{"role": "user", "content": "[M G] Ok, nächster schritt\n\n[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 125 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Renderer v1 gebaut\n\nIch habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.\n\nKurz gesagt:\n\n```text\nRender Manifest JSON\n→ Pillow erzeugt vertikale Szenen-Frames\n→ FFmpeg baut daraus MP4\n```\n\nAlso: **kein LLM rendert das Video**, sondern ein deterministischer Renderer. Das LLM/Manifest gibt nur vor, *was wann passieren soll*. Der Renderer setzt es technisch um.\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/preview_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_preview.py\n```\n\nTests:\n\n```text\ntests/test_preview_renderer.py\n```\n\nDependency ergänzt:\n\n```text\nPillow>=10.0\n```\n\n## Erstes Testvideo gerendert\n\nFür:\n\n```text\nai-004 — Stop asking AI to think for you\n```\n\nOutput:\n\n```text\ndata/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n```\n\nTechnische Prüfung:\n\n```text\nwidth=540\nheight=960\navg_frame_rate=24/1\nduration=43.041667\n```\n\nPreview-Video:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4\n\n## Was der Renderer v1 aktuell macht\n\n- nimmt das Render Manifest\n- baut 5 visuelle Szenen\n- nutzt pro Szene:\n  - Asset Type\n  - On-screen Text\n  - Timing\n  - Retention Goal\n- erzeugt vertikale Preview-Cards\n- exportiert daraus ein MP4 via FFmpeg\n\nDas ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.\n\n## Tests / Git\n\nFull suite:\n\n```text\n24 passed in 0.88s\n```\n\nNeuer Commit:\n\n```text\n07bbc57 feat: add manifest preview renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nRemote verifiziert:\n\n```text\nlocal == remote\n```\n\nPR-Link bleibt:\n\nhttps://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:\n\n- Hook text animation verbessern\n- echte Caption-Overlays über alle Beats\n- simple motion/pan/zoom\n- optional TTS-Audio-Spur\n- danach wieder ein neues MP4 zur Freigabe\n\nAktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti.", "timestamp": "2026-05-23T20:31:04.572355"}
{"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 nächster schritt\"\n\n## Goal\nDer User möchte AutoShortsBot/Hermes von einer einfachen KI-Video-/Card-Rendering-Pipeline zu einem halbautomatischen **Retention-Studio für Shorts** weiterentwickeln. Ziel ist ein System, das datenbasierte, animierte Mechanismus-Erklärvideos produziert: starke Hooks in den ersten 1–2 Sekunden, visuelle Veränderung alle 1–2 Sekunden, keine statischen Slideshow-Videos, sondern „mechanism reveal / visual explanation / mini-investigation“-Content mit Fokus auf Hidden Money, AI Systems, Auto-/Finanzmechaniken und unsichtbare Systemeffekte.\n\n## Constraints & Preferences\n- Sprache/Kommunikation: Deutsch; User wird teilweise mit „Sir“ angesprochen.\n- Der User lehnt finale statische Textkarten-/KI-Bild-Slideshows ab; sie sind nur Preview/Storyboard, nicht Endprodukt.\n- Finale Videos sollen bewegte Sequenzen, AI-video Assets, Screen-Recording-Simulationen, Mechanismus-Animationen, kinetic captions, Sound/SFX und schnelle visuelle Wechsel enthalten.\n- Der aktuelle Card-/Preview-Renderer ist nur Timing-/Storyboard-Werkzeug.\n- `static_card_limit = 0` als Qualitätsprinzip für finale Inhalte.\n- Content-Richtung: kein generischer KI-Avatar-News-Kanal, kein „Was wäre wenn…“-KI-Müll, kein Reused-Content-Klon.\n- Avatare höchstens als Nebenfigur/Branding-Element, nicht als Hauptformat.\n- Bevorzugte Nische: **Hidden Money & AI Systems**.\n- Zielserie: **Unsichtbare Systeme in 30 Sekunden**.\n- Claim: „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“\n- Erfolgsmechanik: Hook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung.\n- Inspirationsmechaniken: Zack-D-Films-artige Mechanism Reveals, MrBeast/Jenny-Hoyos sofortiges Versprechen, TikTok Creative Code: Hook/Body/Close, dynamisches Editing, Bewegung, Text-Overlays, Sound/Voice/SFX.\n- User erlaubt, die untracked Telegram-Review-Dateien zu ignorieren, weil sie versehentlich durch einen falschen Telegram-Kanal entstanden sind.\n- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen oder Artefakten bewahren; ggf. mit `[REDACTED]` ersetzen.\n\n## Completed Actions\n1. ANALYSED Preview-Renderer Typography Issue — festgestellt, dass Textgrößen im Preview uneinheitlich wirkten: Haupttext groß, Retention-/Erklärungstext unten deutlich kleiner; erklärt, dass diese Zwei-Ebenen-Logik technisch beabsichtigt, aber für Shorts visuell schwach ist [tool: conversation].\n2. UPDATED Preview-Renderer-Konzept — Renderer um adaptive Font-Fitting, konsistentere Text-Hierarchie, Mindestgrößen, maximale Zeilenanzahl und Typography-Metadaten erweitert; Ziel war weniger zu kleine/zu stark schwankende Textgrößen im Storyboard-Preview [tool: code/terminal claimed].\n3. ADDED Typography Test Rules für `540x960` Preview — Regeln dokumentiert/geprüft: `main_font_size >= 34`, `max(main_font_size) - min(main_font_size) <= 8`, `retention_font_size >= 22`, `main_line_count <= 4`, `retention_line_count <= 3` [tool: tests claimed].\n4. RENDERED Top-3 Previews neu — erzeugt unter `data/previews/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.mp4`, `ai-005-the-4-box-prompt-for-mental-overload.mp4`, `attention-002-the-algorithm-does-not-need-you-happy.mp4` [tool: terminal claimed].\n5. VERIFIED Preview `ai-004` — technische Werte: `width=540`, `height=960`, `avg_frame_rate=24/1`, `duration=43.041667`; Typography: `main_font_size: 34–42`, `retention_font_size: 25–26` [tool: ffprobe/tests claimed].\n6. TEST full suite nach Typography-Änderung — Ergebnis: `25 passed in 0.96s` [tool: pytest claimed].\n7. COMMITTED Typography-Fix — Commit `3cc201b fix: improve preview renderer text sizing` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n8. ANSWERED User-Frage zum Endergebnis — klargestellt: aktueller Preview-Renderer ist nicht finales Content-Format; Endprodukt soll Videosequenzen, Bewegung, AI-video Assets, Screen-Recordings, kinetic captions, Voiceover und Sounddesign nutzen, nicht statische Cards [tool: conversation].\n9. DEFINED bessere Zielrichtung für `ai-004` — vorgeschlagen: ca. 40% Screen Recording/UI Simulation, 30% cinematic human-at-desk/decision moments, 20% kinetic captions, 10% Cards nur für Prompt-Fragmente [tool: conversation].\n10. CREATED `autoshorts/asset_plan.py` — neue Asset-Plan-v2-Komponente für motion-led Asset Pipeline; entscheidet pro Szene zwischen `cinematic_ai_video`, `screen_recording_simulation`, `animated_card`, `kinetic_text_overlay` [tool: code claimed].\n11. CREATED `scripts/generate_asset_plans.py` — CLI zum Generieren von Asset-Plänen aus vorhandenen Render-/Storyboard-Daten [tool: code claimed].\n12. CREATED `tests/test_asset_plan.py` — Tests für Asset Plan Builder, u.a. Provider-Prompting und keine finalen statischen Cards [tool: code claimed].\n13. GENERATED Asset-Pläne Top-3 — Dateien erzeugt unter `data/asset_plans/testbatch_002_top3/`: `ai-004-stop-asking-ai-to-think-for-you.json`, `ai-005-the-4-box-prompt-for-mental-overload.json`, `attention-002-the-algorithm-does-not-need-you-happy.json` [tool: terminal claimed].\n14. SET Asset Pipeline `static_card_limit = 0` — finale statische Textkarten explizit ausgeschlossen; Preview bleibt Storyboard/Timing-Werkzeug [tool: code claimed].\n15. BUILT Provider-ready Prompting für cinematic Szenen — Prompt-Felder mit photorealistischer vertikaler 9:16-Clip-Beschreibung, cinematic lighting, realistic motion, negative space for captions und „do not include readable text“ ergänzt [tool: code claimed].\n16. BUILT Negative Prompts für AI-video Provider — u.a. `no readable text`, `no subtitles`, `no logos`, `no watermarks`, `no fake UI text`, `no slideshow`, `no static image`, `no uncanny faces` [tool: code claimed].\n17. TEST full suite nach Asset Plan v2 — Ergebnis: `32 passed in 0.96s` [tool: pytest claimed].\n18. COMMITTED Motion-led Asset Planning — Commit `b60981b feat: add motion-led asset planning` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n19. IDENTIFIED untracked Telegram Review Package — nicht angefasst: `autoshorts/review_package.py`, `data/telegram_reviews/`, `scripts/generate_telegram_review_packages.py`, `tests/test_review_package.py` [tool: git status claimed].\n20. CREATED `autoshorts/retention_studio.py` — neue Strategiekomponente, die AutoShortsBot konzeptionell als `semi_automatic_retention_studio` statt KI-Video-Fabrik verankert [tool: code claimed].\n21. CREATED `scripts/generate_retention_studio.py` — CLI zum Generieren von Retention-Studio-Strategieartefakten [tool: code claimed].\n22. CREATED `tests/test_retention_studio.py` — Tests für Strategie, Dramaturgie, visuelle Bewegung, Sound Cues und Follow Trigger [tool: code claimed].\n23. GENERATED Retention-Studio-Artefakte — erzeugt unter `data/retention_studio/`: `strategy.json`, `pilot_ideas.json`, `mechanism_explainers.json` [tool: terminal claimed].\n24. SET Positionierung in Strategie — `Hidden Money & AI Systems`; Serie `Unsichtbare Systeme in 30 Sekunden`; Claim „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“ [tool: code claimed].\n25. EXCLUDED unerwünschte Content-Formate in Strategie — `ki_avatar_news`, `static_ai_slideshow`, `reused_content_clone`; Quality Gate `static_card_limit = 0` [tool: code claimed].\n26. IMPLEMENTED Mechanism-Explainer-Formel — Timing/Beats: `0.0–1.5s Pattern Interrupt`, `1.5–5s Problem`, `5–18s Mechanism Reveal`, `18–28s Twist`, `28–35s Follow Trigger` [tool: code claimed].\n27. ADDED Tests für Retention-Beats — Pattern Interrupt endet ≤ 1.5 Sekunden; jeder Beat braucht visuelle Bewegung und Sound Cue; kein generisches „Follow for more“; Follow Trigger als Serienversprechen [tool: pytest claimed].\n28. ADDED Format-Taxonomie — `mechanism_reveal`, `mistake_breakdown`, `hidden_cost`, `before_after`, `myth_vs_reality`, `mini_simulation`, `nobody_notices_this` [tool: code claimed].\n29. ADDED Content-Pillars — `hidden_money_systems`, `auto_trade_mechanics`, `ai_agents_explained`, `swiss_systems_explained`, `dangerous_thinking_errors`, plus `hidden_attention_systems` [tool: code claimed].\n30. GENERATED 10 Pilot-Ideen mit Scores — Top 3: Score 90 `hidden_attention_systems`/„Warum Shorts dich nicht wegen dem Inhalt halten“, Score 88 `hidden_money_systems`/„Warum ein billiges Auto teuer sein kann“, Score 86 `hidden_money_systems`/„Warum 0%-Finanzierung nicht kostenlos ist“ [tool: terminal claimed].\n31. SET Produktionsentscheidung — Empfehlung `produce_count = 5 von 10` Pilot-Ideen [tool: code claimed].\n32. BUILT Beispiel-Mechanism-Explainer für „Warum ein billiges Auto teuer sein kann“ — Beat-Struktur mit Occasion-Käufer, 3D-Car-X-Ray, Preis/Reparaturrisiko/Standtage/Händler-Marge/Liquidität, Twist „teuerster Fehler ist nicht Kaufpreis, sondern Zeitpunkt“ [tool: code claimed].\n33. TEST full suite nach Retention Studio — Ergebnis: `36 passed in 0.99s` [tool: pytest claimed].\n34. COMMITTED Retention-Studio-Strategie — Commit `a9fe897 feat: add retention studio strategy` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n35. UPDATED Skill-Notiz — Social-Shorts-/Retention-Studio-Skill um Retention Studio Strategy, Mechanism Explainer Shorts, Hidden Money & AI Systems und `static_card_limit = 0` erweitert [tool: file edit claimed].\n36. CREATED `autoshorts/mechanism_pipeline.py` — Adapter von Retention-Studio Mechanism Explainer zu renderer-nahem Mechanism Shot Plan [tool: code claimed].\n37. CREATED `scripts/generate_mechanism_shot_plans.py` — CLI zum Generieren von Mechanism Shot Plans aus Mechanism Explainern [tool: code claimed].\n38. CREATED `tests/test_mechanism_pipeline.py` — Tests für Adapter/Shot-Plan-Erzeugung, Timing, Asset Types, Bewegungs-/Sound-Metadaten und `static_card_limit = 0` [tool: code claimed].\n39. GENERATED Pilot-Shot-Plan — Datei `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` erzeugt [tool: terminal claimed].\n40. MAPPED Mechanism Beats zu Scenes — `pattern_interrupt`, `problem`, `mechanism`, `twist`, `follow_trigger` wurden in Szenen mit Timing, Asset Type, On-screen Text, Voiceover, Visual Direction, Visual Motion, Sound Cue, Retention Goal, Caption Beats und Final Render Intent übersetzt [tool: code claimed].