforms, blank required fields, missing titles/hashtags, invalid durations, missing human approval, and any side effects. This is the first platform-facing post draft, but still performs no upload, no Telegram call, no rendering, and no credential access.\\n \\n+`autoshorts.social.review.render_social_media_draft_review()` turns a valid `SocialMediaDraft` into a Telegram-safe approval message with candidate id, platforms, duration, hook, title variants, caption, hashtags, CTA, preview reference, quality/risk summary, explicit human-approval status, and approval commands:\\n+\\n+- `Freigabe YouTube`\\n+- `Freigabe TikTok`\\n+- `Freigabe Instagram`\\n+- `Freigabe alle`\\n+- `Ablehnen`\\n+- `Ändern: <requested change>`\\n+\\n+The renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_SZ3p81FxLktVbBnOdetKhgWO", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_FbXPhY8U67USVxrL6iRFR982", "call_id": "call_FbXPhY8U67USVxrL6iRFR982", "response_item_id": "fc_060035eb87c40e14016a12f858416081919f9ca11f525d104e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.social.draft import build_social_media_draft\\nfrom autoshorts.social.review import render_social_media_draft_review\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\ncandidate = convert_script_draft_to_candidate(brief, script)\\npreview = build_html_preview(build_render_manifest(create_shot_plan(script)))\\ntext = render_social_media_draft_review(build_social_media_draft(candidate, preview))\\nprint(text.splitlines()[0])\\nprint(len(text))\\nprint('Freigabe alle' in text)\\nprint('publish' in text.casefold(), 'post' in text.casefold())\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 82%]\\n...............                                                          [100%]\\n87 passed in 0.16s\\n## Social Media Draft Review\\n776\\nTrue\\nFalse False\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_FbXPhY8U67USVxrL6iRFR982", "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/social/review.py\\n?? tests/test_social_review.py\\n[feat/core-approval-quality-gates a7d10fa] feat: render social media draft reviews\\n 3 files changed, 152 insertions(+)\\n create mode 100644 autoshorts/social/review.py\\n create mode 100644 tests/test_social_review.py\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\\nd118a76 feat: add retention shot planning\\n6e6569a feat: add content demo review pipeline\\n72dc887 feat: convert content drafts to video candidates\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_EPAh2PagFmWr4kjEVvoAHbcg", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Deciding on delivery**\n\nI'm thinking about whether I should continue with the delivery process since the user indicated \"so weiter.\" My previous plan involves drafting and reviewing before going ahead with the delivery. It seems reasonable to proceed with the delivery as well. Also, I need to ensure that everything is tested properly to guarantee a smooth process. Making sure all parts are in place is crucial!", "finish_reason": "tool_calls", "reasoning_content": "**Deciding on delivery**\n\nI'm thinking about whether I should continue with the delivery process since the user indicated \"so weiter.\" My previous plan involves drafting and reviewing before going ahead with the delivery. It seems reasonable to proceed with the delivery as well. Also, I need to ensure that everything is tested properly to guarantee a smooth process. Making sure all parts are in place is crucial!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvholQ8LvtELGdawE0TXanS6rFC8uy7VYXS-pYjliFch-P6f46_Ni_t1Myxm1nGQvqSOsXuUmEcoqeNjwE5oE8BgtDSLFYRfHHMtrV3gwNj4G9tCGj1hJcr6afzrWGts_b3HfsFwsW0kkSP4w9uxZRLtERyroqufjAIUjVbunDSL2l6U4fblinuPDlQoVY-8yhhA1tC5m0MElHPvu-MtiTTndY_cA1NHoWHjzxLE9gMIBhLPDvBb-atmM4Pyj2eIjjCSPQeWBA3vPFSOzTLxpibCKdR_ksNQJONu7hDnFS8ynOW9YRVf0xqc9sKQUsj232ZzQZ-r_Ch279O-fzZkqspB9Wsla6zbJy4tAZFSzXH6EqR_GmVp8plG02p_KJXszaeOSYmvZIp0AbiQS7fiDsdwb977CjL9EOTuKg2zHxCIX4-WO6S250r63sx5zbDeERmJqO-e_44jaz2X5rqo9L4ef7nzsi_3sqolqEeK4jJ0PWNY0sW1-qz5VtXB_MKDvbCpyTX2vvrbjEwZwERENaBvOGQeJWqDRRH_rRN_7UyyihsgfUJ0z2doX0Ai0ym_PlQNm6Q0F0wxbGL14i4Mcaf5yapGOPz38vj4EGUqzP7SBRVxeRN3v-QSTuGXPLXsnVYsMVbJ0VIE978xTWucisyI06ei-HNmgw-umEo6tdmD2E9izKxw5AjaZPY2VddIh5Kt98or8jTlEb1OAW-v1Q-CIjTyWhtL6MqdFCow81Ls1EHBWvScNjWMfky1yI5G5qwra-IQY2MndjLma3GWTo5e03K9T2S9o6lNwmOLcvYQJ3hG1Oer7AeBWRiFrH4o-dHFilpH4cXaD1fUA3nMd_D6KQEr1jeeX1FRpUPCyDo-xZo6YSypyaB6uzDflFbk3P1zImvfElZra38t1Unf_txU6esYUkwSG8nZhK3TNx8m-Emfsz_WbLQjxmgD0dgKXvGz1xBE11OW80-B0GXLWwv1J557HO2tUPJZsBV2VduJ2QNsE0gqNayP5yelZhd-r0fttXIbU3iH4-eJFW8czD0_LrieBWRy54zk9pkfadU_vm9nF7TVh0bcRgvmgIwaHdHGT7yhkIk3c-qoj9RwbGrDww==", "id": "rs_060035eb87c40e14016a12f86694a481919e550e298ae45370", "summary": [{"type": "summary_text", "text": "**Deciding on delivery**\n\nI'm thinking about whether I should continue with the delivery process since the user indicated \"so weiter.\" My previous plan involves drafting and reviewing before going ahead with the delivery. It seems reasonable to proceed with the delivery as well. Also, I need to ensure that everything is tested properly to guarantee a smooth process. Making sure all parts are in place is crucial!"}]}], "tool_calls": [{"id": "call_5NmmlUXTdv2Kv7Dt2dd5EYHI", "call_id": "call_5NmmlUXTdv2Kv7Dt2dd5EYHI", "response_item_id": "fc_060035eb87c40e14016a12f868d7908191a2c0e41b05a732ea", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"import pytest\\n\\nfrom autoshorts.social.delivery import SocialDraftDelivery, prepare_social_draft_delivery\\n\\n\\ndef test_prepare_social_draft_delivery_wraps_review_text_without_side_effects():\\n    text = \\\"## Social Media Draft Review\\\\nCandidate: example\\\\nFreigabe alle\\\"\\n\\n    delivery = prepare_social_draft_delivery(text, topic=\\\"review\\\")\\n\\n    assert isinstance(delivery, SocialDraftDelivery)\\n    assert delivery.delivery_type == \\\"social_draft_review\\\"\\n    assert delivery.topic == \\\"review\\\"\\n    assert delivery.text == text\\n    assert delivery.requires_human_approval is True\\n    assert delivery.side_effects == ()\\n\\n\\ndef test_prepare_social_draft_delivery_rejects_blank_text():\\n    with pytest.raises(ValueError, match=\\\"review text is required\\\"):\\n        prepare_social_draft_delivery(\\\"   \\\")\\n\\n\\ndef test_prepare_social_draft_delivery_rejects_long_text():\\n    with pytest.raises(ValueError, match=\\\"Telegram message exceeds 4096 characters\\\"):\\n        prepare_social_draft_delivery(\\\"x\\\" * 4097)\\n\\n\\ndef test_prepare_social_draft_delivery_rejects_action_language():\\n    with pytest.raises(ValueError, match=\\\"social draft delivery must not imply publishing\\\"):\\n        prepare_social_draft_delivery(\\\"please publish this\\\")\\n\\n    with pytest.raises(ValueError, match=\\\"social draft delivery must not imply publishing\\\"):\\n        prepare_social_draft_delivery(\\\"please post this\\\")\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_delivery.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1357, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_delivery.py\\\">\\nERROR [3:6] Import \\\"autoshorts.social.delivery\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_5NmmlUXTdv2Kv7Dt2dd5EYHI", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_dMR5nzR5QLzOgMORZOimyMDQ", "call_id": "call_dMR5nzR5QLzOgMORZOimyMDQ", "response_item_id": "fc_060035eb87c40e14016a12f87198788191b258b84602456c8f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_social_delivery.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_social_delivery.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_social_delivery.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_social_delivery.py:3: in <module>\\n    from autoshorts.social.delivery import SocialDraftDelivery, prepare_social_draft_delivery\\nE   ModuleNotFoundError: No module named 'autoshorts.social.delivery'\\n=========================== short test summary info ============================\\nERROR tests/test_social_delivery.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.11s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_dMR5nzR5QLzOgMORZOimyMDQ", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_JIG0B7FDoEF2satRbvClale3", "call_id": "call_JIG0B7FDoEF2satRbvClale3", "response_item_id": "fc_060035eb87c40e14016a12f8766ad4819197bdda929db1ff07", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Side-effect-free delivery planning for social media draft reviews.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\n_TELEGRAM_MESSAGE_LIMIT = 4096\\n_FORBIDDEN_ACTION_WORDS = (\\\"publish\\\", \\\"post\\\")\\n\\n\\n@dataclass(frozen=True)\\nclass SocialDraftDelivery:\\n    text: str\\n    topic: str = \\\"review\\\"\\n    delivery_type: str = \\\"social_draft_review\\\"\\n    requires_human_approval: bool = True\\n    side_effects: tuple[str, ...] = ()\\n\\n\\ndef prepare_social_draft_delivery(review_text: str, *, topic: str = \\\"review\\\") -> SocialDraftDelivery:\\n    \\\"\\\"\\\"Prepare a Telegram-bound social draft review without sending it.\\\"\\\"\\\"\\n\\n    if not (review_text or \\\"\\\").strip():\\n        raise ValueError(\\\"review text is required\\\")\\n    if len(review_text) > _TELEGRAM_MESSAGE_LIMIT:\\n        raise ValueError(\\\"Telegram message exceeds 4096 characters\\\")\\n\\n    lowered = review_text.casefold()\\n    if any(word in lowered for word in _FORBIDDEN_ACTION_WORDS):\\n        raise ValueError(\\\"social draft delivery must not imply publishing\\\")\\n\\n    return SocialDraftDelivery(\\n        text=review_text,\\n        topic=topic,\\n        delivery_type=\\\"social_draft_review\\\",\\n        requires_human_approval=True,\\n        side_effects=(),\\n    )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/social/delivery.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"....                                                                     [100%]\\n4 passed in 0.04s\\n........................................................................ [ 79%]\\n...................                                                      [100%]\\n91 passed in 0.15s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_if9iSgZa57vqxd2hbg9PZNof", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_NKkoaAlRZNdpQDQsn5ZIQYCY", "call_id": "call_NKkoaAlRZNdpQDQsn5ZIQYCY", "response_item_id": "fc_060035eb87c40e14016a12f88a6b50819184ea16ebfcae2f4a", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.social.delivery` — side-effect-free delivery plan for social media draft review text.\\n- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\",\"old_string\":\"- `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n- `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -25,6 +25,7 @@\\n - `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n - `autoshorts.review.package` — Telegram-friendly review package renderer for human approval.\\n+- `autoshorts.social.delivery` — side-effect-free delivery plan for social media draft review text.\\n - `autoshorts.social.draft` — approval-first social media draft model from candidate plus render preview.\\n - `autoshorts.social.review` — Telegram-safe approval text renderer for social media drafts.\\n - `autoshorts.review.render` — renders complete review batches into Telegram-safe text.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_NKkoaAlRZNdpQDQsn5ZIQYCY", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_xkXiVwa2HAaiFx2KRk1KvIBQ", "call_id": "call_xkXiVwa2HAaiFx2KRk1KvIBQ", "response_item_id": "fc_060035eb87c40e14016a12f891588481918f29d12dcc1d9804", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"The renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n\\n`autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n\\n## Human approval commands\",\"old_string\":\"The renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -136,6 +136,8 @@\\n \\n The renderer avoids publish/post language and keeps approval semantics separate from any future platform action.\\n \\n+`autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_xkXiVwa2HAaiFx2KRk1KvIBQ", "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 79%]\\n...................                                                      [100%]\\n91 passed in 0.14s\\n M README.md\\n?? autoshorts/social/delivery.py\\n?? tests/test_social_delivery.py\\n[feat/core-approval-quality-gates 515c549] feat: add social draft delivery preparation\\n 3 files changed, 75 insertions(+)\\n create mode 100644 autoshorts/social/delivery.py\\n create mode 100644 tests/test_social_delivery.py\\n515c549 feat: add social draft delivery preparation\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\\nd118a76 feat: add retention shot planning\\n6e6569a feat: add content demo review pipeline\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_pVLhQwL1F8jC71U5RLhdmFnc", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Considering CLI and next steps**\n\nI’m thinking about whether I can tackle the CLI now. The earlier plan was to include a demo for the social draft. It seems like a logical next step to continue with that. Plus, I realize we have some time available now to get this done. So, let’s go for it! I’ll work on the CLI and the demo together.", "finish_reason": "tool_calls", "reasoning_content": "**Considering CLI and next steps**\n\nI’m thinking about whether I can tackle the CLI now. The earlier plan was to include a demo for the social draft. It seems like a logical next step to continue with that. Plus, I realize we have some time available now to get this done. So, let’s go for it! I’ll work on the CLI and the demo together.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvihZxKlUK49jAG_-dokh1SQEqULayDwXlo8GVZn0F4u0yh24-H4T5w4lqQemTVtWbrcNbgO2GQRUrqwNMZxzhk2tLMzfRpmIs1kU5KNAn7aQJUMy-tAd-PN4Jj6BO1kCr1qvYG7JjBdKlF9f375mJIGuVKnfxbuTWzKeDJvbixBBQYkzt9tAC0Zq0PHxHe3cZaIkv0f-7sTKUtgiAUuD6wsywbvsbZLdWU5D0ZPwN8fpnu_l-552V9uFjNMe_dAS3yy8EDwicEhzzLj-SuOq3HoaknWNT82YbcTjrENhs0xpjmFmkBkGa148pPfnYUkNRff1TNynAS8_PRZNd_cdjLkMmXOR02ceS67pnVrq0ztDAdu3nAJv5oLRQ8XmqNp12xU8MqltdHoECag72rFg0_G3-81lq7LYO_JOjLTof_Q_rWqAQj0WxWL1S6lNo5tNWgHBWkoBMo9yyimxFy5085Vjvr6SzeVlfqXKoeuI3EqQLYJTH32cokmOW26ZL-40iHwZRlnTHWmjSBVS5g70UNtN_sQai1EI75WBK1vm-UANaGAwRnOZPFxzbUOGZCoYykDfix5ZwAGFp-L8p9sLCJEeJV6xoT9lpKzcqcHyS6V_oErZyH36FfTP7AldEqQYRYpUAuilJLEQ653uSPDtedOkL1mNBDSFDQXn4AHP2V3BNwl0Tmx9IyvtSC1etP8OW0CEPvtXCUtr5PfPA96hWuPhQLgduJbcM_SZPofsvfoNrR2UVLAA39b9BuD7KYz_511Ob3pSuWBeE4mPGdAR_nioJWZMfvw38syc28nIAbvst8nPKAjoAe79Wmsic-ngKaSyCBXfexta_sd7iWY-RH5BJxbfPeJWjMJUrEmlKnvYlwzJTPkcYQSr5nwg3IcLC8iey5IzLBg1u6TwAUbhHUMrLIe0u2RXLs0YnFlDPeX8wVoZTjHhAFM4RvXXbjJeal6V5k4gB5o_tLcCI-vK2rPqTSUzsGqjpBVfOgylzNuhD7XaIdteDFLS_BdHLCzxjTUtSlH3vVJ_9x0bX6Oi8wN8n66-fbLCvvkaXqxI2pjxjM=", "id": "rs_060035eb87c40e14016a12f8a026ac8191934f2a68f36c1812", "summary": [{"type": "summary_text", "text": "**Considering CLI and next steps**\n\nI’m thinking about whether I can tackle the CLI now. The earlier plan was to include a demo for the social draft. It seems like a logical next step to continue with that. Plus, I realize we have some time available now to get this done. So, let’s go for it! I’ll work on the CLI and the demo together."}]}], "tool_calls": [{"id": "call_qXYY55Uk1RSKJ9MmiiboPDV7", "call_id": "call_qXYY55Uk1RSKJ9MmiiboPDV7", "response_item_id": "fc_060035eb87c40e14016a12f8a1baf881918c4da46922cbf27a", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from autoshorts.cli.demo_social_draft import main\\n\\n\\ndef test_demo_social_draft_cli_prints_delivery_metadata_and_review_text(capsys):\\n    exit_code = main([])\\n\\n    captured = capsys.readouterr()\\n\\n    assert exit_code == 0\\n    assert \\\"Delivery type: social_draft_review\\\" in captured.out\\n    assert \\\"Topic: review\\\" in captured.out\\n    assert \\\"Requires human approval: yes\\\" in captured.out\\n    assert \\\"Side effects: none\\\" in captured.out\\n    assert \\\"Text length:\\\" in captured.out\\n    assert \\\"## Social Media Draft Review\\\" in captured.out\\n    assert \\\"Candidate: ai_life_systems-ai-weekly-review-routine-35s\\\" in captured.out\\n    assert \\\"Platforms: YouTube Shorts, TikTok, Instagram Reels\\\" in captured.out\\n    assert \\\"Freigabe alle\\\" in captured.out\\n    assert captured.err == \\\"\\\"\\n    assert \\\"publish\\\" not in captured.out.casefold()\\n    assert \\\"post\\\" not in captured.out.casefold()\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_social_draft_cli.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_____________ ERROR collecting tests/test_demo_social_draft_cli.py _____________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_social_draft_cli.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_demo_social_draft_cli.py:1: in <module>\\n    from autoshorts.cli.demo_social_draft import main\\nE   ModuleNotFoundError: No module named 'autoshorts.cli.demo_social_draft'\\n=========================== short test summary info ============================\\nERROR tests/test_demo_social_draft_cli.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.11s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_Z1MDP8ts4gtRC3BPcJd1WGMb", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_5UEr1TeCvufsXCQTY8tItxDn", "call_id": "call_5UEr1TeCvufsXCQTY8tItxDn", "response_item_id": "fc_060035eb87c40e14016a12f8ae455c8191bf35e12351ccc310", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Print the deterministic social media draft review delivery.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport sys\\nfrom collections.abc import Sequence\\nfrom typing import TextIO\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.content.to_candidate import convert_script_draft_to_candidate\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.preview import build_html_preview\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\nfrom autoshorts.social.delivery import prepare_social_draft_delivery\\nfrom autoshorts.social.draft import build_social_media_draft\\nfrom autoshorts.social.review import render_social_media_draft_review\\n\\n\\ndef _build_demo_delivery():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    candidate = convert_script_draft_to_candidate(brief, script)\\n    preview = build_html_preview(build_render_manifest(create_shot_plan(script)))\\n    draft = build_social_media_draft(candidate, preview)\\n    review_text = render_social_media_draft_review(draft)\\n    return prepare_social_draft_delivery(review_text, topic=\\\"review\\\")\\n\\n\\ndef main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int:\\n    \\\"\\\"\\\"Print social draft delivery metadata and review text without sending anything.\\\"\\\"\\\"\\n\\n    _ = argv or ()\\n    output = stdout or sys.stdout\\n    delivery = _build_demo_delivery()\\n    side_effects = \\\", \\\".join(delivery.side_effects) if delivery.side_effects else \\\"none\\\"\\n    approval = \\\"yes\\\" if delivery.requires_human_approval else \\\"no\\\"\\n\\n    print(f\\\"Delivery type: {delivery.delivery_type}\\\", file=output)\\n    print(f\\\"Topic: {delivery.topic}\\\", file=output)\\n    print(f\\\"Requires human approval: {approval}\\\", file=output)\\n    print(f\\\"Side effects: {side_effects}\\\", file=output)\\n    print(f\\\"Text length: {len(delivery.text)}\\\", file=output)\\n    print(\\\"\\\", file=output)\\n    print(delivery.text, file=output)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_social_draft.py\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \".                                                                        [100%]\\n1 passed in 0.05s\\n........................................................................ [ 78%]\\n....................                                                     [100%]\\n92 passed in 0.16s\\n892\\nDelivery type: social_draft_review\\nTopic: review\\nRequires human approval: yes\\nSide effects: none\\nText length: 776\\n\\n## Social Media Draft Review\\n\\nTrue\\nFalse False\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_stIMPkkdQgVY6TerIOw0jmW4", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_Y3ocUBJWYtd8mL0Tj6qiWFUR", "call_id": "call_Y3ocUBJWYtd8mL0Tj6qiWFUR", "response_item_id": "fc_060035eb87c40e14016a12f8c3e4808191b0b550e91d915b2e", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n- `autoshorts.cli.demo_social_draft` — local CLI entry point that prints a side-effect-free social media draft review delivery.\\n- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\",\"old_string\":\"- `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n- `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n- `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -10,6 +10,7 @@\\n \\n - `autoshorts.cli.demo_render_preview` — local CLI entry point that prints side-effect-free render-preview metadata plus HTML.\\n - `autoshorts.cli.demo_review_batch` — local CLI entry point that prints the deterministic review demo.\\n+- `autoshorts.cli.demo_social_draft` — local CLI entry point that prints a side-effect-free social media draft review delivery.\\n - `autoshorts.cli.demo_telegram_delivery` — local CLI entry point that prints side-effect-free Telegram delivery metadata plus review text.\\n - `autoshorts.config.pillars` — content pillar registry, default AI/life-systems positioning, and pillar fit validation.\\n - `autoshorts.content.brief` — strategy brief model for retention-led content development before scripting.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_Y3ocUBJWYtd8mL0Tj6qiWFUR", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_SUYoL6BgfaKa8NxRmBqpTGwt", "call_id": "call_SUYoL6BgfaKa8NxRmBqpTGwt", "response_item_id": "fc_060035eb87c40e14016a12f8cc14c08191bd07ebaf7908bff8", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n\\nPreview the complete social draft delivery locally with:\\n\\n```bash\\npython -m autoshorts.cli.demo_social_draft\\n```\\n\\nIt prints delivery metadata first, then the social draft review text. It still sends nothing and does not call platform APIs.\\n\\n## Human approval commands\",\"old_string\":\"`autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n\\n## Human approval commands\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:11:18.976797"}
{"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@@ -139,6 +139,14 @@\\n \\n `autoshorts.social.delivery.prepare_social_draft_delivery()` wraps social draft review text in a side-effect-free `SocialDraftDelivery` object with delivery type `social_draft_review`, topic metadata, `requires_human_approval=True`, and `side_effects=()`. It validates non-empty text, Telegram's 4096-character limit, and the same no publish/post language boundary.\\n \\n+Preview the complete social draft delivery locally with:\\n+\\n+```bash\\n+python -m autoshorts.cli.demo_social_draft\\n+```\\n+\\n+It prints delivery metadata first, then the social draft review text. It still sends nothing and does not call platform APIs.\\n+\\n ## Human approval commands\\n \\n Supported review responses:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_SUYoL6BgfaKa8NxRmBqpTGwt", "timestamp": "2026-05-24T15:11:18.976797"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 78%]\\n....................                                                     [100%]\\n92 passed in 0.14s\\n M README.md\\n?? autoshorts/cli/demo_social_draft.py\\n?? tests/test_demo_social_draft_cli.py\\n[feat/core-approval-quality-gates 3b387f4] feat: add demo social draft cli\\n 3 files changed, 85 insertions(+)\\n create mode 100644 autoshorts/cli/demo_social_draft.py\\n create mode 100644 tests/test_demo_social_draft_cli.py\\n3b387f4 feat: add demo social draft cli\\n515c549 feat: add social draft delivery preparation\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\\nd118a76 feat: add retention shot planning\\n6e6569a feat: add content demo review pipeline\\n72dc887 feat: convert content drafts to video candidates\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7YfvBswWDb98PPLf0qJNwm3V", "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "Erledigt, Sir.\n\nWir haben den nächsten Block vollständig durchgezogen:\n\n```text\nSocialMediaDraft\n→ Social Draft Review Text\n→ Social Draft Delivery Preparation\n→ Demo CLI\n```\n\nAlles weiterhin ohne Upload, ohne Telegram-Call, ohne Renderer-Call, ohne Credentials. Also ausnahmsweise eine Social-Media-Pipeline ohne eingebauten Selbstzerstörungsknopf.\n\n## 1. SocialMediaDraft Model\n\nNeue Dateien:\n\n```text\nautoshorts/social/__init__.py\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nNeue API:\n\n```python\nbuild_social_media_draft(candidate, preview) -> SocialMediaDraft\nvalidate_social_media_draft(draft) -> ValidationResult\n```\n\nDer Draft enthält:\n\n- Candidate ID\n- Plattformen:\n  - YouTube Shorts\n  - TikTok\n  - Instagram Reels\n- Titelvarianten\n- Caption\n- Hashtags\n- CTA\n- Hook\n- geplante Dauer\n- Quality/Risk Summary\n- Preview Reference\n- Preview Type\n- `requires_human_approval=True`\n- `side_effects=()`\n\nVerifiziert:\n\n```text\nai_life_systems-ai-weekly-review-routine-35s\n('YouTube Shorts', 'TikTok', 'Instagram Reels')\nHTML timeline preview available\nTrue ()\nTrue\n```\n\nCommit:\n\n```text\n1f6aa2c feat: add social media draft model\n```\n\n## 2. Social Draft Review Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nNeue API:\n\n```python\nrender_social_media_draft_review(draft: SocialMediaDraft) -> str\n```\n\nErzeugt Telegram-safe Review Text:\n\n```text\n## Social Media Draft Review\n\nCandidate: ai_life_systems-ai-weekly-review-routine-35s\nPlatforms: YouTube Shorts, TikTok, Instagram Reels\nDuration: 35s\n\nHook:\n...