\n41. CREATED Shot Plan für „Warum ein billiges Auto teuer sein kann“ — Scene 1 `0.0s–1.4s cinematic_ai_video`, Scene 2 `1.4s–5.0s cinematic_ai_video`, Scene 3 `5.0s–18.0s mechanism_animation`, Scene 4 `18.0s–28.0s mechanism_animation`, Scene 5 `28.0s–34.0s kinetic_text_overlay` [tool: terminal claimed].\n42. SET Shot-Plan-Constraints — `static_card_limit = 0`, `visual_change_every_seconds = 2`, `primary_style = mechanism_reveal_motion_explainer` [tool: code claimed].\n43. TEST full suite nach Mechanism Pipeline — Ergebnis: `40 passed in 0.97s` [tool: pytest claimed].\n44. COMMITTED Mechanism Explainer Adapter — Commit `5644c8b feat: adapt mechanism explainers to shot plans` auf Branch `feat/initial-growth-engine`; Remote als `local == remote` verifiziert [tool: git claimed].\n45. UPDATED Retention-Studio-Skill-Notiz — Pipeline festgehalten: `strategy → scored ideas → mechanism explainer → mechanism shot plan → render manifest → asset plan → final renderer` [tool: file edit claimed].\n46. CONFIRMED Telegram Review Files ignored — User erklärte, dass die separaten Working-Tree-Dateien versehentlich durch falschen Telegram-Kanal entstanden sind; sie bleiben unberührt und uncommitted [tool: conversation].\n\n## Active State\n- Repository/Projekt: `/home/agent/jarvis_runtime/AutoShortsBot`.\n- Branch: `feat/initial-growth-engine`.\n- Remote-Status zuletzt berichtet: `local == remote`.\n- Letzter bekannter Commit: `5644c8b feat: adapt mechanism explainers to shot plans`.\n- Vorherige relevante Commits:\n  - `3cc201b fix: improve preview renderer text sizing`\n  - `b60981b feat: add motion-led asset planning`\n  - `a9fe897 feat: add retention studio strategy`\n- Letzter Teststatus: `40 passed in 0.97s`.\n- Aktuelles Pipeline-Level erreicht:\n  - Retention Studio Strategie vorhanden.\n  - Pilot-Ideen gescored.\n  - Mechanism Explainer vorhanden.\n  - Mechanism Shot Plan für Pilot „Warum ein billiges Auto teuer sein kann“ vorhanden.\n- Nächster logischer Schritt noch nicht erledigt:\n  - `Mechanism Shot Plan → Render Manifest → Asset Plan` für den neuen Pilot, nicht mehr nur für alte AI-Life-Top-3.\n- Untracked/zu ignorieren:\n  - `autoshorts/review_package.py`\n  - `data/telegram_reviews/`\n  - `scripts/generate_telegram_review_packages.py`\n  - `tests/test_review_package.py`\n- Keine laufenden Server oder Prozesse erwähnt.\n- Keine Secrets/Credentials bewahren; falls vorhanden, als `[REDACTED]`.\n\n## In Progress\n- Der User hat „ok nächster schritt“ gesagt. Gemeinter nächster Arbeitsschritt laut vorheriger Antwort:\n  - Mechanism Shot Plan für „Warum ein billiges Auto teuer sein kann“\n  - → Render Manifest\n  - → Asset Plan\n  - Ziel: neue Retention-Studio-/Mechanism-Explainer-Pipeline an die bestehende Render-/Asset-Plan-Infrastruktur anbinden.\n- Noch nicht umgesetzt:\n  - Adapter/CLI/Tests für `Mechanism Shot Plan → Render Manifest`.\n  - Generierung eines Render Manifests für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.\n  - Asset Plan für diesen neuen Pilot auf Basis des neuen Render Manifests.\n  - Provider Job Specs für cinematic Szenen des Pilots.\n\n## Blocked\nKeine bekannten Blocker.  \nKeine Fehlermeldungen offen.  \nEinziger Hinweis: frühere 125 Messages konnten nicht zusammengefasst werden; daraus können ältere Details fehlen. Die aktuelle Richtung und letzten Artefakte sind aber in den neuen Turns beschrieben.\n\n## Key Decisions\n1. Der Preview-Renderer ist nicht das finale Produkt, sondern nur Storyboard-/Timing-Werkzeug. Grund: Statische Cards wirken langweilig und konkurrieren nicht mit modernen Shorts/Kling/Hailuo/Runway-Qualität.\n2. Finale Videos müssen motion-led sein: AI-video Clips, Screen-Recording-Simulationen, mechanism animations, kinetic captions, Voiceover, SFX und schnelle Schnitte.\n3. `static_card_limit = 0` ist ein strategisches Qualitätsgate für finale Videos. Grund: User lehnt statische KI-Bilder + Text/Voiceover als KI-Müll-Massenware ab.\n4. AutoShortsBot/Hermes wird als halbautomatisches Retention-Studio positioniert, nicht als KI-Video-Fabrik. Grund: Erfolg hängt von Hook, offenen Loops, visueller Stimulation und Payoff ab, nicht vom KI-Look.\n5. Gewählte Content-Nische: Hidden Money & AI Systems. Grund: verbindet Hermes, Autotrading, Finanzen, KI-Agenten, Politik/Systemverständnis und hat spätere Monetarisierungsmöglichkeiten über Newsletter, Tools, Beratung, Affiliate, Software oder Kurse.\n6. Kernformat: Mechanism Reveal / Visual Explanation / Mini-Investigation. Grund: zeigt unsichtbare Mechanismen visuell und bietet echten Erkenntniswert statt generischem Voiceover.\n7. Avatare nicht als Hauptformat. Grund: viele KI-Avatare wirken generisch, halten Retention schlecht, und es gibt Disclosure-/Trust-Risiken bei realistischen synthetischen Menschen.\n8. Re-Kuratierung erfolgreicher Videos ist strategisch erlaubt, aber nicht als Kopie/Neuvertontung. Grund: YouTube Reused Content vermeiden; Mechaniken adaptieren, nicht Inhalte klonen.\n9. Pilot-Fokus: „Warum ein billiges Auto teuer sein kann“. Grund: passt zu Money/Auto, klares visuelles Objekt, starke 3D-/X-Ray-/Layer-Animation möglich, hohe Monetarisierbarkeit.\n10. Untracked Telegram-Review-Dateien werden ignoriert. Grund: User schrieb versehentlich in falschen Telegram-Kanal; Änderungen sollen nicht mit dieser Pipeline vermischt werden.\n\n## Resolved Questions\n1. User fragte, warum Text oben groß und unten klein ist. Antwort: Der Preview-Renderer nutzt Haupttext plus kleineren Retention-/Erklärungstext; technisch beabsichtigt, aber visuell für Shorts ungeeignet. Finale Videos sollen weniger Text pro Frame und große rhythmische Captions nutzen.\n2. User fragte, ob Endergebnis Videosequenzen oder wieder statische Bilder sein sollen. Antwort: Nicht statische Bilder. Endziel sind Videosequenzen mit AI-video Assets, Screen Recordings, kinetic captions, Voiceover, Musik/SFX und fast cuts.\n3. User fragte, warum diese Videos besser sein sollen als bisheriger Content. Antwort: Nur wenn Scripting, visuelle Umsetzung und Retention-Struktur besser werden: starke Hooks, motion-led visuals, alle 1–2 Sekunden visueller Reiz, echter Mechanismus-Payoff.\n4. User fragte implizit, ob statische Cards reichen. Antwort: Nein, statische Cards sind nur Preview/Storyboard und als finales Format strategisch zu schwach.\n5. User brachte „Chloe vs History“, Kling, Hailuo, photorealistische KI-Videos als Qualitätsreferenz ein. Antwort: Diese Richtung wurde aufgenommen; finale Pipeline soll provider-ready prompts für cinematic AI-video Szenen erzeugen, aber nicht in generischem KI-Look enden.\n6. User schlug Retention-Studio statt KI-Video-Content vor. Antwort: Konzept wurde als `semi_automatic_retention_studio` umgesetzt und in Strategieartefakten verankert.\n7. User schlug Zack-D-Films-artige Mechanism Reveals vor. Antwort: Mechanism-Explainer-Formel, Beat-Struktur und Pilot „Warum ein billiges Auto teuer sein kann“ wurden umgesetzt.\n8. User erklärte, der separate Working Tree könne ignoriert werden. Antwort: Telegram-Review-Dateien wurden nicht angefasst und nicht committet.\n\n## Pending User Asks\n- „ok nächster schritt“ ist noch offen. Gemeint ist nach aktuellem Stand der nächste Build-Schritt:\n  - Mechanism Shot Plan → Render Manifest → Asset Plan für den Pilot „Warum ein billiges Auto teuer sein kann“.\n\n## Relevant Files\n- `autoshorts/asset_plan.py` — Asset-Plan-v2-Komponente für motion-led finale Asset-Auswahl pro Szene.\n- `scripts/generate_asset_plans.py` — CLI zur Asset-Plan-Generierung.\n- `tests/test_asset_plan.py` — Tests für Asset-Plan-Logik.\n- `data/asset_plans/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `data/asset_plans/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.json` — Asset Plan für alten AI-Life-Top-3-Preview.\n- `autoshorts/retention_studio.py` — Strategie-/Pilot-/Mechanism-Explainer-Komponente für Retention Studio.\n- `scripts/generate_retention_studio.py` — CLI zur Generierung von Retention-Studio-Artefakten.\n- `tests/test_retention_studio.py` — Tests für Strategie, Beats, visuelle Bewegung, Sound Cues, Follow Trigger.\n- `data/retention_studio/strategy.json` — Retention-Studio-Strategie.\n- `data/retention_studio/pilot_ideas.json` — gescorte Pilot-Ideen.\n- `data/retention_studio/mechanism_explainers.json` — Mechanism-Explainer-Daten.\n- `autoshorts/mechanism_pipeline.py` — Adapter von Mechanism Explainer zu Mechanism Shot Plan.\n- `scripts/generate_mechanism_shot_plans.py` — CLI zur Shot-Plan-Generierung.\n- `tests/test_mechanism_pipeline.py` — Tests für Mechanism Shot Plan Adapter.\n- `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` — generierter Pilot-Shot-Plan.\n- `data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4` — neu gerenderter Preview.\n- `data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4` — neu gerenderter Preview.\n- `autoshorts/review_package.py` — untracked/zu ignorieren.\n- `data/telegram_reviews/` — untracked/zu ignorieren.\n- `scripts/generate_telegram_review_packages.py` — untracked/zu ignorieren.\n- `tests/test_review_package.py` — untracked/zu ignorieren.\n\n## Remaining Work\n- Neuen Adapter bauen: `Mechanism Shot Plan → Render Manifest`.\n- Render Manifest für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` erzeugen.\n- Asset Plan v2 auf diesen neuen Mechanism-Render-Manifest anwenden.\n- Provider Job Specs für cinematic AI-video Szenen exportieren, z.B. Kling/Hailuo/Runway-kompatible Prompts.\n- Mechanism Animation Specs definieren für 3D-Car-X-Ray, Preis-/Risiko-/Marge-/Timing-Layer.\n- Final Renderer erweitern, damit er echte Clip-Dateien, Screen-Recording-Simulationen, mechanism animations und kinetic captions zusammensetzt.\n- Optional: Preview-Storyboard weiter nutzen, aber klar als nicht-final markieren.\n- Später: Analysepipeline für Retention nach 1s/3s, durchschnittliche Wiedergabedauer, Replays, Kommentare pro 1’000 Views, Follows pro 1’000 Views.\n\n## Critical Context\n- Frühere Kontextzusammenfassung war nicht verfügbar: „Summary generation was unavailable. 125 message(s) were removed…“. Frühere Details vor den neuen Turns können fehlen.\n- Branch: `feat/initial-growth-engine`.\n- PR-Link bleibt: `https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine`.\n- Letzter bekannter Teststatus: `40 passed in 0.97s`.\n- Wichtigster aktueller Pilot: `Warum ein billiges Auto teuer sein kann`.\n- Pilot-Shot-Plan-Szenen:\n  - Scene 1 — `pattern_interrupt`, `0.0s–1.4s`, `cinematic_ai_video`, Visual: 3D car x-ray with price tag layers and risk meters freezes mid-action, Motion: hard zoom/freeze-frame/red signal pulse, Sound: bass hit + camera snap.\n  - Scene 2 — `problem`, `1.4s–5.0s`, `cinematic_ai_video`, Visual: surface layer peels away to reveal hidden system, Motion: layer peel + parallax push.\n  - Scene 3 — `mechanism_reveal`, `5.0s–18.0s`, `mechanism_animation`, Visual: 3D car x-ray with price tag layers and risk meters, Motion: sequential layer reveal every 1–2 seconds.\n  - Scene 4 — `twist`, `18.0s–28.0s`, `mechanism_animation`, Visual: wrong cause fades out, real cause appears, Motion: snap zoom + wrong answer collapses.\n  - Scene 5 — `follow_trigger`, `28.0s–34.0s`, `kinetic_text_overlay`, Visual: animated system map, Motion: map draws itself, final node glows.\n- Strategy constraints to preserve:\n  - `static_card_limit = 0`\n  - `visual_change_every_seconds = 2`\n  - `primary_style = mechanism_reveal_motion_explainer`\n- Provider negative prompt concepts:\n  - no readable text\n  - no subtitles\n  - no logos\n  - no watermarks\n  - no fake UI text\n  - no slideshow\n  - no static image\n  - no uncanny faces\n- Do not include or preserve any credentials/secrets; replace with `[REDACTED]` if encountered.\n\n--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:\nSummary generation was unavailable. 115 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. 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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_2jpQuzfJDwoUEKoxz3gLxST9", "call_id": "call_2jpQuzfJDwoUEKoxz3gLxST9", "response_item_id": "fc_0d1a4d10654d56b4016a11d0023b8c819188aada64c4366d3f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_scene01_wan_probe_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'subfolder':'',...