\n\nTitle variants:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\n- Freigabe YouTube\n- Freigabe TikTok\n- Freigabe Instagram\n- Freigabe alle\n- Ablehnen\n- Ändern: <requested change>\n```\n\nGuardrails:\n\n- validiert Draft vorher\n- max. Telegram-sicher im aktuellen Output\n- keine `publish`-/`post`-Sprache\n- Approval bleibt explizit getrennt\n\nVerifiziert:\n\n```text\n## Social Media Draft Review\n776\nTrue\nFalse False\n```\n\nBedeutung:\n\n- Text beginnt korrekt\n- Länge `776`\n- enthält `Freigabe alle`\n- enthält kein `publish`\n- enthält kein `post`\n\nCommit:\n\n```text\na7d10fa feat: render social media draft reviews\n```\n\n## 3. Social Draft Delivery Preparation\n\nNeue Dateien:\n\n```text\nautoshorts/social/delivery.py\ntests/test_social_delivery.py\n```\n\nNeue API:\n\n```python\nprepare_social_draft_delivery(review_text, topic=\"review\") -> SocialDraftDelivery\n```\n\nDelivery Objekt:\n\n```python\nSocialDraftDelivery(\n    delivery_type=\"social_draft_review\",\n    topic=\"review\",\n    requires_human_approval=True,\n    side_effects=(),\n)\n```\n\nValidiert:\n\n- Text nicht leer\n- Telegram Limit `4096`\n- keine `publish`-/`post`-Sprache\n- keine Side Effects\n\nCommit:\n\n```text\n515c549 feat: add social draft delivery preparation\n```\n\n## 4. Demo CLI für ersten Social-Media-Freigabeentwurf\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_social_draft.py\ntests/test_demo_social_draft_cli.py\n```\n\nNeuer Command:\n\n```bash\npython -m autoshorts.cli.demo_social_draft\n```\n\nDer läuft deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review Text\n→ SocialDraftDelivery\n→ stdout\n```\n\nOutput beginnt:\n\n```text\nDelivery type: social_draft_review\nTopic: review\nRequires human approval: yes\nSide effects: none\nText length: 776\n\n## Social Media Draft Review\n```\n\nManuell verifiziert:\n\n```text\n892\nDelivery type: social_draft_review\nTopic: review\nRequires human approval: yes\nSide effects: none\nText length: 776\n\n## Social Media Draft Review\n\nTrue\nFalse False\n```\n\nBedeutung:\n\n- Gesamtoutput Länge `892`\n- Delivery Metadata korrekt\n- Review Text enthalten\n- `Freigabe alle` enthalten\n- kein `publish`\n- kein `post`\n\nCommit:\n\n```text\n3b387f4 feat: add demo social draft cli\n```\n\n## Tests\n\nFinaler Stand:\n\n```text\n92 passed in 0.14s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\n3b387f4 feat: add demo social draft cli\n515c549 feat: add social draft delivery preparation\na7d10fa feat: render social media draft reviews\n1f6aa2c feat: add social media draft model\nf8c5588 feat: add demo render preview cli\n42ac32f feat: add side-effect-free render preview\ne258635 feat: add render manifest planning\nd118a76 feat: add retention shot planning\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Wo wir jetzt stehen\n\nDie kontrollierte Approval-Pipeline ist jetzt:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ Demo CLI\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt erst würde ich Richtung erstem visuellen Video-Artefakt gehen:\n\n```text\nRenderManifest\n→ Local MP4 Timing Preview\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/local_preview.py\ntests/test_local_preview.py\n```\n\nZiel:\n\n- kein AI-Video\n- kein ComfyUI\n- kein Wan\n- kein Qwen-Image\n- lokal deterministische Preview\n- aus Manifest:\n  - simple vertical scene cards\n  - captions renderer-owned\n  - scene timings\n  - placeholder visuals\n  - optional FFmpeg MP4 Assembly später\n\nErster Schritt wäre wahrscheinlich noch side-effect-free:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n```\n\nDanach:\n\n```text\nLocalPreviewPlan\n→ actual files in temp/output dir\n```\n\nSo trennen wir Planung von Dateischreiben. Langweilig, aber genau die Sorte langweilig, die verhindert, dass ein Bot “mal eben” 400 MB Clip-Müll in Git ablegt.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvjb5Yxy1kaZn4RFcfcoRsMfRYIu-5gdykdZigCiM8HmQxxTZP52flLWni3FRke1yZC7HdhtLGUVGWosy_7zOM82DqberjdHjkYLWrKd_3-r_ZTvEUn4Ikw2fvtMd77kS26lRJAJ4ik483SQWq0vE56cGeFACayi5fY9aVwp4NbejXZgtgy5w7jwKLZlhoa6lrEPkp9eKeq2rxaa8oir0QIYnc02nFu_uNQZdIWrdRcJ1qvj6oi7kID7uPZ6Psn_K7mtsFMOTz8AUZsvb-kGXeo8Rxw4DG2nO9sFxG5eTBnDQw9ItNDDn6ejPmw1dnR34xNUF2Q96FI4EpXtS9uu_WzOZD7AeupMpR4AU2TjfnysDUQKy9nRToGoBmK9tKPaYYrTVP8RYVYAFkyP33APzD59Fb84_EV1zE265S5s6BWZbKR6v3y9WTxKzzXWx_inOJUNExHkL4XC9r6HaF1oT_M11f6NpgsbTmU84Doj8qWVhAoUBfN766owuk-ifYPqDncvulZXbg3hvxBXnaTWYzI_NC_qh6tfm_dyLu7WrjiW9fJ12NJxp3bYeDy9A2B6V6VIi3vEKHWxgYFjLFpV5sMTsRCTACdgFRUrHvBP5lRlE93vWahGqEg_cR_xsubqUyPxIZZIMA8R144SRZkUPAQocC9nie8fIyIXExPBYfzJOsiq_tp73l6Wf4Q6fHUAI0q2HaSKS0gfXRYO8LuX5ZOqhP80yeN1EQRBK1OQ0olc9AMGAKeJmZNhHH803C7pEsmjFT1RdnnD1W5eLz9cBiTJKd7tnQRSMoEp0LsKWYxpRrgAcXvtHbHZtG7fhabC9W6PAjkh1Urw0By70-zo5PtG0vXy7ViMl5d2hGIzu_nFjJGMvdlKsc7wAWPPbEEp748Q6m_ouJLMC5ddH77McXZH8KGTAGv0Dpe76O6bvgeJolnJd8TrooTCDynfd00NQlw042l95M17AB9UhiyafLRMgK0hBPvNZpfVGjYXw2FJDnbvKIUdprWv-FaG_gg28DoyTsA8nhFqJKUdmOKhd5ob4Q==", "id": "rs_060035eb87c40e14016a12f8dabfd081919a0b805353ef3ebe", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\nWir haben den nächsten Block vollständig durchgezogen:\n\n```text\nSocialMediaDraft\n→ Social Draft Review Text\n→ Social Draft Delivery Preparation\n→ Demo CLI\n```\n\nAlles weiterhin ohne Upload, ohne Telegram-Call, ohne Renderer-Call, ohne Credentials. Also ausnahmsweise eine Social-Media-Pipeline ohne eingebauten Selbstzerstörungsknopf.\n\n## 1. SocialMediaDraft Model\n\nNeue Dateien:\n\n```text\nautoshorts/social/__init__.py\nautoshorts/social/draft.py\ntests/test_social_draft.py\n```\n\nNeue API:\n\n```python\nbuild_social_media_draft(candidate, preview) -> SocialMediaDraft\nvalidate_social_media_draft(draft) -> ValidationResult\n```\n\nDer Draft enthält:\n\n- Candidate ID\n- Plattformen:\n  - YouTube Shorts\n  - TikTok\n  - Instagram Reels\n- Titelvarianten\n- Caption\n- Hashtags\n- CTA\n- Hook\n- geplante Dauer\n- Quality/Risk Summary\n- Preview Reference\n- Preview Type\n- `requires_human_approval=True`\n- `side_effects=()`\n\nVerifiziert:\n\n```text\nai_life_systems-ai-weekly-review-routine-35s\n('YouTube Shorts', 'TikTok', 'Instagram Reels')\nHTML timeline preview available\nTrue ()\nTrue\n```\n\nCommit:\n\n```text\n1f6aa2c feat: add social media draft model\n```\n\n## 2. Social Draft Review Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/social/review.py\ntests/test_social_review.py\n```\n\nNeue API:\n\n```python\nrender_social_media_draft_review(draft: SocialMediaDraft) -> str\n```\n\nErzeugt Telegram-safe Review Text:\n\n```text\n## Social Media Draft Review\n\nCandidate: ai_life_systems-ai-weekly-review-routine-35s\nPlatforms: YouTube Shorts, TikTok, Instagram Reels\nDuration: 35s\n\nHook:\n...\n\nTitle variants:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\n- Freigabe YouTube\n- Freigabe TikTok\n- Freigabe Instagram\n- Freigabe alle\n- Ablehnen\n- Ändern: <requested change>\n```\n\nGuardrails:\n\n- validiert Draft vorher\n- max. Telegram-sicher im aktuellen Output\n- keine `publish`-/`post`-Sprache\n- Approval bleibt explizit getrennt\n\nVerifiziert:\n\n```text\n## Social Media Draft Review\n776\nTrue\nFalse False\n```\n\nBedeutung:\n\n- Text beginnt korrekt\n- Länge `776`\n- enthält `Freigabe alle`\n- enthält kein `publish`\n- enthält kein `post`\n\nCommit:\n\n```text\na7d10fa feat: render social media draft reviews\n```\n\n## 3. Social Draft Delivery Preparation\n\nNeue Dateien:\n\n```text\nautoshorts/social/delivery.py\ntests/test_social_delivery.py\n```\n\nNeue API:\n\n```python\nprepare_social_draft_delivery(review_text, topic=\"review\") -> SocialDraftDelivery\n```\n\nDelivery Objekt:\n\n```python\nSocialDraftDelivery(\n    delivery_type=\"social_draft_review\",\n    topic=\"review\",\n    requires_human_approval=True,\n    side_effects=(),\n)\n```\n\nValidiert:\n\n- Text nicht leer\n- Telegram Limit `4096`\n- keine `publish`-/`post`-Sprache\n- keine Side Effects\n\nCommit:\n\n```text\n515c549 feat: add social draft delivery preparation\n```\n\n## 4. Demo CLI für ersten Social-Media-Freigabeentwurf\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_social_draft.py\ntests/test_demo_social_draft_cli.py\n```\n\nNeuer Command:\n\n```bash\npython -m autoshorts.cli.demo_social_draft\n```\n\nDer läuft deterministisch durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review Text\n→ SocialDraftDelivery\n→ stdout\n```\n\nOutput beginnt:\n\n```text\nDelivery type: social_draft_review\nTopic: review\nRequires human approval: yes\nSide effects: none\nText length: 776\n\n## Social Media Draft Review\n```\n\nManuell verifiziert:\n\n```text\n892\nDelivery type: social_draft_review\nTopic: review\nRequires human approval: yes\nSide effects: none\nText length: 776\n\n## Social Media Draft Review\n\nTrue\nFalse False\n```\n\nBedeutung:\n\n- Gesamtoutput Länge `892`\n- Delivery Metadata korrekt\n- Review Text enthalten\n- `Freigabe alle` enthalten\n- kein `publish`\n- kein `post`\n\nCommit:\n\n```text\n3b387f4 feat: add demo social draft cli\n```\n\n## Tests\n\nFinaler Stand:\n\n```text\n92 passed in 0.14s\n```\n\n## Aktueller Commit-Verlauf\n\n```text\n3b387f4 feat: add demo social draft cli\n515c549 feat: add social draft delivery preparation\na7d10fa feat: render social media draft reviews\n1f6aa2c feat: add social media draft model\nf8c5588 feat: add demo render preview cli\n42ac32f feat: add side-effect-free render preview\ne258635 feat: add render manifest planning\nd118a76 feat: add retention shot planning\n6e6569a feat: add content demo review pipeline\n72dc887 feat: convert content drafts to video candidates\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Wo wir jetzt stehen\n\nDie kontrollierte Approval-Pipeline ist jetzt:\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ Demo CLI\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt erst würde ich Richtung erstem visuellen Video-Artefakt gehen:\n\n```text\nRenderManifest\n→ Local MP4 Timing Preview\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/local_preview.py\ntests/test_local_preview.py\n```\n\nZiel:\n\n- kein AI-Video\n- kein ComfyUI\n- kein Wan\n- kein Qwen-Image\n- lokal deterministische Preview\n- aus Manifest:\n  - simple vertical scene cards\n  - captions renderer-owned\n  - scene timings\n  - placeholder visuals\n  - optional FFmpeg MP4 Assembly später\n\nErster Schritt wäre wahrscheinlich noch side-effect-free:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n```\n\nDanach:\n\n```text\nLocalPreviewPlan\n→ actual files in temp/output dir\n```\n\nSo trennen wir Planung von Dateischreiben. Langweilig, aber genau die Sorte langweilig, die verhindert, dass ein Bot “mal eben” 400 MB Clip-Müll in Git ablegt."}], "id": "msg_060035eb87c40e14016a12f8dba4c08191b1e0998378123301", "phase": "final_answer"}], "timestamp": "2026-05-24T15:11:18.976797"}
{"role": "assistant", "content": "", "reasoning": "**Determining necessary actions**\n\nI need to figure out what actions to take here. First, I’m wondering if I should load skills that are already relevant. Before replying, it’s crucial to scan through what’s available. I might consider integrating TDD or social features, or perhaps looking into the manifest-preview-renderer reference. It seems like I should utilize the tools I have on hand to get everything right.", "finish_reason": "tool_calls", "reasoning_content": "**Determining necessary actions**\n\nI need to figure out what actions to take here. First, I’m wondering if I should load skills that are already relevant. Before replying, it’s crucial to scan through what’s available. I might consider integrating TDD or social features, or perhaps looking into the manifest-preview-renderer reference. It seems like I should utilize the tools I have on hand to get everything right.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvmw4jb97io2OXdjF642ZIJnp6LAWiXyMCagaC4T0ZVUwHDUcpBSKnsLzYUleeIHITiWVJGcDz_kC2wS_hCIIUpLkxHVtmYoj4QszLyE7tUTk6XMRWq3NuDuoHT6tY4iPzpRNiWrjqFysKGtRaX3s7ADnm_mVQu9zQYOwPgF3XqSuEhq_ivkJ-txd_h9h2jqOWyPuJNcZSdKUwBruK5cYJNPLF_U_RT7GgnqsL8c0CvyQvAAUHKdRB4QhiwVDqy2WEtXioLSzdPnJcGaKBS125zRXX8NLpmxmAFaQiGHJ-LGgNjvwkTPUK1EkgVKpnOPX1KKKosw8lYo87Q03TtGHOC8EfTt-ApzOkrqrW4Wi1taPnhh_pn6T9qTp9tALwIZm_HOMnoOcFJjTQVLFztaaEaY2A-ErZ24oFM6OMUXmdwi6YAi0r0gTV2nFhHsbgXzEKrm_Eesd_1CjmE3xAsmcewL4zFMOELB_io5TZjhJQPY0Vtl_B3SWdbRHHV_5xNLuv6O2UakM46BQLQ6N0I9e61ZYIDMFaNGU0hg6o4Jcck0tGUMlriPxIl9DyrI7OObcJgnj_pkC9r_sbW4eetFhRqzFXrZXB4wK0NIe8YShPcnTcEY3VKc4eCgsgWdcZ4LuA7FV6sUr1g3HF0HXHgg2hKS7ppr1vsHhlxpSwErMLQ2ly30oHqHNCeAR_WeQbB9BI75aNCH8iC8pBxu6QUbCzFlvEQmw82NRw4zKZ2TlGN9hgkZmT5lRa4y9ugm2DeVPJUJfS9YHq0VY6UIA5M9YfUvdHQo2dEfQ8UoBMxkiVU_tLgrpZD7KHgJQl2SEBqLdEPiMoAobRkJp-ePx_0nQmfQqQSunRxmiEcgKakqQuV8XKnGD3jREdcr_iAeOsobhar531YtedbZW5zX_p-QIgM6d0lN_AiBPsDUbTB6ITS-6xU4R6BpUn6cdfftjKGf4DC83xaTTjv-JKeDK4Eoo0CZan2tIK87O8pf1zJlwSzhs_5pJCyYRImVro4-ilVklxaEwVRhWzC-IkU-vT_huSi_-OEK_RD__LzWGz__92v57aN2Syo9SNKYn6Twbq3v0eLwoZSQZQRsMLP2oUMPY3_osUH1mER2Tr03zH73aJlL6h8=", "id": "rs_060035eb87c40e14016a12f9ace22881919d1b595418368d16", "summary": [{"type": "summary_text", "text": "**Determining necessary actions**\n\nI need to figure out what actions to take here. First, I’m wondering if I should load skills that are already relevant. Before replying, it’s crucial to scan through what’s available. I might consider integrating TDD or social features, or perhaps looking into the manifest-preview-renderer reference. It seems like I should utilize the tools I have on hand to get everything right."}]}], "tool_calls": [{"id": "call_GF6KgFSSZZcrVAYDj4NPycJN", "call_id": "call_GF6KgFSSZZcrVAYDj4NPycJN", "response_item_id": "fc_060035eb87c40e14016a12f9b0ef048191b82a2ba8898c913a", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_NFPKYCY8UbeEN6XU4mY4BpEG", "call_id": "call_NFPKYCY8UbeEN6XU4mY4BpEG", "response_item_id": "fc_060035eb87c40e14016a12f9b0f0708191a41e015554913c8c", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-automation\",\"file_path\":\"references/manifest-preview-renderer.md\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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_GF6KgFSSZZcrVAYDj4NPycJN", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/manifest-preview-renderer.md\", \"content\": \"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)`\\n  - one representative image per visual timeline item\\n  - uses the manifest text, asset type, timing, and retention goal\\n- `write_frame_images(frames, output_dir)`\\n  - writes numbered PNGs for inspection/debugging\\n- `render_preview_video(manifest, output_path, width, height, fps)`\\n  - renders frames to temporary PNGs\\n  - writes an FFmpeg concat file with per-scene durations\\n  - exports `yuv420p` MP4 with `+faststart`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n  data/render_manifests/<batch>/<video>.json \\\\\\n  --out-dir data/previews/<batch> \\\\\\n  --width 540 \\\\\\n  --height 960 \\\\\\n  --fps 24\\n```\\n\\nUse reduced preview resolution like `540x960` for fast local review. Keep final `1080x1920` as a later renderer-quality target.\\n\\n## Repo hygiene\\n\\n- Keep generated MP4/PNG media out of Git via `.gitignore`.\\n- Commit renderer code, tests, scripts, manifests, and lightweight planning artifacts.\\n- Do not commit rendered previews unless explicitly needed as small fixtures.\\n- Add runtime dependencies explicitly, e.g. `Pillow>=10.0`; treat FFmpeg as an external system prerequisite checked at runtime.\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset supplier.\\n- Do not over-polish the first renderer. Its job is approval/timing validation.\\n- Do not publish from this step. Preview rendering still requires human approval before posting.\\n- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal readability before scaling to more videos.\", \"file_type\": \".md\"}", "tool_call_id": "call_NFPKYCY8UbeEN6XU4mY4BpEG", "timestamp": "2026-05-24T15:16:32.736700"}
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{"role": "assistant", "content": "", "reasoning": "**Testing local preview plan**\n\nI need to start with tests for the local_preview.py plan. Maybe I should use frame specifications to guide this. It's essential to gather the relevant manifest data as part of the process. I'm also considering implementing an approach that ensures side effects are kept to a minimum. I want to ensure that everything functions smoothly without unwanted changes. Let's lay the groundwork properly and get this sorted out!", "finish_reason": "tool_calls", "reasoning_content": "**Testing local preview plan**\n\nI need to start with tests for the local_preview.py plan. Maybe I should use frame specifications to guide this. It's essential to gather the relevant manifest data as part of the process. I'm also considering implementing an approach that ensures side effects are kept to a minimum. I want to ensure that everything functions smoothly without unwanted changes. 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Maybe I should use frame specifications to guide this. It's essential to gather the relevant manifest data as part of the process. I'm also considering implementing an approach that ensures side effects are kept to a minimum. I want to ensure that everything functions smoothly without unwanted changes. Let's lay the groundwork properly and get this sorted out!"}]}], "tool_calls": [{"id": "call_M4giFmm3C9PSDc38fyshV36d", "call_id": "call_M4giFmm3C9PSDc38fyshV36d", "response_item_id": "fc_060035eb87c40e14016a12f9bdaca8819189fe6074abdb121d", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_preview.py\",\"content\":\"import pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.local_preview import (\\n    LocalPreviewPlan,\\n    build_local_preview_plan,\\n    validate_local_preview_plan,\\n)\\nfrom autoshorts.rendering.manifest import RenderManifest, RenderSettings, build_render_manifest\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _manifest():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    return build_render_manifest(create_shot_plan(script))\\n\\n\\ndef test_build_local_preview_plan_creates_side_effect_free_frame_plan():\\n    manifest = _manifest()\\n\\n    plan = build_local_preview_plan(manifest)\\n\\n    assert isinstance(plan, LocalPreviewPlan)\\n    assert plan.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert plan.title == \\\"AI weekly review routine\\\"\\n    assert plan.width == 540\\n    assert plan.height == 960\\n    assert plan.fps == 24\\n    assert plan.output_dir == \\\"data/previews/ai_life_systems-ai-weekly-review-routine\\\"\\n    assert plan.side_effects == ()\\n    assert plan.external_calls == ()\\n    assert len(plan.frames) == len(manifest.timeline)\\n    assert plan.frames[0].frame_id == \\\"frame-01-first_frame_hook\\\"\\n    assert plan.frames[0].start_ms == 0\\n    assert plan.frames[0].duration_ms == 3000\\n    assert plan.frames[0].scene_purpose == \\\"first_frame_hook\\\"\\n    assert plan.frames[0].caption_text == \\\"If AI runs your week, you have already lost control.\\\"\\n    assert plan.frames[0].asset_mode == \\\"kinetic_text_overlay\\\"\\n    assert plan.frames[0].placeholder_image_path == (\\n        \\\"data/previews/ai_life_systems-ai-weekly-review-routine/frame-01-first_frame_hook.png\\\"\\n    )\\n    assert plan.frames[-1].start_ms == 30000\\n    assert plan.frames[-1].duration_ms == 5000\\n\\n\\ndef test_validate_local_preview_plan_accepts_default_plan():\\n    result = validate_local_preview_plan(build_local_preview_plan(_manifest()))\\n\\n    assert result.is_valid is True\\n    assert result.errors == ()\\n\\n\\ndef test_build_local_preview_plan_rejects_invalid_manifest():\\n    manifest = _manifest()\\n    invalid = RenderManifest(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        settings=RenderSettings(width=1920, height=1080),\\n        timeline=manifest.timeline,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"render size must be vertical 1080x1920\\\"):\\n        build_local_preview_plan(invalid)\\n\\n\\ndef test_validate_local_preview_plan_rejects_side_effects_external_calls_and_bad_size():\\n    plan = build_local_preview_plan(_manifest())\\n    unsafe = LocalPreviewPlan(\\n        script_id=plan.script_id,\\n        title=plan.title,\\n        width=1920,\\n        height=1080,\\n        fps=0,\\n        output_dir=plan.output_dir,\\n        frames=plan.frames,\\n        side_effects=(\\\"write_png\\\",),\\n        external_calls=(\\\"ffmpeg\\\",),\\n    )\\n\\n    result = validate_local_preview_plan(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"preview size must be vertical\\\" in result.errors\\n    assert \\\"fps must be positive\\\" in result.errors\\n    assert \\\"preview plan must not have side effects\\\" in result.errors\\n    assert \\\"preview plan must not call external tools\\\" in result.errors\\n\\n\\ndef test_validate_local_preview_plan_rejects_bad_frame_timing_and_missing_fields():\\n    plan = build_local_preview_plan(_manifest())\\n    first = plan.frames[0]\\n    bad_frame = type(first)(\\n        frame_id=\\\"\\\",\\n        scene_purpose=\\\"\\\",\\n        start_ms=1000,\\n        duration_ms=0,\\n        caption_text=\\\"\\\",\\n        label_text=\\\"\\\",\\n        asset_mode=\\\"\\\",\\n        motion_instruction=\\\"\\\",\\n        placeholder_image_path=\\\"\\\",\\n    )\\n    unsafe = LocalPreviewPlan(\\n        script_id=plan.script_id,\\n        title=plan.title,\\n        width=plan.width,\\n        height=plan.height,\\n        fps=plan.fps,\\n        output_dir=plan.output_dir,\\n        frames=(bad_frame,) + plan.frames[1:],\\n        side_effects=(),\\n        external_calls=(),\\n    )\\n\\n    result = validate_local_preview_plan(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"frame 1 frame_id is required\\\" in result.errors\\n    assert \\\"frame 1 duration_ms must be positive\\\" in result.errors\\n    assert \\\"first frame must start at 0ms\\\" in result.errors\\n    assert \\\"frame 1 caption_text is required\\\" in result.errors\\n    assert \\\"frame 1 placeholder_image_path is required\\\" in result.errors\\n\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 4607, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_preview.py\\\">\\nERROR [5:6] Import \\\"autoshorts.rendering.local_preview\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_M4giFmm3C9PSDc38fyshV36d", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_YeBW8PC5S63cFh28IRxS11fI", "call_id": "call_YeBW8PC5S63cFh28IRxS11fI", "response_item_id": "fc_060035eb87c40e14016a12f9d7f3d481918a5aa744a9393359", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_local_preview.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_local_preview.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_preview.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_local_preview.py:5: in <module>\\n    from autoshorts.rendering.local_preview import (\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.local_preview'\\n=========================== short test summary info ============================\\nERROR tests/test_local_preview.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.11s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_YeBW8PC5S63cFh28IRxS11fI", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_tVRYD3vGFNFw5uFex3OA3PFD", "call_id": "call_tVRYD3vGFNFw5uFex3OA3PFD", "response_item_id": "fc_060035eb87c40e14016a12f9dd02f4819192e86b765c1be5bc", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Side-effect-free local preview planning from render manifests.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.manifest import RenderManifest, validate_render_manifest\\n\\n\\n@dataclass(frozen=True)\\nclass LocalPreviewFrame:\\n    frame_id: str\\n    scene_purpose: str\\n    start_ms: int\\n    duration_ms: int\\n    caption_text: str\\n    label_text: str\\n    asset_mode: str\\n    motion_instruction: str\\n    placeholder_image_path: str\\n\\n\\n@dataclass(frozen=True)\\nclass LocalPreviewPlan:\\n    script_id: str\\n    title: str\\n    width: int\\n    height: int\\n    fps: int\\n    output_dir: str\\n    frames: tuple[LocalPreviewFrame, ...]\\n    side_effects: tuple[str, ...] = ()\\n    external_calls: tuple[str, ...] = ()\\n\\n\\ndef _is_blank(value: str) -> bool:\\n    return not (value or \\\"\\\").strip()\\n\\n\\ndef _frame_id(index: int, purpose: str) -> str:\\n    return f\\\"frame-{index:02d}-{purpose}\\\"\\n\\n\\ndef build_local_preview_plan(\\n    manifest: RenderManifest,\\n    *,\\n    width: int = 540,\\n    height: int = 960,\\n    fps: int = 24,\\n) -> LocalPreviewPlan:\\n    \\\"\\\"\\\"Build a deterministic frame plan without writing images or calling FFmpeg.\\\"\\\"\\\"\\n\\n    manifest_validation = validate_render_manifest(manifest)\\n    if not manifest_validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(manifest_validation.errors))\\n\\n    output_dir = f\\\"data/previews/{manifest.script_id}\\\"\\n    frames = tuple(\\n        LocalPreviewFrame(\\n            frame_id=_frame_id(index, item.scene_purpose),\\n            scene_purpose=item.scene_purpose,\\n            start_ms=item.start_seconds * 1000,\\n            duration_ms=(item.end_seconds - item.start_seconds) * 1000,\\n            caption_text=item.caption_layer.text,\\n            label_text=item.label_layer.text,\\n            asset_mode=item.asset_mode,\\n            motion_instruction=item.motion_instruction,\\n            placeholder_image_path=f\\\"{output_dir}/{_frame_id(index, item.scene_purpose)}.png\\\",\\n        )\\n        for index, item in enumerate(manifest.timeline, start=1)\\n    )\\n    plan = LocalPreviewPlan(\\n        script_id=manifest.script_id,\\n        title=manifest.title,\\n        width=width,\\n        height=height,\\n        fps=fps,\\n        output_dir=output_dir,\\n        frames=frames,\\n        side_effects=(),\\n        external_calls=(),\\n    )\\n    validation = validate_local_preview_plan(plan)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n    return plan\\n\\n\\ndef validate_local_preview_plan(plan: LocalPreviewPlan) -> ValidationResult:\\n    errors: list[str] = []\\n    if _is_blank(plan.script_id):\\n        errors.append(\\\"script_id is required\\\")\\n    if _is_blank(plan.title):\\n        errors.append(\\\"title is required\\\")\\n    if plan.width <= 0 or plan.height <= 0 or plan.height <= plan.width:\\n        errors.append(\\\"preview size must be vertical\\\")\\n    if plan.fps <= 0:\\n        errors.append(\\\"fps must be positive\\\")\\n    if _is_blank(plan.output_dir):\\n        errors.append(\\\"output_dir is required\\\")\\n    if plan.side_effects:\\n        errors.append(\\\"preview plan must not have side effects\\\")\\n    if plan.external_calls:\\n        errors.append(\\\"preview plan must not call external tools\\\")\\n    if not plan.frames:\\n        errors.append(\\\"at least one preview frame is required\\\")\\n\\n    previous_end: int | None = None\\n    for index, frame in enumerate(plan.frames, start=1):\\n        for field_name, value in {\\n            \\\"frame_id\\\": frame.frame_id,\\n            \\\"scene_purpose\\\": frame.scene_purpose,\\n            \\\"caption_text\\\": frame.caption_text,\\n            \\\"label_text\\\": frame.label_text,\\n            \\\"asset_mode\\\": frame.asset_mode,\\n            \\\"motion_instruction\\\": frame.motion_instruction,\\n            \\\"placeholder_image_path\\\": frame.placeholder_image_path,\\n        }.items():\\n            if _is_blank(value):\\n                errors.append(f\\\"frame {index} {field_name} is required\\\")\\n        if frame.duration_ms <= 0:\\n            errors.append(f\\\"frame {index} duration_ms must be positive\\\")\\n        if index == 1 and frame.start_ms != 0:\\n            errors.append(\\\"first frame must start at 0ms\\\")\\n        if previous_end is not None and frame.start_ms < previous_end:\\n            errors.append(f\\\"frame {index} start_ms overlaps previous frame\\\")\\n        previous_end = frame.start_ms + frame.duration_ms\\n\\n    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_preview.py\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \".....                                                                    [100%]\\n5 passed in 0.05s\\n........................................................................ [ 74%]\\n.........................                                                [100%]\\n97 passed in 0.14s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_Rvp07wMbCo1aBdMRaxTbRGVv", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEvn-tf64motytC3VuByMnLEV9hdRoHeZZHkaBVla6Fc5O2h9W5vDLSUgMqHO7CqgpCCY9Ok0rBV-Sx8Wf8rb_mz6nkC9fyr0VeUuf2l5GVNw8uJQR-ba1zDFi8H5P7IXVEecinMVocswSm7r-LTUQOrLaU0PXdnPdTdLejKQGrWS2yfO_-_uP7k1T4YLcYiZfAqhQK9SCCZWUVtmBQacC2d87F540FfStpErTtf-Fd-maqbLajx8CgV-959zdTyvrpINUxkduUt-zr3eOrLRozkzbto68OqWuzIezoJclYAyd3jQzoaqLWJG9RViKWAI7ErE-wxE1GG_ME5CnKcF7eWgPl8QAuflTgwoP54ZfQ9Am0zJMM_T9NOgjsIFeBpiUCgsGud3YtIdSadZLSvxggH9Z50J2ODmbBvAVHxTsulGFjLwaYm6535UR1y8jhApwrMRKP2YtJKlLvJtG8pqPyJENvVYiQeDkM0ddBMn2x2OROQYwBL9KVSclhJlEfyB8ZgogAINnxOixFDG8u3shujltkijkS7M00-pGDtiRrBxgXPtm9KnRRZB-lVUuBTXA5A_trbFvZ_Mgy1efZHFZ6mk4VouRbxDMamg_ObOJGTI-Qnws9A2tUEUzj0uEjnLZyXctX_njkOB2R_AzPpxXoHvQMqLydCO1qQdedLWKxlpMMRyja4FEN_glU-fPyLOgEHTDuL4_v5yY5d80M9pt3XkefYVjMqsb6l43lbUwA1ZveZr9p7W6YLG6INcCFQaUOdORadRV7UesUAUn2-yCaCtfEH4kdzoFLViRHR4htOJqGfXwVrwYKTucnPXChVbdQwQ1bh2m_GJV1QV85XH1i_3TWb2T0ti0ahnQaZlWxmCpmZXAbbsBwCV8GJsgx_e1oYCbOZLLreU_6LwYoqs6l4PHBsO8YCyuESaXFYjP3_4s08=", "id": "rs_060035eb87c40e14016a12f9fe596481919b5aef9e727ed988", "summary": []}], "tool_calls": [{"id": "call_MNzlkQwrTAQqtLPirKNrmK28", "call_id": "call_MNzlkQwrTAQqtLPirKNrmK28", "response_item_id": "fc_060035eb87c40e14016a12f9fed218819187cf45aa407e19b2", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\",\"new_string\":\"- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n- `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"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@@ -22,6 +22,7 @@\\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 - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n - `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n - `autoshorts.rendering.shot_plan` — retention-led shot planner before render manifests or video generation.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_MNzlkQwrTAQqtLPirKNrmK28", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_LpXpg3YXxpyObCLRPTvCr6EA", "call_id": "call_LpXpg3YXxpyObCLRPTvCr6EA", "response_item_id": "fc_060035eb87c40e14016a12fa05d09481918cb579e7859490c9", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n\\n`autoshorts.rendering.local_preview.build_local_preview_plan()` turns a validated `RenderManifest` into a deterministic `LocalPreviewPlan` for later frame/video generation. The default plan uses reduced local preview settings `540x960` at `24fps`, creates one `LocalPreviewFrame` per manifest timeline item, converts scene timing to milliseconds, and assigns deterministic placeholder image paths under `data/previews/<script_id>/`.\\n\\n`validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n\\n## Social media draft\",\"old_string\":\"It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n\\n## Social media draft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"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@@ -111,6 +111,10 @@\\n \\n It prints preview metadata first, then the HTML document. Redirect stdout to a local `.html` file if you want to inspect it in a browser; the CLI itself still writes nothing.\\n \\n+`autoshorts.rendering.local_preview.build_local_preview_plan()` turns a validated `RenderManifest` into a deterministic `LocalPreviewPlan` for later frame/video generation. The default plan uses reduced local preview settings `540x960` at `24fps`, creates one `LocalPreviewFrame` per manifest timeline item, converts scene timing to milliseconds, and assigns deterministic placeholder image paths under `data/previews/<script_id>/`.\\n+\\n+`validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n+\\n ## Social media draft\\n \\n `autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_LpXpg3YXxpyObCLRPTvCr6EA", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_N5kKSrSLlu8McSxCaZc04S38", "call_id": "call_N5kKSrSLlu8McSxCaZc04S38", "response_item_id": "fc_060035eb87c40e14016a12fa0ebc008191933c265118260a22", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\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, validate_local_preview_plan\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\nplan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\nprint(plan.script_id)\\nprint((plan.width, plan.height, plan.fps))\\nprint(len(plan.frames))\\nprint(plan.frames[0].frame_id, plan.frames[0].start_ms, plan.frames[0].duration_ms)\\nprint(plan.frames[-1].start_ms, plan.frames[-1].duration_ms)\\nprint(plan.side_effects, plan.external_calls)\\nprint(validate_local_preview_plan(plan).is_valid)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_hrOHlDyq8pA5KMRtkXoioiEN", "call_id": "call_hrOHlDyq8pA5KMRtkXoioiEN", "response_item_id": "fc_060035eb87c40e14016a12fa1a19a88191abd0d5133276a5db", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/rendering/local_preview.py tests/test_local_preview.py && git commit -m \\\"feat: add local preview frame planning\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/local_preview.py\\n?? tests/test_local_preview.py\\n[feat/core-approval-quality-gates 02ecf59] feat: add local preview frame planning\\n 3 files changed, 264 insertions(+)\\n create mode 100644 autoshorts/rendering/local_preview.py\\n create mode 100644 tests/test_local_preview.py\\n02ecf59 feat: add local preview frame planning\\n3b387f4 feat: add demo social draft cli\\n515c549 feat: add social draft delivery preparation\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\\ne258635 feat: add render manifest planning\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_hrOHlDyq8pA5KMRtkXoioiEN", "timestamp": "2026-05-24T15:16:32.736700"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Local Preview Frame Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/local_preview.py\ntests/test_local_preview.py\n```\n\n## Neue API\n\n```python\nbuild_local_preview_plan(manifest: RenderManifest) -> LocalPreviewPlan\nvalidate_local_preview_plan(plan: LocalPreviewPlan) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n```\n\nNoch kein Dateischreiben, kein PNG, kein FFmpeg, kein MP4. Nur ein sauberer, deterministischer Plan für den späteren lokalen Preview-Renderer.\n\n## LocalPreviewPlan\n\nDefault Settings:\n\n```text\nwidth: 540\nheight: 960\nfps: 24\noutput_dir: data/previews/<script_id>\nside_effects: ()\nexternal_calls: ()\n```\n\nWarum `540x960` statt direkt `1080x1920`?\n\nWeil das der schnelle Timing-/Approval-Preview ist. Finale Qualität kommt später. Erst prüfen wir Timing, Textgewicht und Scene Flow — dann darf die Maschine schwitzen.\n\n## LocalPreviewFrame\n\nPro Manifest-Timeline-Item wird ein Frame-Plan erzeugt:\n\n```text\nframe_id\nscene_purpose\nstart_ms\nduration_ms\ncaption_text\nlabel_text\nasset_mode\nmotion_instruction\nplaceholder_image_path\n```\n\nBeispiel verifiziert:\n\n```text\nframe-01-first_frame_hook\nstart_ms: 0\nduration_ms: 3000\nplaceholder:\ndata/previews/ai_life_systems-ai-weekly-review-routine/frame-01-first_frame_hook.png\n```\n\nLetzte Szene:\n\n```text\nstart_ms: 30000\nduration_ms: 5000\n```\n\n## Guardrails\n\n`validate_local_preview_plan()` rejected:\n\n- nicht-vertikale Preview Size\n- `fps <= 0`\n- leerer Output-Pfad\n- keine Frames\n- fehlende Frame-Felder\n- schlechte Frame-Durations\n- erster Frame startet nicht bei `0ms`\n- überlappende Frames\n- Side Effects\n- externe Tool Calls\n\nAlso wieder ein netter kleiner Zaun gegen “mal eben rendern wir irgendwas”. Zäune sind gut. Besonders um Bots.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.local_preview'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n97 passed in 0.15s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n(540, 960, 24)\n5\nframe-01-first_frame_hook 0 3000\n30000 5000\n() ()\nTrue\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.local_preview`\n- `LocalPreviewPlan`\n- `LocalPreviewFrame`\n- Default Preview Settings\n- Guardrails\n- weiterhin keine Datei-/Renderer-Side-Effects\n\n## Commit\n\n```text\n02ecf59 feat: add local preview frame planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir den ersten echten lokalen Artefakt-Schritt bauen:\n\n```text\nLocalPreviewPlan\n→ PNG scene cards\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/local_frame_writer.py\ntests/test_local_frame_writer.py\n```\n\nZiel:\n\n- schreibt deterministische PNG Scene Cards in ein explizites Output-Verzeichnis\n- nutzt `Pillow`, falls verfügbar\n- pro Frame:\n  - vertikale Canvas\n  - Scene Label\n  - Caption\n  - Asset Mode\n  - Motion Instruction kurz\n  - Timing\n- Rückgabe als `LocalFrameWriteResult`\n  - `output_dir`\n  - `frame_paths`\n  - `side_effects=(\"write_png\",)`\n  - keine externen Calls\n- keine MP4-Erzeugung yet\n- kein FFmpeg yet\n- Medien bleiben unter `data/previews/...` und müssen gitignored sein\n\nDas wäre der erste kontrollierte Schritt von “Plan” zu “sichtbarem Artefakt”. Danach bauen wir erst:\n\n```text\nPNG scene cards\n→ FFmpeg concat plan\n→ MP4 timing preview\n```", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Local Preview Frame Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/local_preview.py\ntests/test_local_preview.py\n```\n\n## Neue API\n\n```python\nbuild_local_preview_plan(manifest: RenderManifest) -> LocalPreviewPlan\nvalidate_local_preview_plan(plan: LocalPreviewPlan) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n```\n\nNoch kein Dateischreiben, kein PNG, kein FFmpeg, kein MP4. Nur ein sauberer, deterministischer Plan für den späteren lokalen Preview-Renderer.\n\n## LocalPreviewPlan\n\nDefault Settings:\n\n```text\nwidth: 540\nheight: 960\nfps: 24\noutput_dir: data/previews/<script_id>\nside_effects: ()\nexternal_calls: ()\n```\n\nWarum `540x960` statt direkt `1080x1920`?\n\nWeil das der schnelle Timing-/Approval-Preview ist. Finale Qualität kommt später. Erst prüfen wir Timing, Textgewicht und Scene Flow — dann darf die Maschine schwitzen.\n\n## LocalPreviewFrame\n\nPro Manifest-Timeline-Item wird ein Frame-Plan erzeugt:\n\n```text\nframe_id\nscene_purpose\nstart_ms\nduration_ms\ncaption_text\nlabel_text\nasset_mode\nmotion_instruction\nplaceholder_image_path\n```\n\nBeispiel verifiziert:\n\n```text\nframe-01-first_frame_hook\nstart_ms: 0\nduration_ms: 3000\nplaceholder:\ndata/previews/ai_life_systems-ai-weekly-review-routine/frame-01-first_frame_hook.png\n```\n\nLetzte Szene:\n\n```text\nstart_ms: 30000\nduration_ms: 5000\n```\n\n## Guardrails\n\n`validate_local_preview_plan()` rejected:\n\n- nicht-vertikale Preview Size\n- `fps <= 0`\n- leerer Output-Pfad\n- keine Frames\n- fehlende Frame-Felder\n- schlechte Frame-Durations\n- erster Frame startet nicht bei `0ms`\n- überlappende Frames\n- Side Effects\n- externe Tool Calls\n\nAlso wieder ein netter kleiner Zaun gegen “mal eben rendern wir irgendwas”. Zäune sind gut. Besonders um Bots.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.local_preview'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n97 passed in 0.15s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n(540, 960, 24)\n5\nframe-01-first_frame_hook 0 3000\n30000 5000\n() ()\nTrue\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.local_preview`\n- `LocalPreviewPlan`\n- `LocalPreviewFrame`\n- Default Preview Settings\n- Guardrails\n- weiterhin keine Datei-/Renderer-Side-Effects\n\n## Commit\n\n```text\n02ecf59 feat: add local preview frame planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt können wir den ersten echten lokalen Artefakt-Schritt bauen:\n\n```text\nLocalPreviewPlan\n→ PNG scene cards\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/local_frame_writer.py\ntests/test_local_frame_writer.py\n```\n\nZiel:\n\n- schreibt deterministische PNG Scene Cards in ein explizites Output-Verzeichnis\n- nutzt `Pillow`, falls verfügbar\n- pro Frame:\n  - vertikale Canvas\n  - Scene Label\n  - Caption\n  - Asset Mode\n  - Motion Instruction kurz\n  - Timing\n- Rückgabe als `LocalFrameWriteResult`\n  - `output_dir`\n  - `frame_paths`\n  - `side_effects=(\"write_png\",)`\n  - keine externen Calls\n- keine MP4-Erzeugung yet\n- kein FFmpeg yet\n- Medien bleiben unter `data/previews/...` und müssen gitignored sein\n\nDas wäre der erste kontrollierte Schritt von “Plan” zu “sichtbarem Artefakt”. Danach bauen wir erst:\n\n```text\nPNG scene cards\n→ FFmpeg concat plan\n→ MP4 timing preview\n```"}], "id": "msg_060035eb87c40e14016a12fa1e729081919e7484ff0df5c33d", "phase": "final_answer"}], "timestamp": "2026-05-24T15:16:32.736700"}
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{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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_CIOGKNYxUukg8q5IoWu1yygD", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/manifest-preview-renderer.md\", \"content\": \"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)`\\n  - one representative image per visual timeline item\\n  - uses the manifest text, asset type, timing, and retention goal\\n- `write_frame_images(frames, output_dir)`\\n  - writes numbered PNGs for inspection/debugging\\n- `render_preview_video(manifest, output_path, width, height, fps)`\\n  - renders frames to temporary PNGs\\n  - writes an FFmpeg concat file with per-scene durations\\n  - exports `yuv420p` MP4 with `+faststart`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n  data/render_manifests/<batch>/<video>.json \\\\\\n  --out-dir data/previews/<batch> \\\\\\n  --width 540 \\\\\\n  --height 960 \\\\\\n  --fps 24\\n```\\n\\nUse reduced preview resolution like `540x960` for fast local review. Keep final `1080x1920` as a later renderer-quality target.\\n\\n## Repo hygiene\\n\\n- Keep generated MP4/PNG media out of Git via `.gitignore`.\\n- Commit renderer code, tests, scripts, manifests, and lightweight planning artifacts.\\n- Do not commit rendered previews unless explicitly needed as small fixtures.\\n- Add runtime dependencies explicitly, e.g. `Pillow>=10.0`; treat FFmpeg as an external system prerequisite checked at runtime.\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset supplier.\\n- Do not over-polish the first renderer. Its job is approval/timing validation.\\n- Do not publish from this step. Preview rendering still requires human approval before posting.\\n- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal readability before scaling to more videos.\", \"file_type\": \".md\"}", "tool_call_id": "call_9KZK4CugHw8ehAetXuUcs0B1", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": "**Considering implementation requirements**\n\nI need to implement a frame writer, which means I'll have to check if Pillow is installed. There's also the possibility that I need to look into the dependencies listed in the pyproject file. I wonder if using the terminal will be necessary to verify the current package version, as it might provide important information. It's crucial to confirm that everything is set up correctly before moving forward!", "finish_reason": "tool_calls", "reasoning_content": "**Considering implementation requirements**\n\nI need to implement a frame writer, which means I'll have to check if Pillow is installed. There's also the possibility that I need to look into the dependencies listed in the pyproject file. I wonder if using the terminal will be necessary to verify the current package version, as it might provide important information. It's crucial to confirm that everything is set up correctly before moving forward!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEwIXzmJCYO3jYpmgP8hx5oWWfs89lKD-pUGOULiWQXl8nOxP8_mNt4-NjLh4emEE6VuF0MCtSHDDrwgCNxMVDT2mIalx6soNobMpDFzX6VZ7kPri-hy0ER84yZxPbc50QresXEV1SUbSL7ft54urW2ROb8DHNtpK9vOtdJWdL90Zp42Y5COh05QZvGDyVFg3kYSpYaYFfPVbM-IjwLS_wwjm3ElQHOp3rieU3ngzYFJsqMd4SK7n-qvA3uk5fm8k9w7EdR1ZiaTQWTu6Ds4He2YT8GWLyouu9Oa2QQpkeRokRoLiEtCSbwDXeEy9iNS4xsGZGEwwCQW10wB0xWBQ5DygbAhT5OqxEkTIAH0OMgN0aj64yyWPovHo0_HSL0ZONdGCR_o8scF1pqnf9HSIuzqv56ZYmsKqeZPYtje5HKfvK6lP1-hYPnjMQOeE9Ij0BNvpRLJYLJ_yEZef-vGH_4TSrdEilxMr9b3cW2WWPAvLFuxc71svS2h5FRa9Eqo1D1hd5vkq4mhapKCht97oospZ20le_pZTQAT3Lpv4OXITqk64hb5OgxO0Bqn8Z6pl1qZkvI4hvZWZiyKK5B_p-csc_6QLFJWZYgM34MtjFDOvV88fbwyeXUc2k-ct3v-zsEqvAH2oli2JinVT7Fpoa2n6ziKBBaJY-gvVB9A6TDWaYT9qJwxz6_a8L5PP8by7P47JptYN3RkKS7CZfcfsLqw8VVBibOITc2hN4qCQMgKYRcRKFsceEtO-5Z8O4ILwp7948Dhr62uybKhvGYFNA15zqpuZQseaN5AYSEyY4p5jRCrdG9xeiwt1H0aeQ3nuIwaC6WSTQWRYkjvj8ij1HwrA9tf4-1mpDnnAhD0ck29ePaH9qBPumv2EX9DW8cwFS06xNSiDlMAr0BGyF1Un-zMMZR7VBiyfoH3d7h3dbjokTfaxdGiAI9JpfVTAb6O_7Qy1VxZyPFWtRytHHCOqzIw_N2ercgQ7sqCDSfpPsqed6DO9eu6boutvTNWA4vYjS-WrKB2sbAmm8qJZRK7oLdA3Q8uQG7_NohGcoGW5jj0VYSO01lGuS70w1MQfClxgw_fAZL5ANaPFymTMrbMUG2VFTA==", "id": "rs_060035eb87c40e14016a1302119c088191908c0019cba2943d", "summary": [{"type": "summary_text", "text": "**Considering implementation requirements**\n\nI need to implement a frame writer, which means I'll have to check if Pillow is installed. There's also the possibility that I need to look into the dependencies listed in the pyproject file. I wonder if using the terminal will be necessary to verify the current package version, as it might provide important information. It's crucial to confirm that everything is set up correctly before moving forward!"}]}], "tool_calls": [{"id": "call_2tepBpkYnfdSGBv2sceHRFXK", "call_id": "call_2tepBpkYnfdSGBv2sceHRFXK", "response_item_id": "fc_060035eb87c40e14016a13021778b48191bb7bc3e55885187e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\ntry:\\n    import PIL\\n    print('PIL available', PIL.__version__)\\nexcept Exception as exc:\\n    print('PIL missing', type(exc).__name__, exc)\\nPY\\n\\ngit status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}, {"id": "call_82o2Gqxni5Z6L5wFTLWTCJC8", "call_id": "call_82o2Gqxni5Z6L5wFTLWTCJC8", "response_item_id": "fc_060035eb87c40e14016a13021778d88191a9e87f840b0be3f9", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\",\"pattern\":\"pyproject.toml\",\"target\":\"files\",\"limit\":20}"}}, {"id": "call_GTgyHIMQePNpcLdQVX5xLpyN", "call_id": "call_GTgyHIMQePNpcLdQVX5xLpyN", "response_item_id": "fc_060035eb87c40e14016a13021778f081919e712b46233a1883", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/.gitignore\",\"limit\":200}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|# Secrets / local config\\n     2|.env\\n     3|.env.*\\n     4|*.token\\n     5|*secret*\\n     6|\\n     7|# Python\\n     8|__pycache__/\\n     9|*.py[cod]\\n    10|.pytest_cache/\\n    11|.ruff_cache/\\n    12|.mypy_cache/\\n    13|.venv/\\n    14|venv/\\n    15|\\n    16|# Generated media and runtime data\\n    17|data/local_motion_renders/\\n    18|data/imported_ai_clips/\\n    19|data/generated/\\n    20|outputs/\\n    21|*.mp4\\n    22|*.mov\\n    23|*.webm\\n    24|*.wav\\n    25|*.mp3\\n    26|*.png\\n    27|*.jpg\\n    28|*.jpeg\\n    29|*.webp\\n    30|\\n    31|# OS/editor\\n    32|.DS_Store\\n    33|.vscode/\\n    34|.idea/\\n    35|\", \"total_lines\": 34, \"file_size\": 349, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_GTgyHIMQePNpcLdQVX5xLpyN", "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "assistant", "content": "", "reasoning": "**Evaluating dependencies and tests**\n\nI need to consider adding the Pillow dependency because it's installed, but the pyproject dependencies are empty. If I'm using PIL, I should definitely add that dependency. For my TDD tests, I’ll write tests that import local_frame_writer and use tmp_path to verify dimensions with PIL. I’m thinking maybe I shouldn’t commit the PNG since it's temporary. I also want to focus on result side effects without external calls. So, let's get started on the writing!", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating dependencies and tests**\n\nI need to consider adding the Pillow dependency because it's installed, but the pyproject dependencies are empty. If I'm using PIL, I should definitely add that dependency. For my TDD tests, I’ll write tests that import local_frame_writer and use tmp_path to verify dimensions with PIL. I’m thinking maybe I shouldn’t commit the PNG since it's temporary. I also want to focus on result side effects without external calls. So, let's get started on the writing!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEwIr819UsVhzO4WGrxNKn4p4rdmBwlwMfnxSqh6q2rGcRU1ERXmYJLFvUmHN6VFOIQf0kLe-A20SxIqraUoEpb4PIKhUNQBtqBQE-uLb1wXQwNxkVlvjYHrent35XOmHPzj4P1Xq_AxeOjH97GTq6Uw51Ve3yJ10qNqxDjd1uTwUEj-hXmmKmskFHcrpwWP9b5Qaiv-gP_IVPMjmmbQTmEPWDC0jGvuEvWfRKaOD0wNfNG8E6AgmuFWnuaPXqjK-mcT8NX-CXwflgMzMCnq1lgAUiosgGvqYyaBeNsgpbWqnrh08ABjjSkn3GwzxqG84FbnK80oLFIX6TOxcL2E6tBw23VwnvnsGondjChBay6OgIow62peSgh7NZXEm9fuchzq59sLgyZ3F4_5N98zI0qHzCI6bzNxqQwcS-odn2cChllYigcW9ad_efxvCFPPcq3MKHEyud5EF7fPC0ylG7dF8wgy5sCXkYMclwjeCl8htc2VE3EAk52Nq70JxOPJsrTR5DtK9kHUEIErlvUjOo1WM6vcGpTwEF537zD00X8N_HFVa_e0wi2zVKv18TdEDXSuje3h0Ah1J3GRDNkUFZNShPnR_TnHsolVo0rbHPr3TfnEnwGWl0svwvgrKYdK0gxq2itC2f-eusdTfqoZS8ZwlK2J2iOZH9ud7fl4l8o1b18-saduoGzfBXMEj4Dqbkzei4u1SC7csRsraWbXZxc9jsgExtLA3pmE6Mho5mh7h9Iu2DSh6x6s4TEaRePSjxVEmhCbHjnDFcAQhCnDbZtWsZLOfxbb9TuAyJINV0O4xwJyhr1SZ9TFehk-K6t7QCvDgBOOzKTOexMXTYiJxtJ9ckjchQ2Evs8j8IrJAtrfoHFch6GCtkUXIE9PVh5z4TYECikvGUyxMfjHQR8KpNEyN5Iif24UExQ--ziMOJPkuFRPeqNZwa6FhnLu8KFVKZbt_3ZTcZUr0sH-49P6XiVFvLC202aDGbOmXj9emrtG5EicxxutqtIWNQYjjwJ0a_S5rTY95ZZuhAjARpf-yhiYLd_Ei0XjBHMpvpJRVRnZCnKYNpmylTzXcg2CxF7SZ8N7NW3avaXPSCY06p4zb7rKxM4-CzyEnRUbc-iCt19mLUQadfTTzJGnzAphfNQpv0xIJL1-mfMh38mqsbdBbps1ksdagyQ4QOgHf4SwFHtpwi-eJmzz1NOTfNKK6RrPeNNROFfBCO1vbsi71QxDWd6Byl_2k8Nw9_yTx48GLU_vuP6gwVZhdbto1f5bGx4q0tWwGzDERmqX6dA_7jsw-bFZd_lExChLRHRsLu0x7WFye32XIvkB-2t33dQGPsiEyuDd9VjJ5EkxZ8PD2ddibhDxeVA_Lce6zTNzUMjx939wwk5s=", "id": "rs_060035eb87c40e14016a13022804008191a76881b213c4fa4d", "summary": [{"type": "summary_text", "text": "**Evaluating dependencies and tests**\n\nI need to consider adding the Pillow dependency because it's installed, but the pyproject dependencies are empty. If I'm using PIL, I should definitely add that dependency. For my TDD tests, I’ll write tests that import local_frame_writer and use tmp_path to verify dimensions with PIL. I’m thinking maybe I shouldn’t commit the PNG since it's temporary. I also want to focus on result side effects without external calls. So, let's get started on the writing!"}]}], "tool_calls": [{"id": "call_5txXNLm4jTJXr5og9dMaWWw4", "call_id": "call_5txXNLm4jTJXr5og9dMaWWw4", "response_item_id": "fc_060035eb87c40e14016a13022b6a408191b688115fdd73c0fe", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from pathlib import Path\\n\\nimport pytest\\nfrom PIL import Image\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.local_frame_writer import LocalFrameWriteResult, write_local_preview_frames\\nfrom autoshorts.rendering.local_preview import LocalPreviewPlan, build_local_preview_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _plan():\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    return build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\n\\n\\ndef test_write_local_preview_frames_writes_valid_png_scene_cards(tmp_path):\\n    plan = _plan()\\n\\n    result = write_local_preview_frames(plan, output_dir=tmp_path)\\n\\n    assert isinstance(result, LocalFrameWriteResult)\\n    assert result.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert result.output_dir == str(tmp_path)\\n    assert result.side_effects == (\\\"write_png\\\",)\\n    assert result.external_calls == ()\\n    assert len(result.frame_paths) == len(plan.frames)\\n    assert result.frame_paths[0].endswith(\\\"frame-01-first_frame_hook.png\\\")\\n\\n    first_path = Path(result.frame_paths[0])\\n    assert first_path.exists()\\n    assert first_path.stat().st_size > 0\\n    with Image.open(first_path) as image:\\n        assert image.format == \\\"PNG\\\"\\n        assert image.size == (540, 960)\\n\\n\\ndef test_write_local_preview_frames_preserves_plan_order_and_paths(tmp_path):\\n    plan = _plan()\\n\\n    result = write_local_preview_frames(plan, output_dir=tmp_path)\\n\\n    assert [Path(path).name for path in result.frame_paths] == [\\n        \\\"frame-01-first_frame_hook.png\\\",\\n        \\\"frame-02-pattern_interrupt.png\\\",\\n        \\\"frame-03-core_explanation.png\\\",\\n        \\\"frame-04-screen_or_card_visual.png\\\",\\n        \\\"frame-05-cta.png\\\",\\n    ]\\n\\n\\ndef test_write_local_preview_frames_rejects_invalid_plan(tmp_path):\\n    plan = _plan()\\n    invalid = LocalPreviewPlan(\\n        script_id=plan.script_id,\\n        title=plan.title,\\n        width=1920,\\n        height=1080,\\n        fps=plan.fps,\\n        output_dir=plan.output_dir,\\n        frames=plan.frames,\\n        side_effects=plan.side_effects,\\n        external_calls=plan.external_calls,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"preview size must be vertical\\\"):\\n        write_local_preview_frames(invalid, output_dir=tmp_path)\\n\\n\\ndef test_write_local_preview_frames_rejects_output_dir_outside_runtime_area(tmp_path):\\n    plan = _plan()\\n    unsafe_dir = tmp_path / \\\"..\\\" / \\\"unsafe\\\"\\n\\n    with pytest.raises(ValueError, match=\\\"output_dir must be explicit and normalized\\\"):\\n        write_local_preview_frames(plan, output_dir=unsafe_dir)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2997, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.py\\\">\\nERROR [8:6] Import \\\"autoshorts.rendering.local_frame_writer\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_5txXNLm4jTJXr5og9dMaWWw4", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_JDiWIAFuK2I5gbNuGdB5bSxy", "call_id": "call_JDiWIAFuK2I5gbNuGdB5bSxy", "response_item_id": "fc_060035eb87c40e14016a130244df4c8191b3e2e939b126fca0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_local_frame_writer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n______________ ERROR collecting tests/test_local_frame_writer.py _______________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_local_frame_writer.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_local_frame_writer.py:8: in <module>\\n    from autoshorts.rendering.local_frame_writer import LocalFrameWriteResult, write_local_preview_frames\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.local_frame_writer'\\n=========================== short test summary info ============================\\nERROR tests/test_local_frame_writer.