[truncated]\", \"timeout\": 90, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdACqwlK4sNI0c6c7Q2KPhxPBaMYwPrzk_O8qQhIlePrneHdTjRY0Wx9wMtSN_aXQ2lYw4PIHcjzPkZk0Jj9Btogs7kisR7DK2mSG-TZueQI6kxvK-xJCPe8_KfADprZL2q57NSJRBn798jE_GRD7H6vsWYidPVzsNaCR2G8Ac6ca_2Gg9dZ7tjG5jXtO1naT_gnjTlk86f8tBrs9yH8bfI7_SPZ8wWPGxzJeMVimLzJHMf8nQrkxT4CkLCd-RLqo5x4xAFQkZVpI8zWEvM_17jq2-KE2jd_rE1gNamUgSXB5XEJbKFgMT2i6GvqYrig7_xdbcqQfVdG2JISsGmvY4ftq3N5t1vOmd7DmhHQDnCTZBKlv4SbsjO-mtLKpzvz060RWi7VUEUqvZcrcdO3Ab0Yvi70tvvshiGa_ICy_g4zqpxQPsIDQ6jhQbX-uH67kzkrjdcyAfZxPl-WoZQ1u-yMY-Hf4vh4sWw2s0M9I1WBj5rRVV70UUDKiPG-lHJL_6563m5_hQAAQfczBHS5wxe-QOqVkJe85AbP7eq3_lVU-gkKk0E5NmCOYQpIWwUfp6n2fS1k7My2fOttIl7HO3xPICswmgoMMrbJE9Jdv4jV9F96d5Zr03J-Z7wuXGfDCchSjrV06pPJ0fuHKTFWcuLs_UCHU2YZDl7ip-rsLwBXF-LRCV_hEt7QekMMCzBAa5ZwPQUm75SqKDB_Vs54_spHf86_o6qGXqB2FpTkhomNI0qe_-s2u3dB-JovcZNiLfnRMnwPz-TZOcc1rG9vJs0ERwXy5FUS8DAW3xf2yEPFD5rfRT8FreSNuDCUg7XPFXuQSaQfm8Zs4QCK2B9r5GF8m61NqGzIpjJmlZDAPFdyZFuWf49jQpWdQ3UhYYQULnsXoWIAHiWhnwsUV8ke5V9zbibt000_RPB4qnDv1F7APFo=", "id": "rs_0d1a4d10654d56b4016a11d001550c81919e75425b91fd3485", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.parse, urllib.request, pathlib\nbase='http://100...` -> exit 0, 1 lines output", "tool_call_id": "call_2jpQuzfJDwoUEKoxz3gLxST9", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_8DJWY67D1L6eI3iyUKxIjmXT", "call_id": "call_8DJWY67D1L6eI3iyUKxIjmXT", "response_item_id": "fc_0d1a4d10654d56b4016a11d00c82f0819182a264278faded97", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/scene01_probe_frame.jpg\",\"question\":\"Beschreibe den Frame kurz: wirkt er wie ein plausibler winziger Wan-Probeclip für Auto/Garage oder hauptsächlich Artefakte? Beurteile knapp.\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[vision_analyze] 'Beschreibe den Frame kurz: wirkt er wie ein plausi' (271 chars)", "tool_call_id": "call_8DJWY67D1L6eI3iyUKxIjmXT", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_X1IXiWYeVCs4bVl6GeYfyQf0", "call_id": "call_X1IXiWYeVCs4bVl6GeYfyQf0", "response_item_id": "fc_0d1a4d10654d56b4016a11d01889b081919bed8e688efb2bb8", "type": "function", "function": {"name": "memory", "arguments": "{\"action\":\"add\",\"target\":\"memory\",\"content\":\"AutoShorts GPU host: ComfyUI reachable via Tailscale at http://100.101.173.25:8188; host has NVIDIA RTX A5000 Laptop GPU 16GB VRAM, ComfyUI 0.22.0, PyTorch 2.12.0+cu126, WanVideoWrapper nodes and Wan2.1 T2V 1.3B FP8 models installed.\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdAYr6ZYp_TZfP7NQNbVp4cBKPdfnrph_bgyGtTemhItEYLe5NM9UB-boWKnHRjnRw6Q-kE36whz0YBJftw-MoDGMvolUd5ljMm6ivVOSZ5XjHXu6B4_XHpHDXzjoqWFs4rLwDoCp-GeggcGJ9l3qut8IsLQa0FWWylHwqdCXAYOe6KGZgxFX3W2698Yway5R5SovAeafV3KGwk3AO_7mw5dDd-yiiqOUAZocDskQGv__feA84fbAbSbdxD8GTAw96jzQnMEWXtg3NknnaoHT4BCMIzWlsa7WCiNmgv37gMxzVYVc4HA60r5wk8HIS2IN224Ts8Wa7IdIJGt9tMclmYwc0Q9tpzRvv3MztqvPiZK1yAA9dM8tkcmggp6gsn8BAYCAp6TsPcozxLPe7D4E3tyrTDERR-eZCgWb6SM4lFGQkwGrpVRlrQZ8go1m_BUhqJTLiD7CxNFByqvPAg-CUWowWqKCi1KmRtEfzpGtMIGM9VVQHcffy2G_pMvANdgrGPb-P5D1u1oYhGHulDZdCKvOiGKoamgl_SpP8RTxyOyK-vyOdERp-A99SXadFHnvl-XAkp6MGn37YQH_WLH8ZuziomjOC4FfNIy_0A9gjPtcYgcGSwTVBhiG9dDHOYEXGCJv9KaezC8QBRl2nZsGVRvp4OiVzAFnZ6gPbXAvFb4FLiEraGtRdzLKXpBjwo3lqLE0AmfuRKs3peJQ9cx198_xRUxYwrsG9iwrFvtX4h7wtgQDTHQEW_M7yIsCEG0nH9YppO4FCIX8_5V8JLOf6T2BTqDxRd-ebVgaz15tTaBIgUN3wAKpUEiou4z4ek_1oSAMP4AqTvQubzmzFPdJsyAhm5Qh4qQkfSPqX1zhYeNGoAauZHSeHkvArgkcdtnc_WPXJgJHm4vjc8_ZAUmwntb7wK-wtxWEHs5_7dEd0RW745DuxcUDpBI8B6g2aNVwUFVl_D-lIcNbOoOdHtKiGbD6xDJMgkX6uQmrprT9y-14FqQlTRv-MQTkedJxUiE3O3bVPkw0yT789N41xbIwIPf2Fs1bGG26cxEuUhbuR3LcmGl4HvDhwt35S0krGZ69CDyOgwFbJRAtKDGZDmIz2_2dgcCrcVAgYEw3HzPtb4kqIFg34sMXJYb62pk3jfEd_297euoAOLILLavwDYEGoXDPFwqgg52k1MfDlns8o5MXRWc8IA2u4awC-f8wTjNpNQSNDibjWDr2RRmEzZun8pMO3Iu9R4E5J_e-RWzx0f7kZXa5I3k5yG27jIMTjX5JeKg2mZ_WULwpFhAr439i0tQUFs6015DvmPwUZV-M8aC65E=", "id": "rs_0d1a4d10654d56b4016a11d01606fc8191bec5fef94ee93777", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[memory] add on memory", "tool_call_id": "call_X1IXiWYeVCs4bVl6GeYfyQf0", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, Sir — **zweiter Versuch erfolgreich**.\n\n## Ergebnis\n\nIch konnte ComfyUI über Tailscale erreichen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nComfyUI läuft jetzt mit:\n\n```text\n--lowvram\n```\n\nund meldet:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: ca. 16.49 GB\nRAM free: ca. 18.8 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\n```\n\nDas ist jetzt stabil genug für erste lokale Wan-Tests.\n\n---\n\n## Smoke-Test 1: erfolgreich\n\nIch habe einen winzigen Wan-Test über die API queued:\n\n```text\n128x224\n9 Frames\n2 Steps\n8 fps\nWan2.1 T2V 1.3B FP8\nText Encoder auf CPU\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 128\nheight: 224\nfps: 8\nduration: 1.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n\nVisuell: stark artefaktig, aber das war nur ein Minimaltest. Wichtig war: **Pipeline läuft durch und crasht nicht mehr.**\n\n---\n\n## Smoke-Test 2: Scene-01 Probe erfolgreich\n\nDanach habe ich einen etwas größeren Test für unsere Auto-Szene gestartet:\n\n```text\n240x416\n17 Frames\n4 Steps\n8 fps\nPrompt: cheap used car in dim garage, cinematic push-in\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 240\nheight: 416\nfps: 8\nduration: 2.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n\nVisuelle Diagnose:  \nMan erkennt dunkle, horizontale Formen und rote Lichtpunkte — **eher abstrakter Auto/Garage-Vibe**, aber noch kein brauchbarer realistischer Clip. Für 240x416, 4 Steps und 1.3B FP8 ist das erwartbar. Also: technisch erfolgreich, ästhetisch noch „Noir-Fiebertraum mit Rücklichtern“. Immerhin lokal und kostenlos.\n\n---\n\n## Wichtige Erkenntnisse\n\n## 1. Der Crash war RAM-bedingt\n\nVorher hattest du nur ca. 5 GB freien RAM. Jetzt mit ca. 17 GB verfügbar läuft es.\n\n## 2. `--lowvram` hilft\n\nBitte ComfyUI für diese Tests weiter so starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --lowvram\n```\n\n## 3. Text Encoder auf CPU ist stabiler\n\nIch habe im Workflow gesetzt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nDas hält VRAM sauberer und scheint auf deinem Setup stabil zu laufen.\n\n## 4. Modelle und Nodes sind korrekt installiert\n\nBestätigt:\n\n```text\nWanVideoModelLoader\nLoadWanVideoT5TextEncoder\nWanVideoTextEncode\nWanVideoSampler\nWanVideoDecode\nWanVideoVAELoader\nCreateVideo\nSaveVideo\n```\n\nAlle vorhanden und ausführbar.\n\n---\n\n# Nächster sinnvoller Schritt\n\nJetzt baue ich im AutoShortsBot den echten **ComfyUI Wan Client**:\n\n```text\nAutoShortsBot Scene Spec\n→ ComfyUI Workflow JSON\n→ /prompt API\n→ Poll Queue/History\n→ Download MP4 via /view\n→ speichern als imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisch iterieren:\n\n```text\nProbe 1: 240x416, 4 steps\nProbe 2: 360x640, 6–8 steps\nProbe 3: 432x768, 8–12 steps\n```\n\nund schauen, wo auf der A5000 der Sweet Spot liegt.\n\n## Für brauchbarere Qualität würde ich als nächstes testen\n\n```text\nwidth: 360\nheight: 640\nframes: 25\nfps: 8\nsteps: 8\nduration: ca. 3.1s\n```\n\nWenn das stabil läuft, gehen wir Richtung:\n\n```text\n432x768\n33 Frames\n8–12 Steps\n```\n\nFür finale Shorts müssen wir nicht direkt 1080x1920 aus Wan erzeugen. Besser:\n\n```text\nWan Clip kleiner generieren\n→ lokal upscalen / compositen\n→ Captions & Mechanism-Layer in 1080x1920\n```\n\nDas ist bei 16 GB VRAM realistischer.\n\n---\n\n## Fazit\n\n**Ja, die lokale GPU-Video-Pipeline funktioniert.**\n\nNoch nicht hübsch, aber technisch durchgängig:\n\n```text\nHermes VM\n→ Tailscale\n→ ComfyUI Host RTX A5000\n→ Wan2.1 T2V 1.3B FP8\n→ MP4 zurück in AutoShortsBot\n```\n\nDamit ist der Browser-Free-Tier-Weg nicht mehr Hauptpfad, sondern nur noch Fallback. Sehr gut. Kein Credits-Gebettel mehr.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdAhgg8755sBj8O3VWhzI09YiE1oZ5hVVg-U01hIQ_4vPGrLly8qWcKfBHN88J3eau3vblrGXRm2aMisW-Z5unOQxP4Msp_BGxIT22kXb7xmypGRIw3UZwK5TsXIrU8Vbt3MwsKCv9CjmN2ry5vk6gVbeZjuShMFn6zqPKQJ5Bl8kYaGCQqLG8RNhs_Q63SIebsx6702c65-rjKZdlxbctjagYxTQrJOl2PVWUncXYWb8hzIKNC79QARsi74-IGos8popDnA7erIleGVk4gnHKcyFNCY3Z1k9cCih4oCYwY_kpfqcoZa79ZpQFmXXJNWuH8MYxdaVE4G2bY4u4g8mDVC3icBHHdMwrH_7sQ_jXsMFC4r__vrMbFIUkiXdgWBeX1JxIIAqNM0fg05zgBn89HUFSF6G4gOYeTqM_uoQOnUZiZJYkcWEElLQZnTEiN7bgiSfrPdoPkkrEYZccL7HyZ8zmv4qdKQECZ1mSofjRTsS6YaUqSczzqaZWJiCvm41FqV7rzQ75aSWSiQ4bszIw0jqZMrxb4hCgV-DVg-9hXo_HH5Df_otEvJXhJGAwB4nnMGml9XvH0jDGv_OQEznzI01tIGR8-sa8vkkuMe9YPRSlWji29z2avHszrQAOp_Q2qAbdNntQ7AzfSf5QFz0kz7hHceIcX5Ls8qbJUdW7yPFAew7jt4bdUzsT6He8kEHmEebUQdEoFKsurONhDrpND7glKB3VWGcXWIB-okOV7jnpquuEH4XGxdeR3V0V_2uNKIh2Cri-JOTwWAZYdru5s495kbePL20Aen_It_El4T2aFG7xCez3dbdcOUSg2WpNAualPynxm9U9NF1-OlaL_vC0K0z41Snpni1C0-x2fXbT8Z3SBPAYV3POeSxN01I1kEu9pcUytWBXMq2jqyV96X59GimeUtGO0WqMnKSlg7uzxef3dMIA-FmXnB3P1o01wT0Pfji7ZnTGcIJzwA5v8mZPe-jFCY78BNQvd4vuB5LVc1YN7Tmx7VcdvRtdStSjzytNp_WUsFirhnUdLJOOVszkOluQj3sKM1KEg-WQVr7F_dbrroOWKSrtU_ubxBVzcOrWMlE12_hKlaBTuGMxCo1MZVTbReE4YJq4tX1qfuZMN-vLfZPYSblq691JWZXIzau14ZajZ9bH666wliRlBE9GlXj7ONIc5EoRk73dTfCc2h1NYIRoDX6Pb099TAM3130geERYMkUj7wNuSsK_A3qZGknIuM6Du3hAnnb2wZm_Hu1DQJwiBHHM5_XWvjbVxLpor4cOkfaJ6BiHy9waQny9mGrpAMPyq8IUe1xdkdFn4=", "id": "rs_0d1a4d10654d56b4016a11d01f97d08191a077eba33b44b6cf", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — **zweiter Versuch erfolgreich**.\n\n## Ergebnis\n\nIch konnte ComfyUI über Tailscale erreichen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nComfyUI läuft jetzt mit:\n\n```text\n--lowvram\n```\n\nund meldet:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: ca. 16.49 GB\nRAM free: ca. 18.8 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\n```\n\nDas ist jetzt stabil genug für erste lokale Wan-Tests.\n\n---\n\n## Smoke-Test 1: erfolgreich\n\nIch habe einen winzigen Wan-Test über die API queued:\n\n```text\n128x224\n9 Frames\n2 Steps\n8 fps\nWan2.1 T2V 1.3B FP8\nText Encoder auf CPU\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 128\nheight: 224\nfps: 8\nduration: 1.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan_micro_smoke_00001_.mp4\n\nVisuell: stark artefaktig, aber das war nur ein Minimaltest. Wichtig war: **Pipeline läuft durch und crasht nicht mehr.**\n\n---\n\n## Smoke-Test 2: Scene-01 Probe erfolgreich\n\nDanach habe ich einen etwas größeren Test für unsere Auto-Szene gestartet:\n\n```text\n240x416\n17 Frames\n4 Steps\n8 fps\nPrompt: cheap used car in dim garage, cinematic push-in\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 240\nheight: 416\nfps: 8\nduration: 2.125s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_wan_probe_00001_.mp4\n\nVisuelle Diagnose:  \nMan erkennt dunkle, horizontale Formen und rote Lichtpunkte — **eher abstrakter Auto/Garage-Vibe**, aber noch kein brauchbarer realistischer Clip. Für 240x416, 4 Steps und 1.3B FP8 ist das erwartbar. Also: technisch erfolgreich, ästhetisch noch „Noir-Fiebertraum mit Rücklichtern“. Immerhin lokal und kostenlos.\n\n---\n\n## Wichtige Erkenntnisse\n\n## 1. Der Crash war RAM-bedingt\n\nVorher hattest du nur ca. 5 GB freien RAM. Jetzt mit ca. 17 GB verfügbar läuft es.\n\n## 2. `--lowvram` hilft\n\nBitte ComfyUI für diese Tests weiter so starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --lowvram\n```\n\n## 3. Text Encoder auf CPU ist stabiler\n\nIch habe im Workflow gesetzt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nDas hält VRAM sauberer und scheint auf deinem Setup stabil zu laufen.\n\n## 4. Modelle und Nodes sind korrekt installiert\n\nBestätigt:\n\n```text\nWanVideoModelLoader\nLoadWanVideoT5TextEncoder\nWanVideoTextEncode\nWanVideoSampler\nWanVideoDecode\nWanVideoVAELoader\nCreateVideo\nSaveVideo\n```\n\nAlle vorhanden und ausführbar.\n\n---\n\n# Nächster sinnvoller Schritt\n\nJetzt baue ich im AutoShortsBot den echten **ComfyUI Wan Client**:\n\n```text\nAutoShortsBot Scene Spec\n→ ComfyUI Workflow JSON\n→ /prompt API\n→ Poll Queue/History\n→ Download MP4 via /view\n→ speichern als imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisch iterieren:\n\n```text\nProbe 1: 240x416, 4 steps\nProbe 2: 360x640, 6–8 steps\nProbe 3: 432x768, 8–12 steps\n```\n\nund schauen, wo auf der A5000 der Sweet Spot liegt.\n\n## Für brauchbarere Qualität würde ich als nächstes testen\n\n```text\nwidth: 360\nheight: 640\nframes: 25\nfps: 8\nsteps: 8\nduration: ca. 3.1s\n```\n\nWenn das stabil läuft, gehen wir Richtung:\n\n```text\n432x768\n33 Frames\n8–12 Steps\n```\n\nFür finale Shorts müssen wir nicht direkt 1080x1920 aus Wan erzeugen. Besser:\n\n```text\nWan Clip kleiner generieren\n→ lokal upscalen / compositen\n→ Captions & Mechanism-Layer in 1080x1920\n```\n\nDas ist bei 16 GB VRAM realistischer.\n\n---\n\n## Fazit\n\n**Ja, die lokale GPU-Video-Pipeline funktioniert.**\n\nNoch nicht hübsch, aber technisch durchgängig:\n\n```text\nHermes VM\n→ Tailscale\n→ ComfyUI Host RTX A5000\n→ Wan2.1 T2V 1.3B FP8\n→ MP4 zurück in AutoShortsBot\n```\n\nDamit ist der Browser-Free-Tier-Weg nicht mehr Hauptpfad, sondern nur noch Fallback. Sehr gut. Kein Credits-Gebettel mehr."}], "id": "msg_0d1a4d10654d56b4016a11d0215f2481919a210d587ef27e06", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] auf den videos ist nichts zu erkennen, nur blinkende farben. hier noch ein paar infos von der KI; bitte berücksichtigen. nenne mir den neuen Startbefehl bzw. die Anpassungen die wir machen müssen um effizient lokal videos zu generieren. es scheint so als würde die GPU nicht genutzt (Nvidia-SMI zeigt gerade mal 1gb benutzt an). hier die infos: 23.05.2026, 18:02 Schweizer Zeit\n\nJa — Wan 2.1 läuft bei dir, aber dein Log zeigt: nicht optimal effizient eingestellt.\n\nDer entscheidende Punkt:\n\nT5Encoder: 01:05\nT5Encoder: 00:46\nSampling: 2 steps in ~1s\nPrompt executed in 120.71 seconds\n\nDas heisst: Das eigentliche Video-Sampling ist schnell, aber der T5-Textencoder frisst fast die ganze Zeit. Wahrscheinlich läuft der Textencoder wegen --lowvram oder Workflow-Device-Settings auf CPU/offload. Für echte Effizienz solltest du zuerst versuchen, ohne `--lowvram` zu starten.\n\n## Was ist ComfyUI?\n\nComfyUI ist kein llama.cpp für Videos. Llama.cpp ist eher eine schlanke Runtime für LLMs/GGUF-Modelle. ComfyUI ist eher ein visueller Node-Workflow-Editor + Server für Bild-, Video- und Diffusion-Pipelines: Modelle laden, Text encoden, Sampler, VAE, Upscaler, Video-Combine usw. als verschaltbare Nodes. ComfyUI unterstützt Wan inzwischen auch nativ; Custom-Nodes wie Kijai WanVideoWrapper sind eher für Zusatzfeatures/Experimente sinnvoll. Der Wrapper-Autor schreibt selbst, dass man native Wan-Nodes nutzen sollte, ausser man braucht ein Feature, das nativ noch fehlt. ([GitHub][1])\n\n## Dein Startbefehl: besser so\n\nFür deinen Fall würde ich zuerst diesen Start testen:\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n\n--listen bindet ComfyUI an eine IP; Standard wäre nur 127.0.0.1, und der Web-Port ist typischerweise 8188. Dein Tailscale-Ansatz ist daher sinnvoll, solange du ComfyUI nicht öffentlich ins Internet hängst. ([ComfyUI Dokumentation][2])\n\n## Wichtig: --lowvram nur bei Out-of-Memory\n\nComfyUIs aktuelle CLI-Beschreibung sagt: --lowvram bringt bei aktiviertem Dynamic VRAM nichts; wenn Dynamic VRAM nicht aktiv ist, lässt es Textencoder auf CPU laufen. Genau das passt zu deinem Log: T5-Encoding dauert ewig, Sampling ist schnell. ([GitHub][3])\n\nDarum:\n\n# Normal / empfohlen\npython main.py --listen \"$TS_IP\" --port 8188 --reserve-vram 1.0\n\n# Nur testen, wenn du genug VRAM hast und wiederholt gleiche Modelle nutzt\npython main.py --listen \"$TS_IP\" --port 8188 --highvram --reserve-vram 1.0\n\n# Nur Notfall bei OOM\npython main.py --listen \"$TS_IP\" --port 8188 --lowvram\n\n--highvram hält Modelle eher im GPU-Speicher, was bei wiederholten Runs schneller sein kann, aber natürlich OOM riskieren kann. ([GitHub][3])\n\n## Welche Modellgrösse?\n\nFür dich aktuell:\n\n| Ziel                                                                | Empfehlung                      |\n| ------------------------------------------------------------------- | ------------------------------- |\n| Schnell testen, lokal stabil, wenig VRAM                            | Wan 2.1 T2V 1.3B            |\n| Bessere finale Qualität, aber langsam/schwer                        | Wan 2.1 14B fp8/quantisiert |\n| Bestes Verhältnis 2026, falls du nicht strikt bei 2.1 bleiben musst | Wan 2.2 5B TI2V prüfen      |\n\nWan 2.1 1.3B ist offiziell für Consumer-GPUs gedacht; die Wan-Entwickler nennen ca. 8.19 GB VRAM und etwa 4 Minuten für 5 Sekunden 480p auf einer RTX 4090 ohne zusätzliche Optimierungen. ([GitHub][4]) Die 14B-Variante ist sichtbar schwerer: ComfyUIs Wan2.1-Doku beschreibt 1.3B als lokale Lightweight-Variante und 14B als High-Performance-Modell mit 32GB+ Modellgrösse und höheren VRAM-Anforderungen. ([ComfyUI Dokumentation][5])\n\nMeine praktische Empfehlung: 1.3B für Iteration, 14B nur für finale Shots. Für Social-Media-Produktion zählt Durchsatz. Du willst viele Prompts, Shots und Varianten testen können.\n\n## Deine aktuellen\nTestwerte sind zu klein für echte Beurteilung\n\nDu generierst gerade:\n\n9 frames at 128x224\n2 steps\n\nDas ist nur ein Smoke-Test. Für echte Videos:\n\nPreview:\n480x832 oder 512x896\n33–49 Frames\n8–12 Steps mit Distill/LightX2V/CausVid\n20–30 Steps ohne Distill\n\nFinal:\n480x832 oder 720x1280\n49–81 Frames\n14B/fp8 nur für beste Clips\n\n2 Steps ohne spezielles Distill-Modell sind qualitativ praktisch zu wenig.\n\n## Workflow-Settings, die ich ändern würde\n\nIn ComfyUI selbst:\n\n1. Textencoder prüfen\n   Stelle im Wan/T5/CLIP-Loader sicher, dass du umt5_xxl_fp8_e4m3fn_scaled.safetensors nutzt, nicht fp16, wenn VRAM knapp ist. ComfyUIs Wan-Doku nutzt genau diesen fp8 Textencoder in den Wan2.1/Wan2.2 Workflows. ([ComfyUI Dokumentation][5])\n\n2. T5 nicht auf CPU, wenn VRAM reicht\n   Dein Bottleneck ist T5. Also Textencoder möglichst GPU/fp8 statt CPU/offload.\n\n3. VAE tiled nur bei OOM\n   Tiled VAE spart Speicher, kann aber bremsen. Wenn keine OOMs kommen, nicht unnötig aggressiv tilen.\n\n4. Nicht jedes Mal Modell wechseln\n   Gleiche Auflösung, gleiche Framezahl, gleiche Modelle halten. Torch/Comfy/Caches profitieren davon.\n\n5. Native Wan zuerst\n   Für normale Wan2.1/2.2 Workflows zuerst native ComfyUI-Templates nutzen. Kijai WanVideoWrapper nur, wenn du TeaCache, Spezial-LoRAs, GGUF/fp8-Varianten oder experimentelle Features brauchst. ([GitHub][1])\n\n## Sofort-Test\n\nMach genau diesen Vergleich:\n\n# 1. Start ohne lowvram\npython main.py --listen \"$TS_IP\" --port 8188 --reserve-vram 1.0\n\nDann denselben Prompt nochmal laufen lassen. Wenn der T5Encoder nicht mehr 60+ Sekunden braucht, war `--lowvram`/CPU-Offload das Problem.\n\nZusätzlich:\n\nnvidia-smi\n\nWenn du mir die Ausgabe von nvidia-smi schickst, kann ich dir die passende Einstellung für deine echte VRAM-Grösse sagen. Compute Capability 8.6 sagt nur grob Ampere/CUDA-fähig, aber nicht, ob du 8, 10, 12, 16 oder 24 GB VRAM hast.\n\n[1]: https://github.com/kijai/ComfyUI-WanVideoWrapper \"GitHub - kijai/ComfyUI-WanVideoWrapper · GitHub\"\n[2]: https://docs.comfy.org/interface/settings/server-config \"Server Config - ComfyUI\"\n[3]: https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/cli_args.py \"ComfyUI/comfy/cli_args.py at master · Comfy-Org/ComfyUI · GitHub\"\n[4]: https://github.com/Wan-Video/Wan2.1 \"GitHub - Wan-Video/Wan2.1: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub\"\n[5]: https://docs.comfy.org/tutorials/video/wan/fun-control \"ComfyUI Wan2.1 Fun Control Video Examples - ComfyUI\"", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_GG6PIsKdVaW36Y1KPa065NVk", "call_id": "call_GG6PIsKdVaW36Y1KPa065NVk", "response_item_id": "fc_0d1a4d10654d56b4016a11d0c022f481919608ea2f77eb8562", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\ntry:\\n    data=json.load(urllib.request.urlopen(base+'/system_stats', timeout=10))\\n    print('argv', data['system'].get('ar...[truncated]\", \"timeout\": 30, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdDAWnJt7cBuiF89z6a6Hjv6ckTrUs_w4c2MWymFtlJ-Qjgv9LBlM-TF-fUS03sBmues2T8GSy6mdGgLUVkViCXV6K_uFzMFFYRs1KLsRLsfiH3muJOzqff20tvUBuObgoMTLV1kp1fgY3TIvJTXHTrsBnQlzjLeqGG6PRptgKGrCZnpnckalqCHc57Mv0_sOr8qvH1Is1BSDjK9Kpq19-gPfuClLWypBd9nUWEsNFBJkCPwKrU-Ytk6Lv2ZOF8cVsfAMMHjjUVeT1XgPyAR-2bRI2PLWlp6dqawdwFwa2Y6Zm6S_HRXGjSY2QjpG7zULba8ewkHzSH4yICtEaB0ePXT0xRbpYjzr0xzUboIEPgaEwF_dglHrM5-2WH9fDl4wvrDuIQIVf4ALG2IG1YVUFisruKYCSLPo3Ue3xhTanYAxaUoHFT1Rymyf5CQFCcKvqfFaAGs_ATz8rvGqg2lzmrK9Z4f-KVhj-C1_0w8qVzs3_0E9RR0FB-TuaRJ9ot9Il0c-OkxCtceIlLVZf3nScV_XJ1KCAap0no7wCIWyzCr3WJOBZA-xGQwF_La6xZFTDHd9ND3-zRuBq5_LnxHwOvB6KCvcU1gOhT-rPzOrRLfvt8gkTTVb6A_SoVP4q9pggc5_d-kCDuh0pG00s5ww0iV10cEjYly-SyDW18ShGR9PpeR7vifQJOqCRXBmbVt2-OsFqC1EbVHarjTuIUCsNJW8hUnezJvBzE5JsBLO8JPCYSVCYPtpOdXqXf8kOQXBdI6qwZxCmcTUqPYLWHeu8YBHPXWF5yb8HnC-pXxZFsOOT7V5rp7sbyg7n70egEBeyDOzHruunQ1Muavp2JztcApmeL2nHZpB4t0yZVsDyjEAUMwR_-XyioCfpXW_F5OVilJUtqhgrC4murQQ1vKnbw-7_0y2oZtorNvp4QcEng8Ra5y8XN5Q61PThmhEdhXkylQXdOWmoEDrJf1_rlPENu6hQ==", "id": "rs_0d1a4d10654d56b4016a11d0bd92408191a133306f1fbc80b4", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport json, urllib.request\nbase='http://100.101.173.25:8188'...` -> exit 0, 1 lines output", "tool_call_id": "call_GG6PIsKdVaW36Y1KPa065NVk", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_blcEsENeHDDMgI7AjCT2M0Mc", "call_id": "call_blcEsENeHDDMgI7AjCT2M0Mc", "response_item_id": "fc_0d1a4d10654d56b4016a11d0ce4a94819190bf6e40d89f184b", "type": "function", "function": {"name": "skill_manage", "arguments": "{\"action\": \"patch\", \"name\": \"comfyui\", \"old_string\": \"13. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8, `--lowvram`, CPU text enco...[truncated]\", \"new_string\": \"13. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 and a micro smoke test. Use...[truncated]\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdDOWrm0w-KaDi2_03-T7tuV3Oq8fffLc3vJDA8ln10rKT6kT4iOUMr3Q-DurCTJJSzVwzHPNb-aJaQiigsmOtuVTbG11sz2kkrM-7n3CpE9t2s6x5MirR8cpxCu0OXySJj9wynWqbqMG5L_0hei_XgxiVgOLTqxtjGlQOREBYI3NqhNvsg-L_3DefCOH7y1c76giWMcMVHxBm3Um-6nPjBEkO-CmuVL0QNXm3XgsPjaEANvvDicbsOGmD6dU7W_ZiZoK-oxe3CydAMrPMW09wJoBPhXTStiKoFNvwP4RAUEUmn3LvIzwc3l_CDhFFuk3o1YCOZyOVO9MHSjZDBrWW7hQ1w52Wc6MWO8mmkdYB1-KlGqTtvkHFsVUZMM779HvZUZQ9lpvHX6Nr-rOdjZ7x9VmYQ2dBCdrMjyw0EWf5balfVmkkraL1E7W3NFAGV8BZcuoFoMh14I8SHfVdxj8g_-uoTdNNBiimZU9xEa6zDkGfSdi65gcpFNnLJlOJMpB2avjJPlaCB_0EPgSweNsOiW3Jmq4iP-rHr6aZtK2IpMWFwJ08tjwhBRxb9Ae-usQ9hcOXnwWMDp9VHvaw9DkKEeYGNs1HOE31gMSvuoEgrS9cm3QobGvi0JvJX0UpOBy8Zq2_x-uQ5MMbqrCyv53IjI_oV_VC64KnoFjH38TAr7Dv458PF1IY7IB9pz4Y7ir3BTtbpJHDGmUS6PyzK2DS3b8G6zNJbIArsuxMsiYK8J8JkogumS_gtf5_f0IeGMGtM_hd-IVcDF6RPwMcYZarQ0FwTk4RzT1aCuG6-ueGDN1Qo9vYOHMkJu7_GzgRR4jod1P8c0d04QG3tYdUtFroLJRP2kHc5Y-THcfAnzdVxSwM6VGIpwMcAvs8OS7aziAdrYiyXZckPK6QbcINPuIUkYFiEZtkqZGTx-NN7dt2hd7Vo5-DW3CtPiStMLgzvAd5tM7IYS6vBLEhx7hUYwc4ry0Fza4c2a_NqANo6NYzCQRbWlYl1dVkEe1uVoxlpW3Sxqxqran5WgdMUK9hmHHk1GXlIgTYTJf08NZGqxVOHXk-TATQnGwA5hzKByewMkg1VepmCHWI6IIhouzR6lf0OU1W7cjsnGGMxRTZfpckzP3JzvTTEq3IU5o-ktoFYq0jRSaV1_6dyHApw5WNDIUfbO01AQ_sPNgM5CS4uMgO0yl6J1PDMD5N2UdJ63gNwTpIcgUVigoe1B9Wf2OW9MFE43ZbI3gxkgTERDdKoG1mhVP-8EfZRRiZW25CYiN0EaYTDjijjJqaOxFDc4ek9y2vqV08I7zgeVoaCXIS1v2XpcETZxMzAWxFqTn260ETsGfxBAB8X8gIPzg1VLfbq5Jl1HOXm0W1Bl53dDGIZzUfz4D-RbeMNKkKkHZm_eIRmkcwr9z9ofGRFoWkENeBhs8ff7YwuoEYOZLV4A9JdPZjzjs7_4koRndlgUxm_vZsGIIfDFxr7UoFb0KQ1qhzfJ_2Nx0pw4UjRE2-tC22zZbDdoFS4tbp6hTZH1r7aoVBTMgKzg2gvkF3cjV5It_oU152zok3vCgAUN5yaRL6FOwC1n3kzT4RjQWJo1ZO8vpdZHDW8zS_HWp_cSHWNSbhZqz9n6WI7oOsDQLz5bN03XX11Sb3MGzNOXvObtEyhx3wO5hXjBXxbvh5Qh-y141uSlUFNxtpp2O36UfeWGR5QtRv_UN_NKoZ78mUI_merLqOMSamuhUUExwF-VuCWmiyA15QPaL4iShyXXOMZpN2jgpP4qGIOfTjctok6qMnXzlHOBr7Vkwr-wl-pGtFDfbX2y4c6kVWZkpu5e79R5u4EXziAUJDf63MDW7DtPQwnEehtVowP_iV1h_6dU65W3NBQlE58zQ7tw5FbityrHXhmZOQWPl4hjtCG2u4UWhYfAOawmy1u8kYMoyCdy0u5msPnbTZk7Wq1YjJ6QqO_vWyEywCm_p6IY-GhtI2w6L5g96dKQRZ6L39DgVYkMMp5lZ6jimD0tF_yXyKugXE0wE7J3Tip7GLpCmsYJRzC1BOGycS398CAf1UM5E7oPnjqUtekngDyUezRZ5xV4IPBjaTF45ph7PkTyTXNI-VJB69wxOxkpgZ_7FdssAWSOtK-ZKtAPU5athdIyrNpiW9kOSnHivm6AKQMaqjX13NHxFe3qd_a_CAWSwGRbTsoi_VSvgxNp3PLrNjdtd50hHk4G7cJsVyAF2rTzO5Mbd_EvLf2GHEU5sE2wwnqDTe0e_oHJN5veosX35VnlLQ7UgRtuOnJAuDF910kHUe4TMoV-6iT4HIdSpoTwJoeu9JAQ-NqwBO5mtUWnRQ-Yb4EkSFzNNBfpcxqszcXyybKDQJoo-aLnDOyQl7JVMWeZ8j1cLp_YmFpAxJQ72AsWUuzzjnuLiQUykEqygc77UDEUgiM6r3LX7XKezRs_CpTMCfWVLCgYddIcrSEhIcZngVX9ARhshftINoxVi09xVyYd1obAnA-Xfn6He4j6Ud7XHQsB6cA6DxwIGOAvqyxDYU3i4MVnWUSWb-4UPxgOVaX6U9xXM2zDHERXuUH9ZkA8dyyTxFdq_vtjrTIgvTPphELHiCLh-lIhxgpezkop4JHYzUznltjMkwIcS7AvTBh6FLp1c1UU661PnIDBPMJNKIb0bJKUPGj2Ru6wlE17hFJx3xttrjRXb1OGKhNPF1ZjIqTVwPC6Uk9kRwp6vpOimwjzMNKjwaWbUc5w4ggc1ebFguGIs1JGqnndZDQxTWyg3NZOf5aFv9yJ3AKNW7FCfehd4A6kOJlldVpntPtmbJuqVB-1aAalNArZfFi4B4XIwi6egqwsfkgd4v7ye52F3IDSmVTx4YabFrC0X9ZjHRaug_2IWOqypZ6k1ZP5ul_O1TRGoCGWhc0dPTVoHPOPwZKT10b0q-TbkEpzDk8qcD75gqiLI4Wtv8Em7zBSAenwasd5xTg-6Q1Cb8I32vXour7fNDCKZiG7NQuUQQ3dtqJSpSASdOqhXC-lnYSciwCAr47Ps5G0RRZFnMbEAw==", "id": "rs_0d1a4d10654d56b4016a11d0c69050819184e0e3137e68e877", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'comfyui' (1 replacement).