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_JDiWIAFuK2I5gbNuGdB5bSxy", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEwJLLRv7DSYE_o4HtY1xx7UC5TuhoS1sciOw-js-kXxKUAmu7iXc8pLLjenS4Nv7vM7YhyCz6pf3DF28joQVU7ZZ0WcZVhMVOY8NDfwawolWCMkCMHBh1b7D6y3Y4phsFqGXZ4OGAmrn0ay9nhI8u6wuVu_pVdSW2w1f44MI8vyHphFezNUEDt7sgSrXrXXNScPDOTBhQhRedIVtibC-aFijBJDj3Z02OIwiDv8-udMNR3BOJqRA2fCFNgr16w1xeR-RM1J6nM6Mfkio21dUqnWPiyEJB_5XiUGk1iarUbQ2qKVIaaqLmbc_h4v2p2yOznsgB21PoQ2JBMsQiJNzWKjdbuQcF-yyA8UUOoFdS4Fyd4bnHsSsRId3DTTCkA3id2GBff4IUgylbYpODUJiAb1VT5pRBe0NUbY_1c0IrEPeQC-iXAreBmx3eXrZ8_DOGCxIjxN8buI_-budRx6y_iuazLR1QHWLryd47eFFlCGOiJEBAHWM6B0BSmfBz5gZjS55AWvJMHqvSP6VfZOpixK4nKq4jfsSGtDJOKpavCM1CsImZYWwZex13A6t9jSOJJj_FhyhE7uIHhAnNyQMaJCn0ZExCPNIj_8nLQr27BbslaEti1p7_TAPW9RVX0drD5rMEfejgzkHPPU3bJXu8gVJ0uydXmumCNVmpgmuS5LbA-jmPSQMYRMV0CqnTRPD2Ep27aUfn9EO4WdHVlq29dVZDq9HihY3-qT1_zhhrLOQdZtifr3PVBFZJnHus4BVn3HjSR0alwdMgtQ4h7LQEyKxBKXPabKZaF42ffUcsQkEkcZUUvcTYYymTKmnYOj4SX9DwWHYOfZidqXdNj996ej_K9kvhjvMJLo_E6P3TwxVgkz4M10tYIHrIfHvZJB_L9lGj2OCpV0u_czT_OrFgB3aMLNi4yNke7oZixBXjh8xBDtZdp5UgFZ8pw3cNaX-D-46FyTWtjLc71umCdW7Ne4pPQeDeNyDHpAahL5qAO4OX_W4q0Fj76x0wtjdQ_T_1XC4IH4SSlIciJZWzjDCyknZiHe6AvKc3GHTRoQfakFEKdCQif8tGQ__olvvGelhsIN9itgwc2_pVMxdnqWB3xOOzeM-LiIacGj_Y-p3SB0kSPLE3w5jbOjSIOJ25N7sA6X3wBiueOAgCR3VSy5IHwDc9ohREGeEZ8lPsZfxjXq9VAKWEYCXv-JZsJR70fW0qG5oHCJDM83Fvd7_rRJ0TxUsH7cFg3in65vYTIf9yksoz_5Sqa9xoKo2bVRTw6mSvBZ8nwisHjWEDoAtBH9fMjbEk-lzRoKm_A1jNmP-YbnDsL-SdMfpP08AGPuSoawM-wS43XlyrXvBGBOjym02vI6QylbCu1Nf4mZnJozoRFVZTYINZSAf72haymnNC9Bqo2lnVdYuaI2eriY-gWodu002UOcx539QHYgjow85r0yv2S3qdzBfF2s7ZgLpWXEuKrQUJOpplnEdTnP2MOn89eTWUTUtckiDy0fJ3rWoIi6EPT3LSg9AZ_YgeWCegrHsIv3pmZyIouuHQG8__WHCB0lGAap6tlAAsj8OKRoSgiqOtMswkpuSva8axy5UTcDldmB7f0Ge45-LrUCTnIHWuDqrGAsBIT_EOw84KWXIL_hW82vuMT67M_xCShIyvaXKfvbzm6bbRecM-V0x8B6DfCrXkeoH-X0fIDkpKVzR-rpX2jdyTtJlxART-RNmLSnQ7yL6", "id": "rs_060035eb87c40e14016a13024934688191a38ccaa10a4d1d89", "summary": []}], "tool_calls": [{"id": "call_RGViMEGVJTg2kO1C7DGzxAKu", "call_id": "call_RGViMEGVJTg2kO1C7DGzxAKu", "response_item_id": "fc_060035eb87c40e14016a13024bfe5c8191b71e4a086facfcaf", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Write deterministic local preview PNG scene cards.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\nfrom textwrap import wrap\\n\\nfrom PIL import Image, ImageDraw, ImageFont\\n\\nfrom autoshorts.rendering.local_preview import LocalPreviewPlan, validate_local_preview_plan\\n\\n\\n@dataclass(frozen=True)\\nclass LocalFrameWriteResult:\\n    script_id: str\\n    output_dir: str\\n    frame_paths: tuple[str, ...]\\n    side_effects: tuple[str, ...] = (\\\"write_png\\\",)\\n    external_calls: tuple[str, ...] = ()\\n\\n\\ndef _validate_output_dir(output_dir: str | Path) -> Path:\\n    path = Path(output_dir)\\n    if not str(path).strip() or \\\"..\\\" in path.parts:\\n        raise ValueError(\\\"output_dir must be explicit and normalized\\\")\\n    return path\\n\\n\\ndef _font(size: int):\\n    try:\\n        return ImageFont.truetype(\\\"DejaVuSans.ttf\\\", size=size)\\n    except OSError:\\n        return ImageFont.load_default()\\n\\n\\ndef _wrapped(text: str, width: int) -> str:\\n    lines: list[str] = []\\n    for paragraph in text.splitlines() or [text]:\\n        lines.extend(wrap(paragraph, width=width) or [\\\"\\\"])\\n    return \\\"\\\\n\\\".join(lines)\\n\\n\\ndef _palette(asset_mode: str) -> tuple[tuple[int, int, int], tuple[int, int, int]]:\\n    palettes = {\\n        \\\"kinetic_text_overlay\\\": ((15, 23, 42), (96, 165, 250)),\\n        \\\"mechanism_animation\\\": ((17, 24, 39), (52, 211, 153)),\\n        \\\"screen_recording_simulation\\\": ((24, 24, 27), (250, 204, 21)),\\n        \\\"cinematic_ai_video\\\": ((28, 25, 23), (251, 146, 60)),\\n    }\\n    return palettes.get(asset_mode, ((17, 24, 39), (148, 163, 184)))\\n\\n\\ndef _draw_card(plan: LocalPreviewPlan, frame) -> Image.Image:\\n    background, accent = _palette(frame.asset_mode)\\n    image = Image.new(\\\"RGB\\\", (plan.width, plan.height), background)\\n    draw = ImageDraw.Draw(image)\\n\\n    margin = max(24, plan.width // 18)\\n    title_font = _font(max(26, plan.width // 13))\\n    body_font = _font(max(18, plan.width // 24))\\n    small_font = _font(max(14, plan.width // 34))\\n\\n    draw.rectangle((0, 0, plan.width, 18), fill=accent)\\n    draw.text((margin, margin), frame.scene_purpose.replace(\\\"_\\\", \\\" \\\").upper(), fill=accent, font=small_font)\\n    draw.text((margin, margin + 38), _wrapped(frame.caption_text, 24), fill=(248, 250, 252), font=title_font, spacing=8)\\n\\n    info_top = plan.height // 2\\n    draw.rounded_rectangle(\\n        (margin, info_top, plan.width - margin, plan.height - margin),\\n        radius=18,\\n        outline=accent,\\n        width=2,\\n        fill=(background[0] + 8, background[1] + 8, background[2] + 8),\\n    )\\n    body = (\\n        f\\\"Timing: {frame.start_ms}ms + {frame.duration_ms}ms\\\\n\\\"\\n        f\\\"Asset: {frame.asset_mode}\\\\n\\\"\\n        f\\\"Label: {frame.label_text}\\\\n\\\\n\\\"\\n        f\\\"Motion: {frame.motion_instruction}\\\"\\n    )\\n    draw.multiline_text((margin + 18, info_top + 18), _wrapped(body, 42), fill=(226, 232, 240), font=body_font, spacing=6)\\n    return image\\n\\n\\ndef write_local_preview_frames(\\n    plan: LocalPreviewPlan,\\n    *,\\n    output_dir: str | Path | None = None,\\n) -> LocalFrameWriteResult:\\n    \\\"\\\"\\\"Write deterministic PNG scene cards for a validated local preview plan.\\\"\\\"\\\"\\n\\n    validation = validate_local_preview_plan(plan)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n    target_dir = _validate_output_dir(output_dir or plan.output_dir)\\n    target_dir.mkdir(parents=True, exist_ok=True)\\n\\n    frame_paths: list[str] = []\\n    for frame in plan.frames:\\n        path = target_dir / f\\\"{frame.frame_id}.png\\\"\\n        image = _draw_card(plan, frame)\\n        image.save(path, format=\\\"PNG\\\")\\n        frame_paths.append(str(path))\\n\\n    return LocalFrameWriteResult(\\n        script_id=plan.script_id,\\n        output_dir=str(target_dir),\\n        frame_paths=tuple(frame_paths),\\n        side_effects=(\\\"write_png\\\",),\\n        external_calls=(),\\n    )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\\n+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\\n@@ -3,7 +3,9 @@\\n version = \\\"0.1.0\\\"\\n description = \\\"Semi-automatic short-form video growth engine with human approval gates.\\\"\\n requires-python = \\\">=3.11\\\"\\n-dependencies = []\\n+dependencies = [\\n+    \\\"Pillow>=10.0\\\",\\n+]\\n \\n [tool.pytest.ini_options]\\n pythonpath = [\\\".\\\"]\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/pyproject.toml\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_u7CmohbggolqlliOcfHkCoQ6", "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "assistant", "content": "", "reasoning": "**Updating documentation**\n\nI think I might need to update the README. I’m considering whether to verify things manually and possibly write to the /tmp path instead of using /data. It could be easier to generate temporary files there. I also realize that I might have to update the documentation further. And hey, I wonder if running a git status might show the status of the pyproject file. I’ll have to keep an eye on that!", "finish_reason": "tool_calls", "reasoning_content": "**Updating documentation**\n\nI think I might need to update the README. I’m considering whether to verify things manually and possibly write to the /tmp path instead of using /data. It could be easier to generate temporary files there. I also realize that I might have to update the documentation further. And hey, I wonder if running a git status might show the status of the pyproject file. I’ll have to keep an eye on that!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEwmKBHRYeeYPJV6n5J0WUH7fTYBx_kOsliV3J52ROK7RGCJ_yujucYsCveY4k7aPs5qn6_DyN-2r0wnduQX7Tle7A4hNYlX8JelFGloTi9lHK7xoccAqeHXn8gUe4ZaEf21VxrfOj6hgOGu9ZfvFPP1iVXpv5dcAwboN9itDA_uoJmQnIPVtJ8Z2-GgWHC-OqoC5l19aSGy1CKleA-Th6wMUGMcGYCntxwBTkdw7oCHibeVzCUN_osjPWX4p2OJXdwN2VNUaeIg8kgoJ652ubrf_bxZWDCc8SQyE-WZ84470WP928xTaMXt4AM6NqY2TimZImB-OyoONJ-hm5_ii8534YWdex5-7BwP3ZukrM2EuZtP1xPaNR9Vnm5Y4cuICK5292ZNbMJxDVp_B3I68KESbCuKvn0G1ojxIsmzsAsNN7KJ7lMfjXOXHNTBNk2M-gp6cBIuTKQTlmpSYXYnO4ocW1Y19z8N3RSeJagFG56YvqaiDvbhYnmdjPOO1m1F-16em_e1x5Mk31cPacmwSLbuMGoASDS4MgSMopCbwHSmESafQoZuu8YrZiyShzQOfbai2X5RxGr0IIGwcnWODsvWEfFRqFjg2sjm0J5YDhQIG2vxcJGMBo_C8g3aWcQAMGGa9sci9csHS4Xyz022swFZYOlOZvkNLj0jp7FO5w1xBbWMWh8JGJKiWvN82fAGwlSf68iBOPPFYgKmLVUqQrL47i3vRJP4w6tWSHNjnYuNHN2zXeHIVzOwPgckzj_6sYumxnRtdKklHjHT0mrq9ALuhhDsRt5EYF8XlFOwts9LS1gMVqoCSXFXE6nPz7vpp1i8gBLEfTuultsuZpFl9Fl9os1_2sT58smLRSrBKclICYml_rL3vzw5k9MwQmjlUdbC1FnOGSsCW98D1NQRhgrLHtnY_Tgg4Ah9ylnwu2me9KulOlQjShEwZCCiXht9BgGiHqrSu5brXaCt0Ok531eO1QLg827U7aYz7hKrSyTbDX3Lc8YSrTxqkVNqT9C_LO-z9w9yHfTHrgDBnGJXkyD5mTDDEP3oJBcaepA9Mp4bi3Jk1RKAY4OCQKtsY9nmNvtnD5CEDxnxN6UzCOI3MY4N9ujl7zXPoyTAWHnQ1GfuKsc18KN9Vi9UTsuJ30Lyu2Arl", "id": "rs_060035eb87c40e14016a130987cae481918bc151db20674548", "summary": [{"type": "summary_text", "text": "**Updating documentation**\n\nI think I might need to update the README. I’m considering whether to verify things manually and possibly write to the /tmp path instead of using /data. It could be easier to generate temporary files there. I also realize that I might have to update the documentation further. And hey, I wonder if running a git status might show the status of the pyproject file. I’ll have to keep an eye on that!"}]}], "tool_calls": [{"id": "call_k8wxWa4VW72cfKqgK3SHNnoi", "call_id": "call_k8wxWa4VW72cfKqgK3SHNnoi", "response_item_id": "fc_060035eb87c40e14016a13098afa4c8191bebc2a30cea4ae05", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\",\"old_string\":\"- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n- `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
{"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@@ -22,6 +22,7 @@\\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 - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n - `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n - `autoshorts.rendering.preview` — side-effect-free HTML timeline previews for render manifests.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_k8wxWa4VW72cfKqgK3SHNnoi", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_JSedIkqDWd3VBob4uknCSuGv", "call_id": "call_JSedIkqDWd3VBob4uknCSuGv", "response_item_id": "fc_060035eb87c40e14016a13098ff6d081919db20790b51d6419", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n\\n`autoshorts.rendering.local_frame_writer.write_local_preview_frames()` is the first explicit artifact-writing boundary. It consumes a validated `LocalPreviewPlan` and writes deterministic PNG scene cards using Pillow. Each card uses manifest-derived frame data: scene purpose, caption, label, timing, asset mode, and motion instruction. It returns `LocalFrameWriteResult` with `side_effects=(\\\"write_png\\\",)` and `external_calls=()`.\\n\\nGenerated preview frames should stay under runtime preview directories such as `data/previews/<script_id>/` or temporary test directories. PNG media is ignored by Git.\\n\\n## Social media draft\",\"old_string\":\"`validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n\\n## Social media draft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
{"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@@ -116,6 +116,10 @@\\n \\n `validate_local_preview_plan()` rejects non-vertical preview sizes, non-positive fps, blank output paths, empty frame lists, missing frame fields, bad frame durations, non-zero first-frame start time, overlapping frames, side effects, and external tool calls. This is still a plan only: it writes no PNGs, calls no FFmpeg, and renders no MP4.\\n \\n+`autoshorts.rendering.local_frame_writer.write_local_preview_frames()` is the first explicit artifact-writing boundary. It consumes a validated `LocalPreviewPlan` and writes deterministic PNG scene cards using Pillow. Each card uses manifest-derived frame data: scene purpose, caption, label, timing, asset mode, and motion instruction. It returns `LocalFrameWriteResult` with `side_effects=(\\\"write_png\\\",)` and `external_calls=()`.\\n+\\n+Generated preview frames should stay under runtime preview directories such as `data/previews/<script_id>/` or temporary test directories. PNG media is ignored by Git.\\n+\\n ## Social media draft\\n \\n `autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_JSedIkqDWd3VBob4uknCSuGv", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_EDmYdhXOlwpLvEdA6W6Y2dWm", "call_id": "call_EDmYdhXOlwpLvEdA6W6Y2dWm", "response_item_id": "fc_060035eb87c40e14016a13099bcd148191a9eac48e757d8ad1", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"Run tests:\\n\\n```bash\\npython -m pytest tests -q\\n```\\n\\nRuntime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is not used yet.\\n\\n## Planned architecture\",\"old_string\":\"Run tests:\\n\\n```bash\\npython -m pytest tests -q\\n```\\n\\n## Planned architecture\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
{"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@@ -289,6 +289,8 @@\\n python -m pytest tests -q\\n ```\\n \\n+Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is not used yet.\\n+\\n ## Planned architecture\\n \\n ```text\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_EDmYdhXOlwpLvEdA6W6Y2dWm", "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_aKyfaU9ybscz1DtHZ6ADSzrF", "call_id": "call_aKyfaU9ybscz1DtHZ6ADSzrF", "response_item_id": "fc_060035eb87c40e14016a1309a212848191b439ec69128e3356", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom pathlib import Path\\nfrom tempfile import TemporaryDirectory\\nfrom PIL import Image\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\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\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\nplan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\nwith TemporaryDirectory() as tmp:\\n    result = write_local_preview_frames(plan, output_dir=tmp)\\n    first = Path(result.frame_paths[0])\\n    with Image.open(first) as img:\\n        print(result.script_id)\\n        print(len(result.frame_paths))\\n        print(first.name)\\n        print(img.format, img.size)\\n        print(result.side_effects, result.external_calls)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "assistant", "content": "", "reasoning": "**Checking git status**\n\nI need to get my commit ready, but first, it’s probably a good idea to check the git status. This way, I’ll see what changes I’ve made and what files are staged. It’ll help ensure I’m not missing anything important before I proceed. I want to make sure everything is set up correctly, so let's go ahead and see what’s currently happening in the repository!", "finish_reason": "tool_calls", "reasoning_content": "**Checking git status**\n\nI need to get my commit ready, but first, it’s probably a good idea to check the git status. This way, I’ll see what changes I’ve made and what files are staged. It’ll help ensure I’m not missing anything important before I proceed. I want to make sure everything is set up correctly, so let's go ahead and see what’s currently happening in the repository!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEwmzV4YR_lQYhK1UodYjFXpNUUo2-jz0d5kml6nvD55TALG9VD7BIF1_PYcrVBylidwBdk458GrVBZbSPWRWMOn6w1eP2-AtgE5jt4q9YlAdhM3xLkGjUzCRJl8CdS82w0eLepCr7qUwQL_0YS40FLAb7tXU3pxiTdYpeeLyLlcujqHr5Jc_OIxrM5fHfC7XZtOyh1btc-3JPzBjekmYi0u60-YQ5tnGXJgl5RdPpT4jzwk-a1IMQ2XkM4TpfdYyFr5z6WU7DvJKCp254fLvRRLQf26P8raGxNPnE45b_DXAm3yCuvENrueH66X4eq5_RHxrNNmKLyH7IkS3BvgCzHkpHFXKeD90gPJYh6_BdSnWn3Ri1iPqc9gJl7TLh9ZmvH8vR3umHUgQovMeRmLHizt702qXeOtLn8Ojj5sDuiiJrQjewvu-vPvhD6Hp4lWEjzV38XdDpd0ge5dbrIovw3sFEYraa2ckAd_DjLN8KGzCgcitwyMrNNFctA5b0ybsSGcjW0U6F-yhwn8ADFshY7f4Y_x1e4-vXWoReQibwXEd2yln8xvmv6jvyM2V6GRDXdaCA06SzOMDVaZhekq5FGkrfif4Fsrt337CKvvGgXmY1VqkiqwRROr2ntLYHdueqcX3mv3y70HHSz0zum_TU3qSKwWuIQWL5JrQCTQgb1AAIqZMJs8N3JAdp5AZ6bxXQUlgPV9lNrli4DJJ3c_PPLcdzZnN8A0XoFTcYa97WtmH90A3O9OejQRyjtR9rDbzrvtDurYDDJo_xhSYynX1-xFUgWkOALxjxJ_BbO3FWd77UrUbt6cni1io1JQ7RXzjqNIfaeijWmFBrLhy3ZC7vamH8vUYWLIWB8_arwSM_VfEuHPqRISIJIeBfLf3lkCdABkHujN3Mfa-itKbZrTebgb4LUocMkfsYLRyqz7u7kPxdniJr8aZsdqqd1P_C105WVcboxfh4ZyFf0XLyqFZoeixZA==", "id": "rs_060035eb87c40e14016a1309b104e88191809dd4efa5fae406", "summary": [{"type": "summary_text", "text": "**Checking git status**\n\nI need to get my commit ready, but first, it’s probably a good idea to check the git status. This way, I’ll see what changes I’ve made and what files are staged. It’ll help ensure I’m not missing anything important before I proceed. I want to make sure everything is set up correctly, so let's go ahead and see what’s currently happening in the repository!"}]}], "tool_calls": [{"id": "call_W8Qh37NPSRlib6zbTEZyvEgE", "call_id": "call_W8Qh37NPSRlib6zbTEZyvEgE", "response_item_id": "fc_060035eb87c40e14016a1309b3b9cc819199a3637399cee146", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md pyproject.toml autoshorts/rendering/local_frame_writer.py tests/test_local_frame_writer.py && git commit -m \\\"feat: write local preview frame cards\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:23:06.439264"}
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{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Local Preview Frame Writer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/local_frame_writer.py\ntests/test_local_frame_writer.py\n```\n\nGeändert:\n\n```text\npyproject.toml\nREADME.md\n```\n\n## Neue API\n\n```python\nwrite_local_preview_frames(plan: LocalPreviewPlan, *, output_dir: str | Path | None = None) -> LocalFrameWriteResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n```\n\nDas ist der erste explizite Artefakt-Schritt: hier wird wirklich geschrieben — aber nur PNG Scene Cards, keine MP4, kein FFmpeg, kein ComfyUI, kein Qwen/Wan, kein Telegram.\n\n## LocalFrameWriteResult\n\n```python\nLocalFrameWriteResult(\n    script_id=...,\n    output_dir=...,\n    frame_paths=(...),\n    side_effects=(\"write_png\",),\n    external_calls=(),\n)\n```\n\nAlso klar markiert: **dieser Boundary schreibt Dateien**, ruft aber nichts Externes auf.\n\n## Was die PNG Scene Cards enthalten\n\nPro `LocalPreviewFrame` wird eine vertikale PNG-Karte erzeugt mit:\n\n- Scene Purpose\n- Caption\n- Timing\n- Asset Mode\n- Label\n- Motion Instruction\n\nFormat:\n\n```text\n540x960\nPNG\n```\n\nBeispiel verifiziert:\n\n```text\nframe-01-first_frame_hook.png\nPNG (540, 960)\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n5\nframe-01-first_frame_hook.png\nPNG (540, 960)\n('write_png',) ()\n```\n\nBedeutung:\n\n- Script korrekt\n- 5 Scene Cards geschrieben\n- erste Datei korrekt benannt\n- PNG valide\n- Dimensionen korrekt\n- Side Effects: nur `write_png`\n- keine External Calls\n\n## Guardrails\n\nTests prüfen:\n\n- PNG-Dateien existieren\n- PNG-Dateien sind non-empty\n- Pillow kann sie öffnen\n- Dimensionen stimmen\n- Frame-Reihenfolge bleibt stabil\n- ungültiger PreviewPlan wird rejected\n- unsaubere Output-Pfade mit `..` werden rejected\n\n## Dependency\n\n`pyproject.toml` enthält jetzt:\n\n```toml\ndependencies = [\n    \"Pillow>=10.0\",\n]\n```\n\nPillow ist nötig für deterministische lokale PNG Preview Frames.\n\n## Tests\n\nRED war wie gewünscht:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.local_frame_writer'\n```\n\nGREEN:\n\n```text\n4 passed\n```\n\nFull suite:\n\n```text\n101 passed in 0.37s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.local_frame_writer`\n- PNG Scene Card Boundary\n- Side Effects bewusst `(\"write_png\",)`\n- keine externen Calls\n- PNGs bleiben runtime/generated media und sind durch `.gitignore` abgedeckt\n- Pillow als Runtime Dependency\n- FFmpeg noch nicht genutzt\n\n## Commit\n\n```text\n7e881b3 feat: write local preview frame cards\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt:\n\n```text\nPNG scene cards\n→ FFmpeg concat plan\n```\n\nNoch **nicht** direkt MP4 rendern. Erst bauen wir den planbaren FFmpeg-Layer:\n\n```text\nautoshorts/rendering/ffmpeg_plan.py\ntests/test_ffmpeg_plan.py\n```\n\nZiel:\n\n- aus `LocalFrameWriteResult + LocalPreviewPlan`\n- erzeugt ein `FFmpegConcatPlan`\n- enthält:\n  - frame paths\n  - per-frame durations\n  - concat file content\n  - intended output path\n  - fps\n  - width/height\n- Side Effects:\n  - erstmal `()`\n- External Calls:\n  - erstmal `()`\n- validiert:\n  - gleiche Anzahl Frames wie Plan\n  - alle Pfade `.png`\n  - durations > 0\n  - output `.mp4`\n  - keine echten FFmpeg Calls\n\nDanach erst:\n\n```text\nFFmpegConcatPlan\n→ write concat file\n→ call ffmpeg\n→ MP4 timing preview\n```\n\nEin Schritt nach dem anderen. Sonst sind wir schneller bei “warum liegt da ein kaputtes 900MB Video im Repo?” als bei “gutes Short”.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Local Preview Frame Writer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/local_frame_writer.py\ntests/test_local_frame_writer.py\n```\n\nGeändert:\n\n```text\npyproject.toml\nREADME.md\n```\n\n## Neue API\n\n```python\nwrite_local_preview_frames(plan: LocalPreviewPlan, *, output_dir: str | Path | None = None) -> LocalFrameWriteResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n```\n\nDas ist der erste explizite Artefakt-Schritt: hier wird wirklich geschrieben — aber nur PNG Scene Cards, keine MP4, kein FFmpeg, kein ComfyUI, kein Qwen/Wan, kein Telegram.\n\n## LocalFrameWriteResult\n\n```python\nLocalFrameWriteResult(\n    script_id=...,\n    output_dir=...,\n    frame_paths=(...),\n    side_effects=(\"write_png\",),\n    external_calls=(),\n)\n```\n\nAlso klar markiert: **dieser Boundary schreibt Dateien**, ruft aber nichts Externes auf.\n\n## Was die PNG Scene Cards enthalten\n\nPro `LocalPreviewFrame` wird eine vertikale PNG-Karte erzeugt mit:\n\n- Scene Purpose\n- Caption\n- Timing\n- Asset Mode\n- Label\n- Motion Instruction\n\nFormat:\n\n```text\n540x960\nPNG\n```\n\nBeispiel verifiziert:\n\n```text\nframe-01-first_frame_hook.png\nPNG (540, 960)\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\n5\nframe-01-first_frame_hook.png\nPNG (540, 960)\n('write_png',) ()\n```\n\nBedeutung:\n\n- Script korrekt\n- 5 Scene Cards geschrieben\n- erste Datei korrekt benannt\n- PNG valide\n- Dimensionen korrekt\n- Side Effects: nur `write_png`\n- keine External Calls\n\n## Guardrails\n\nTests prüfen:\n\n- PNG-Dateien existieren\n- PNG-Dateien sind non-empty\n- Pillow kann sie öffnen\n- Dimensionen stimmen\n- Frame-Reihenfolge bleibt stabil\n- ungültiger PreviewPlan wird rejected\n- unsaubere Output-Pfade mit `..` werden rejected\n\n## Dependency\n\n`pyproject.toml` enthält jetzt:\n\n```toml\ndependencies = [\n    \"Pillow>=10.0\",\n]\n```\n\nPillow ist nötig für deterministische lokale PNG Preview Frames.\n\n## Tests\n\nRED war wie gewünscht:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.local_frame_writer'\n```\n\nGREEN:\n\n```text\n4 passed\n```\n\nFull suite:\n\n```text\n101 passed in 0.37s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.local_frame_writer`\n- PNG Scene Card Boundary\n- Side Effects bewusst `(\"write_png\",)`\n- keine externen Calls\n- PNGs bleiben runtime/generated media und sind durch `.gitignore` abgedeckt\n- Pillow als Runtime Dependency\n- FFmpeg noch nicht genutzt\n\n## Commit\n\n```text\n7e881b3 feat: write local preview frame cards\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt:\n\n```text\nPNG scene cards\n→ FFmpeg concat plan\n```\n\nNoch **nicht** direkt MP4 rendern. Erst bauen wir den planbaren FFmpeg-Layer:\n\n```text\nautoshorts/rendering/ffmpeg_plan.py\ntests/test_ffmpeg_plan.py\n```\n\nZiel:\n\n- aus `LocalFrameWriteResult + LocalPreviewPlan`\n- erzeugt ein `FFmpegConcatPlan`\n- enthält:\n  - frame paths\n  - per-frame durations\n  - concat file content\n  - intended output path\n  - fps\n  - width/height\n- Side Effects:\n  - erstmal `()`\n- External Calls:\n  - erstmal `()`\n- validiert:\n  - gleiche Anzahl Frames wie Plan\n  - alle Pfade `.png`\n  - durations > 0\n  - output `.mp4`\n  - keine echten FFmpeg Calls\n\nDanach erst:\n\n```text\nFFmpegConcatPlan\n→ write concat file\n→ call ffmpeg\n→ MP4 timing preview\n```\n\nEin Schritt nach dem anderen. Sonst sind wir schneller bei “warum liegt da ein kaputtes 900MB Video im Repo?” als bei “gutes Short”."}], "id": "msg_060035eb87c40e14016a1309b8e1508191b78a7908b81863d1", "phase": "final_answer"}], "timestamp": "2026-05-24T16:23:06.439264"}