\"}", "tool_call_id": "call_blcEsENeHDDMgI7AjCT2M0Mc", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, die KI-Analyse ist korrekt, Sir. **Unser aktuelles Setup ist stabil, aber absichtlich zu defensiv** — dadurch wird die GPU kaum genutzt.\n\nIch habe gerade von hier geprüft: ComfyUI läuft noch mit:\n\n```text\nargv: main.py --listen 100.101.173.25 --port 8188 --lowvram\nVRAM free: ca. 15.25 GB\nRAM free: ca. 13.13 GB\n```\n\nDas erklärt exakt dein Problem:\n\n```text\n--lowvram\n+ TextEncode device=cpu\n+ load_device=offload_device\n+ 2–4 Steps\n+ winzige Auflösung\n= kaum GPU-Auslastung, lange T5-Zeit, blinkender Pixelbrei\n```\n\nWir haben damit bewiesen: Pipeline läuft.  \nJetzt schalten wir von **Überlebensmodus** auf **Produktionsmodus**. Endlich darf die GPU arbeiten. Schockierend moderne Idee.\n\n---\n\n# Neuer Startbefehl\n\nBitte ComfyUI stoppen:\n\n```bash\nCTRL+C\n```\n\nFalls es im Hintergrund hängt:\n\n```bash\npkill -f \"python main.py\"\n```\n\nDann neu starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas ist ab jetzt unser **Standard-Startbefehl**.\n\nNicht mehr:\n\n```bash\n--lowvram\n```\n\naußer bei OOM/Crash.\n\n---\n\n# Wenn das stabil läuft: High-VRAM-Test\n\nErst nach einem erfolgreichen normalen Test:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --highvram \\\n  --reserve-vram 1.0\n```\n\n`--highvram` kann bei wiederholten Runs schneller sein, weil Modelle eher im VRAM bleiben. Aber: höheres OOM-Risiko.\n\nFür jetzt bitte zuerst:\n\n```bash\n--reserve-vram 1.0\n```\n\nohne `--highvram`.\n\n---\n\n# Workflow-Anpassungen\n\nIch ändere in den nächsten API-Workflows diese Punkte:\n\n## 1. T5/Textencoder auf GPU\n\nAlt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\n```\n\nAlt:\n\n```text\nLoadWanVideoT5TextEncoder load_device = offload_device\n```\n\nNeu:\n\n```text\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nGrund: T5 war dein Bottleneck. Nicht Sampling.\n\n---\n\n## 2. Model Loader weniger offloaden\n\nAlt:\n\n```text\nWanVideoModelLoader load_device = offload_device\n```\n\nNeu für 1.3B FP8:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nBei 1.3B FP8 sollte das auf 16 GB VRAM gehen.\n\nWenn OOM:\n\n```text\nzurück auf offload_device\n```\n\n---\n\n## 3. VAE Tiling nicht aggressiv erzwingen\n\nAlt:\n\n```text\nenable_vae_tiling = true\ntile_x = 128\ntile_y = 128\n```\n\nNeu für Preview:\n\n```text\nenable_vae_tiling = false\n```\n\nWenn OOM beim Decode:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nTiling spart Speicher, kann aber bremsen und Artefakte fördern.\n\n---\n\n## 4. Realistische Testparameter statt Smoke-Test-Müll\n\nBisher:\n\n```text\n128x224 / 240x416\n9–17 Frames\n2–4 Steps\n```\n\nDas war nur Funktionstest. Für visuelle Beurteilung nutzlos.\n\nNeuer Preview-Test:\n\n```text\nwidth: 480\nheight: 832\nframes: 33\nfps: 12\nsteps: 10\ncfg: 3.5\nscheduler: unipc\n```\n\nDauer:\n\n```text\n33 / 12 = 2.75 Sekunden\n```\n\nDas ist erstmals halbwegs beurteilbar.\n\nFalls OOM oder zu langsam:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\n---\n\n# Bitte jetzt ausführen\n\n## 1. ComfyUI stoppen\n\n```bash\npkill -f \"python main.py\"\n```\n\n## 2. Neu starten\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## 3. Healthcheck\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n## 4. GPU-Monitor parallel laufen lassen\n\nWährend ich dann den nächsten Job starte:\n\n```bash\nwatch -n 1 nvidia-smi\n```\n\nDu solltest beim T5-Encoding/Sampling deutlich mehr als 1 GB sehen, wenn GPU-Encoding greift.\n\n---\n\n# Falls der Start sofort crasht oder OOM kommt\n\nDann nehmen wir Zwischenmodus:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 2.0\n```\n\nFalls immer noch Problem:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 3.0\n```\n\nErst danach zurück zu:\n\n```bash\n--lowvram\n```\n\n`--lowvram` ist jetzt nur noch Notfallmodus, nicht Produktionsmodus.\n\n---\n\n# Warum `nvidia-smi` vorher kaum GPU zeigte\n\nDrei Gründe:\n\n1. **T5 lief auf CPU/offload**  \n   Deshalb 45–65 Sekunden Textencoder, aber kaum GPU.\n\n2. **Sampling war winzig und extrem kurz**  \n   2–4 Steps bei Mini-Auflösung sind in Sekunden vorbei. `nvidia-smi` kann den Spike leicht verpassen.\n\n3. **Das Modell wurde absichtlich offloaded**  \n   Wir hatten alles auf Stabilität getrimmt, nicht auf Speed/Qualität.\n\n---\n\n# Native Wan vs Kijai Wrapper\n\nDie Info ist auch richtig: Für Standard-Wan sollten wir langfristig **native ComfyUI Wan-Workflows** testen.\n\nAber kurzfristig:\n\n```text\nKijai Wrapper läuft bereits\nModelle werden erkannt\nAPI-Workflow funktioniert\n```\n\nDeshalb machen wir jetzt zuerst:\n\n```text\nWrapper optimieren → brauchbaren Clip erzeugen\n```\n\nDanach:\n\n```text\nnative Wan Template vergleichen\n```\n\nNicht beides gleichzeitig. Sonst debuggen wir Workflow-Religion statt Videos. Und davon hat die Welt wahrlich genug.\n\n---\n\n# Sobald du neu gestartet hast\n\nSchreib einfach:\n\n```text\nläuft ohne lowvram\n```\n\nDann starte ich von hier den nächsten richtigen Test:\n\n```text\n480x832\n33 Frames\n10 Steps\nT5 GPU\nModel main_device\nVAE tiling off\n```\n\nWenn das durchläuft, haben wir den ersten echten lokalen Video-Kandidaten.", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, die KI-Analyse ist korrekt, Sir. **Unser aktuelles Setup ist stabil, aber absichtlich zu defensiv** — dadurch wird die GPU kaum genutzt.\n\nIch habe gerade von hier geprüft: ComfyUI läuft noch mit:\n\n```text\nargv: main.py --listen 100.101.173.25 --port 8188 --lowvram\nVRAM free: ca. 15.25 GB\nRAM free: ca. 13.13 GB\n```\n\nDas erklärt exakt dein Problem:\n\n```text\n--lowvram\n+ TextEncode device=cpu\n+ load_device=offload_device\n+ 2–4 Steps\n+ winzige Auflösung\n= kaum GPU-Auslastung, lange T5-Zeit, blinkender Pixelbrei\n```\n\nWir haben damit bewiesen: Pipeline läuft.  \nJetzt schalten wir von **Überlebensmodus** auf **Produktionsmodus**. Endlich darf die GPU arbeiten. Schockierend moderne Idee.\n\n---\n\n# Neuer Startbefehl\n\nBitte ComfyUI stoppen:\n\n```bash\nCTRL+C\n```\n\nFalls es im Hintergrund hängt:\n\n```bash\npkill -f \"python main.py\"\n```\n\nDann neu starten:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas ist ab jetzt unser **Standard-Startbefehl**.\n\nNicht mehr:\n\n```bash\n--lowvram\n```\n\naußer bei OOM/Crash.\n\n---\n\n# Wenn das stabil läuft: High-VRAM-Test\n\nErst nach einem erfolgreichen normalen Test:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --highvram \\\n  --reserve-vram 1.0\n```\n\n`--highvram` kann bei wiederholten Runs schneller sein, weil Modelle eher im VRAM bleiben. Aber: höheres OOM-Risiko.\n\nFür jetzt bitte zuerst:\n\n```bash\n--reserve-vram 1.0\n```\n\nohne `--highvram`.\n\n---\n\n# Workflow-Anpassungen\n\nIch ändere in den nächsten API-Workflows diese Punkte:\n\n## 1. T5/Textencoder auf GPU\n\nAlt:\n\n```text\nWanVideoTextEncode device = cpu\n```\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\n```\n\nAlt:\n\n```text\nLoadWanVideoT5TextEncoder load_device = offload_device\n```\n\nNeu:\n\n```text\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nGrund: T5 war dein Bottleneck. Nicht Sampling.\n\n---\n\n## 2. Model Loader weniger offloaden\n\nAlt:\n\n```text\nWanVideoModelLoader load_device = offload_device\n```\n\nNeu für 1.3B FP8:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nBei 1.3B FP8 sollte das auf 16 GB VRAM gehen.\n\nWenn OOM:\n\n```text\nzurück auf offload_device\n```\n\n---\n\n## 3. VAE Tiling nicht aggressiv erzwingen\n\nAlt:\n\n```text\nenable_vae_tiling = true\ntile_x = 128\ntile_y = 128\n```\n\nNeu für Preview:\n\n```text\nenable_vae_tiling = false\n```\n\nWenn OOM beim Decode:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nTiling spart Speicher, kann aber bremsen und Artefakte fördern.\n\n---\n\n## 4. Realistische Testparameter statt Smoke-Test-Müll\n\nBisher:\n\n```text\n128x224 / 240x416\n9–17 Frames\n2–4 Steps\n```\n\nDas war nur Funktionstest. Für visuelle Beurteilung nutzlos.\n\nNeuer Preview-Test:\n\n```text\nwidth: 480\nheight: 832\nframes: 33\nfps: 12\nsteps: 10\ncfg: 3.5\nscheduler: unipc\n```\n\nDauer:\n\n```text\n33 / 12 = 2.75 Sekunden\n```\n\nDas ist erstmals halbwegs beurteilbar.\n\nFalls OOM oder zu langsam:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\n---\n\n# Bitte jetzt ausführen\n\n## 1. ComfyUI stoppen\n\n```bash\npkill -f \"python main.py\"\n```\n\n## 2. Neu starten\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## 3. Healthcheck\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n## 4. GPU-Monitor parallel laufen lassen\n\nWährend ich dann den nächsten Job starte:\n\n```bash\nwatch -n 1 nvidia-smi\n```\n\nDu solltest beim T5-Encoding/Sampling deutlich mehr als 1 GB sehen, wenn GPU-Encoding greift.\n\n---\n\n# Falls der Start sofort crasht oder OOM kommt\n\nDann nehmen wir Zwischenmodus:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 2.0\n```\n\nFalls immer noch Problem:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 3.0\n```\n\nErst danach zurück zu:\n\n```bash\n--lowvram\n```\n\n`--lowvram` ist jetzt nur noch Notfallmodus, nicht Produktionsmodus.\n\n---\n\n# Warum `nvidia-smi` vorher kaum GPU zeigte\n\nDrei Gründe:\n\n1. **T5 lief auf CPU/offload**  \n   Deshalb 45–65 Sekunden Textencoder, aber kaum GPU.\n\n2. **Sampling war winzig und extrem kurz**  \n   2–4 Steps bei Mini-Auflösung sind in Sekunden vorbei. `nvidia-smi` kann den Spike leicht verpassen.\n\n3. **Das Modell wurde absichtlich offloaded**  \n   Wir hatten alles auf Stabilität getrimmt, nicht auf Speed/Qualität.\n\n---\n\n# Native Wan vs Kijai Wrapper\n\nDie Info ist auch richtig: Für Standard-Wan sollten wir langfristig **native ComfyUI Wan-Workflows** testen.\n\nAber kurzfristig:\n\n```text\nKijai Wrapper läuft bereits\nModelle werden erkannt\nAPI-Workflow funktioniert\n```\n\nDeshalb machen wir jetzt zuerst:\n\n```text\nWrapper optimieren → brauchbaren Clip erzeugen\n```\n\nDanach:\n\n```text\nnative Wan Template vergleichen\n```\n\nNicht beides gleichzeitig. Sonst debuggen wir Workflow-Religion statt Videos. Und davon hat die Welt wahrlich genug.\n\n---\n\n# Sobald du neu gestartet hast\n\nSchreib einfach:\n\n```text\nläuft ohne lowvram\n```\n\nDann starte ich von hier den nächsten richtigen Test:\n\n```text\n480x832\n33 Frames\n10 Steps\nT5 GPU\nModel main_device\nVAE tiling off\n```\n\nWenn das durchläuft, haben wir den ersten echten lokalen Video-Kandidaten."}], "id": "msg_0d1a4d10654d56b4016a11d0da35e481919fceb44fd2ec763a", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "assistant", "content": "Now monitor maybe nvidia stats via system_stats includes vram. Need poll.", "tool_calls": [{"id": "call_bl6WEVMu3aG7QOyJyuxV9hpy", "call_id": "call_bl6WEVMu3aG7QOyJyuxV9hpy", "response_item_id": "fc_0d1a4d10654d56b4016a11d2b17cdc8191b1b2a73fc168c1c6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport json, time, urllib.request, urllib.error\\nbase='http://100.101.173.25:8188'\\npid='8d6126de-549e-411f-b61d-fe11b4f42935'\\nfor i in range(100):\\n    try:\\n        q=json.load(urllib.re...[truncated]\", \"timeout\": 600, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Now monitor maybe nvidia stats via system_stats includes vram. Need poll."}], "id": "msg_0d1a4d10654d56b4016a11d2b0ed44819180dcd9daaff4dea9", "phase": "commentary"}], "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_38jT3dgTor8Sv6ywlVhAZKJ0", "call_id": "call_38jT3dgTor8Sv6ywlVhAZKJ0", "response_item_id": "fc_0d1a4d10654d56b4016a11d304aea481918d2a39dad25ba7f5", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport json, time, urllib.request\\nbase='http://100.101.173.25:8188'\\npid='283de2c2-e617-4bbd-b9bb-1b3a4f5c73f2'\\nfor i in range(120):\\n    q=json.load(urllib.request.urlopen(base+'/queue'...[truncated]\", \"timeout\": 600, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport json, time, urllib.request\nbase='http://100.101.173.25...` -> exit 0, 1 lines output", "tool_call_id": "call_38jT3dgTor8Sv6ywlVhAZKJ0", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Download.", "tool_calls": [{"id": "call_WYEreUQv9XwU7AmbLrSrD9TB", "call_id": "call_WYEreUQv9XwU7AmbLrSrD9TB", "response_item_id": "fc_0d1a4d10654d56b4016a11d3a96f108191a2ed665c76e15fbb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_scene01_gpu_480x832_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'subfolder':'...[truncated]\", \"timeout\": 180, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Download."}], "id": "msg_0d1a4d10654d56b4016a11d3a9279c81918c3ae44dd6adbcb6", "phase": "commentary"}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.parse, urllib.request, pathlib\nbase='http://100...` -> exit 0, 1 lines output", "tool_call_id": "call_WYEreUQv9XwU7AmbLrSrD9TB", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_knTiqgEyE7cBPeOf9Iuieyjr", "call_id": "call_knTiqgEyE7cBPeOf9Iuieyjr", "response_item_id": "fc_0d1a4d10654d56b4016a11d3b250f08191b7fc28afdc536d3a", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/scene01_gpu_480_mid.jpg\",\"question\":\"Beurteile knapp den Frame: Ist ein Auto/Garagenmotiv klar erkennbar? Ist der Clip als B-Roll/Hook-Grundlage brauchbar oder noch zu artefaktisch?\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[vision_analyze] 'Beurteile knapp den Frame: Ist ein Auto/Garagenmot' (275 chars)", "tool_call_id": "call_knTiqgEyE7cBPeOf9Iuieyjr", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Perfekt, Sir. Ich habe es direkt getestet: **ohne `--lowvram` funktioniert es deutlich besser.**\n\n## Ergebnis\n\nComfyUI läuft jetzt korrekt mit:\n\n```text\nmain.py --listen 100.101.173.25 --port 8188 --reserve-vram 1.0\n```\n\nIch habe von hier aus zwei neue GPU-Workflows gestartet.\n\n---\n\n## Test 1 — 416×736, 25 Frames, 8 Steps\n\nSettings:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 47 Sekunden\n```\n\nGPU-Auslastung laut ComfyUI-Systemstats:\n\n```text\nVRAM used: ca. 8–10.5 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n\nVisuell: **endlich erkennbar** — rotes Auto in dunkler Garage. Noch nicht final, aber kein blinkender Pixelbrei mehr.\n\n---\n\n## Test 2 — 480×832, 33 Frames, 10 Steps\n\nSettings:\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\nDauer: 2.75 Sekunden\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 160 Sekunden\n```\n\nGPU-Auslastung:\n\n```text\nVRAM used: ca. 8.2–10.3 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n\nVisuelle Diagnose:  \n**Auto/Garage ist klar erkennbar.** Dunkel, filmisch, rotes Auto, Close-up. Als Hook-B-Roll-Grundlage brauchbar. Noch nicht „finaler YouTube-Shorts-Killer“, aber jetzt sind wir in echtem Video-Territorium statt „blinkender GPU-Schamanismus“.\n\n---\n\n# Neuer Standard-Startbefehl\n\nBitte ab jetzt für lokale Video-Generierung:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## Nicht mehr standardmäßig verwenden\n\n```bash\n--lowvram\n```\n\n`--lowvram` nur noch bei Crash/OOM.\n\n---\n\n# Wenn du noch mehr Speed bei wiederholten Runs willst\n\nSpäter testen wir:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --highvram \\\n  --reserve-vram 1.0\n```\n\nAber erst später. Aktuell ist der normale Modus stabil und nutzt die GPU.\n\n---\n\n# Workflow-Anpassungen, die jetzt gelten\n\n## 1. Textencoder auf GPU\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nNicht mehr:\n\n```text\ndevice = cpu\nload_device = offload_device\n```\n\nDas war der Grund, warum T5 ewig dauerte.\n\n---\n\n## 2. Wan Model auf GPU/main_device\n\nNeu:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nNicht mehr:\n\n```text\noffload_device\n```\n\nFür `Wan2.1 T2V 1.3B FP8` passt das auf deiner A5000.\n\n---\n\n## 3. VAE Tiling aus\n\nNeu:\n\n```text\nenable_vae_tiling = false\n```\n\nNur bei OOM:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\n---\n\n# Empfohlene Produktionsprofile\n\n## Fast Preview / Prompt-Iteration\n\nDas ist aktuell der Sweet Spot:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\n```\n\nDauer:\n\n```text\n~45–60 Sekunden\n```\n\nNutzen:\n\n```text\nschnell genug für Varianten\ngut genug, um Motiv/Komposition zu beurteilen\n```\n\n## Better Preview / Kandidat\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\n```\n\nDauer:\n\n```text\n~2.5–3 Minuten\n```\n\nNutzen:\n\n```text\nbrauchbare B-Roll-Basis\nbesserer Bildaufbau\nmehr Bewegung\n```\n\n## Noch nicht sofort\n\n```text\n720x1280\n49–81 Frames\n20+ Steps\n14B Modell\n```\n\nDas wäre jetzt unnötig schwer. Erst Prompt-/Shot-Struktur sauber bekommen.\n\n---\n\n# Warum `nvidia-smi` vorher nur ~1 GB zeigte\n\nWeil wir im Notfallmodus waren:\n\n```text\n--lowvram\nTextencoder auf CPU\nModel offload_device\nwinzige Auflösung\n2–4 Steps\n```\n\nJetzt sah ich während der Jobs:\n\n```text\n8–10.5 GB VRAM used\n```\n\nAlso: GPU wird jetzt genutzt.\n\n---\n\n# Nächster Schritt\n\nIch würde jetzt im Repo den **ComfyUI Wan Client** als feste Pipeline-Komponente bauen:\n\n```text\nScene Spec\n→ Workflow JSON\n→ ComfyUI /prompt\n→ Poll /queue + /history\n→ Download /view\n→ speichern als:\ndata/imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisiert Varianten erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nund die beste Variante in den Hybrid-Renderer ziehen.\n\nKurz: **Startbefehl ist jetzt `--reserve-vram 1.0`, nicht `--lowvram`. GPU-Pipeline funktioniert. Erste brauchbare Auto-B-Roll ist erzeugt.**", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdPCBJ7qUdM_hjRHVQpmc-1x8tMA8cZxT9Xym-hyq45NRCvry9iH4PBj9oasoTyuzVQ0XzDcI3lkIprGCuGCwuXjIdXO7i4kvzZlTici1lPhnN-HquJwa3rp1cj5-UVhK2ifQDgmFSLTWi4tcYKxJBhc_RTFqr0cpuTq7H9kE7nBUxhYHC6oCpQMCdk4UHuoLLKjWsiH-8L61AsVj-6W6lURs_1A3uNMExAQdZNMdvTJbxL8d7hyDQ2WD7xwz9hTQskXq1X53wuBynZB-o-hX2ceCy0dYfA5CX5jZa3MNN7HoGu1ro6iHe8Jrl4zPGQLoIQur9AuVl6gT92XKcS-2dfeMsxVCuvPO427Li5GUIUTYD0VI67pujVivSR2xmovDj893VV0H0WR-7-bk9_LJue3w0h-sx5gpnrvmOubYvSXvNETvKaz9D1J3-G2rLR7ybPIhNAH559_P02_uCa9bOxGhHXjHQQImlKi-6mFZguAjshMo7s6vM8NWVqDbATaxXF5IyPsbyZR1cRaDmCG1s2hRekOzZGCGoVeP3e8mfN4bHCW4PRAPWi3cRI-lQ4scSOwJBVDVs03tUIktj1ybXB9At8n6ibiqWmoumv6GA-v3THHTrc0G4WJ4HzlXHDxloN22dlqzBAE-zNJRH30GuXNifVZguAPI3bnmPgzEmaGitFCxDY32JoeDxNrk9BrN3tKH9QuKnYGqDYUzD1itfkMvnbrWCjWF-rMsRV38zD0s2p7CFudMpuZriq6VkN-xHD597kJkAFGBSUu48X49sU5pexRk1HrGAINnmbtzVWy-ZsqDlhJ51qYR04Ae4N8sKDqYwuDNjjxozVKgcDjjUSIpLqi3CL7Kq6REXI0bEgoEa3IZeTJbZCxI_vi52fKVTwqzSgY3M3sNdlLyLpq6T2970_8I9RA9oRdbwYi5Umke3_buOdLwAiefb525xTGqI4CuVtnvl6WDI5s_6FZ6HquY5P_n1UclM5lJI_buRmH1wEgVezBjSRMLrIo4XFck11odW80hm_jgKxSjqpRpal8KFA1Ayp39FGkr6q_R98SkY85bYzA3Nr6qovl7shkcOOHzaQCvGQeIfndJIJ73gHVGv-_eA7GoEdZE4kfkJilSLK4bsWU5-PZIdJO_EWKcUPnN728m3h-5kKevHreaNvU05yXa8U6lqJsuhgujsv4vfeMpnQsDXzt-6myCtTKLA5WHHhsRqblt3UAVHwo2xgvjgBKB8VPORtZ2U0IKLWl-ogD_U_0VWpGvniBbnrAp8aoyMCD8w7U5EG96CcQDamJNqgZlBjBzOI9euvvYIK24EcxmaHjC5_cKmlKjpw32Lk8f7RCI7Gcph2W5gTzzxGml3j8_olkpRgIqBMCeXl2h3OKUAr7JF1wf0dpG-XlzIHl-9fOJeWXDoiSmva_BY1TnT9Pf5nD2H1JnS1neOBpKs6joIMTF9yj9NpD9yK0lvd3P713CbhRqa1lxOFKjG3d22dFvSXTqqhENB4KMpecE0lma_r3auPYNS9hUyM1kw93rIX9kcStHm9gXgrqMC8yWgK95XXE7eJfy0dKvzYk7pzPjO1Fona7SXfMhlGxk8NnzhB6OhvCYAWuJ3h5bahMOqwd_cG4SllUrpnk6FIM-V9Ht7wvjsd7Sovii5CfbLhl4KxLqGiWxXeQ6ldiHsN0cngvbz7hukO_2-3cPA9VEKRSdRVkRNLnMdGfhS3ti6IbXV6qG3xmXw8s_bAHtKvOExx96lvmKez7gfe-FdabrHGcX7vqNz788gA5dp8ynQ9sVeXCSTVI_jphx1AITw55mcanisVdM-X_tR5oyPnUOjD55E6Ybo6Pk2JnmKsq4veWYssOwkDKbW086M2IDw_Y338X1aDr3FjO5tNpPIWDdkFoJi5zu046VisNRLg642hRudwrJBXsyxIgb9efy0gYk35yVCHNkAW6KNzVCzprlb1eGaUEog_xzpQ1kZAK3ogij-ekzlQzffhxu2W6myRd3sg65-dQ1NBZqf5jkP3MvuK7l1A1udG-7GWlJa-ZK2uUCJHQqgDCieHqQmB20enaCgUsR9jjLrUN1c1U7cpe-nkuIOPA02KUuRCsclzBOJens_jt02KVfL0q0-xn33SJAYRnopLZuk2DgfgCZ4qfuPyTKNRgDDdCvxS9raOKu-UhGKblTXoz19J_6QLzQ52Z-5jgSqpjGzo36YXlf6YhcjCFG-36sracABFXWfKrIbNfGICy0HJbyHpinI--lhOsKYMNXAGOrS9J9m90fE2UC1H_tfUR9KyaYm7Ad77LaZbhce3CQQMExyLM1gI4MRd7EGLDDrUKo43yfBFB7PspXY53EftNNI8QZethJgRtePBE3FRZ-WxSQu1Ae-rSCNyyag5F20CooLBpTHDpX9VyFgRuUhYHoi5IOgbaufxMV4TBrRR-N7j2AzjZe8pgf2KaOITy1KvSXjo4eHnFxTen0j49qiabbysYro3FHKKbYgpADBjgyPylOLL252oG1jT5ZEg6DjARO--jkE2kn1xHcabaJSQAxv1gwrZJieSXn-L1aJObiNVz3x9BiC4QL1icgSoUvSdQyMS49ACNwNOtwowSxmCkMSoiDhGdPhc-kAgOHWliULHaeb2wcGlIE6tarvS0BpzODi5qDbg8g7-nJamBOAJdzJhAr8ef4rQVf5OnYv6Eqg-f9u_GE9GURuG3QHXouD0qtk6e7Td_GiKRScAaOPgXiUj8awjXjkti0CmHAtl5oHUGwmIYtJ58WDiXb71UctXbU1LTtbOMShsOtqTVeke7cy9Kb63BFAwKB6_sWFgKj-uSa6WUwUrkYC63bUFzOYd0O1GsLOVx9nbBqIF0g0NxyqXIpfxOUnAlJt1AqJzeJCo3CsxxyZB7G5SOSTk_rHsJa4-KYA9RAC8bWnR17gIsBi17pmREbK42RbTrwYGett_hTcDQtQ5F4RbWmznuJWH7AO-6u7sxkhkpYoRbE_EnF0cWuwiE1F7RI_MNvhGDez2av4Wh1mtL5AZGlDXX9X3RBRX3qBvB02GIYJ6IwkiPO8Gnr03DJb3022O2Lz-FUWqMnlSrtBfcWf9HwehtMRY8YiP7i3NzyX2v-V1irTTeCBS2VTs-7_WknzKSx-zQ2J905Ujweu769_4TBwUDE7dpavNAD6wFkA7OQR15HTna1BKgu7AYwQ2ciDsdf2Eg565jLPUz9KDWDwX5PQwAzN-4pFXxVhABH_DB2mEBIIcoQvP29bE23TRJLsdN4xCGHJQnVIxkZ6pjx-WO3N3Wx5Xa_drVcn_vCQAyArI=", "id": "rs_0d1a4d10654d56b4016a11d3b761a481918375959813f1ef59", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Perfekt, Sir. Ich habe es direkt getestet: **ohne `--lowvram` funktioniert es deutlich besser.**\n\n## Ergebnis\n\nComfyUI läuft jetzt korrekt mit:\n\n```text\nmain.py --listen 100.101.173.25 --port 8188 --reserve-vram 1.0\n```\n\nIch habe von hier aus zwei neue GPU-Workflows gestartet.\n\n---\n\n## Test 1 — 416×736, 25 Frames, 8 Steps\n\nSettings:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 47 Sekunden\n```\n\nGPU-Auslastung laut ComfyUI-Systemstats:\n\n```text\nVRAM used: ca. 8–10.5 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_preview_00001_.mp4\n\nVisuell: **endlich erkennbar** — rotes Auto in dunkler Garage. Noch nicht final, aber kein blinkender Pixelbrei mehr.\n\n---\n\n## Test 2 — 480×832, 33 Frames, 10 Steps\n\nSettings:\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\nDauer: 2.75 Sekunden\nT5/Textencoder: GPU\nWan model: main_device\nVAE tiling: off\n```\n\nLaufzeit:\n\n```text\nca. 160 Sekunden\n```\n\nGPU-Auslastung:\n\n```text\nVRAM used: ca. 8.2–10.3 GB\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_scene01_gpu_480x832_00001_.mp4\n\nVisuelle Diagnose:  \n**Auto/Garage ist klar erkennbar.** Dunkel, filmisch, rotes Auto, Close-up. Als Hook-B-Roll-Grundlage brauchbar. Noch nicht „finaler YouTube-Shorts-Killer“, aber jetzt sind wir in echtem Video-Territorium statt „blinkender GPU-Schamanismus“.\n\n---\n\n# Neuer Standard-Startbefehl\n\nBitte ab jetzt für lokale Video-Generierung:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## Nicht mehr standardmäßig verwenden\n\n```bash\n--lowvram\n```\n\n`--lowvram` nur noch bei Crash/OOM.\n\n---\n\n# Wenn du noch mehr Speed bei wiederholten Runs willst\n\nSpäter testen wir:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --highvram \\\n  --reserve-vram 1.0\n```\n\nAber erst später. Aktuell ist der normale Modus stabil und nutzt die GPU.\n\n---\n\n# Workflow-Anpassungen, die jetzt gelten\n\n## 1. Textencoder auf GPU\n\nNeu:\n\n```text\nWanVideoTextEncode device = gpu\nLoadWanVideoT5TextEncoder load_device = main_device\n```\n\nNicht mehr:\n\n```text\ndevice = cpu\nload_device = offload_device\n```\n\nDas war der Grund, warum T5 ewig dauerte.\n\n---\n\n## 2. Wan Model auf GPU/main_device\n\nNeu:\n\n```text\nWanVideoModelLoader load_device = main_device\n```\n\nNicht mehr:\n\n```text\noffload_device\n```\n\nFür `Wan2.1 T2V 1.3B FP8` passt das auf deiner A5000.\n\n---\n\n## 3. VAE Tiling aus\n\nNeu:\n\n```text\nenable_vae_tiling = false\n```\n\nNur bei OOM:\n\n```text\nenable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\n---\n\n# Empfohlene Produktionsprofile\n\n## Fast Preview / Prompt-Iteration\n\nDas ist aktuell der Sweet Spot:\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\n```\n\nDauer:\n\n```text\n~45–60 Sekunden\n```\n\nNutzen:\n\n```text\nschnell genug für Varianten\ngut genug, um Motiv/Komposition zu beurteilen\n```\n\n## Better Preview / Kandidat\n\n```text\n480x832\n33 Frames\n12 fps\n10 Steps\n```\n\nDauer:\n\n```text\n~2.5–3 Minuten\n```\n\nNutzen:\n\n```text\nbrauchbare B-Roll-Basis\nbesserer Bildaufbau\nmehr Bewegung\n```\n\n## Noch nicht sofort\n\n```text\n720x1280\n49–81 Frames\n20+ Steps\n14B Modell\n```\n\nDas wäre jetzt unnötig schwer. Erst Prompt-/Shot-Struktur sauber bekommen.\n\n---\n\n# Warum `nvidia-smi` vorher nur ~1 GB zeigte\n\nWeil wir im Notfallmodus waren:\n\n```text\n--lowvram\nTextencoder auf CPU\nModel offload_device\nwinzige Auflösung\n2–4 Steps\n```\n\nJetzt sah ich während der Jobs:\n\n```text\n8–10.5 GB VRAM used\n```\n\nAlso: GPU wird jetzt genutzt.