{"role": "assistant", "content": "", "reasoning": "**Planning implementation steps**\n\nI need to take action here. First, I should load the relevant skills that I’ll need. After that, I'll implement the ffmpeg_plan using test-driven development (TDD). It’s essential to follow through on these steps systematically to ensure everything works smoothly. I'll stay focused on getting this done effectively, and that involves making sure I don’t skip any steps along the way! Let’s get to it!", "finish_reason": "tool_calls", "reasoning_content": "**Planning implementation steps**\n\nI need to take action here. First, I should load the relevant skills that I’ll need. After that, I'll implement the ffmpeg_plan using test-driven development (TDD). It’s essential to follow through on these steps systematically to ensure everything works smoothly. I'll stay focused on getting this done effectively, and that involves making sure I don’t skip any steps along the way! Let’s get to it!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEw1djtm867DBWaRKJRLK-3o-UtzuxH6dOLk1MLXo-FPJpq8as2cLIHvPGdPDiIH9MZ0xXHuP1yv-cR1Rx7KnQoZi79oQmLj8TAEyM3WO_55Z2lGkidboctfY_On7T-iFh-EIh542S5ttcs7ReQ8dGiMBEvgAaDSnx3LTrKPIfSR8nALRw8y2kXg2tfW4e5azaGgg3y4GIPFojIIR5WouHCEmdP-To_1jlCFcD2bVlGeO6vCePx2bLbWpq1SwmfVG_qPYqOxnqXOAgKMvO3M_9ELd9-gzcqjgraGiDD_9s6Y6C-I3cE8NriTE8EOEnrAlMkfGrVzoZZJ4UJrStlEotXrMY5P8ZfJHBEB9NWX0gAWotzT_qN1NGmbNXSSaZVxG44U46aMq8Qwxl_kBNIuN3j5bbYIJmNGktlkApCO0yy60QRLKfwc_zDV37HNznp6ebCIG-jCkGsg2_faXocig5tnWzw7svAmfvI2LNgMLR-O2XhzeRek6VRbBicJNEAx7yuy_G0d36JmyQ_y28Tc3UltcmZqdnaxtoe21ZDyk0ccH4xZnEBJLMRMhKE3XWIQ1uZ6FtCnH7KOGobO20SKnZDPQn-rZ8evgJbsa0_MynY1LMXfiZFI6DSzjyTAVSx-uwyOH2lmV7DGqTf4PVmRd1D3fdjAhjprvnVLV-7BrJyJj3ybxg7UEVtBWAIuGDdPVNn_lkoZwSgUwPD9oFdDUvKBJlyRaAwphG3XKBlBu-IEciTFxkeLpu8Z6deRLKmV8MJIPXDr3ElvdhxodP8vaOk5JpN0pz7V5jA9ebEqqjfM4uqM3sIsVifZHNxpZom6EjGgsU70P2aUQvwYTYGHP4OiUsDdb6X0zvmjMwLLEj_9XEaNUeKXDG9RSdxKx70E0kh4of9LX6vLjcdUIQBpSCaSEop_YeyfGE6_ljViXJUTGzU2GbrVMGMjzWxxqybuRyKHwiHyfs9unqBKAo338eT3cjUoH0XLwA30Jn9dlfFQMhc49CvGUAJkTBPwHBdgs-hM-", "id": "rs_060035eb87c40e14016a130d5afb4481919f6d703f7869de1c", "summary": [{"type": "summary_text", "text": "**Planning implementation steps**\n\nI need to take action here. First, I should load the relevant skills that I’ll need. After that, I'll implement the ffmpeg_plan using test-driven development (TDD). It’s essential to follow through on these steps systematically to ensure everything works smoothly. I'll stay focused on getting this done effectively, and that involves making sure I don’t skip any steps along the way! Let’s get to it!"}]}], "tool_calls": [{"id": "call_jzHoQLCPl1dgtwWy6XGddu1X", "call_id": "call_jzHoQLCPl1dgtwWy6XGddu1X", "response_item_id": "fc_060035eb87c40e14016a130d5d20288191a8553b8d8b5da4be", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_1NzfzJiatgEqBYhWwcfuArB6", "call_id": "call_1NzfzJiatgEqBYhWwcfuArB6", "response_item_id": "fc_060035eb87c40e14016a130d5d204081918308e7810e84eefa", "type": "function", "function": {"name": "skill_view", "arguments": "{\"file_path\":\"references/manifest-preview-renderer.md\",\"name\":\"social-media-shorts-automation\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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_jzHoQLCPl1dgtwWy6XGddu1X", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/manifest-preview-renderer.md\", \"content\": \"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)`\\n  - one representative image per visual timeline item\\n  - uses the manifest text, asset type, timing, and retention goal\\n- `write_frame_images(frames, output_dir)`\\n  - writes numbered PNGs for inspection/debugging\\n- `render_preview_video(manifest, output_path, width, height, fps)`\\n  - renders frames to temporary PNGs\\n  - writes an FFmpeg concat file with per-scene durations\\n  - exports `yuv420p` MP4 with `+faststart`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n  data/render_manifests/<batch>/<video>.json \\\\\\n  --out-dir data/previews/<batch> \\\\\\n  --width 540 \\\\\\n  --height 960 \\\\\\n  --fps 24\\n```\\n\\nUse reduced preview resolution like `540x960` for fast local review. Keep final `1080x1920` as a later renderer-quality target.\\n\\n## Repo hygiene\\n\\n- Keep generated MP4/PNG media out of Git via `.gitignore`.\\n- Commit renderer code, tests, scripts, manifests, and lightweight planning artifacts.\\n- Do not commit rendered previews unless explicitly needed as small fixtures.\\n- Add runtime dependencies explicitly, e.g. `Pillow>=10.0`; treat FFmpeg as an external system prerequisite checked at runtime.\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset supplier.\\n- Do not over-polish the first renderer. Its job is approval/timing validation.\\n- Do not publish from this step. Preview rendering still requires human approval before posting.\\n- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal readability before scaling to more videos.\", \"file_type\": \".md\"}", "tool_call_id": "call_1NzfzJiatgEqBYhWwcfuArB6", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "Implement. Need inspect current.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Implement. Need inspect current."}], "id": "msg_060035eb87c40e14016a130d6202cc8191ab35931182a32739", "phase": "commentary"}], "tool_calls": [{"id": "call_BTzSRk9K0pz61a1UTBomjOho", "call_id": "call_BTzSRk9K0pz61a1UTBomjOho", "response_item_id": "fc_060035eb87c40e14016a130d64fb7881919aafdf865d7ab596", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}, {"id": "call_ULzARYZp8Jc72JwoE3mcgmuZ", "call_id": "call_ULzARYZp8Jc72JwoE3mcgmuZ", "response_item_id": "fc_060035eb87c40e14016a130d64fcec8191950ad4cd3059ff2a", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_preview.py\",\"limit\":240}"}}, {"id": "call_0nf4nQj4VEfvEDqoYLAr9cnB", "call_id": "call_0nf4nQj4VEfvEDqoYLAr9cnB", "response_item_id": "fc_060035eb87c40e14016a130d64fd00819196a413ea29d126d5", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py\",\"limit\":240}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_BTzSRk9K0pz61a1UTBomjOho", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Side-effect-free local preview planning from render manifests.\\\"\\\"\\\"\\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|from autoshorts.rendering.manifest import RenderManifest, validate_render_manifest\\n     9|\\n    10|\\n    11|@dataclass(frozen=True)\\n    12|class LocalPreviewFrame:\\n    13|    frame_id: str\\n    14|    scene_purpose: str\\n    15|    start_ms: int\\n    16|    duration_ms: int\\n    17|    caption_text: str\\n    18|    label_text: str\\n    19|    asset_mode: str\\n    20|    motion_instruction: str\\n    21|    placeholder_image_path: str\\n    22|\\n    23|\\n    24|@dataclass(frozen=True)\\n    25|class LocalPreviewPlan:\\n    26|    script_id: str\\n    27|    title: str\\n    28|    width: int\\n    29|    height: int\\n    30|    fps: int\\n    31|    output_dir: str\\n    32|    frames: tuple[LocalPreviewFrame, ...]\\n    33|    side_effects: tuple[str, ...] = ()\\n    34|    external_calls: tuple[str, ...] = ()\\n    35|\\n    36|\\n    37|def _is_blank(value: str) -> bool:\\n    38|    return not (value or \\\"\\\").strip()\\n    39|\\n    40|\\n    41|def _frame_id(index: int, purpose: str) -> str:\\n    42|    return f\\\"frame-{index:02d}-{purpose}\\\"\\n    43|\\n    44|\\n    45|def build_local_preview_plan(\\n    46|    manifest: RenderManifest,\\n    47|    *,\\n    48|    width: int = 540,\\n    49|    height: int = 960,\\n    50|    fps: int = 24,\\n    51|) -> LocalPreviewPlan:\\n    52|    \\\"\\\"\\\"Build a deterministic frame plan without writing images or calling FFmpeg.\\\"\\\"\\\"\\n    53|\\n    54|    manifest_validation = validate_render_manifest(manifest)\\n    55|    if not manifest_validation.is_valid:\\n    56|        raise ValueError(\\\"; \\\".join(manifest_validation.errors))\\n    57|\\n    58|    output_dir = f\\\"data/previews/{manifest.script_id}\\\"\\n    59|    frames = tuple(\\n    60|        LocalPreviewFrame(\\n    61|            frame_id=_frame_id(index, item.scene_purpose),\\n    62|            scene_purpose=item.scene_purpose,\\n    63|            start_ms=item.start_seconds * 1000,\\n    64|            duration_ms=(item.end_seconds - item.start_seconds) * 1000,\\n    65|            caption_text=item.caption_layer.text,\\n    66|            label_text=item.label_layer.text,\\n    67|            asset_mode=item.asset_mode,\\n    68|            motion_instruction=item.motion_instruction,\\n    69|            placeholder_image_path=f\\\"{output_dir}/{_frame_id(index, item.scene_purpose)}.png\\\",\\n    70|        )\\n    71|        for index, item in enumerate(manifest.timeline, start=1)\\n    72|    )\\n    73|    plan = LocalPreviewPlan(\\n    74|        script_id=manifest.script_id,\\n    75|        title=manifest.title,\\n    76|        width=width,\\n    77|        height=height,\\n    78|        fps=fps,\\n    79|        output_dir=output_dir,\\n    80|        frames=frames,\\n    81|        side_effects=(),\\n    82|        external_calls=(),\\n    83|    )\\n    84|    validation = validate_local_preview_plan(plan)\\n    85|    if not validation.is_valid:\\n    86|        raise ValueError(\\\"; \\\".join(validation.errors))\\n    87|    return plan\\n    88|\\n    89|\\n    90|def validate_local_preview_plan(plan: LocalPreviewPlan) -> ValidationResult:\\n    91|    errors: list[str] = []\\n    92|    if _is_blank(plan.script_id):\\n    93|        errors.append(\\\"script_id is required\\\")\\n    94|    if _is_blank(plan.title):\\n    95|        errors.append(\\\"title is required\\\")\\n    96|    if plan.width <= 0 or plan.height <= 0 or plan.height <= plan.width:\\n    97|        errors.append(\\\"preview size must be vertical\\\")\\n    98|    if plan.fps <= 0:\\n    99|        errors.append(\\\"fps must be positive\\\")\\n   100|    if _is_blank(plan.output_dir):\\n   101|        errors.append(\\\"output_dir is required\\\")\\n   102|    if plan.side_effects:\\n   103|        errors.append(\\\"preview plan must not have side effects\\\")\\n   104|    if plan.external_calls:\\n   105|        errors.append(\\\"preview plan must not call external tools\\\")\\n   106|    if not plan.frames:\\n   107|        errors.append(\\\"at least one preview frame is required\\\")\\n   108|\\n   109|    previous_end: int | None = None\\n   110|    for index, frame in enumerate(plan.frames, start=1):\\n   111|        for field_name, value in {\\n   112|            \\\"frame_id\\\": frame.frame_id,\\n   113|            \\\"scene_purpose\\\": frame.scene_purpose,\\n   114|            \\\"caption_text\\\": frame.caption_text,\\n   115|            \\\"label_text\\\": frame.label_text,\\n   116|            \\\"asset_mode\\\": frame.asset_mode,\\n   117|            \\\"motion_instruction\\\": frame.motion_instruction,\\n   118|            \\\"placeholder_image_path\\\": frame.placeholder_image_path,\\n   119|        }.items():\\n   120|            if _is_blank(value):\\n   121|                errors.append(f\\\"frame {index} {field_name} is required\\\")\\n   122|        if frame.duration_ms <= 0:\\n   123|            errors.append(f\\\"frame {index} duration_ms must be positive\\\")\\n   124|        if index == 1 and frame.start_ms != 0:\\n   125|            errors.append(\\\"first frame must start at 0ms\\\")\\n   126|        if previous_end is not None and frame.start_ms < previous_end:\\n   127|            errors.append(f\\\"frame {index} start_ms overlaps previous frame\\\")\\n   128|        previous_end = frame.start_ms + frame.duration_ms\\n   129|\\n   130|    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n   131|\", \"total_lines\": 130, \"file_size\": 4410, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_ULzARYZp8Jc72JwoE3mcgmuZ", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "read_file", "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|\\n     9|from PIL import Image, ImageDraw, ImageFont\\n    10|\\n    11|from autoshorts.rendering.local_preview import LocalPreviewPlan, validate_local_preview_plan\\n    12|\\n    13|\\n    14|@dataclass(frozen=True)\\n    15|class LocalFrameWriteResult:\\n    16|    script_id: str\\n    17|    output_dir: str\\n    18|    frame_paths: tuple[str, ...]\\n    19|    side_effects: tuple[str, ...] = (\\\"write_png\\\",)\\n    20|    external_calls: tuple[str, ...] = ()\\n    21|\\n    22|\\n    23|def _validate_output_dir(output_dir: str | Path) -> Path:\\n    24|    path = Path(output_dir)\\n    25|    if not str(path).strip() or \\\"..\\\" in path.parts:\\n    26|        raise ValueError(\\\"output_dir must be explicit and normalized\\\")\\n    27|    return path\\n    28|\\n    29|\\n    30|def _font(size: int):\\n    31|    try:\\n    32|        return ImageFont.truetype(\\\"DejaVuSans.ttf\\\", size=size)\\n    33|    except OSError:\\n    34|        return ImageFont.load_default()\\n    35|\\n    36|\\n    37|def _wrapped(text: str, width: int) -> str:\\n    38|    lines: list[str] = []\\n    39|    for paragraph in text.splitlines() or [text]:\\n    40|        lines.extend(wrap(paragraph, width=width) or [\\\"\\\"])\\n    41|    return \\\"\\\\n\\\".join(lines)\\n    42|\\n    43|\\n    44|def _palette(asset_mode: str) -> tuple[tuple[int, int, int], tuple[int, int, int]]:\\n    45|    palettes = {\\n    46|        \\\"kinetic_text_overlay\\\": ((15, 23, 42), (96, 165, 250)),\\n    47|        \\\"mechanism_animation\\\": ((17, 24, 39), (52, 211, 153)),\\n    48|        \\\"screen_recording_simulation\\\": ((24, 24, 27), (250, 204, 21)),\\n    49|        \\\"cinematic_ai_video\\\": ((28, 25, 23), (251, 146, 60)),\\n    50|    }\\n    51|    return palettes.get(asset_mode, ((17, 24, 39), (148, 163, 184)))\\n    52|\\n    53|\\n    54|def _draw_card(plan: LocalPreviewPlan, frame) -> Image.Image:\\n    55|    background, accent = _palette(frame.asset_mode)\\n    56|    image = Image.new(\\\"RGB\\\", (plan.width, plan.height), background)\\n    57|    draw = ImageDraw.Draw(image)\\n    58|\\n    59|    margin = max(24, plan.width // 18)\\n    60|    title_font = _font(max(26, plan.width // 13))\\n    61|    body_font = _font(max(18, plan.width // 24))\\n    62|    small_font = _font(max(14, plan.width // 34))\\n    63|\\n    64|    draw.rectangle((0, 0, plan.width, 18), fill=accent)\\n    65|    draw.text((margin, margin), frame.scene_purpose.replace(\\\"_\\\", \\\" \\\").upper(), fill=accent, font=small_font)\\n    66|    draw.text((margin, margin + 38), _wrapped(frame.caption_text, 24), fill=(248, 250, 252), font=title_font, spacing=8)\\n    67|\\n    68|    info_top = plan.height // 2\\n    69|    draw.rounded_rectangle(\\n    70|        (margin, info_top, plan.width - margin, plan.height - margin),\\n    71|        radius=18,\\n    72|        outline=accent,\\n    73|        width=2,\\n    74|        fill=(background[0] + 8, background[1] + 8, background[2] + 8),\\n    75|    )\\n    76|    body = (\\n    77|        f\\\"Timing: {frame.start_ms}ms + {frame.duration_ms}ms\\\\n\\\"\\n    78|        f\\\"Asset: {frame.asset_mode}\\\\n\\\"\\n    79|        f\\\"Label: {frame.label_text}\\\\n\\\\n\\\"\\n    80|        f\\\"Motion: {frame.motion_instruction}\\\"\\n    81|    )\\n    82|    draw.multiline_text((margin + 18, info_top + 18), _wrapped(body, 42), fill=(226, 232, 240), font=body_font, spacing=6)\\n    83|    return image\\n    84|\\n    85|\\n    86|def write_local_preview_frames(\\n    87|    plan: LocalPreviewPlan,\\n    88|    *,\\n    89|    output_dir: str | Path | None = None,\\n    90|) -> LocalFrameWriteResult:\\n    91|    \\\"\\\"\\\"Write deterministic PNG scene cards for a validated local preview plan.\\\"\\\"\\\"\\n    92|\\n    93|    validation = validate_local_preview_plan(plan)\\n    94|    if not validation.is_valid:\\n    95|        raise ValueError(\\\"; \\\".join(validation.errors))\\n    96|\\n    97|    target_dir = _validate_output_dir(output_dir or plan.output_dir)\\n    98|    target_dir.mkdir(parents=True, exist_ok=True)\\n    99|\\n   100|    frame_paths: list[str] = []\\n   101|    for frame in plan.frames:\\n   102|        path = target_dir / f\\\"{frame.frame_id}.png\\\"\\n   103|        image = _draw_card(plan, frame)\\n   104|        image.save(path, format=\\\"PNG\\\")\\n   105|        frame_paths.append(str(path))\\n   106|\\n   107|    return LocalFrameWriteResult(\\n   108|        script_id=plan.script_id,\\n   109|        output_dir=str(target_dir),\\n   110|        frame_paths=tuple(frame_paths),\\n   111|        side_effects=(\\\"write_png\\\",),\\n   112|        external_calls=(),\\n   113|    )\\n   114|\", \"total_lines\": 113, \"file_size\": 3806, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_0nf4nQj4VEfvEDqoYLAr9cnB", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "Write tests. Need maybe create temp frames using writer. Use ffmpeg_plan module.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Write tests. Need maybe create temp frames using writer. Use ffmpeg_plan module."}], "id": "msg_060035eb87c40e14016a130d6c36888191be91b0d44a1dac72", "phase": "commentary"}], "tool_calls": [{"id": "call_10qJ9nPqf44lC1MEmAaX6wan", "call_id": "call_10qJ9nPqf44lC1MEmAaX6wan", "response_item_id": "fc_060035eb87c40e14016a130d6ccccc8191bb7ec2c08e1a3e8d", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_plan.py\",\"content\":\"from pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.ffmpeg_plan import FFmpegConcatPlan, build_ffmpeg_concat_plan, validate_ffmpeg_concat_plan\\nfrom autoshorts.rendering.local_frame_writer import LocalFrameWriteResult, write_local_preview_frames\\nfrom autoshorts.rendering.local_preview import build_local_preview_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _preview_plan_and_frames(tmp_path):\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    preview_plan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\n    write_result = write_local_preview_frames(preview_plan, output_dir=tmp_path)\\n    return preview_plan, write_result\\n\\n\\ndef test_build_ffmpeg_concat_plan_creates_side_effect_free_concat_plan(tmp_path):\\n    preview_plan, write_result = _preview_plan_and_frames(tmp_path)\\n\\n    plan = build_ffmpeg_concat_plan(preview_plan, write_result, output_path=tmp_path / \\\"preview.mp4\\\")\\n\\n    assert isinstance(plan, FFmpegConcatPlan)\\n    assert plan.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert plan.output_path == str(tmp_path / \\\"preview.mp4\\\")\\n    assert plan.concat_file_path == str(tmp_path / \\\"preview.concat.txt\\\")\\n    assert plan.width == 540\\n    assert plan.height == 960\\n    assert plan.fps == 24\\n    assert plan.side_effects == ()\\n    assert plan.external_calls == ()\\n    assert len(plan.entries) == len(preview_plan.frames)\\n    assert plan.entries[0].image_path.endswith(\\\"frame-01-first_frame_hook.png\\\")\\n    assert plan.entries[0].duration_seconds == 3.0\\n    assert plan.entries[-1].duration_seconds == 5.0\\n    assert \\\"file '\\\" in plan.concat_file_content\\n    assert \\\"duration 3.000\\\" in plan.concat_file_content\\n    assert plan.concat_file_content.strip().endswith(\\\"frame-05-cta.png'\\\")\\n\\n\\ndef test_validate_ffmpeg_concat_plan_accepts_default_plan(tmp_path):\\n    preview_plan, write_result = _preview_plan_and_frames(tmp_path)\\n\\n    result = validate_ffmpeg_concat_plan(\\n        build_ffmpeg_concat_plan(preview_plan, write_result, output_path=tmp_path / \\\"preview.mp4\\\")\\n    )\\n\\n    assert result.is_valid is True\\n    assert result.errors == ()\\n\\n\\ndef test_build_ffmpeg_concat_plan_rejects_mismatched_frame_count(tmp_path):\\n    preview_plan, write_result = _preview_plan_and_frames(tmp_path)\\n    mismatched = LocalFrameWriteResult(\\n        script_id=write_result.script_id,\\n        output_dir=write_result.output_dir,\\n        frame_paths=write_result.frame_paths[:-1],\\n        side_effects=write_result.side_effects,\\n        external_calls=write_result.external_calls,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"frame count must match preview plan\\\"):\\n        build_ffmpeg_concat_plan(preview_plan, mismatched, output_path=tmp_path / \\\"preview.mp4\\\")\\n\\n\\ndef test_build_ffmpeg_concat_plan_rejects_non_png_frame_paths(tmp_path):\\n    preview_plan, write_result = _preview_plan_and_frames(tmp_path)\\n    bad = LocalFrameWriteResult(\\n        script_id=write_result.script_id,\\n        output_dir=write_result.output_dir,\\n        frame_paths=(str(Path(write_result.frame_paths[0]).with_suffix(\\\".jpg\\\")),) + write_result.frame_paths[1:],\\n        side_effects=write_result.side_effects,\\n        external_calls=write_result.external_calls,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"frame paths must be png files\\\"):\\n        build_ffmpeg_concat_plan(preview_plan, bad, output_path=tmp_path / \\\"preview.mp4\\\")\\n\\n\\ndef test_validate_ffmpeg_concat_plan_rejects_bad_output_and_external_calls(tmp_path):\\n    preview_plan, write_result = _preview_plan_and_frames(tmp_path)\\n    plan = build_ffmpeg_concat_plan(preview_plan, write_result, output_path=tmp_path / \\\"preview.mp4\\\")\\n    unsafe = FFmpegConcatPlan(\\n        script_id=plan.script_id,\\n        output_path=str(tmp_path / \\\"preview.mov\\\"),\\n        concat_file_path=plan.concat_file_path,\\n        width=0,\\n        height=plan.height,\\n        fps=0,\\n        entries=plan.entries,\\n        concat_file_content=plan.concat_file_content,\\n        side_effects=(\\\"write_concat\\\",),\\n        external_calls=(\\\"ffmpeg\\\",),\\n    )\\n\\n    result = validate_ffmpeg_concat_plan(unsafe)\\n\\n    assert result.is_valid is False\\n    assert \\\"output_path must end with .mp4\\\" in result.errors\\n    assert \\\"width and height must be positive\\\" in result.errors\\n    assert \\\"fps must be positive\\\" in result.errors\\n    assert \\\"ffmpeg concat plans must not have side effects\\\" in result.errors\\n    assert \\\"ffmpeg concat plans must not call external tools\\\" in result.errors\\n\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 4913, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_plan.py\\\">\\nERROR [7:6] Import \\\"autoshorts.rendering.ffmpeg_plan\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_10qJ9nPqf44lC1MEmAaX6wan", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_N8QDB6BIwstGILQogmO8qPJ0", "call_id": "call_N8QDB6BIwstGILQogmO8qPJ0", "response_item_id": "fc_060035eb87c40e14016a130d8971148191a7ce4cd78ce0c107", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ffmpeg_plan.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n__________________ ERROR collecting tests/test_ffmpeg_plan.py __________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_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_ffmpeg_plan.py:7: in <module>\\n    from autoshorts.rendering.ffmpeg_plan import FFmpegConcatPlan, build_ffmpeg_concat_plan, validate_ffmpeg_concat_plan\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_plan'\\n=========================== short test summary info ============================\\nERROR tests/test_ffmpeg_plan.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_N8QDB6BIwstGILQogmO8qPJ0", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "Implement. Need escape single quote in ffmpeg concat maybe simple. ensure content ends with last file repeated? concat demuxer requires last file repeated no duration maybe test expects ends with frame-05. Good.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Implement. Need escape single quote in ffmpeg concat maybe simple. ensure content ends with last file repeated? concat demuxer requires last file repeated no duration maybe test expects ends with frame-05. Good."}], "id": "msg_060035eb87c40e14016a130d8ee7fc8191a720740b7910aade", "phase": "commentary"}], "tool_calls": [{"id": "call_PpMZJoJkv5zwIHMWB8ny0qpD", "call_id": "call_PpMZJoJkv5zwIHMWB8ny0qpD", "response_item_id": "fc_060035eb87c40e14016a130d8ff14c81918b3c05d5ebb334e2", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Side-effect-free FFmpeg concat planning for local preview frames.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\n\\nfrom autoshorts.ideas.candidate import ValidationResult\\nfrom autoshorts.rendering.local_frame_writer import LocalFrameWriteResult\\nfrom autoshorts.rendering.local_preview import LocalPreviewPlan, validate_local_preview_plan\\n\\n\\n@dataclass(frozen=True)\\nclass FFmpegConcatEntry:\\n    image_path: str\\n    duration_seconds: float\\n\\n\\n@dataclass(frozen=True)\\nclass FFmpegConcatPlan:\\n    script_id: str\\n    output_path: str\\n    concat_file_path: str\\n    width: int\\n    height: int\\n    fps: int\\n    entries: tuple[FFmpegConcatEntry, ...]\\n    concat_file_content: str\\n    side_effects: tuple[str, ...] = ()\\n    external_calls: tuple[str, ...] = ()\\n\\n\\ndef _is_blank(value: str) -> bool:\\n    return not (value or \\\"\\\").strip()\\n\\n\\ndef _quote_concat_path(path: str) -> str:\\n    return path.replace(\\\"'\\\", \\\"'\\\\\\\\''\\\")\\n\\n\\ndef _concat_content(entries: tuple[FFmpegConcatEntry, ...]) -> str:\\n    lines: list[str] = []\\n    for entry in entries:\\n        lines.append(f\\\"file '{_quote_concat_path(entry.image_path)}'\\\")\\n        lines.append(f\\\"duration {entry.duration_seconds:.3f}\\\")\\n    if entries:\\n        lines.append(f\\\"file '{_quote_concat_path(entries[-1].image_path)}'\\\")\\n    return \\\"\\\\n\\\".join(lines) + \\\"\\\\n\\\"\\n\\n\\ndef build_ffmpeg_concat_plan(\\n    preview_plan: LocalPreviewPlan,\\n    write_result: LocalFrameWriteResult,\\n    *,\\n    output_path: str | Path,\\n) -> FFmpegConcatPlan:\\n    \\\"\\\"\\\"Build an FFmpeg concat plan without writing files or calling FFmpeg.\\\"\\\"\\\"\\n\\n    preview_validation = validate_local_preview_plan(preview_plan)\\n    if not preview_validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(preview_validation.errors))\\n    if write_result.script_id != preview_plan.script_id:\\n        raise ValueError(\\\"write result script_id must match preview plan\\\")\\n    if len(write_result.frame_paths) != len(preview_plan.frames):\\n        raise ValueError(\\\"frame count must match preview plan\\\")\\n    if any(not path.endswith(\\\".png\\\") for path in write_result.frame_paths):\\n        raise ValueError(\\\"frame paths must be png files\\\")\\n    if write_result.external_calls:\\n        raise ValueError(\\\"frame write result must not include external calls\\\")\\n\\n    entries = tuple(\\n        FFmpegConcatEntry(\\n            image_path=image_path,\\n            duration_seconds=frame.duration_ms / 1000,\\n        )\\n        for frame, image_path in zip(preview_plan.frames, write_result.frame_paths, strict=True)\\n    )\\n    out_path = Path(output_path)\\n    concat_path = out_path.with_suffix(\\\".concat.txt\\\")\\n    plan = FFmpegConcatPlan(\\n        script_id=preview_plan.script_id,\\n        output_path=str(out_path),\\n        concat_file_path=str(concat_path),\\n        width=preview_plan.width,\\n        height=preview_plan.height,\\n        fps=preview_plan.fps,\\n        entries=entries,\\n        concat_file_content=_concat_content(entries),\\n        side_effects=(),\\n        external_calls=(),\\n    )\\n    validation = validate_ffmpeg_concat_plan(plan)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n    return plan\\n\\n\\ndef validate_ffmpeg_concat_plan(plan: FFmpegConcatPlan) -> ValidationResult:\\n    errors: list[str] = []\\n    if _is_blank(plan.script_id):\\n        errors.append(\\\"script_id is required\\\")\\n    if _is_blank(plan.output_path):\\n        errors.append(\\\"output_path is required\\\")\\n    elif not plan.output_path.endswith(\\\".mp4\\\"):\\n        errors.append(\\\"output_path must end with .mp4\\\")\\n    if _is_blank(plan.concat_file_path):\\n        errors.append(\\\"concat_file_path is required\\\")\\n    elif not plan.concat_file_path.endswith(\\\".concat.txt\\\"):\\n        errors.append(\\\"concat_file_path must end with .concat.txt\\\")\\n    if plan.width <= 0 or plan.height <= 0:\\n        errors.append(\\\"width and height must be positive\\\")\\n    if plan.fps <= 0:\\n        errors.append(\\\"fps must be positive\\\")\\n    if plan.side_effects:\\n        errors.append(\\\"ffmpeg concat plans must not have side effects\\\")\\n    if plan.external_calls:\\n        errors.append(\\\"ffmpeg concat plans must not call external tools\\\")\\n    if not plan.entries:\\n        errors.append(\\\"at least one concat entry is required\\\")\\n    if _is_blank(plan.concat_file_content):\\n        errors.append(\\\"concat_file_content is required\\\")\\n\\n    for index, entry in enumerate(plan.entries, start=1):\\n        if _is_blank(entry.image_path):\\n            errors.append(f\\\"entry {index} image_path is required\\\")\\n        elif not entry.image_path.endswith(\\\".png\\\"):\\n            errors.append(\\\"frame paths must be png files\\\")\\n        if entry.duration_seconds <= 0:\\n            errors.append(f\\\"entry {index} duration_seconds must be positive\\\")\\n\\n    return ValidationResult(is_valid=not errors, errors=tuple(errors))\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_plan.py\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