\n\n---\n\n# Nächster Schritt\n\nIch würde jetzt im Repo den **ComfyUI Wan Client** als feste Pipeline-Komponente bauen:\n\n```text\nScene Spec\n→ Workflow JSON\n→ ComfyUI /prompt\n→ Poll /queue + /history\n→ Download /view\n→ speichern als:\ndata/imported_ai_clips/.../scene_01.mp4\n```\n\nDann können wir automatisiert Varianten erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nund die beste Variante in den Hybrid-Renderer ziehen.\n\nKurz: **Startbefehl ist jetzt `--reserve-vram 1.0`, nicht `--lowvram`. GPU-Pipeline funktioniert. Erste brauchbare Auto-B-Roll ist erzeugt.**"}], "id": "msg_0d1a4d10654d56b4016a11d3c217448191b46b29c4dacf4521", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] sollen wir Wan 2.2 TI2V 5B testen für bessere qualität? Wan 2.2 wurde mit deutlich mehr Daten trainiert: laut Modellkarte mit +65.6 % mehr Bildern und +83.2 % mehr Videos gegenüber Wan 2.1. Das soll besonders Bewegung, Semantik und Ästhetik verbessern. Ausserdem unterstützt das Wan2.2-TI2V-5B Text-to-Video und Image-to-Video in einem Modell und ist für 720p bei 24 FPS ausgelegt.", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_zPg5jgD0qbCGp9A4D5Pdj9Wr", "call_id": "call_zPg5jgD0qbCGp9A4D5Pdj9Wr", "response_item_id": "fc_0d1a4d10654d56b4016a11d55b7cb48191b05c1f2ff07f1e89", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport json, urllib.request\\nrepos=['Kijai/WanVideo_comfy','Kijai/WanVideo_comfy_fp8_scaled','Comfy-Org/Wan_2.2_ComfyUI_Repackaged']\\nfor repo in repos:\\n print('\\\\n##',repo)\\n try:\\n  data=...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEdVb0HgXjNPEs2Z2NodfhAaw3A1YETBxIf1N_61trJuRzz9fYRhwbjX1uD0Cr3JugKql7S0oGspaoz-lCGl7j0KLaqI7Nxn_Siga-eS5Mmp76KbP_X1JhlXtB1sybyLVm9MTes3dHk07ev7ZWdKQH0GfV2AR3m0wHxJAb9zS7NykQ8Yy6Gkt5WKXeTa816vH5iHnhcFG508ER_GKkDo7ZSuW6a7XVDXLaBNVr99Xgc4J-ryyW5RMwrI9UPA1LuF1y-WlJDZ9y7HctT7Twt1XYs1ZUIm7M7YEKMR_lM5oLWIeiVyXZ1leM7PivKgqsqrj5SaHUmqrgI7oVaznImY6aErrGNmisT2FwSWitzmg_shF8M0IMtqtJdkBawcZBq6qLwts7818-2uSYph9i8RGSmcJEeiuh5VhSaNIpatZNGI_3i3oKQq__-okq1RBtsXpZDDyJdixhVLsuahuOcIIR5v9VIikytBM_WBl6bMpwGwF0HOb22Eozwg9FqEAKkGRqTJ5bhIdzoEQkUeQvr_Bfrdjbr_qVJcbjNY05GRteY8vzpxNMz9SAxCvhugatuIFXy5ynFBBzbD2Dbeg9bq1sf-5HO8OaDwtgHZQHPV0cqu27vkJvSnmK0eaAxDAeNPqOfwqxUqsdH7QyQWa8OSRQhENHOcGxRXUC34qrq-sNxVa_mqvo-HJiAbGLPpULX2hcC336Bilt5m4Uh5im18kKg1VkCx9I0nDZV68xdIPVc8L4cIOdDAeuxk2U61r-aMcfbETVwHIWBN8S50pVRBhjU5CxvtLmQya9Du_vY1ZLmwsv-wsGR2cxhTIipOzYKAQV70l_XRwXWpILoo5xVH7I6FwFk3P3r0_NULZClHCR6WlaIOSZAd-mM-bSK-rY1MY2m4bsrrhKDcA0W7Vbg8Q0tRC_gvSaBdWyiYEC-yiXFGmPJ27V-E8xXLiLTc8gLzOwDROssR7zkn7pJuTaNkrc2m_f7LCZlHfwTbePlfBu0M0EHltPNcIS0ZN-yDwz_9kATOqFmXjxtnqXNNoZgbHQdFtiGqEdvyDSg2gutCkts93PR4Gxxs-dMcjUKtPbYotylyXaptOV1XKLebwXMGiPo6dK9JgyZi7kYfx1j68VZ5ZZjGs_uLbHEZjKSiTk6p1Gc1Qe9t1NR4_T8GNQm5OTo-h2aXj1AuFvWUIKgavMEEWOQZivi4PzxCxHxIMMK_bd4bRAHqCsrWeonKBA8L27PcS4IiKeI5dMuYGH-JKwSVpe3_FDR9jSOg9nFx_XjVM_3fHc-V6eMt88Ap_kKxKhdFPtLIddGwinsun3A8WVm6nyY6gxQjMLEPQbguUR74CNzoAEswQaB_tSdUoMqYFW37lSxe1D_9XG3MlL4voixH0bjBkiRJpaARDdjT7s7k_y1W9eKwMKdHEn_Y3AYcmROEsOLcK2CkFaxOIodHc_-RgM1xI6YLE7Ihvq2KvJ2TkFebSFn8eiUFeEXak2fcn0ShChHN434IADnBd3DWmKeh4ma9TGEdQMPvyfjLtIjCNlOMT", "id": "rs_0d1a4d10654d56b4016a11d55933b88191af1d94a6b385f0a1", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport json, urllib.request\nrepos=['Kijai/WanVideo_comfy','Ki...` -> exit 0, 1 lines output", "tool_call_id": "call_zPg5jgD0qbCGp9A4D5Pdj9Wr", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, Sir — **Wan 2.2 TI2V 5B sollten wir testen.**  \nAber bitte **nicht blind als “720p 24 FPS final”**, sondern als kontrollierter A/B-Test gegen Wan 2.1.\n\nDie Datenlage spricht dafür:\n\n- Wan 2.2 ist besser trainiert.\n- TI2V 5B kann Text-to-Video und Image-to-Video.\n- 5B ist qualitativ deutlich interessanter als 1.3B.\n- Auf deiner RTX A5000 16 GB ist es realistisch — **aber nur mit FP8 / scaled FP8**, nicht bequem mit BF16/FP16.\n\nDer richtige Testpfad ist daher:\n\n```text\nWan 2.1 1.3B FP8 = schneller Iterationsmodus\nWan 2.2 TI2V 5B FP8 scaled = Qualitätsmodus für Kandidaten\n```\n\nNicht:\n\n```text\nWan 2.2 14B / 720p / 24fps / lange Clips\n```\n\nDas wäre auf 16 GB VRAM unnötig brutal.\n\n---\n\n# Empfehlung\n\n## Ja, installieren und testen\n\nAber wir nehmen diese Variante:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nGröße laut HuggingFace-Listing:\n\n```text\nca. 4.91 GB\n```\n\nNicht zuerst:\n\n```text\nWan2_2-TI2V-5B_fp16.safetensors\n```\n\nDie FP16/BF16-Variante liegt bei ca. 9.3 GB. Zusammen mit T5 + VAE + Overhead wird das auf 16 GB schnell eng.\n\n---\n\n# 1. ComfyUI stoppen\n\nAuf dem GPU-Host:\n\n```bash\npkill -f \"python main.py\"\n```\n\n---\n\n# 2. Genug Speicher prüfen\n\n```bash\ndf -h ~/ai\nfree -h\n```\n\nFür Wan 2.2 Test brauchst du zusätzlich ungefähr:\n\n```text\n7–10 GB Download/Cache\n```\n\nBesser:\n\n```text\n20 GB frei\n```\n\n---\n\n# 3. Modelle herunterladen\n\nVenv aktivieren:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\n## Wan 2.2 TI2V 5B FP8 scaled\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nDanach prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/TI2V/\n```\n\nErwartet:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\n---\n\n## Wan 2.2 VAE\n\nWan 2.2 nutzt eine andere VAE als Wan 2.1. Bitte laden:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_2_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet zusätzlich:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.3 GB\n```\n\n---\n\n## Besserer / passender UMT5 FP8 Text Encoder\n\nDu hast aktuell:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nFür Wan 2.2 / native Workflows wird oft diese Variante genutzt:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nLaden:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_text_encoder\n```\n\nDann kopieren:\n\n```bash\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nDu solltest dann haben:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 4. ComfyUI neu starten\n\nBitte wieder **ohne `--lowvram`**:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas ist weiterhin unser Standard.\n\n---\n\n# 5. Wenn OOM kommt\n\nDann zuerst mehr reservieren:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 2.0\n```\n\nFalls immer noch OOM:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 3.0\n```\n\nErst wenn das nicht reicht:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --lowvram\n```\n\nAber `--lowvram` ist wieder Notfall, weil dann T5/Offload langsam wird.\n\n---\n\n# 6. Prüfen, ob ComfyUI die Modelle sieht\n\nNach Neustart:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nUnd im GUI prüfen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nIm WanVideo Model Loader sollte dann etwas auftauchen wie:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nIm VAE Loader:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nIm T5/Text Encoder Loader:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 7. Testprofil für Wan 2.2 TI2V 5B\n\nNicht direkt 720p. Wir testen so:\n\n## Probe A — sicherer Start\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nWan 2.2 TI2V 5B FP8 scaled\nT5 GPU/main_device\nVAE Wan2.2\nVAE tiling off\n```\n\nZiel:\n\n```text\nLäuft stabil?\nGPU-Auslastung?\nIst Motiv klarer als Wan 2.1?\n```\n\n## Probe B — Qualitätsvergleich\n\nWenn A läuft:\n\n```text\n480x832\n33 Frames\n12 fps\n10–12 Steps\n```\n\nDas ist der direkte Vergleich zu unserem bisherigen besten Wan 2.1 Clip.\n\n## Probe C — Kandidat\n\nWenn B gut aussieht:\n\n```text\n576x1024\n49 Frames\n12 fps\n12–16 Steps\n```\n\nNoch nicht 720p. Erst schauen, ob Qualität/Speed sinnvoll ist.\n\n---\n\n# 8. Warum nicht sofort 720p 24 FPS?\n\nWeil 720p/24fps zwar im Modellmarketing steht, aber dein echtes Produktionsziel anders ist:\n\n```text\nviele Varianten\nschnelle Auswahl\nRetention-Test\nlokales Compositing\nCaptions/SFX/Mechanism-Layer\n```\n\nFür Shorts reicht es völlig, wenn der AI-Clip z.B. in:\n\n```text\n480x832 oder 576x1024\n```\n\ngut aussieht und danach lokal in:\n\n```text\n1080x1920\n```\n\ncomposited/upscaled wird.\n\n720p nativ ist später für finale Winner-Shots interessant, nicht für Prompt-Findung.\n\n---\n\n# Kurzurteil\n\n## Sollen wir Wan 2.2 TI2V 5B testen?\n\n**Ja. Definitiv.**\n\nAber als:\n\n```text\nWan 2.2 TI2V 5B FP8 scaled\n416x736 → 480x832 → 576x1024\n```\n\nNicht als:\n\n```text\nFP16/BF16\n720p\n24fps\nlange Clips\n```\n\n## Erwartung\n\nIch erwarte:\n\n- bessere Semantik\n- stabilere Objekte\n- weniger abstrakte Lichtflächen\n- bessere Bewegung\n- bessere Ästhetik\n\nAber:\n\n- langsamer als 1.3B\n- mehr VRAM/RAM\n- ggf. OOM bei zu hohen Settings\n\n---\n\n# Bitte jetzt ausführen\n\nKurzform der wichtigsten Befehle:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n\nhf download Kijai/WanVideo_comfy \\\n  Wan2_2_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_text_encoder\n\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\nDann starten:\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nWenn du danach sagst:\n\n```text\nWan 2.2 läuft\n```\n\nprüfe ich von hier `/object_info`, baue den Wan-2.2-Workflow und starte den A/B-Test gegen unseren Wan-2.1-Clip.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": 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"id": "rs_0d1a4d10654d56b4016a11d56421d48191aaeef3dba1430c72", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — **Wan 2.2 TI2V 5B sollten wir testen.**  \nAber bitte **nicht blind als “720p 24 FPS final”**, sondern als kontrollierter A/B-Test gegen Wan 2.1.\n\nDie Datenlage spricht dafür:\n\n- Wan 2.2 ist besser trainiert.\n- TI2V 5B kann Text-to-Video und Image-to-Video.\n- 5B ist qualitativ deutlich interessanter als 1.3B.\n- Auf deiner RTX A5000 16 GB ist es realistisch — **aber nur mit FP8 / scaled FP8**, nicht bequem mit BF16/FP16.\n\nDer richtige Testpfad ist daher:\n\n```text\nWan 2.1 1.3B FP8 = schneller Iterationsmodus\nWan 2.2 TI2V 5B FP8 scaled = Qualitätsmodus für Kandidaten\n```\n\nNicht:\n\n```text\nWan 2.2 14B / 720p / 24fps / lange Clips\n```\n\nDas wäre auf 16 GB VRAM unnötig brutal.\n\n---\n\n# Empfehlung\n\n## Ja, installieren und testen\n\nAber wir nehmen diese Variante:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nGröße laut HuggingFace-Listing:\n\n```text\nca. 4.91 GB\n```\n\nNicht zuerst:\n\n```text\nWan2_2-TI2V-5B_fp16.safetensors\n```\n\nDie FP16/BF16-Variante liegt bei ca. 9.3 GB. Zusammen mit T5 + VAE + Overhead wird das auf 16 GB schnell eng.\n\n---\n\n# 1. ComfyUI stoppen\n\nAuf dem GPU-Host:\n\n```bash\npkill -f \"python main.py\"\n```\n\n---\n\n# 2. Genug Speicher prüfen\n\n```bash\ndf -h ~/ai\nfree -h\n```\n\nFür Wan 2.2 Test brauchst du zusätzlich ungefähr:\n\n```text\n7–10 GB Download/Cache\n```\n\nBesser:\n\n```text\n20 GB frei\n```\n\n---\n\n# 3. Modelle herunterladen\n\nVenv aktivieren:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\n## Wan 2.2 TI2V 5B FP8 scaled\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nDanach prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/TI2V/\n```\n\nErwartet:\n\n```text\nWan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\n---\n\n## Wan 2.2 VAE\n\nWan 2.2 nutzt eine andere VAE als Wan 2.1. Bitte laden:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_2_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/vae/\n```\n\nErwartet zusätzlich:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.3 GB\n```\n\n---\n\n## Besserer / passender UMT5 FP8 Text Encoder\n\nDu hast aktuell:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nFür Wan 2.2 / native Workflows wird oft diese Variante genutzt:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nLaden:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_text_encoder\n```\n\nDann kopieren:\n\n```bash\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nDu solltest dann haben:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 4. ComfyUI neu starten\n\nBitte wieder **ohne `--lowvram`**:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas ist weiterhin unser Standard.\n\n---\n\n# 5. Wenn OOM kommt\n\nDann zuerst mehr reservieren:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 2.0\n```\n\nFalls immer noch OOM:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 3.0\n```\n\nErst wenn das nicht reicht:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --lowvram\n```\n\nAber `--lowvram` ist wieder Notfall, weil dann T5/Offload langsam wird.