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{"role": "assistant", "content": "Patch README.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Patch README."}], "id": "msg_060035eb87c40e14016a130dbc7b2c8191be9930bacf3da96e", "phase": "commentary"}], "tool_calls": [{"id": "call_spiw5KT2JXkLOGdODKTiDo7Y", "call_id": "call_spiw5KT2JXkLOGdODKTiDo7Y", "response_item_id": "fc_060035eb87c40e14016a130dbcc80c81919748fbbb903f66ca", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\",\"old_string\":\"- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\",\"new_string\":\"- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n- `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"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@@ -22,6 +22,7 @@\\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 - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\n+- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n - `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\\n - `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_spiw5KT2JXkLOGdODKTiDo7Y", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_hdG5gRlrGiuqPA8lpKqdCOWc", "call_id": "call_hdG5gRlrGiuqPA8lpKqdCOWc", "response_item_id": "fc_060035eb87c40e14016a130dc400c48191b802aa966247f86f", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"Generated preview frames should stay under runtime preview directories such as `data/previews/<script_id>/` or temporary test directories. PNG media is ignored by Git.\\n\\n`autoshorts.rendering.ffmpeg_plan.build_ffmpeg_concat_plan()` turns a `LocalPreviewPlan` plus `LocalFrameWriteResult` into a side-effect-free `FFmpegConcatPlan`. It records ordered PNG paths, per-frame durations, output `.mp4` path, derived `.concat.txt` path, preview dimensions, fps, and FFmpeg concat demuxer file content.\\n\\n`validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n\\n## Social media draft\",\"old_string\":\"Generated preview frames should stay under runtime preview directories such as `data/previews/<script_id>/` or temporary test directories. PNG media is ignored by Git.\\n\\n## Social media draft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"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@@ -121,6 +121,10 @@\\n \\n Generated preview frames should stay under runtime preview directories such as `data/previews/<script_id>/` or temporary test directories. PNG media is ignored by Git.\\n \\n+`autoshorts.rendering.ffmpeg_plan.build_ffmpeg_concat_plan()` turns a `LocalPreviewPlan` plus `LocalFrameWriteResult` into a side-effect-free `FFmpegConcatPlan`. It records ordered PNG paths, per-frame durations, output `.mp4` path, derived `.concat.txt` path, preview dimensions, fps, and FFmpeg concat demuxer file content.\\n+\\n+`validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n+\\n ## Social media draft\\n \\n `autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_hdG5gRlrGiuqPA8lpKqdCOWc", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_MQu28Gbts4nJ6TWSIwL0ac7c", "call_id": "call_MQu28Gbts4nJ6TWSIwL0ac7c", "response_item_id": "fc_060035eb87c40e14016a130dcfc42081919f4593026f3d9407", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom tempfile import TemporaryDirectory\\nfrom pathlib import Path\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\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, validate_ffmpeg_concat_plan\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\npreview_plan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\nwith TemporaryDirectory() as tmp:\\n    write_result = write_local_preview_frames(preview_plan, output_dir=tmp)\\n    plan = build_ffmpeg_concat_plan(preview_plan, write_result, output_path=Path(tmp) / 'preview.mp4')\\n    print(plan.script_id)\\n    print((plan.width, plan.height, plan.fps))\\n    print(len(plan.entries))\\n    print(plan.entries[0].duration_seconds, plan.entries[-1].duration_seconds)\\n    print(Path(plan.output_path).name, Path(plan.concat_file_path).name)\\n    print(plan.side_effects, plan.external_calls)\\n    print(validate_ffmpeg_concat_plan(plan).is_valid)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"........................................................................ [ 67%]\\n..................................                                       [100%]\\n106 passed in 0.86s\\nai_life_systems-ai-weekly-review-routine\\n(540, 960, 24)\\n5\\n3.0 5.0\\npreview.mp4 preview.concat.txt\\n() ()\\nTrue\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_MQu28Gbts4nJ6TWSIwL0ac7c", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_2tO8I8aC3VLxb9FhGY0DmLAP", "call_id": "call_2tO8I8aC3VLxb9FhGY0DmLAP", "response_item_id": "fc_060035eb87c40e14016a130ddc0aec819196ca7755dc8a420a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/rendering/ffmpeg_plan.py tests/test_ffmpeg_plan.py && git commit -m \\\"feat: add ffmpeg concat planning\\\" && git log --oneline -n 8 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/ffmpeg_plan.py\\n?? tests/test_ffmpeg_plan.py\\n[feat/core-approval-quality-gates 1128b7c] feat: add ffmpeg concat planning\\n 3 files changed, 248 insertions(+)\\n create mode 100644 autoshorts/rendering/ffmpeg_plan.py\\n create mode 100644 tests/test_ffmpeg_plan.py\\n1128b7c feat: add ffmpeg concat planning\\n7e881b3 feat: write local preview frame cards\\n02ecf59 feat: add local preview frame planning\\n3b387f4 feat: add demo social draft cli\\n515c549 feat: add social draft delivery preparation\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_2tO8I8aC3VLxb9FhGY0DmLAP", "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg Concat Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_plan.py\ntests/test_ffmpeg_plan.py\n```\n\n## Neue API\n\n```python\nbuild_ffmpeg_concat_plan(\n    preview_plan: LocalPreviewPlan,\n    write_result: LocalFrameWriteResult,\n    *,\n    output_path: str | Path,\n) -> FFmpegConcatPlan\n\nvalidate_ffmpeg_concat_plan(plan: FFmpegConcatPlan) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n```\n\nWichtig: Dieser Schritt ruft **noch kein FFmpeg** auf und schreibt **noch keine concat file**. Er plant nur sauber.\n\n## FFmpegConcatPlan\n\nEnthält:\n\n```text\nscript_id\noutput_path\nconcat_file_path\nwidth\nheight\nfps\nentries\nconcat_file_content\nside_effects=()\nexternal_calls=()\n```\n\nPro Entry:\n\n```text\nimage_path\nduration_seconds\n```\n\nBeispiel verifiziert:\n\n```text\nai_life_systems-ai-weekly-review-routine\n(540, 960, 24)\n5\n3.0 5.0\npreview.mp4 preview.concat.txt\n() ()\nTrue\n```\n\nBedeutung:\n\n- Script ID korrekt\n- Preview size/fps korrekt\n- 5 PNG Frames\n- erste Dauer `3.0s`\n- letzte Dauer `5.0s`\n- Output `.mp4`\n- Concat file path `.concat.txt`\n- keine Side Effects\n- keine External Calls\n- Validation grün\n\n## Concat File Content\n\nDer Plan erzeugt bereits FFmpeg concat-demuxer-kompatiblen Inhalt:\n\n```text\nfile '.../frame-01-first_frame_hook.png'\nduration 3.000\nfile '.../frame-02-pattern_interrupt.png'\nduration 5.000\n...\nfile '.../frame-05-cta.png'\nduration 5.000\nfile '.../frame-05-cta.png'\n```\n\nDas letzte File wird wiederholt, wie FFmpeg concat demuxer es für die letzte Duration gern hat. Natürlich etwas altmodisch. FFmpeg ist eben ein mächtiger Werkzeugkasten aus der Ära “was wäre, wenn UX optional wäre”.\n\n## Guardrails\n\n`build_ffmpeg_concat_plan()` / `validate_ffmpeg_concat_plan()` prüfen:\n\n- gültiger `LocalPreviewPlan`\n- `script_id` Match zwischen Plan und FrameWriteResult\n- gleiche Anzahl Preview Frames und PNG Paths\n- Frame Paths müssen `.png` sein\n- Output Path muss `.mp4` sein\n- Concat Path muss `.concat.txt` sein\n- Width/Height/FPS positiv\n- Durations positiv\n- keine Side Effects\n- keine External Calls\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_plan'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n106 passed in 0.86s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_plan`\n- `FFmpegConcatPlan`\n- Entries/Durations\n- concat demuxer content\n- `.mp4` / `.concat.txt` validation\n- weiterhin kein FFmpeg Call\n\n## Commit\n\n```text\n1128b7c feat: add ffmpeg concat planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt der echte FFmpeg Boundary — aber sauber getrennt:\n\n```text\nFFmpegConcatPlan\n→ write concat file\n→ call ffmpeg\n→ MP4 timing preview\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\nZiel:\n\n- schreibt `.concat.txt`\n- ruft `ffmpeg`\n- erzeugt `.mp4`\n- gibt `FFmpegRenderResult` zurück:\n  - `output_path`\n  - `concat_file_path`\n  - `side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\")`\n  - `external_calls=(\"ffmpeg\",)`\n- Tests:\n  - `ffmpeg -version` check/skip wenn nicht installiert\n  - MP4 exists\n  - MP4 non-empty\n  - optional `ffprobe`:\n    - width `540`\n    - height `960`\n    - duration ungefähr `35s`\n    - fps `24`\n\nDas wäre der erste echte Video-Preview. Noch nicht schön, aber kontrolliert — und kontrolliert schlägt “hübscher Zufallsunfall” jeden Tag.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg Concat Planning\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_plan.py\ntests/test_ffmpeg_plan.py\n```\n\n## Neue API\n\n```python\nbuild_ffmpeg_concat_plan(\n    preview_plan: LocalPreviewPlan,\n    write_result: LocalFrameWriteResult,\n    *,\n    output_path: str | Path,\n) -> FFmpegConcatPlan\n\nvalidate_ffmpeg_concat_plan(plan: FFmpegConcatPlan) -> ValidationResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n```\n\nWichtig: Dieser Schritt ruft **noch kein FFmpeg** auf und schreibt **noch keine concat file**. Er plant nur sauber.\n\n## FFmpegConcatPlan\n\nEnthält:\n\n```text\nscript_id\noutput_path\nconcat_file_path\nwidth\nheight\nfps\nentries\nconcat_file_content\nside_effects=()\nexternal_calls=()\n```\n\nPro Entry:\n\n```text\nimage_path\nduration_seconds\n```\n\nBeispiel verifiziert:\n\n```text\nai_life_systems-ai-weekly-review-routine\n(540, 960, 24)\n5\n3.0 5.0\npreview.mp4 preview.concat.txt\n() ()\nTrue\n```\n\nBedeutung:\n\n- Script ID korrekt\n- Preview size/fps korrekt\n- 5 PNG Frames\n- erste Dauer `3.0s`\n- letzte Dauer `5.0s`\n- Output `.mp4`\n- Concat file path `.concat.txt`\n- keine Side Effects\n- keine External Calls\n- Validation grün\n\n## Concat File Content\n\nDer Plan erzeugt bereits FFmpeg concat-demuxer-kompatiblen Inhalt:\n\n```text\nfile '.../frame-01-first_frame_hook.png'\nduration 3.000\nfile '.../frame-02-pattern_interrupt.png'\nduration 5.000\n...\nfile '.../frame-05-cta.png'\nduration 5.000\nfile '.../frame-05-cta.png'\n```\n\nDas letzte File wird wiederholt, wie FFmpeg concat demuxer es für die letzte Duration gern hat. Natürlich etwas altmodisch. FFmpeg ist eben ein mächtiger Werkzeugkasten aus der Ära “was wäre, wenn UX optional wäre”.\n\n## Guardrails\n\n`build_ffmpeg_concat_plan()` / `validate_ffmpeg_concat_plan()` prüfen:\n\n- gültiger `LocalPreviewPlan`\n- `script_id` Match zwischen Plan und FrameWriteResult\n- gleiche Anzahl Preview Frames und PNG Paths\n- Frame Paths müssen `.png` sein\n- Output Path muss `.mp4` sein\n- Concat Path muss `.concat.txt` sein\n- Width/Height/FPS positiv\n- Durations positiv\n- keine Side Effects\n- keine External Calls\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_plan'\n```\n\nGREEN:\n\n```text\n5 passed\n```\n\nFull suite:\n\n```text\n106 passed in 0.86s\n```\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_plan`\n- `FFmpegConcatPlan`\n- Entries/Durations\n- concat demuxer content\n- `.mp4` / `.concat.txt` validation\n- weiterhin kein FFmpeg Call\n\n## Commit\n\n```text\n1128b7c feat: add ffmpeg concat planning\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Nächster sinnvoller Schritt\n\nJetzt kommt der echte FFmpeg Boundary — aber sauber getrennt:\n\n```text\nFFmpegConcatPlan\n→ write concat file\n→ call ffmpeg\n→ MP4 timing preview\n```\n\nVorschlag:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\nZiel:\n\n- schreibt `.concat.txt`\n- ruft `ffmpeg`\n- erzeugt `.mp4`\n- gibt `FFmpegRenderResult` zurück:\n  - `output_path`\n  - `concat_file_path`\n  - `side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\")`\n  - `external_calls=(\"ffmpeg\",)`\n- Tests:\n  - `ffmpeg -version` check/skip wenn nicht installiert\n  - MP4 exists\n  - MP4 non-empty\n  - optional `ffprobe`:\n    - width `540`\n    - height `960`\n    - duration ungefähr `35s`\n    - fps `24`\n\nDas wäre der erste echte Video-Preview. Noch nicht schön, aber kontrolliert — und kontrolliert schlägt “hübscher Zufallsunfall” jeden Tag."}], "id": "msg_060035eb87c40e14016a130de0b58481919e3c50f1c850f8af", "phase": "final_answer"}], "timestamp": "2026-05-24T16:40:51.271176"}
{"role": "assistant", "content": "", "reasoning": "**Implementing ffmpeg_renderer**\n\nI need to keep working on implementing the ffmpeg_renderer. It's important for the task I'm handling right now. I should focus on utilizing my skills effectively to ensure a smooth process. There’s definitely a lot to think about in terms of details, but it all comes together when I stay organized. I’ll just take it step by step and figure things out as I go along!", "finish_reason": "tool_calls", "reasoning_content": "**Implementing ffmpeg_renderer**\n\nI need to keep working on implementing the ffmpeg_renderer. It's important for the task I'm handling right now. I should focus on utilizing my skills effectively to ensure a smooth process. There’s definitely a lot to think about in terms of details, but it all comes together when I stay organized. I’ll just take it step by step and figure things out as I go along!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExCRNf0JzqTnwBHk3osZ7G_aj4yZEiV3K_sU3cdVFvYJKoaf_dY6hH4LZzsVKlmil977bF3rXAfTU0ev4blIxP6jKkypBhCk7WBAPZWEwSnSmu825smdl2u10sYAJv550FS-KDnQNNunfDDh5VAj6AiMIb7n9QXuFmfD-RgOIomUj6JnNYuZaf-1iK4Xlg_E0lFzCKG-3P8NXD7Cn-KxGc4UzRW29S3Fyfcw0qyrpcNRgG_2oYTtcjw3PGY0rsOLP_XUUSkMPBCcw4mJdlqRdq4QUZmWF7hbKv89hjPTmPM-SCGX7H8ASYXnKAK5bt7pdEijpf6uIyU7Mxy7Z7uZNDYB58Pw4zoNt9fXl91-dcy17L1DEdfr9okb0xtMKILe4WLr_LWqmKBhZGeRPJnUiK8C3yHSUdCuNssMeE2AUAg2-Y8HoXJtMcBkzXTh8bQIjWLNQ0Z_hbGJ9YM1SFtl6rBAig_VOqyWhoLDUpGbCEiG3AopKGvhVq9D1VPMuP7Mpkh2EWTgX1S7jcCNnNhVgGukbE6P-GPUI2p8sBRbfazM7_UstyK3JXlVw3fPPpKmSjIDcyp2fva8P9PaxKDNMyLRcJL9poWRgf9_LFJS9D3ljs5T9I_uxHdMtjhGelKotOWcjyFq-GWcLoh0IDg4Letq-NqInc4cZIsETA3Yp8A5bsN54I-PYWLJEllgc42BMOJrZ1QQUYdRwCYQXKelAaPbEIXlQaZ3ZaK7ZaFKBMp32Bez4Z8N_NkcDwSBU5f2QKcm3znpLL76vZJxJsLjdpW1Jyu_8HYlxncGHM8H9qB-xK3M0GKwxc_iUo5IXqIpvcklUW2TlGIokrbxE8R4Xp51nQ_czdKIwJbL1nh6Bb6QtMMQqS-89347WbxMh_zsgw-drNG55o3lCejpN-BQiGmsrGWSMFSSpdbxuVk2UkgWdpAMZ2aAx7TDDlGWka_KWM9O8s7Vj5Q1z4znQrL7fHP_RFnaSnHrS_QDpoChEnfGO_w=", "id": "rs_060035eb87c40e14016a13108c6c808191812e548859769a5d", "summary": [{"type": "summary_text", "text": "**Implementing ffmpeg_renderer**\n\nI need to keep working on implementing the ffmpeg_renderer. It's important for the task I'm handling right now. I should focus on utilizing my skills effectively to ensure a smooth process. There’s definitely a lot to think about in terms of details, but it all comes together when I stay organized. I’ll just take it step by step and figure things out as I go along!"}]}], "tool_calls": [{"id": "call_m4tYoo8w72BxTFxJ7RGaKhSI", "call_id": "call_m4tYoo8w72BxTFxJ7RGaKhSI", "response_item_id": "fc_060035eb87c40e14016a131091310c8191807c42a9e26494aa", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_TFpr4TwylJDLo3Yx7nLNIdRG", "call_id": "call_TFpr4TwylJDLo3Yx7nLNIdRG", "response_item_id": "fc_060035eb87c40e14016a131091311c8191ad964025bf088cbe", "type": "function", "function": {"name": "skill_view", "arguments": "{\"file_path\":\"references/manifest-preview-renderer.md\",\"name\":\"social-media-shorts-automation\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"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)`, or `prepare_preview(manifest)`.\\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), and placeholder 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, and bad timings.\\n6. 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_m4tYoo8w72BxTFxJ7RGaKhSI", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/manifest-preview-renderer.md\", \"content\": \"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)`\\n  - one representative image per visual timeline item\\n  - uses the manifest text, asset type, timing, and retention goal\\n- `write_frame_images(frames, output_dir)`\\n  - writes numbered PNGs for inspection/debugging\\n- `render_preview_video(manifest, output_path, width, height, fps)`\\n  - renders frames to temporary PNGs\\n  - writes an FFmpeg concat file with per-scene durations\\n  - exports `yuv420p` MP4 with `+faststart`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n  data/render_manifests/<batch>/<video>.json \\\\\\n  --out-dir data/previews/<batch> \\\\\\n  --width 540 \\\\\\n  --height 960 \\\\\\n  --fps 24\\n```\\n\\nUse reduced preview resolution like `540x960` for fast local review. Keep final `1080x1920` as a later renderer-quality target.\\n\\n## Repo hygiene\\n\\n- Keep generated MP4/PNG media out of Git via `.gitignore`.\\n- Commit renderer code, tests, scripts, manifests, and lightweight planning artifacts.\\n- Do not commit rendered previews unless explicitly needed as small fixtures.\\n- Add runtime dependencies explicitly, e.g. `Pillow>=10.0`; treat FFmpeg as an external system prerequisite checked at runtime.\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset supplier.\\n- Do not over-polish the first renderer. Its job is approval/timing validation.\\n- Do not publish from this step. Preview rendering still requires human approval before posting.\\n- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal readability before scaling to more videos.\", \"file_type\": \".md\"}", "tool_call_id": "call_TFpr4TwylJDLo3Yx7nLNIdRG", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!", "finish_reason": "tool_calls", "reasoning_content": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExCYHgcgAnwHILgSNiEUl1N9obiL1G2tl_8G4pAWHiQajbJYPFrl_hX-O5xK4i9ydX2rFzjRi8cI__dNFnlqqqpML16a9Q4oCehnueqJ4p-oiN5P2SRCcya8t5QlAYqPySF1ABEYPfCxkxdXBNxNDjfce8Xk7yLllK4FzJSG5latd6FdzmpRmVbNvljNXIP2A0T7inUqaSeW6e1Tux8QMxq5rRK9KP8SkgGV1-tmK7OAAkdl9OBQTuySNdqaTIb3gM9ya63bFlNQkwwjqVG866jl-3SAcVHVlTd1sDbMSb6DTlPBimZUFbU7fyoTF-ixAD1JXnY0cRl4fwfCh81HGqY9fRK-ba9ujHJ7ikcG3js03wTT9PYqgTlIC9WB4VsD6D_y9G5BRx9IkI9mRxqoq3YOonTDknHlpxSvzmBxxtEt3-NHQO9OiWlZMSFqJ4hbmi59f6lUof62jqXZnAez1k8t8tSUuhFkbtbgnw62yZNe3ZXEd0L9zBoy0cEN6czKEXpfxB9KNiXK1C7fTITvxWKTWVqEliXtTqhBxY-8gUe8BRfHGC-qAfhPvJRwPpL3Vh-Q246oJ7s0kGeh0d2I7N1lxMb1uIzT1LmpnPQWBz8sazT5hpvsCNe4MUXyHishB-PIWy-aVtL34rbdwno8_BAm0SQiSesIYGQ3WgmDFdn4Dkuz2aD5RWqQ5T4ZnhOtPk4jm2uT7gkYElPnIVG674yK4H8lsOEs4JWdIStVa0CWezxZDVWfYM7VN2HbkyjeVb614dbX9OQSn5tE8mt9ZIOIi11Ty80Vcgqc_NjyRSqUV4-3uoFbxSD3wr8r0XVj1XHeyDBIobvZfok-efp-ZBJUZF3KUc2uIitpCptrvOq5iGIOxbMGM_6at_D5DZ-yB1HLSh5KIC0YSVq3YAX6BOEIKdSJi6iFFKSqwe7M_DGGcZCiMs8ZNhVko7bhV2w4eZx3Bv8tuUejM2XLm11TH-2_ncijeS8-Q9ymi32-1PCl1hyjISSTLGZpdmRGZKrmJBrI", "id": "rs_060035eb87c40e14016a13109569e081918165d278415a9904", "summary": [{"type": "summary_text", "text": "**Checking ffmpeg availability**\n\nI need to implement something and first, I want to check if ffmpeg is available. It seems like testing could be skipped for now, and I can use the terminal to do this. I'll run a quick command to see if ffmpeg is installed and functioning properly. If it's there, I'll be able to proceed; if not, I’ll need to figure out how to install it. Let's see what the terminal tells me!"}]}], "tool_calls": [{"id": "call_N5Ot8sgWfogJyYo7pKIrM3En", "call_id": "call_N5Ot8sgWfogJyYo7pKIrM3En", "response_item_id": "fc_060035eb87c40e14016a131098692c8191b20c538e32c59ee0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"command -v ffmpeg || true; command -v ffprobe || true; git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"/usr/bin/ffmpeg\\n/usr/bin/ffprobe\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_N5Ot8sgWfogJyYo7pKIrM3En", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!", "finish_reason": "tool_calls", "reasoning_content": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExCfJ-MXyd-FuW2Yzn9oPo8oCTm3sfhjduYUah-vISyn2wMq54luo2GuvIJ6WFcZ8Wg4pv8XHLxlcnBy8ufqN-8oryjNmR6O6zLnWYv4vf8xF2scKPGAYEhv_Hyeo0p2aLPbjpnPk1kCisUcxFnGBIJiI-axcjouonFTXOQKxzTP2QUjqbQMDmsWEJQ3_iFhuSiiS_Rc98x1rRm0DUev4WvETXZ-M3UHpkUfH9-2Aje5uYhIvSx3-kTmc0uBvPhbwXctrMEqNaE0miNraaYMIiB9PQrE2aNxor-QswevJqO42esuWgcQSrxeJ-sHem-kyPXqMwx-V4_LerAqlmztu99_DRJy3y-A7qhVyj-u19ghSOs8Y3lUNqm__dGnYO7ZoBmiDem8yz4NbrXPdyDTgewLlUjKPMfeoPvkoCxT7_3bfBiHN2MxPEYER8PZFOlA-BQlqa8yL70PYIXOfhp96SfIsIvYgVbnXJzCrz7KftbTCSH3SMbRoSjBqgsDZFGTOq7AAKTo8OTb6Z1Xq3DmHGXVAd8n8qGfmAyRME24nBWTd_2KmXGg13MNa-xheNTGke31E4YkxIP955FnSePQ14M4AAFjXHONHdnt3cy9a9u1y37JOG1DldN_Mr87XzdxqBPfA_I_yag_9vtHn0qodRtAVNYgW-kCdf51Q7uh4SxOtthNAMpZyvulp6UaFpla8k6nqhLvF1WTwM3niBfcbZO05D3HtmpdRS6IYY8vGQFYM1NEjRh-0oY_tRCE6cTESvpnfE4yQawjp6f8EtOKz0gAInzYdQqMZJju6DpKQsAZ2sfZF5-COLLi5qaSs51CE_VVeg-KuzHD_qlOgK75ppL3Q9xLHwVg8rQiLgX4LT6zk7-G8i3RO3lKv3yHx6J8F-yJJvDYQ4aLy7dIGHNAsgbVh7XEFs0IY86MSEOvQxBOODFy4jmRPcNqEc0SmsgBWijapF8jybzjgs0k8-I8ZQ6NJHseDaA7o4JGZQjLTQAU7bxTKMJfIeJqKud8T8n-gYUwi69v1cPG1gPmPTmPyrxiRw3yAjmadVRegQGZcsP-INilwIdxByei856FxjtdVndskx8Xnag8i9dQDacEVKtUTg==", "id": "rs_060035eb87c40e14016a13109d11f48191a8f5197327698a8f", "summary": [{"type": "summary_text", "text": "**Considering test setup**\n\nI'm thinking about writing a test and using a subprocess for ffprobe, which might work. I have to keep the duration manageable—maybe around 35 seconds—with five PNGs. I’ll need to figure out the best way to construct this to ensure it all runs smoothly. It’s a bit tricky, but I think it’s doable if I plan out how to tackle the encoding as well. Let’s make this work!"}]}], "tool_calls": [{"id": "call_B1l3vy7tRMttV4dwSqflmL00", "call_id": "call_B1l3vy7tRMttV4dwSqflmL00", "response_item_id": "fc_060035eb87c40e14016a13109fbd088191b1e510686766f93d", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"import json\\nimport shutil\\nimport subprocess\\nfrom pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.ffmpeg_plan import build_ffmpeg_concat_plan\\nfrom autoshorts.rendering.ffmpeg_renderer import FFmpegRenderResult, render_ffmpeg_preview_video\\nfrom autoshorts.rendering.local_frame_writer import write_local_preview_frames\\nfrom autoshorts.rendering.local_preview import build_local_preview_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\npytestmark = pytest.mark.skipif(shutil.which(\\\"ffmpeg\\\") is None, reason=\\\"ffmpeg is required\\\")\\n\\n\\ndef _concat_plan(tmp_path):\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    preview_plan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\n    write_result = write_local_preview_frames(preview_plan, output_dir=tmp_path / \\\"frames\\\")\\n    return build_ffmpeg_concat_plan(preview_plan, write_result, output_path=tmp_path / \\\"preview.mp4\\\")\\n\\n\\ndef test_render_ffmpeg_preview_video_writes_concat_file_and_mp4(tmp_path):\\n    plan = _concat_plan(tmp_path)\\n\\n    result = render_ffmpeg_preview_video(plan)\\n\\n    assert isinstance(result, FFmpegRenderResult)\\n    assert result.script_id == \\\"ai_life_systems-ai-weekly-review-routine\\\"\\n    assert result.output_path == plan.output_path\\n    assert result.concat_file_path == plan.concat_file_path\\n    assert result.side_effects == (\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n    assert result.external_calls == (\\\"ffmpeg\\\",)\\n    assert result.returncode == 0\\n    assert Path(result.concat_file_path).exists()\\n    assert Path(result.concat_file_path).read_text() == plan.concat_file_content\\n    output = Path(result.output_path)\\n    assert output.exists()\\n    assert output.stat().st_size > 0\\n\\n\\n@pytest.mark.skipif(shutil.which(\\\"ffprobe\\\") is None, reason=\\\"ffprobe is required\\\")\\ndef test_render_ffmpeg_preview_video_smoke_probes_dimensions_and_duration(tmp_path):\\n    plan = _concat_plan(tmp_path)\\n\\n    result = render_ffmpeg_preview_video(plan)\\n\\n    probe = subprocess.run(\\n        [\\n            \\\"ffprobe\\\",\\n            \\\"-v\\\",\\n            \\\"error\\\",\\n            \\\"-select_streams\\\",\\n            \\\"v:0\\\",\\n            \\\"-show_entries\\\",\\n            \\\"stream=width,height,r_frame_rate,duration\\\",\\n            \\\"-of\\\",\\n            \\\"json\\\",\\n            result.output_path,\\n        ],\\n        check=True,\\n        capture_output=True,\\n        text=True,\\n    )\\n    stream = json.loads(probe.stdout)[\\\"streams\\\"][0]\\n    assert stream[\\\"width\\\"] == 540\\n    assert stream[\\\"height\\\"] == 960\\n    assert stream[\\\"r_frame_rate\\\"] == \\\"24/1\\\"\\n    assert float(stream[\\\"duration\\\"]) == pytest.approx(35.0, abs=1.0)\\n\\n\\ndef test_render_ffmpeg_preview_video_rejects_invalid_plan(tmp_path):\\n    plan = _concat_plan(tmp_path)\\n    invalid = type(plan)(\\n        script_id=plan.script_id,\\n        output_path=str(tmp_path / \\\"preview.mov\\\"),\\n        concat_file_path=plan.concat_file_path,\\n        width=plan.width,\\n        height=plan.height,\\n        fps=plan.fps,\\n        entries=plan.entries,\\n        concat_file_content=plan.concat_file_content,\\n        side_effects=plan.side_effects,\\n        external_calls=plan.external_calls,\\n    )\\n\\n    with pytest.raises(ValueError, match=\\\"output_path must end with .mp4\\\"):\\n        render_ffmpeg_preview_video(invalid)\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_renderer.py\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3684, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_renderer.py\\\">\\nERROR [11:6] Import \\\"autoshorts.rendering.ffmpeg_renderer\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_B1l3vy7tRMttV4dwSqflmL00", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_qhXSLVz2PPk168HKHsUX2yF2", "call_id": "call_qhXSLVz2PPk168HKHsUX2yF2", "response_item_id": "fc_060035eb87c40e14016a1310b562ac81919f055049f18c428b", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_ffmpeg_renderer.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_ffmpeg_renderer.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_ffmpeg_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_ffmpeg_renderer.py:11: in <module>\\n    from autoshorts.rendering.ffmpeg_renderer import FFmpegRenderResult, render_ffmpeg_preview_video\\nE   ModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_renderer'\\n=========================== short test summary info ============================\\nERROR tests/test_ffmpeg_renderer.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_qhXSLVz2PPk168HKHsUX2yF2", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location.", "finish_reason": "tool_calls", "reasoning_content": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExC-U6GLfQrls9Y24e_1j6AovRJt8Ww7EZAyQmBcqPSWFkNceP5A9jiGS8dmzi8RxXamHRQcd2_-Ft7AnZx1_Hfbszj9v3ivJbXbveCRamtnbIyFkAXYRW0PdotH8tNNSui5lCI43aW3j8OrxsTSvc0E-r8DUu40566vy1mHX4tbqHvhrGNNouUDl2Xec_USaA7MShDe8nl0fKJ5cl2Jvn0NRynYuGzEK-1Ofy8wl0nVSw7IA6oxpQq9QQZwjMql5WpsRqFVAM-fer5oHiv9JwDFXR6MXxViR5ZxT9NrJVJvO_VcrvrKJtsDmxfIaXEBf4F3FIoWggYCyQ4RKDh_BOZcKURfgA0ZyfO4PegoLECKXC7vutDGk62nKsp0XyBL9n7Utgi7_sDLkLbXd8GdO1UOz0KEsvO6s-gWegh-GSTB1YC9yKZHgW0QVauHejeMX_odHFCCpAwRmHOsk5e29Jhb7sXr1KJxoTZuy502ly-si-t9VkFqsHB0IwVtVDQ26voOJxnNL4HMqyRyLiSKY9oAkpGntPOjjNInFCf5fB_Hig9YslDkK0vH_w9gbuJ6j58V0p3ndL4kYDAl7-gjmYdnyUNjnA9XDQZ6vvUhMRRuXNEK6DGojezz9SIrjEnRzfgVBmQRgUTBOWacIomMRIPMldCj1BViZ8szStQ1Bg0GMLYOm6jGHRkcrxXfAuZwFOZlGgHJiiXn578uoDLMU4-eOjsQwz-Dt2TohXYblihwRT8OMPSLdB5d-VKw6tSgI5jMQD6CCW8CzK9XdxiMFcFY8N0s-O0WcbAkdmLOH7HM-TyaxYenTetegCBOF-ti5zGIE4i3TG5E_BRTNcQWT2LfZXDsGpo_5c5Kb9CtY7xRzojj4hvkDeFpF1zIm3TTvJ5NmZL4-31b5UPa5EH7mR8LdyswI_Td5ayrM8z6UPPNXTO8hmh1iFTLyJ_jtvFLY9It4EaUoaHqqtZRJj_x2cKeNRePPo7K76GShLg_vxA57ZMNvU1qgOFc3rFYDSns8QA9LZlBBdDAxwgirXp2tYKpoHnPWOLKK1kzFD7xFwgkL3Cw7LDlZPEq2Gapa7s2KTxNLVYNLd2e9x_MS0lvqY3iqpWCh8FO3pc5KswjhKJVDxNJXRvVZk5i0IbBUj9TmhQFGl_S9KSS2iMEQM46h1k5hoS4Gg1Ii2eZBCwOMqz1jnRPoH40Z-LdqCOk8NPgklON6jVZ2s4qi69NZOiQs0-fDF8i6PXViEENz10Uq35Jn8gBNsEw50yFY2Hkm5WazRSQ7TlYd0IbcR0aJlN7PaMDkaXEjWdcrIrcdfrgAbqtPNlq6LdfQFCVj805-XUa9zJMklLSBVuqRE23sMQCv7KdgqdtPEe3IGqoojnl5bdVbLs-ckHRSr1Nlu6VUkbQ0lDu", "id": "rs_060035eb87c40e14016a1310bac45c8191baef96d981dfb9e2", "summary": [{"type": "summary_text", "text": "**Implementing renderer commands**\n\nI’m working on the renderer and need to concatenate the demuxer command for ffmpeg. It looks like I’ll use `ffmpeg -y -f concat -safe 0 -i concat -vf fps=24,format=yuv420p -movflags +faststart output`. For still images, I want to ensure the output lasts about 35 seconds. I'll use `subprocess.run` and check for stdout and stderr. It’s also important to validate and ensure that the parent directories are correct, using absolute paths for the concat file from the temporary location."}]}], "tool_calls": [{"id": "call_EjgkkXfsV2fyE3oILAvpzrDT", "call_id": "call_EjgkkXfsV2fyE3oILAvpzrDT", "response_item_id": "fc_060035eb87c40e14016a1310bec9688191a0c38c23e1288eac", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"FFmpeg rendering boundary for local preview videos.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nfrom dataclasses import dataclass\\nfrom pathlib import Path\\nimport subprocess\\n\\nfrom autoshorts.rendering.ffmpeg_plan import FFmpegConcatPlan, validate_ffmpeg_concat_plan\\n\\n\\n@dataclass(frozen=True)\\nclass FFmpegRenderResult:\\n    script_id: str\\n    output_path: str\\n    concat_file_path: str\\n    returncode: int\\n    stdout: str\\n    stderr: str\\n    side_effects: tuple[str, ...] = (\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")\\n    external_calls: tuple[str, ...] = (\\\"ffmpeg\\\",)\\n\\n\\ndef render_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult:\\n    \\\"\\\"\\\"Write the concat file and invoke FFmpeg to create a local MP4 preview.\\\"\\\"\\\"\\n\\n    validation = validate_ffmpeg_concat_plan(plan)\\n    if not validation.is_valid:\\n        raise ValueError(\\\"; \\\".join(validation.errors))\\n\\n    concat_path = Path(plan.concat_file_path)\\n    output_path = Path(plan.output_path)\\n    concat_path.parent.mkdir(parents=True, exist_ok=True)\\n    output_path.parent.mkdir(parents=True, exist_ok=True)\\n    concat_path.write_text(plan.concat_file_content)\\n\\n    command = [\\n        \\\"ffmpeg\\\",\\n        \\\"-y\\\",\\n        \\\"-f\\\",\\n        \\\"concat\\\",\\n        \\\"-safe\\\",\\n        \\\"0\\\",\\n        \\\"-i\\\",\\n        str(concat_path),\\n        \\\"-vf\\\",\\n        f\\\"fps={plan.fps},format=yuv420p\\\",\\n        \\\"-movflags\\\",\\n        \\\"+faststart\\\",\\n        str(output_path),\\n    ]\\n    completed = subprocess.run(command, capture_output=True, text=True, check=False)\\n    if completed.returncode != 0:\\n        raise RuntimeError(completed.stderr or \\\"ffmpeg failed\\\")\\n\\n    return FFmpegRenderResult(\\n        script_id=plan.script_id,\\n        output_path=str(output_path),\\n        concat_file_path=str(concat_path),\\n        returncode=completed.returncode,\\n        stdout=completed.stdout,\\n        stderr=completed.stderr,\\n        side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\"),\\n        external_calls=(\\\"ffmpeg\\\",),\\n    )\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/ffmpeg_renderer.py\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExDksY5WzhdAq8HJS9PFebLql7x8dwwCC__R6bUuDaclFdWvogKEtH6E4BJdqSwL7XP-Gp6Yjn5mT9IHoel6k1SY1WotL1YNdtfy6sT4HUzesJbt_SlBwjT76SuTnK6CPjZvhmUNOTZZvEumO7YNyvdiTULpsxzlWF6T3D2q_wznJwhX3sDkkpHnkd5X3nWD70xnsAMUjp5iSXWmUKhVQAQbl21R9jYKha7RSZXsYDfm7SiDi-iDF13r5OuZUKHnP_RZFKScHNcooc55aM701hF2VyfZ7YUU4g7ISHeAdfe80q_M8SVzeKgGc9GPwyO1vbr39YmzgH4t_FPAQG7A1YJ_BjHiTEMN9_1-WI7Za5-mI4bXLr4JabE_nIhtG3vSdx2wqnloqjbVEWbXQqhTSuF9sYqeu9ax2p3bSM_Cw44se9ayw5qW9cIGUX5i_zXLyLvrpA6RNWopyW38Gx4AwJ3da4HJ-wCNWcrOxFWvtWoVFjQkp45_Xi5eA-_KoOTmrIRSN85P1wtiIci-H3larFrb1CQuI7xPZ1dtKU-fDlwrxx1NXjObFJnWCQs5LcEhSu5ICN-NKInsdgMfkF6PRRHts5sAUjGd8pAUxzLV4Nf6TpKQVY0_sNTg-S-8mPo_UcLt-yMJwIfE1lZ5hgA5e2UypWP2i6SOGaAbqzvMjxBLF-LUKWBqpa7S8iF8_Q_6td2PyiLHauAtZ1hXfD_GAtMny7uxEkEHZbeuAY8GNy3np4ez0u4RVgCZPI48Jos2Y-7FkrKGkpJGvmlfFq67PxKF2c6m68-7JNpAY5tKK0lQVQh0RtVtBw-Og1I3kx0A9GpsgHkk2CE5K39VkyPfWpyw4KSaxygXor75PVuAGl1t9uwAR_JTbMWkqucMMtvWH37rDW5Rnh_1fWq5R5R9YGcp29jReVhnUmH8OQqBebi7_AGx1Z3Pgb-7az1BR05wAQcr0fvqDA5nl80qUFxAM1Q78w==", "id": "rs_060035eb87c40e14016a1310e3a77481919471381a53e16bcd", "summary": []}], "tool_calls": [{"id": "call_pgOpkQthEtSHAYt96CTpdHZG", "call_id": "call_pgOpkQthEtSHAYt96CTpdHZG", "response_item_id": "fc_060035eb87c40e14016a1310e453c0819197dd662364c91322", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"- `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.\",\"old_string\":\"- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.\\n- `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"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@@ -23,6 +23,7 @@\\n - `autoshorts.ideas.testbatch` — testbatch validation and renderable-candidate filtering.\\n - `autoshorts.review.approvals` — deterministic Telegram approval command parser.\\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 - `autoshorts.rendering.local_preview` — side-effect-free local preview frame planning from render manifests.\\n - `autoshorts.rendering.manifest` — side-effect-free vertical render manifest generation from shot plans.\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_pgOpkQthEtSHAYt96CTpdHZG", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_kAJM0LvZfACDRMK0SOEaKknr", "call_id": "call_kAJM0LvZfACDRMK0SOEaKknr", "response_item_id": "fc_060035eb87c40e14016a1310ee5290819185c37a27abe07506", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"`validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n\\n`autoshorts.rendering.ffmpeg_renderer.render_ffmpeg_preview_video()` is the explicit local video-rendering boundary. It consumes a validated `FFmpegConcatPlan`, writes the `.concat.txt` file, calls `ffmpeg`, and writes the `.mp4` timing preview. It returns `FFmpegRenderResult` with `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` and `external_calls=(\\\"ffmpeg\\\",)`.\\n\\nThe renderer exports `yuv420p` MP4 with `+faststart`. Tests skip if `ffmpeg`/`ffprobe` are unavailable and otherwise smoke-probe width, height, frame rate, and approximate duration.\\n\\n## Social media draft\",\"old_string\":\"`validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n\\n## Social media draft\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"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@@ -126,6 +126,10 @@\\n \\n `validate_ffmpeg_concat_plan()` rejects mismatched or missing values, non-`.mp4` output paths, non-`.concat.txt` concat paths, non-positive dimensions/fps, missing entries, non-PNG frame paths, non-positive durations, side effects, and external tool calls. This layer still does not write the concat file and does not call FFmpeg.\\n \\n+`autoshorts.rendering.ffmpeg_renderer.render_ffmpeg_preview_video()` is the explicit local video-rendering boundary. It consumes a validated `FFmpegConcatPlan`, writes the `.concat.txt` file, calls `ffmpeg`, and writes the `.mp4` timing preview. It returns `FFmpegRenderResult` with `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` and `external_calls=(\\\"ffmpeg\\\",)`.\\n+\\n+The renderer exports `yuv420p` MP4 with `+faststart`. Tests skip if `ffmpeg`/`ffprobe` are unavailable and otherwise smoke-probe width, height, frame rate, and approximate duration.\\n+\\n ## Social media draft\\n \\n `autoshorts.social.draft.build_social_media_draft()` turns a validated `VideoCandidate` plus `RenderPreview` into an approval-first `SocialMediaDraft` containing:\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_kAJM0LvZfACDRMK0SOEaKknr", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_hcRUmSKGLAp9Q9bZt4AWFjFV", "call_id": "call_hcRUmSKGLAp9Q9bZt4AWFjFV", "response_item_id": "fc_060035eb87c40e14016a1310fb55008191958b458eef3e5f86", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is used only by the explicit local preview renderer boundary and remains an external system prerequisite.\\n\\n## Planned architecture\",\"old_string\":\"Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is not used yet.\\n\\n## Planned architecture\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"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@@ -299,7 +299,7 @@\\n python -m pytest tests -q\\n ```\\n \\n-Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is not used yet.\\n+Runtime dependencies currently include Pillow for deterministic local PNG preview frames. FFmpeg is used only by the explicit local preview renderer boundary and remains an external system prerequisite.\\n \\n ## Planned architecture\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md\"], \"lint\": {\"status\": \"skipped\", \"message\": \"No linter for .md files\"}}", "tool_call_id": "call_hcRUmSKGLAp9Q9bZt4AWFjFV", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "Need manual verify.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need manual verify."}], "id": "msg_060035eb87c40e14016a131101bf4c819190590dee93304280", "phase": "commentary"}], "tool_calls": [{"id": "call_oiC2uTDChOCKKR5v8IgErSHU", "call_id": "call_oiC2uTDChOCKKR5v8IgErSHU", "response_item_id": "fc_060035eb87c40e14016a13110207f4819198efa0e8183c50f8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests -q && python - <<'PY'\\nfrom tempfile import TemporaryDirectory\\nfrom pathlib import Path\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\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\\nbrief = create_content_brief(\\n    pillar_id='ai_life_systems',\\n    topic='AI weekly review routine',\\n    audience_problem='I use AI tools but still lose control of priorities.',\\n    payoff='a 10-minute Sunday review that keeps AI as assistant, not boss',\\n)\\nscript = create_script_draft(brief)\\npreview_plan = build_local_preview_plan(build_render_manifest(create_shot_plan(script)))\\nwith TemporaryDirectory() as tmp:\\n    write_result = write_local_preview_frames(preview_plan, output_dir=Path(tmp)/'frames')\\n    concat_plan = build_ffmpeg_concat_plan(preview_plan, write_result, output_path=Path(tmp)/'preview.mp4')\\n    result = render_ffmpeg_preview_video(concat_plan)\\n    out = Path(result.output_path)\\n    concat = Path(result.concat_file_path)\\n    print(result.script_id)\\n    print(out.name, out.exists(), out.stat().st_size > 0)\\n    print(concat.name, concat.exists())\\n    print(result.side_effects, result.external_calls, result.returncode)\\nPY\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
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{"role": "assistant", "content": "Need git commit.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need git commit."}], "id": "msg_060035eb87c40e14016a13111d526c8191948475aae66a3bed", "phase": "commentary"}], "tool_calls": [{"id": "call_zGo0h2Opda1zMB6GduEOQ4p4", "call_id": "call_zGo0h2Opda1zMB6GduEOQ4p4", "response_item_id": "fc_060035eb87c40e14016a13111da2888191ae676caba4715e99", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git add README.md autoshorts/rendering/ffmpeg_renderer.py tests/test_ffmpeg_renderer.py && git commit -m \\\"feat: render local ffmpeg preview videos\\\" && git log --oneline -n 10 && git status --short\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"M README.md\\n?? autoshorts/rendering/ffmpeg_renderer.py\\n?? tests/test_ffmpeg_renderer.py\\n[feat/core-approval-quality-gates 89a0ebe] feat: render local ffmpeg preview videos\\n 3 files changed, 169 insertions(+), 1 deletion(-)\\n create mode 100644 autoshorts/rendering/ffmpeg_renderer.py\\n create mode 100644 tests/test_ffmpeg_renderer.py\\n89a0ebe feat: render local ffmpeg preview videos\\n1128b7c feat: add ffmpeg concat planning\\n7e881b3 feat: write local preview frame cards\\n02ecf59 feat: add local preview frame planning\\n3b387f4 feat: add demo social draft cli\\n515c549 feat: add social draft delivery preparation\\na7d10fa feat: render social media draft reviews\\n1f6aa2c feat: add social media draft model\\nf8c5588 feat: add demo render preview cli\\n42ac32f feat: add side-effect-free render preview\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_zGo0h2Opda1zMB6GduEOQ4p4", "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg MP4 Preview Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\n## Neue API\n\n```python\nrender_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\nWir haben damit den ersten echten lokalen Video-Artefakt-Schritt.\n\n## FFmpegRenderResult\n\n```python\nFFmpegRenderResult(\n    script_id=...,\n    output_path=...,\n    concat_file_path=...,\n    returncode=0,\n    stdout=...,\n    stderr=...,\n    side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\"),\n    external_calls=(\"ffmpeg\",),\n)\n```\n\nAlso diesmal klar: **ja**, hier wird geschrieben und **ja**, hier wird FFmpeg aufgerufen. Kein stilles Herumgeschmuggel in einem “Formatter”. Vorbildlich langweilig.\n\n## Was der Renderer macht\n\n`render_ffmpeg_preview_video()`:\n\n1. validiert `FFmpegConcatPlan`\n2. schreibt `.concat.txt`\n3. ruft `ffmpeg`\n4. erzeugt `.mp4`\n5. nutzt:\n   - concat demuxer\n   - `fps=<plan.fps>`\n   - `format=yuv420p`\n   - `+faststart`\n\n## Tests\n\nNeue Tests prüfen:\n\n- concat file wird geschrieben\n- MP4 wird erzeugt\n- MP4 ist non-empty\n- Result metadata korrekt\n- Side Effects korrekt:\n  ```text\n  (\"write_concat\", \"call_ffmpeg\", \"write_mp4\")\n  ```\n- External Calls korrekt:\n  ```text\n  (\"ffmpeg\",)\n  ```\n- invalid plan wird rejected\n- `ffprobe` Smoke Test, falls verfügbar:\n  - width `540`\n  - height `960`\n  - fps `24`\n  - duration ungefähr `35s`\n\nTests skippen sauber, falls `ffmpeg` / `ffprobe` nicht installiert sind.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_renderer'\n```\n\nGREEN:\n\n```text\n3 passed\n```\n\nFull suite:\n\n```text\n109 passed in 5.96s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\npreview.mp4 True True\npreview.concat.txt True\n('write_concat', 'call_ffmpeg', 'write_mp4') ('ffmpeg',) 0\n```\n\nBedeutung:\n\n- Script korrekt\n- MP4 existiert\n- MP4 ist non-empty\n- concat file existiert\n- Side Effects korrekt\n- External Call korrekt\n- FFmpeg returncode `0`\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_renderer`\n- explizite FFmpeg Boundary\n- `.concat.txt` + `.mp4`\n- `yuv420p`\n- `+faststart`\n- FFmpeg als externes System-Prerequisite\n\n## Commit\n\n```text\n89a0ebe feat: render local ffmpeg preview videos\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Aktueller Stand der Pipeline\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt brauchen wir einen Demo-CLI für den kompletten lokalen Preview-Render:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo\n```\n\nZiel:\n\n- deterministic Demo Content bauen\n- PNG Frames schreiben\n- FFmpegConcatPlan bauen\n- MP4 rendern\n- Metadaten ausgeben:\n  - output path\n  - concat file path\n  - frame count\n  - side effects\n  - external calls\n- keine Telegram Calls\n- keine Plattform Calls\n- keine AI Calls\n- keine Secrets\n\nDann haben wir einen echten lokalen Command, der einen reviewbaren MP4-Timing-Preview erzeugt. Noch nicht hübsch wie finaler Content — aber als Timing-/Approval-Dummy endlich konkret sichtbar.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: FFmpeg MP4 Preview Renderer\n\nNeue Dateien:\n\n```text\nautoshorts/rendering/ffmpeg_renderer.py\ntests/test_ffmpeg_renderer.py\n```\n\n## Neue API\n\n```python\nrender_ffmpeg_preview_video(plan: FFmpegConcatPlan) -> FFmpegRenderResult\n```\n\nDer Flow ist jetzt:\n\n```text\nRenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\nWir haben damit den ersten echten lokalen Video-Artefakt-Schritt.\n\n## FFmpegRenderResult\n\n```python\nFFmpegRenderResult(\n    script_id=...,\n    output_path=...,\n    concat_file_path=...,\n    returncode=0,\n    stdout=...,\n    stderr=...,\n    side_effects=(\"write_concat\", \"call_ffmpeg\", \"write_mp4\"),\n    external_calls=(\"ffmpeg\",),\n)\n```\n\nAlso diesmal klar: **ja**, hier wird geschrieben und **ja**, hier wird FFmpeg aufgerufen. Kein stilles Herumgeschmuggel in einem “Formatter”. Vorbildlich langweilig.\n\n## Was der Renderer macht\n\n`render_ffmpeg_preview_video()`:\n\n1. validiert `FFmpegConcatPlan`\n2. schreibt `.concat.txt`\n3. ruft `ffmpeg`\n4. erzeugt `.mp4`\n5. nutzt:\n   - concat demuxer\n   - `fps=<plan.fps>`\n   - `format=yuv420p`\n   - `+faststart`\n\n## Tests\n\nNeue Tests prüfen:\n\n- concat file wird geschrieben\n- MP4 wird erzeugt\n- MP4 ist non-empty\n- Result metadata korrekt\n- Side Effects korrekt:\n  ```text\n  (\"write_concat\", \"call_ffmpeg\", \"write_mp4\")\n  ```\n- External Calls korrekt:\n  ```text\n  (\"ffmpeg\",)\n  ```\n- invalid plan wird rejected\n- `ffprobe` Smoke Test, falls verfügbar:\n  - width `540`\n  - height `960`\n  - fps `24`\n  - duration ungefähr `35s`\n\nTests skippen sauber, falls `ffmpeg` / `ffprobe` nicht installiert sind.\n\n## TDD eingehalten\n\nRED:\n\n```text\nModuleNotFoundError: No module named 'autoshorts.rendering.ffmpeg_renderer'\n```\n\nGREEN:\n\n```text\n3 passed\n```\n\nFull suite:\n\n```text\n109 passed in 5.96s\n```\n\n## Manuell verifiziert\n\n```text\nai_life_systems-ai-weekly-review-routine\npreview.mp4 True True\npreview.concat.txt True\n('write_concat', 'call_ffmpeg', 'write_mp4') ('ffmpeg',) 0\n```\n\nBedeutung:\n\n- Script korrekt\n- MP4 existiert\n- MP4 ist non-empty\n- concat file existiert\n- Side Effects korrekt\n- External Call korrekt\n- FFmpeg returncode `0`\n\n## README aktualisiert\n\nDokumentiert:\n\n- `autoshorts.rendering.ffmpeg_renderer`\n- explizite FFmpeg Boundary\n- `.concat.txt` + `.mp4`\n- `yuv420p`\n- `+faststart`\n- FFmpeg als externes System-Prerequisite\n\n## Commit\n\n```text\n89a0ebe feat: render local ffmpeg preview videos\n```\n\nArbeitsverzeichnis ist sauber.\n\n## Aktueller Stand der Pipeline\n\n```text\nContentBrief\n→ ScriptDraft\n→ VideoCandidate\n→ ReviewBatch\n→ Telegram ReviewDelivery\n→ ShotPlan\n→ RenderManifest\n→ HTML Preview\n→ SocialMediaDraft\n→ Social Draft Review\n→ SocialDraftDelivery\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt brauchen wir einen Demo-CLI für den kompletten lokalen Preview-Render:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir data/previews/demo\n```\n\nZiel:\n\n- deterministic Demo Content bauen\n- PNG Frames schreiben\n- FFmpegConcatPlan bauen\n- MP4 rendern\n- Metadaten ausgeben:\n  - output path\n  - concat file path\n  - frame count\n  - side effects\n  - external calls\n- keine Telegram Calls\n- keine Plattform Calls\n- keine AI Calls\n- keine Secrets\n\nDann haben wir einen echten lokalen Command, der einen reviewbaren MP4-Timing-Preview erzeugt. Noch nicht hübsch wie finaler Content — aber als Timing-/Approval-Dummy endlich konkret sichtbar."}], "id": "msg_060035eb87c40e14016a131124acf48191b7350bcfb4812447", "phase": "final_answer"}], "timestamp": "2026-05-24T16:54:47.279842"}
{"role": "assistant", "content": "", "reasoning": "**Implementing CLI and Next Steps**\n\nI need to take action by loading the relevant skills. After that, I should implement the command-line interface (CLI) as requested. It’s essential to outline the next steps clearly. I’ll make sure to use the appropriate tools to complete this task effectively. Alright, let’s get going and figure this out together!", "finish_reason": "tool_calls", "reasoning_content": "**Implementing CLI and Next Steps**\n\nI need to take action by loading the relevant skills. After that, I should implement the command-line interface (CLI) as requested. It’s essential to outline the next steps clearly. I’ll make sure to use the appropriate tools to complete this task effectively. Alright, let’s get going and figure this out together!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExMFHz5d7dIFrubb1M4fM8npUSLewg_qliNwpsrOENhjlkr1jjuXpbL_9YhUwHfx8QGs97Q00vl5belWWNZEnvBmAUZO5hP4yeesU-r4oMogafOr_nbfJ3VxBQyYwxHsfFxNXbj4kN3sPP_gYCpghEFG254wGSAViMaUM-bfe8anqyI-nSoFIi-pZu7pG49qUCfons6U7XY6xmO_mc2fE7Gycv0BbtijPJHJ35z8S3rRNrlcjjV1d94rC0zrYrQaMfAXRd8EuKGue-jljJ6ajipBxSULWT13z29_zwcfWjjHtpA9eN70U6ov3lWdzL1UR3qQc931Yu4aPkzWnrK49MTUgtRhFeD5BTUKhqPkWSXMkmVsRWq9UddilNpTRB1r1YJgBRs-9gHS5XYEw6Xxr_WAjH8--u8Da1iDmdxoIaOojowhgzSFbk8VjoDiazazo9dAqkZcf-UeHMjli1CdZ7yI6UyFsOj3_xIW4moDoU5KSKjRQFIctjNmqxEQ-r6PGMPXyTbfPhdLHhbpZAKjJKeWIF-rbrbG9ZsD9XypwRxnV4nQk_PyX3DPimsP1B6H4HLFhqzKyBh2WqevfAYFEXHFsbCu80iQcBZcmMTIr_wDlpr6JzYBYwlaoBDrEBskbiol60MFU9_02QJNuybj8WHjjL1Gsbq68_jLGjVFC1WI8cCX-6s9uXTIGRSf1jI3PitVlH0wnLkIv4iasFAI91vNpg_yILSIl_Yki0U85i4RYMkG5WaCTk36Q8xIo9Ufudq3NF71SgUaQVyj8EPuTOp_5Kqglg-vLdk_xyCk1IYIA7yrfbyRdydyLgbnv1V8i5QM_Py07vf1h-5ELqDKyLGcX0lILQSS9Eyz6VfdaTWUJPFKCVto-amrlD97RdhjLp0P2KXn88SaN5pnd0mNnabkBqjnF4nUAx2TTt6qKL9uUu4BRPzWfXPsGsPhsaf4tpa7-9dmrakvtuNjvZWPksAQ0f36CiFlKu5Bpm2dv8UTqKZBodWm3syXhhsguHU9HMtgLTvXR96FyZ_tnYrQOF_Q8lWBMJnk_zNsAxYEHTvT-CU=", "id": "rs_060035eb87c40e14016a131301c5a48191bf4083cbea54149f", "summary": [{"type": "summary_text", "text": "**Implementing CLI and Next Steps**\n\nI need to take action by loading the relevant skills. After that, I should implement the command-line interface (CLI) as requested. It’s essential to outline the next steps clearly. I’ll make sure to use the appropriate tools to complete this task effectively. Alright, let’s get going and figure this out together!"}]}], "tool_calls": [{"id": "call_mbQecwMWLQnX3dAgKD1HdhJU", "call_id": "call_mbQecwMWLQnX3dAgKD1HdhJU", "response_item_id": "fc_060035eb87c40e14016a1313058a2c8191bc0bc7709b16b55a", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}, {"id": "call_CZLGTjFFJoNegpwtfjBYFbzO", "call_id": "call_CZLGTjFFJoNegpwtfjBYFbzO", "response_item_id": "fc_060035eb87c40e14016a1313058eb881918782741451174127", "type": "function", "function": {"name": "skill_view", "arguments": "{\"file_path\":\"references/manifest-preview-renderer.md\",\"name\":\"social-media-shorts-automation\"}"}}], "timestamp": "2026-05-24T17:04:58.407704"}
{"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_mbQecwMWLQnX3dAgKD1HdhJU", "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"social-media-shorts-automation\", \"file\": \"references/manifest-preview-renderer.md\", \"content\": \"# Manifest-driven preview renderer for Shorts\\n\\nUse this when a shorts automation project already has render manifests and needs the first video output without slipping into generic AI slideshow rendering.\\n\\n## Durable pattern\\n\\nKeep the renderer deterministic and manifest-driven:\\n\\n```text\\nRender manifest JSON -> scene/card frames -> FFmpeg MP4 -> preview/approval\\n```\\n\\nThe LLM should not render frames. It may help create scripts, shot plans, and manifests, but the renderer should consume structured timeline fields and produce reproducible preview media.\\n\\n## Renderer v1 architecture\\n\\nA pragmatic first renderer can use:\\n\\n- Pillow/PIL to draw vertical scene frames/cards.\\n- FFmpeg concat demuxer to turn frame images into an MP4.\\n- Render manifest fields for timing, text, asset type, visual direction, retention goal, resolution, aspect ratio, and fps.\\n\\nRecommended module shape:\\n\\n- `build_preview_frames(manifest, width, height)` or `build_local_preview_plan(manifest)`\\n  - side-effect-free plan/artifact list, one representative visual item per manifest timeline item\\n  - uses the manifest text, asset type, timing, and retention goal\\n  - records reduced preview settings such as `540x960`, `24fps` for fast timing review\\n- `write_frame_images(frames, output_dir)` or `write_local_preview_frames(plan, output_dir)`\\n  - first explicit artifact-writing boundary\\n  - writes numbered PNGs for inspection/debugging\\n  - returns metadata with `side_effects=(\\\"write_png\\\",)` and `external_calls=()`\\n- `build_ffmpeg_concat_plan(preview_plan, frame_write_result, output_path)`\\n  - side-effect-free FFmpeg plan\\n  - validates frame count, `.png` paths, positive durations, `.mp4` output, `.concat.txt` path\\n  - creates concat-demuxer text but does not write it or call FFmpeg\\n- `render_preview_video(concat_plan)` / `render_ffmpeg_preview_video(plan)`\\n  - explicit FFmpeg boundary only after the concat plan is green\\n  - writes the `.concat.txt`, calls FFmpeg, and writes MP4\\n  - exports `yuv420p` MP4 with `+faststart`\\n  - returns metadata with `side_effects=(\\\"write_concat\\\", \\\"call_ffmpeg\\\", \\\"write_mp4\\\")` and `external_calls=(\\\"ffmpeg\\\",)`\\n\\nThis is a preview/timing renderer, not the final beauty renderer. It validates scene flow, text weight, timing, and approval review before spending time on polished assets. Keep the side-effect-free plan stages separate from artifact-writing/rendering stages so tests and review can catch bad timing before any heavy media step.\\n\\n## TDD checks worth preserving\\n\\nWrite tests before implementation that assert:\\n\\n- side-effect-free preview plans have no external calls and no file writes\\n- preview frames equal the number of visual scene items\\n- first frame starts at `0ms`\\n- frame durations are positive and match manifest timing\\n- image dimensions match requested vertical preview size\\n- first frame text matches the first visual timeline item\\n- PNG frame files are written and valid at the frame-writer boundary\\n- generated media paths stay under runtime/temp preview directories and are gitignored\\n- concat plans are side-effect-free, reference only `.png` frames, produce `.mp4` output paths, and contain positive durations\\n- renderer results explicitly report `write_concat`, `call_ffmpeg`, and `write_mp4` side effects\\n- MP4 output exists and is non-empty\\n- `ffprobe` verifies width, height, frame rate, and duration for real render smoke tests\\n\\n## CLI pattern\\n\\nProvide a small script such as `scripts/render_preview.py`:\\n\\n```bash\\npython3 scripts/render_preview.py \\\\\\n  data/render_manifests/<batch>/<video>.json \\\\\\n  --out-dir data/previews/<batch> \\\\\\n  --width 540 \\\\\\n  --height 960 \\\\\\n  --fps 24\\n```\\n\\nUse reduced preview resolution like `540x960` for fast local review. Keep final `1080x1920` as a later renderer-quality target.\\n\\n## Repo hygiene\\n\\n- Keep generated MP4/PNG media out of Git via `.gitignore`.\\n- Commit renderer code, tests, scripts, manifests, and lightweight planning artifacts.\\n- Do not commit rendered previews unless explicitly needed as small fixtures.\\n- Add runtime dependencies explicitly, e.g. `Pillow>=10.0`; treat FFmpeg as an external system prerequisite checked at runtime.\\n\\n## Pitfalls\\n\\n- Do not let the renderer infer timing or visuals from prose when the manifest already encodes them.\\n- Do not start with AI video generation as the renderer; use it later only as an asset supplier.\\n- Do not over-polish the first renderer. Its job is approval/timing validation.\\n- Do not publish from this step. Preview rendering still requires human approval before posting.\\n- Watch typography consistency in early previews: fixed-size fonts can make some scenes look huge and others tiny. Add tests/metadata for fitted font sizes, max line counts, and minimum retention-goal readability before scaling to more videos.\", \"file_type\": \".md\"}", "tool_call_id": "call_CZLGTjFFJoNegpwtfjBYFbzO", "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "assistant", "content": "", "reasoning": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git.", "finish_reason": "tool_calls", "reasoning_content": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExMRxADC70NJSOPEKelkfHmOb1h0ArNfy8CiwS2apkPbt7dA4w85MaxoY-jb45aWldON0t056zhR8-q8pmNbnthK64YST2VG6NKMNn7QrJJULimtDjURE3-ZvMIaLpomiVS2mUOlpPG5MFgft7fDdfzwAnKkt5dUxaax3Kq8KAE8__FX8SwcMKoBDX3W3WfLfaIArhYOLkYSf0KRz1GPtQM5YVBEZEkavAQz8C-ti811wnW2ZNpX5GjPrecFlyyVzNdnh2wQojFHhuXxFL6YfmePs6LCBgWyd8OVbR-JYn5eQK2-OB9DAE6hqOSXZZoySPzF7vDsL5rdZImEH4zHr1oKEZUqKbDMtKcPBHkJ4dhgCyWxEsqjQQzyOAWcCf5iJlxic54uCShwy2Lj9IjYiBjTuADY59uD_9mrFjgsSibs6SfkHra7JeQwHSvKiujjFg9UF9WUar36bGRjs_DqiXdNlNIWfHOZoNeE0YIH7s8Xsu7scR_qbB2azw7LfchA-Mf90DyjkTZqGOr7rDIu0LLI6UTiHv_apmPD6S-OEUdvz6JKm892G7tBcquE7tT7oVGgZZSleLreQHyDlHuQqjsNiOc03jgh2ZyI0kz8JOed3MBDwzC7P0DMRzeNN8tDdZW1nSFY75TZQvC246LMN4fwDQCNWjS_eWgWTFAbQSpVKLRVeuzu2L8VVq7Hy4C8_OAnIFC9vANn9fxmJtRRDvImZohddGx0ljppnBM2FocYuJjNeLgEHE_K4iSLWY7zElURSaSRPJqWzLLsAHgYVwxmmbf49NqLOOBVIn3p2c7hPf-8nguaRCdPZArLfXT0ppsrAVFSvtCpYL5biaFRIFiFmCCd3UBucIKsqWM2q-svw1buojOn2WChjAxBLLYITYuKg_6PK6JzlTPS4b1E4s_hVWevhPI9oNNGP9scAZEXckzw2PNUNuJ7RnDWf4hI0GJZbVpXpuwVrhnNRHfbTlbaxicOKiR69olwGhIYR01LeFTidM2zBYEtiamkMgd6CNpIhaq3eQ_bok-hIbMseD0JSkfRCDwevmvPbTSL_LE9pI7REwBeWhUFZ73mrJotU7Kij4XJNa1qqtBnLw40vVjqzVfrsbfVWb8WKJovLzBkpviCSc_BaG8k7hpq2Ufb9HMuUVNVsQTiAkcY9lbRwyY561LE94mexuZTYLWo1LLk0VLeaU0U886ExG4285xmc6z0r9OSpr3YIYJ8JauOMLE5xfYuYIvNO-2CAkBdfBkyc2wQdXcjGQhKogKlTmFLeeDCkhmUvbULc6tGJ7O5Q1fU2XpFQlxHx_Cp7fSpCWTaxmRBRn8wgwcsRr5qBjybxEkZacEZHPl6QXqRSW7VtE2itRAHZXqIyX-ULYNr13FAtuax4vDkY3_EcMSBJu-GpwyyCOS5HoxlITG8Vt2J1xfxtIBo-cXcaV0f47wloP3k6cEBHGM_JMZR6iU9SBc5bCJm7QtzTW8q4vPdmEHi3dzFugPM2TZQd7fnGQGPaBHhyZXB6mlnlgL0nAUJS9S9emZ4L6H_dHPbv9CuAWf4EEuJCMRfwp24lmy5eGnH1lW2VSC-oro61aYfsB3ljuwkRY53BX8fGqA0MDcBbd0Dki0Hmwms82jCqCucDbgjhxjVcHNO9fvNQ_K-5ZbkJawnJo95q7v8pHZ5ORgQTwO0ydHTKunl4alBuQeI5I9dS0jYk9rdrns9Be7WKihamRbYH-3C0a9TbZkEAQDQjUETYW7XZbj3y3JaOT43kBIdj5INVNjHf6vTVkDKFGafFyo3DtvAaDYbRmtGREycqCJ5mRw0B31MA3LpYsS3_Zktd4U8qLI5NGiD53lnl9U1lba_tuugnFDzKQcNIrgrNzvr-TXMzB86R5kElf-KCGU21pQCIDXg0it_z6VqcvX-apIn8ptdWUlg5vsK7xYvbdBh4XvPow==", "id": "rs_060035eb87c40e14016a13130a2e0c819180d588e5a7a755f7", "summary": [{"type": "summary_text", "text": "**Planning CLI Implementation**\n\nI need to implement a demo CLI and start by writing a test. It's essential to ensure that the output directory uses a temporary location. The main function should accept arguments like `--out-dir`, and I can test it by invoking `main([\"--out-dir\", str(tmp_path)])`. I’m considering if the test should skip if ffmpeg isn't available. Once that’s set, I might also send media for user approval through a content preview. After creating the CLI, I could generate actual previews in `/tmp`, ensuring generated media is ignored in Git."}]}], "tool_calls": [{"id": "call_mUUvi6zNFHcWxtrNoH1itcg8", "call_id": "call_mUUvi6zNFHcWxtrNoH1itcg8", "response_item_id": "fc_060035eb87c40e14016a13131106f88191a6ce74e4b96faab7", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"import shutil\\nfrom pathlib import Path\\n\\nimport pytest\\n\\nfrom autoshorts.cli.demo_local_preview_video import main\\n\\npytestmark = pytest.mark.skipif(shutil.which(\\\"ffmpeg\\\") is None, reason=\\\"ffmpeg is required\\\")\\n\\n\\ndef test_demo_local_preview_video_cli_renders_mp4_and_prints_metadata(tmp_path, capsys):\\n    exit_code = main([\\\"--out-dir\\\", str(tmp_path)])\\n\\n    captured = capsys.readouterr()\\n    output_path = tmp_path / \\\"preview.mp4\\\"\\n    concat_path = tmp_path / \\\"preview.concat.txt\\\"\\n\\n    assert exit_code == 0\\n    assert output_path.exists()\\n    assert output_path.stat().st_size > 0\\n    assert concat_path.exists()\\n    assert \\\"Preview video: \\\" in captured.out\\n    assert str(output_path) in captured.out\\n    assert \\\"Concat file: \\\" in captured.out\\n    assert str(concat_path) in captured.out\\n    assert \\\"Frame count: 5\\\" in captured.out\\n    assert \\\"Side effects: write_concat, call_ffmpeg, write_mp4\\\" in captured.out\\n    assert \\\"External calls: ffmpeg\\\" in captured.out\\n    assert \\\"Script: ai_life_systems-ai-weekly-review-routine\\\" in captured.out\\n    assert captured.err == \\\"\\\"\\n    assert \\\"publish\\\" not in captured.out.casefold()\\n    assert \\\"post\\\" not in captured.out.casefold()\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\"}"}}], "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1171, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.py\\\">\\nERROR [6:6] Import \\\"autoshorts.cli.demo_local_preview_video\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_mUUvi6zNFHcWxtrNoH1itcg8", "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_WFWl0WnDVFNZnXMh2KGrn55U", "call_id": "call_WFWl0WnDVFNZnXMh2KGrn55U", "response_item_id": "fc_060035eb87c40e14016a131319a4408191a6da05d7d0f5e2e4", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python -m pytest tests/test_demo_local_preview_video_cli.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot\"}"}}], "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________ ERROR collecting tests/test_demo_local_preview_video_cli.py __________\\nImportError while importing test module '/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_demo_local_preview_video_cli.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_demo_local_preview_video_cli.py:6: in <module>\\n    from autoshorts.cli.demo_local_preview_video import main\\nE   ModuleNotFoundError: No module named 'autoshorts.cli.demo_local_preview_video'\\n=========================== short test summary info ============================\\nERROR tests/test_demo_local_preview_video_cli.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.14s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_WFWl0WnDVFNZnXMh2KGrn55U", "timestamp": "2026-05-24T17:04:58.407704"}
{"role": "assistant", "content": "", "reasoning": "**Planning CLI Implementation**\n\nI'm thinking I need to implement a Command Line Interface (CLI) for my project. I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. I’m excited to see how it all comes together!", "finish_reason": "tool_calls", "reasoning_content": "**Planning CLI Implementation**\n\nI'm thinking I need to implement a Command Line Interface (CLI) for my project. I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. I’m excited to see how it all comes together!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExMg2MFh9c6ZjLrz1U1jESwGr62c26CGmpcTuSlK9nkNUk2g_TXkJbxi9Pu0Od-aqVkv5LhAEeJNeuzTvL8Z7GR-Ioz8kv1JgYSXS9bADZh3hahb-UphjeVPjRIxzQhZ0Sv_SOWIu83Bi8oH7v-gUMjn_VwlrtzSq43P6N10dUGPUUcGHtpOOIZX0q__ulcD-SfGlkEJeO_cy_kwtFkgBa8KvQ_PfEN6gMmSjGEBWn6CApD07TehIci7IjdwrwQWXKVOXF6a6Iyz4idAXpkId2q1yxwvorM86Bm8462_gm1XjWdkwOJ-WlNFpeiW70E9gpDfqd0cSOuR54FgCRs6oPT0msHrE-WMZ9El4CmhXujObh47kDgxqdZaJcuF2vWJknkOaHlNoOpBrnxwto4OhZ0ubD6W9axdpAtCd2xtXuiC4Ahef7-Vtl65YUDOB-8frWb17soeJgCZtvq-LXKQSAAd85jDyc6liZhFLQ9YD9O5fsV2qtwc37ekv_2jzE15UuP0kZwOnhvf-dXM0DsOHnUEPyXpbsu_58g4tBwR9Nh2ojlF-scrM2AuUcHwCvPgXjEu5fAdLdWra-_oWllzVv6z0ZGsytymYfhxNbFSMd802okDWhMwOiMyHVnacR4Q9eMf0SxfB7qG0J42ZZdsUbXd_UcPdesNeKxSUVFkU3NEzhE2aoUBcbNGhxtbmX_Y9A8Oa1u6go-k-s2Oi1B7kAxwigDyMSBi4f69DuB9DFLZxXC9xNgfJKrzYGngIGG7Y87ESY9tw8H1M7gyFGeYpIfLDZgjHAMOBzT3Nn-l2AX8bbGmv3xrsFsKa2T6cay301Z4Qhb0GRgyPDHKKrsR63eE1BNqcWyLaiwp1GOekVksaiaaqeQkdpl2eQudNcZ84D_ZQJu7DjXpz2KA4rz7kiJaVl0SXmKzCziYE1Jhzyw6I0JqWLb5fFyL3SQSuxt2PV7aL6UyWSo5KG2KrO37BWvbDQ==", "id": "rs_060035eb87c40e14016a13131e947081918dfbc701b6d8c9fa", "summary": [{"type": "summary_text", "text": "**Planning CLI Implementation**\n\nI'm thinking I need to implement a Command Line Interface (CLI) for my project. I'll use argparse to handle the command line arguments effectively. This will help ensure I build a robust pipeline that can efficiently process commands. I'm already imagining how I'll structure the commands and options to make it user-friendly. It seems like a solid step forward for enhancing accessibility in the project. I’m excited to see how it all comes together!"}]}], "tool_calls": [{"id": "call_FyYTsG9izfK37BgpzSbG3Fsi", "call_id": "call_FyYTsG9izfK37BgpzSbG3Fsi", "response_item_id": "fc_060035eb87c40e14016a131320fa288191b17d167568cb90c1", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Render the deterministic local MP4 timing preview demo.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport sys\\nfrom collections.abc import Sequence\\nfrom pathlib import Path\\nfrom typing import TextIO\\n\\nfrom autoshorts.content.brief import create_content_brief\\nfrom autoshorts.content.script_draft import create_script_draft\\nfrom autoshorts.rendering.ffmpeg_plan import build_ffmpeg_concat_plan\\nfrom autoshorts.rendering.ffmpeg_renderer import render_ffmpeg_preview_video\\nfrom autoshorts.rendering.local_frame_writer import write_local_preview_frames\\nfrom autoshorts.rendering.local_preview import build_local_preview_plan\\nfrom autoshorts.rendering.manifest import build_render_manifest\\nfrom autoshorts.rendering.shot_plan import create_shot_plan\\n\\n\\ndef _parser() -> argparse.ArgumentParser:\\n    parser = argparse.ArgumentParser(description=\\\"Render deterministic AutoShorts local preview MP4.\\\")\\n    parser.add_argument(\\n        \\\"--out-dir\\\",\\n        default=\\\"data/previews/demo-local-preview\\\",\\n        help=\\\"Directory for PNG frames, concat file, and preview MP4.\\\",\\n    )\\n    return parser\\n\\n\\ndef _build_preview_video(output_dir: Path):\\n    brief = create_content_brief(\\n        pillar_id=\\\"ai_life_systems\\\",\\n        topic=\\\"AI weekly review routine\\\",\\n        audience_problem=\\\"I use AI tools but still lose control of priorities.\\\",\\n        payoff=\\\"a 10-minute Sunday review that keeps AI as assistant, not boss\\\",\\n    )\\n    script = create_script_draft(brief)\\n    shot_plan = create_shot_plan(script)\\n    manifest = build_render_manifest(shot_plan)\\n    preview_plan = build_local_preview_plan(manifest)\\n    frame_result = write_local_preview_frames(preview_plan, output_dir=output_dir / \\\"frames\\\")\\n    concat_plan = build_ffmpeg_concat_plan(preview_plan, frame_result, output_path=output_dir / \\\"preview.mp4\\\")\\n    render_result = render_ffmpeg_preview_video(concat_plan)\\n    return preview_plan, frame_result, render_result\\n\\n\\ndef main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int:\\n    \\\"\\\"\\\"Render demo PNG frames and MP4 timing preview, then print metadata.\\\"\\\"\\\"\\n\\n    args = _parser().parse_args(argv)\\n    output = stdout or sys.stdout\\n    output_dir = Path(args.out_dir)\\n    preview_plan, frame_result, render_result = _build_preview_video(output_dir)\\n\\n    print(f\\\"Script: {render_result.script_id}\\\", file=output)\\n    print(f\\\"Preview video: {render_result.output_path}\\\", file=output)\\n    print(f\\\"Concat file: {render_result.concat_file_path}\\\", file=output)\\n    print(f\\\"Frame count: {len(frame_result.frame_paths)}\\\", file=output)\\n    print(f\\\"Preview format: {preview_plan.width}x{preview_plan.height} @ {preview_plan.fps}fps\\\", file=output)\\n    print(f\\\"Frame side effects: {', '.join(frame_result.side_effects)}\\\", file=output)\\n    print(f\\\"Side effects: {', '.join(render_result.side_effects)}\\\", file=output)\\n    print(f\\\"External calls: {', '.join(render_result.external_calls)}\\\", file=output)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/cli/demo_local_preview_video.py\"}"}}], "timestamp": "2026-05-24T17:04:58.407704"}
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{"role": "assistant", "content": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Local Preview Video CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview\n```\n\nDer läuft jetzt komplett durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Verifiziert\n\nOutput:\n\n```text\nScript: ai_life_systems-ai-weekly-review-routine\nPreview video: /tmp/autoshorts-demo-local-preview/preview.mp4\nConcat file: /tmp/autoshorts-demo-local-preview/preview.concat.txt\nFrame count: 5\nPreview format: 540x960 @ 24fps\nFrame side effects: write_png\nSide effects: write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\nexists True\nsize 174359\nframes 5\n```\n\nTests:\n\n```text\n110 passed in 8.46s\n```\n\nCommit:\n\n```text\n496b0ac feat: add demo local preview video cli\n```\n\nArbeitsverzeichnis sauber.\n\nHier ist der erzeugte aktuelle Timing-Preview:\n\nMEDIA:/tmp/autoshorts-demo-local-preview/preview.mp4\n\n---\n\n## Was das jetzt ist\n\nDas ist ein **technischer Timing-/Structure-Preview**, noch kein finaler Content-Preview.\n\nEr zeigt:\n\n- 5 Szenen\n- Timing\n- Hook/Caption Text\n- Scene Labels\n- Asset Mode\n- Motion Instructions\n- grobe vertikale Cards\n\nEr zeigt noch **nicht**:\n\n- echte Motion Graphics\n- Voiceover\n- Musik/SFX\n- AI-/Qwen-/Wan-Visuals\n- final polish\n- echte captions synced to audio\n\nAlso: Es ist der Rohbau. Noch nicht die Villa. Aber immerhin steht kein Zirkuszelt mehr auf der Baustelle.\n\n---\n\n# Nächste Schritte bis zum effektiven Content-Preview zur Freigabe\n\n## Phase 1 — Approval Package mit MP4 + Social Draft\n\nNächster sinnvoller Schritt:\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nZielobjekt:\n\n```python\nContentPreviewApprovalPackage(\n    candidate_id=...,\n    preview_video_path=...,\n    social_review_text=...,\n    approval_commands=(...),\n    requires_human_approval=True,\n    side_effects=(),\n)\n```\n\nDamit haben wir ein einheitliches Ding, das sagt:\n\n> “Hier ist das Video-Preview + Caption/Titel/Hashtags + Freigabe-Kommandos.”\n\n## Phase 2 — Telegram-safe Preview Delivery\n\nDanach:\n\n```text\nContentPreviewApprovalPackage\n→ TelegramPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- Video path vorhanden\n- Approval mandatory\n- kein Upload zu Plattformen\n- kein Auto-Senden ohne explizite Adapter-Grenze\n- keine `publish/post`-Sprache\n\nDann können wir lokal erzeugen:\n\n```text\nMEDIA:/.../preview.mp4\n+\nReview Text\n+\nFreigabe-Kommandos\n```\n\n## Phase 3 — Demo CLI für echten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_content_preview.py\ntests/test_demo_content_preview_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_content_preview --out-dir data/previews/demo-content-preview\n```\n\nOutput:\n\n```text\nPreview video: data/previews/demo-content-preview/preview.mp4\nReview text length: ...\nRequires human approval: yes\nSide effects: write_png, write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\n```\n\nUnd der Text enthält:\n\n```text\n## Content Preview Review\n\nVideo:\n<preview path>\n\nCandidate:\n...\n\nPlatforms:\nYouTube Shorts, TikTok, Instagram Reels\n\nHook:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nAblehnen\nÄndern: <...>\n```\n\n**Das ist der erste echte Freigabe-Preview.**\n\n## Phase 4 — Renderer v2: weniger statisch\n\nAktuell sind es Scene Cards. Für “Content Preview, das nicht beleidigend statisch wirkt” brauchen wir danach:\n\n```text\nautoshorts/rendering/motion_preview.py\n```\n\nZiel:\n\n- simple Ken Burns / pan / zoom\n- kinetic text intro\n- scene transitions\n- progress bar\n- captions renderer-owned\n- sichtbare Bewegung alle 1–2 Sekunden\n\nNoch lokal, noch ohne AI-Video.\n\n## Phase 5 — TTS / Voiceover\n\nDann:\n\n```text\nScriptDraft\n→ VoiceoverScript\n→ TTS audio\n→ caption timing\n```\n\nFür dich relevant:\n\n- Default Voice laut Profil: `de-DE-KillianNeural`\n- Wenn Content englisch bleibt, müssten wir eine englische Voice wählen.\n- Output:\n  - `.mp3` oder `.wav`\n  - später `.srt` / caption timings\n\nDanach:\n\n```text\nMP4 visual preview\n+ TTS audio\n→ audio-synced preview\n```\n\nDas wäre schon deutlich näher an einem echten Content-Preview.\n\n## Phase 6 — Qwen Image / Wan / ComfyUI Assets\n\nErst danach würde ich Qwen/Wan anbinden:\n\n```text\nRenderManifest Scene\n→ Keyframe Prompt\n→ Qwen Image Keyframe\n→ Wan I2V Clip\n→ imported/local asset\n→ deterministic final assembly\n```\n\nWichtig bleibt:\n\n- keine baked-in text in AI assets\n- captions/labels/arrows renderer-owned\n- AI-Video nur für Textur/Bewegung\n- Review jedes Clips vor finaler Assembly\n\n## Meine empfohlene Reihenfolge ab jetzt\n\nKurz und praktisch:\n\n```text\n1. ContentPreviewApprovalPackage\n2. TelegramPreviewDelivery / local delivery object\n3. demo_content_preview CLI\n4. Motion Preview Renderer v2\n5. TTS + synced captions\n6. Qwen/Wan asset layer\n7. Final review package\n8. Erst nach expliziter Freigabe: Plattformadapter\n```\n\n## Direkt nächster Task\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nDamit verbinden wir den vorhandenen MP4-Preview mit dem Social-Media-Freigabetext zu einem echten Content-Preview-Paket. Danach kann ich daraus den Telegram-fähigen Freigabeentwurf machen.", "reasoning": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval.", "finish_reason": "stop", "reasoning_content": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqExOCkjz5xWQK9etJL4VTp7rHPMlVJVtgGoTKWUXufjYstY68jbpg_xCMgSz0zehPScjnIe9mQsmsBohttYOBaYLfb-hrSGglLOszh2XndUm5h3MU3VK5QRbeG9UzoSFlgnx4-DgHE5lVqEgFsZb1y0CeAVpbcRo0PfECCeSyj08hRyz2rurZplWTzLEFtl_wwAuuKzoZB_aXzcfp8D2RSAAmkl1Cn9RyP3uClY-BEygHIbezBYQxsHITpg5N_T9EV6iS6jIq0qQpYbJHuiavxLgeJSo42tab2wAU_9RKRNUf9BdNcQn1mEMV_NLsLY1yLEf9Aq3AP5_cvddsN1kYLjQNH9C01Qjj5VlGxLIBU77c5OBGuc_qhk5_Ipqmh1et571LlD4Ze2rd8rFayVMrfnr4CmA_1S7KAVnbbTjslnzZlNqXRUK-azbvDuVYSZK12qgeAUpP6Hw-1gkuyvAiHPDwrjtb8u4lN0W8JLko2fsu3CoCPTIh8zgbqE3TRCHbqaSS7AVMdA3Y_AMRxa9gNTwwQvzrBONR9zsM5nB3rm3mrdtAg0zQG6RZlzF3KrIvouSxL49pi1a1ewHCICag0TO_W0xvflkfYCaOSeHtThWBnpBFhiTCEYSFtlF0KXRXq2fhwGVFaO5eoramEfWuDjIRTyqhjHgVReUSY8EVQ016Cj96D-tOoEZfRV3Xgm5nSITf1rq4uqW9TTnqAxnNvAvm-IpqF3bUXXAi_30kaBgNiDlAIRrh1TTRXmk69IsyqrSNLw2r0TfqB8fnzJk3OU4u7wfX5ESdWGLxNkIkfb8yBfYdYo_SHA4nBdFjmTu3LNbLmgyZJVuNLFEKAvW-gKXrRvX-RlxoE3zhPgB1ut9k1u6zRC2C8BRb_Jp9NHlZyy3q6zJjsJYMfOdIwa1h4-r9F-RMyPsDjC_Ujiygukqz2mhxbtXrBf-UzbECjyzLI6jKnZfdhgYJoDGVrz_ZHMmsBOAZYn8Met0MSHpPP_dhrHTDL4UcrIhuGGy05IggrKp_rbGYnIxuY3ql9q6IuOPhIMolxyf5thMQAjo87iJjOtfG96K7qyG-4boxdX_ai1wexepTv8bwxWqxEI7WVbyhi1I_KX18Kn1Hgj1q-twn6YdSanSTV2VqCgXGUdkIKl49y1Re-SEpuv2nbfKYIVcp519Te9tI1geXhcp1K07UQ-oSgrCHjcEHInM3jsxF2U2hXkZHu9jB40jX9HKWDYgtfvftf2Wgp4meGj6bPfKD575yhq15ZMoqb9YGZKr2y9J8CV8nkfiL7VxyUMTGuuepImpd5Fn2T5f0YCsdOpiAgnQD1UiCjRT9AhlMitf5I_nIXzBCRQKWpUEjNJWJuMvjFg6aCo1vW6qvCRFEQzXlmSYI8CjBCuSZFot46H-kjom5W9Bux9ZxJOzNKR00dnGT2KVbSw-CoBkrI6eirqIR509RWFiPQ3HZJtd3qvTNk9xQ7sQrqpw5TIjDG9fW06pz5l2oWLmxly7Vzlxfe6cXNZKdgS8hO6vIlzgooCwRn8r6EC2yw7Obt309vI4rhI_onY7G_MVA1IuKI5gxHXP3DfKHKTwgZdRqYZ9Rg-6BPhN6ofkroYiWznfcVYGfm9xdMv3A-N-R7KL9zDl_A7cHrGSGcZyn2qPURVpzDKqahj2nXh8VuqhuA9c3bpKkenkP-2TvzrngwcSi7obQjAiJBi0ZbUKJjoA3qauICv-PnBgeLRi1QdBik7A4vIIi3jD3Pq51M0ca3-Z0M8k-FSB7rvD1D0GXyXCpiRBcqX_Ug_QeMpZ8ADvcoHaF0I5N3L912bLoiTkWVtCq8mePsB9gibqRlWycCR6f4nDz8AnrTrJVPv2sixP-RL0cwEyZoYhUzoqk3GDUzdF3LpboDReGGvBigY3sCTeXAlL46AVV4VNA39OYZ02qsJZjgML9GZIyqjv01b3Ir1bxvcrA-wI8zx5YI4iPHoB9Qw4mjxDfMxIw8PzL8_eg2o25Fpi2ODWUAHjKfOCpYtPvplaQn5rBst8G7HHHpJu8vY823R-BWEI6IhoojtfKhNZrigGqYgcLpcW6ZVJtb9rA4Tue4-jQNaDAuaZxP7W7e0R0nKJFJCL9", "id": "rs_060035eb87c40e14016a13137bb2e0819181a353f3ac1f0169", "summary": [{"type": "summary_text", "text": "**Planning content preview steps**\n\nI need to summarize the steps towards an effective content preview. First, I’ll include the media file location so it’s accessible. I’ll confirm that I’ve completed the command line interface tasks, tests, and commit. The roadmap involves combining the MP4 and social draft into an approval package for delivery, potentially through Telegram. Then, I'll focus on enhancing preview quality with motion, captions, TTS, and audio. The current MP4 is a timing preview, not the final one. Steps include creating the demo content preview package, attaching the MP4, and final approval."}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Neu eingebaut: Demo Local Preview Video CLI\n\nNeue Dateien:\n\n```text\nautoshorts/cli/demo_local_preview_video.py\ntests/test_demo_local_preview_video_cli.py\n```\n\n## Neuer Command\n\n```bash\npython -m autoshorts.cli.demo_local_preview_video --out-dir /tmp/autoshorts-demo-local-preview\n```\n\nDer läuft jetzt komplett durch:\n\n```text\nContentBrief\n→ ScriptDraft\n→ ShotPlan\n→ RenderManifest\n→ LocalPreviewPlan\n→ PNG scene cards\n→ FFmpegConcatPlan\n→ MP4 timing preview\n```\n\n## Verifiziert\n\nOutput:\n\n```text\nScript: ai_life_systems-ai-weekly-review-routine\nPreview video: /tmp/autoshorts-demo-local-preview/preview.mp4\nConcat file: /tmp/autoshorts-demo-local-preview/preview.concat.txt\nFrame count: 5\nPreview format: 540x960 @ 24fps\nFrame side effects: write_png\nSide effects: write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\nexists True\nsize 174359\nframes 5\n```\n\nTests:\n\n```text\n110 passed in 8.46s\n```\n\nCommit:\n\n```text\n496b0ac feat: add demo local preview video cli\n```\n\nArbeitsverzeichnis sauber.\n\nHier ist der erzeugte aktuelle Timing-Preview:\n\nMEDIA:/tmp/autoshorts-demo-local-preview/preview.mp4\n\n---\n\n## Was das jetzt ist\n\nDas ist ein **technischer Timing-/Structure-Preview**, noch kein finaler Content-Preview.\n\nEr zeigt:\n\n- 5 Szenen\n- Timing\n- Hook/Caption Text\n- Scene Labels\n- Asset Mode\n- Motion Instructions\n- grobe vertikale Cards\n\nEr zeigt noch **nicht**:\n\n- echte Motion Graphics\n- Voiceover\n- Musik/SFX\n- AI-/Qwen-/Wan-Visuals\n- final polish\n- echte captions synced to audio\n\nAlso: Es ist der Rohbau. Noch nicht die Villa. Aber immerhin steht kein Zirkuszelt mehr auf der Baustelle.\n\n---\n\n# Nächste Schritte bis zum effektiven Content-Preview zur Freigabe\n\n## Phase 1 — Approval Package mit MP4 + Social Draft\n\nNächster sinnvoller Schritt:\n\n```text\nMP4 timing preview\n+ SocialMediaDraft Review\n→ ContentPreviewApprovalPackage\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nZielobjekt:\n\n```python\nContentPreviewApprovalPackage(\n    candidate_id=...,\n    preview_video_path=...,\n    social_review_text=...,\n    approval_commands=(...),\n    requires_human_approval=True,\n    side_effects=(),\n)\n```\n\nDamit haben wir ein einheitliches Ding, das sagt:\n\n> “Hier ist das Video-Preview + Caption/Titel/Hashtags + Freigabe-Kommandos.”\n\n## Phase 2 — Telegram-safe Preview Delivery\n\nDanach:\n\n```text\nContentPreviewApprovalPackage\n→ TelegramPreviewDelivery\n```\n\nNeue Module:\n\n```text\nautoshorts/preview/delivery.py\ntests/test_content_preview_delivery.py\n```\n\nZiel:\n\n- Text <= Telegram Limit\n- Video path vorhanden\n- Approval mandatory\n- kein Upload zu Plattformen\n- kein Auto-Senden ohne explizite Adapter-Grenze\n- keine `publish/post`-Sprache\n\nDann können wir lokal erzeugen:\n\n```text\nMEDIA:/.../preview.mp4\n+\nReview Text\n+\nFreigabe-Kommandos\n```\n\n## Phase 3 — Demo CLI für echten Freigabeentwurf\n\nDann:\n\n```text\nautoshorts/cli/demo_content_preview.py\ntests/test_demo_content_preview_cli.py\n```\n\nCommand:\n\n```bash\npython -m autoshorts.cli.demo_content_preview --out-dir data/previews/demo-content-preview\n```\n\nOutput:\n\n```text\nPreview video: data/previews/demo-content-preview/preview.mp4\nReview text length: ...\nRequires human approval: yes\nSide effects: write_png, write_concat, call_ffmpeg, write_mp4\nExternal calls: ffmpeg\n```\n\nUnd der Text enthält:\n\n```text\n## Content Preview Review\n\nVideo:\n<preview path>\n\nCandidate:\n...\n\nPlatforms:\nYouTube Shorts, TikTok, Instagram Reels\n\nHook:\n...\n\nCaption:\n...\n\nHashtags:\n...\n\nApproval commands:\nFreigabe alle\nFreigabe YouTube\nFreigabe TikTok\nFreigabe Instagram\nAblehnen\nÄndern: <...>\n```\n\n**Das ist der erste echte Freigabe-Preview.**\n\n## Phase 4 — Renderer v2: weniger statisch\n\nAktuell sind es Scene Cards. Für “Content Preview, das nicht beleidigend statisch wirkt” brauchen wir danach:\n\n```text\nautoshorts/rendering/motion_preview.py\n```\n\nZiel:\n\n- simple Ken Burns / pan / zoom\n- kinetic text intro\n- scene transitions\n- progress bar\n- captions renderer-owned\n- sichtbare Bewegung alle 1–2 Sekunden\n\nNoch lokal, noch ohne AI-Video.\n\n## Phase 5 — TTS / Voiceover\n\nDann:\n\n```text\nScriptDraft\n→ VoiceoverScript\n→ TTS audio\n→ caption timing\n```\n\nFür dich relevant:\n\n- Default Voice laut Profil: `de-DE-KillianNeural`\n- Wenn Content englisch bleibt, müssten wir eine englische Voice wählen.\n- Output:\n  - `.mp3` oder `.wav`\n  - später `.srt` / caption timings\n\nDanach:\n\n```text\nMP4 visual preview\n+ TTS audio\n→ audio-synced preview\n```\n\nDas wäre schon deutlich näher an einem echten Content-Preview.\n\n## Phase 6 — Qwen Image / Wan / ComfyUI Assets\n\nErst danach würde ich Qwen/Wan anbinden:\n\n```text\nRenderManifest Scene\n→ Keyframe Prompt\n→ Qwen Image Keyframe\n→ Wan I2V Clip\n→ imported/local asset\n→ deterministic final assembly\n```\n\nWichtig bleibt:\n\n- keine baked-in text in AI assets\n- captions/labels/arrows renderer-owned\n- AI-Video nur für Textur/Bewegung\n- Review jedes Clips vor finaler Assembly\n\n## Meine empfohlene Reihenfolge ab jetzt\n\nKurz und praktisch:\n\n```text\n1. ContentPreviewApprovalPackage\n2. TelegramPreviewDelivery / local delivery object\n3. demo_content_preview CLI\n4. Motion Preview Renderer v2\n5. TTS + synced captions\n6. Qwen/Wan asset layer\n7. Final review package\n8. Erst nach expliziter Freigabe: Plattformadapter\n```\n\n## Direkt nächster Task\n\nIch würde jetzt bauen:\n\n```text\nautoshorts/preview/package.py\ntests/test_content_preview_package.py\n```\n\nDamit verbinden wir den vorhandenen MP4-Preview mit dem Social-Media-Freigabetext zu einem echten Content-Preview-Paket. Danach kann ich daraus den Telegram-fähigen Freigabeentwurf machen."}], "id": "msg_060035eb87c40e14016a131382decc8191b71fae6b08824198", "phase": "final_answer"}], "timestamp": "2026-05-24T17:04:58.407704"}