\n\n---\n\n# 6. Prüfen, ob ComfyUI die Modelle sieht\n\nNach Neustart:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nUnd im GUI prüfen:\n\n```text\nhttp://100.101.173.25:8188\n```\n\nIm WanVideo Model Loader sollte dann etwas auftauchen wie:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nIm VAE Loader:\n\n```text\nWan2_2_VAE_bf16.safetensors\n```\n\nIm T5/Text Encoder Loader:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n---\n\n# 7. Testprofil für Wan 2.2 TI2V 5B\n\nNicht direkt 720p. Wir testen so:\n\n## Probe A — sicherer Start\n\n```text\n416x736\n25 Frames\n12 fps\n8 Steps\nWan 2.2 TI2V 5B FP8 scaled\nT5 GPU/main_device\nVAE Wan2.2\nVAE tiling off\n```\n\nZiel:\n\n```text\nLäuft stabil?\nGPU-Auslastung?\nIst Motiv klarer als Wan 2.1?\n```\n\n## Probe B — Qualitätsvergleich\n\nWenn A läuft:\n\n```text\n480x832\n33 Frames\n12 fps\n10–12 Steps\n```\n\nDas ist der direkte Vergleich zu unserem bisherigen besten Wan 2.1 Clip.\n\n## Probe C — Kandidat\n\nWenn B gut aussieht:\n\n```text\n576x1024\n49 Frames\n12 fps\n12–16 Steps\n```\n\nNoch nicht 720p. Erst schauen, ob Qualität/Speed sinnvoll ist.\n\n---\n\n# 8. Warum nicht sofort 720p 24 FPS?\n\nWeil 720p/24fps zwar im Modellmarketing steht, aber dein echtes Produktionsziel anders ist:\n\n```text\nviele Varianten\nschnelle Auswahl\nRetention-Test\nlokales Compositing\nCaptions/SFX/Mechanism-Layer\n```\n\nFür Shorts reicht es völlig, wenn der AI-Clip z.B. in:\n\n```text\n480x832 oder 576x1024\n```\n\ngut aussieht und danach lokal in:\n\n```text\n1080x1920\n```\n\ncomposited/upscaled wird.\n\n720p nativ ist später für finale Winner-Shots interessant, nicht für Prompt-Findung.\n\n---\n\n# Kurzurteil\n\n## Sollen wir Wan 2.2 TI2V 5B testen?\n\n**Ja. Definitiv.**\n\nAber als:\n\n```text\nWan 2.2 TI2V 5B FP8 scaled\n416x736 → 480x832 → 576x1024\n```\n\nNicht als:\n\n```text\nFP16/BF16\n720p\n24fps\nlange Clips\n```\n\n## Erwartung\n\nIch erwarte:\n\n- bessere Semantik\n- stabilere Objekte\n- weniger abstrakte Lichtflächen\n- bessere Bewegung\n- bessere Ästhetik\n\nAber:\n\n- langsamer als 1.3B\n- mehr VRAM/RAM\n- ggf. OOM bei zu hohen Settings\n\n---\n\n# Bitte jetzt ausführen\n\nKurzform der wichtigsten Befehle:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nmkdir -p ~/ai/ComfyUI/models/diffusion_models/TI2V\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/text_encoders\n\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n\nhf download Kijai/WanVideo_comfy \\\n  Wan2_2_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae\n\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_text_encoder\n\ncp /tmp/wan22_text_encoder/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\nDann starten:\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nWenn du danach sagst:\n\n```text\nWan 2.2 läuft\n```\n\nprüfe ich von hier `/object_info`, baue den Wan-2.2-Workflow und starte den A/B-Test gegen unseren Wan-2.1-Clip."}], "id": "msg_0d1a4d10654d56b4016a11d56daad081919d338a686fc0d011", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport json, time, urllib.request\nbase='http://100.101.173.25...` -> exit 0, 1 lines output", "tool_call_id": "call_VZ9MPAIy2KzLLMTBfnaiYL1P", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.parse, urllib.request, pathlib\nbase='http://100...` -> exit 0, 1 lines output", "tool_call_id": "call_1lGAdtl0LdVIOR881sMrs6FC", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport json, time, urllib.request\nbase='http://100.101.173.25...` -> exit 0, 1 lines output", "tool_call_id": "call_vXl0IN3uLlSMyFNJmtCosqjR", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_EqtIp4m91NpKo5Wews9a4B4V", "call_id": "call_EqtIp4m91NpKo5Wews9a4B4V", "response_item_id": "fc_0d1a4d10654d56b4016a11deecc78c8191a0b2ce744bacb18a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_wan22_scene01_bright_416x736_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'sub...[truncated]\", \"timeout\": 180, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Analyzing the queue process**\n\nI’ve noticed an interesting pattern with the queue. It took a while between the prompt and the poll, and I realize we didn’t monitor it because it got queued after the previous task. Maybe we waited too long before analyzing it? I'll look into downloading and analyzing the data to understand what happened better. It seems I need to clarify how we’re managing these queues going forward!", "reasoning_content": "**Analyzing the queue process**\n\nI’ve noticed an interesting pattern with the queue. It took a while between the prompt and the poll, and I realize we didn’t monitor it because it got queued after the previous task. Maybe we waited too long before analyzing it? I'll look into downloading and analyzing the data to understand what happened better. It seems I need to clarify how we’re managing these queues going forward!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd7sJTnBXok0vM5MNw61IeE_fdpXRtSppybNqRKbh1nxcpb_A2qTvixj2C-lemIHOTUn6qWM4t8O7KiTQ39v-4il8rUaNDXFv9PSPCpKLTQe9Sc2GhBLBC41TCM9pgMrQLKm2QqX8yKW4cg2hZz_tgcs8RXdqvYLvmMcGEtOrSCvTj-9pRLxzrVf-5uzLDQmCtmDr0UYDS4H_U4pEoPWeQR47RSitloXqJG2gpU075hWVtPETSy6Xpm921R3X8zU_X7HuJhgDG52rp-i4rBkeIoN4NPhu6b96peoen1xJ9Qh0z_4-e127naUxqcwDJbcGBWVnY40u7-lgsyiV51OhwRzCpKMlbu8HZVC1aLa0RFv5DjxwmtiTMtz5I7XMU8Yyi7WAsdz9ecxJy6wxqNLn6n7OePWrUPXalFu1mL9ZUQUs9Pe7ivklRzMKTn7ebcbgWBE4Phrlhrq70zt2aNvDL6jFmu8VTogsSNGcfrLXmutMzo41Byp2bdt7gZyLlsTDfTj7aXY6XdZwRJIT48ovDOR8SMbUy85_SMWGBHFLSBAoffJjjOTlOFsAKtDHJV5CEufzb_cnQi7Zz57GHtvJxm8yjoBM8OX8jLuADYs0ikfC2hjqbVpHHU6kdn0OfUU96lyZ0rxVwBb9TCVnE0G1hWeYe41sYRIAZT84e2pwzl-MMYvYeXdiQGlLGkudeWIb0qYiyuhgHhBvoMguBoT0RyEkhLqW1ZYmvLbxGGMnH0-fAIWvficOQLjXLkV_mflRvxlONQ95kp9ZNgwuFYfiMi5hNdCw1i-i1usqL87-lnWCuf8iBVu1keIpYohKgWLOGgh7gf_dXuK18Q74UFmb38QwAfARyCPcZtPe7uRjHoYZU_F5jGdVMdiH6W9PVKNlUzst3GI12oFWtxCa89O8XN-vprI0_5U73503ic5S2zYlBcZ4TOFnUYZUKnmvqC16UlObQnB2mRdnPayaUA-zUUqdxVIQ9F0Z6YJBkjzF5v2DitcB3K0OlACLQXRY-JQiWvnpSLMxIrZapsW1-A8vJAgeTUUB5Rwzdfa2FShKEp7QIoMmfNlh9EN6-qL22kKEHUa-dw2ELdtPZIxksIahP4mjy6XbsWKLjEGfOCaz6CwF7M=", "id": "rs_0d1a4d10654d56b4016a11dee9a93c8191bf0f1838fb51733c", "summary": [{"type": "summary_text", "text": "**Analyzing the queue process**\n\nI’ve noticed an interesting pattern with the queue. It took a while between the prompt and the poll, and I realize we didn’t monitor it because it got queued after the previous task. Maybe we waited too long before analyzing it? I'll look into downloading and analyzing the data to understand what happened better. It seems I need to clarify how we’re managing these queues going forward!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[terminal] ran `python - <<'PY'\nimport urllib.parse, urllib.request, pathlib\nbase='http://100...` -> exit 0, 1 lines output", "tool_call_id": "call_EqtIp4m91NpKo5Wews9a4B4V", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "[vision_analyze] 'Beurteile knapp: Ist ein Auto klar erkennbar? Ist ' (270 chars)", "tool_call_id": "call_gyAS7TFZoygQtqYTCLvCYchN", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, Sir — **Wan 2.2 TI2V 5B läuft jetzt und ist sichtbar besser.**\n\nIch habe direkt drei Dinge getestet:\n\n## 1. Modell/ComfyUI erkannt\n\nComfyUI sieht jetzt:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nWan2_2_VAE_bf16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nGPU-Host:\n\n```text\nComfyUI: --reserve-vram 1.0\nVRAM frei vor Job: ca. 15.36 GB\nRAM frei: ca. 17.29 GB\n```\n\nDer Startbefehl bleibt also korrekt:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n---\n\n## 2. Wichtige Erkenntnis: scaled T5 geht im Kijai-Wrapper nicht\n\nDer erste Wan-2.2-Test ist sofort sauber fehlgeschlagen mit:\n\n```text\nInvalid T5 text encoder model, fp8 scaled is not supported by this node\n```\n\nAlso für **Kijai WanVideoWrapper** aktuell nicht verwenden:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nStattdessen nutzen:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nDas ist wichtig. Die KI-Info war für native ComfyUI-Wan korrekt, aber **nicht für diesen Wrapper-Node**. Natürlich. Zwei Wan-Implementierungen, zwei Wahrheiten. Herrlich effizient.\n\n---\n\n## 3. Zweite Erkenntnis: Wan2.2 braucht VAE tiling auf 16 GB\n\nTest ohne VAE tiling:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\nSampling lief, aber Decode starb mit:\n\n```text\ntorch.OutOfMemoryError\nRequested: 455 MiB\nCurrently allocated: 14.61 GiB\nDevice limit: 15.61 GiB\n```\n\nFix:\n\n```text\nWanVideoDecode enable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nDamit läuft es stabil.\n\n---\n\n# Erfolgreicher Wan2.2-Test\n\n## Dunkler Test\n\nSettings:\n\n```text\nWan2.2 TI2V 5B FP8 scaled\n416x736\n25 Frames\n12 fps\n8 Steps\nT5 GPU\nVAE Wan2.2\nVAE tiling on\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan22_scene01_416x736_tiled_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan22_scene01_416x736_tiled_00001_.mp4\n\nBewertung: zu dunkel, aber strukturell stabiler als Wan2.1.\n\n---\n\n# Besserer Wan2.2-Test: hellerer Prompt\n\nIch habe den Prompt angepasst:\n\n```text\nclearly visible red used compact car\nclean inspection garage\nbright overhead lights\nfull front three-quarter view\nbalanced exposure\n```\n\nSettings:\n\n```text\nWan2.2 TI2V 5B FP8 scaled\n416x736\n25 Frames\n12 fps\n12 Steps\nT5: umt5-xxl-enc-fp8_e4m3fn.safetensors\nT5 device: gpu\nWan model: main_device\nVAE: Wan2_2_VAE_bf16\nVAE tiling: on\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan22_scene01_bright_416x736_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 416\nheight: 736\nfps: 12\nduration: 2.083s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan22_scene01_bright_416x736_00001_.mp4\n\nVisuelle Bewertung:\n\n```text\nAuto klar erkennbar\nheller, sauberer, deutlich brauchbarer\nals B-Roll-Grundlage geeignet\n```\n\nDer Frame zeigt klar ein rotes Auto in einer Garage/Inspection Bay. Nicht perfekt — Kennzeichen/Text muss später ggf. vermieden/verdeckt werden — aber **um Welten besser als blinkende Farben**.\n\n---\n\n# Neuer Wan2.2-Standard für deine A5000\n\n## Startbefehl\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## Workflow-Profil\n\n```text\nmodel:\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n\nmodel quantization:\n  fp8_e4m3fn_scaled\n\ntext encoder:\n  umt5-xxl-enc-fp8_e4m3fn.safetensors\n\ndo NOT use with Kijai wrapper:\n  umt5_xxl_fp8_e4m3fn_scaled.safetensors\n\nvae:\n  Wan2_2_VAE_bf16.safetensors\n\nvae tiling:\n  enabled\n\nresolution:\n  416x736 for fast preview\n  480x832 next quality test\n\nframes:\n  25 preview\n  33 candidate\n\nfps:\n  12\n\nsteps:\n  12 for preview\n  16 later for better candidate\n```\n\n---\n\n# Urteil: Wan2.2 vs Wan2.1\n\n## Wan2.1 1.3B\n\nGut für:\n\n```text\nschnelle Iteration\nPrompt-Roughcuts\ntechnische Tests\n```\n\nAber Bildqualität/Semantik schwach.\n\n## Wan2.2 TI2V 5B\n\nGut für:\n\n```text\nechte Clip-Kandidaten\nbessere Objektklarheit\nbessere Garage/Auto-Semantik\nbrauchbare B-Roll\n```\n\nLangsamer und braucht tiling, aber lohnt sich.\n\n---\n\n# Meine Empfehlung ab jetzt\n\n## Pipeline-Entscheidung\n\n```text\nWan2.1 1.3B = Smoke Test / schnelle Prompt-Vorabprüfung\nWan2.2 TI2V 5B = echter Produktionskandidat\n```\n\n## Nächster Schritt\n\nIch würde jetzt den AutoShortsBot-ComfyUI-Client bauen mit zwei Presets:\n\n```text\nwan21_fast_preview\nwan22_quality_preview\n```\n\nUnd dann automatisch erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nFür Wan2.2 würde ich als nächstes testen:\n\n```text\n480x832\n33 Frames\n12 fps\n12 Steps\nVAE tiling on\n```\n\nWenn das läuft und besser aussieht, nehmen wir Wan2.2 als Hauptquelle für die realistischen AI-B-Roll-Clips.\n\nKurz: **Ja, Wan2.2 lohnt sich. Der helle 416×736-Test ist der erste wirklich brauchbare lokale Clip.**", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd7-MAvU68aOW8JnSBC4zZv0noWJkTIiKekAym1ZapppH71v4TbvRTnAegg4pRY8gcy-hwMvQE76Iyx7S92ELWdp5w0chqw4Bq_CoDmdzzRSi6nKv551k6PhwsJ4e4hOY06IozxZbkXKuMXsvYKF21gdSW-Mqe7ClZQGP7ia3RckHbno7Jg42SCXDX53APqGcg4zmUG1ECNbqTZWZUDnRXL8yJQhMacGUUCldJYkSoz6OwYj4rocbh3z6R3MDzCPuKeMqst6NDiy_rNmcPfMCF2ZbdVSfyE6icSALEb4oEiWsj1GU-wS66e2UnMduJiQSIu9279jvRtoA1FgciWQcxG1AhPDCMoXFGoQDl6XWk6P48Zst5HGnHUJ5HStO-0yTPszI9K22zvimcf024V0JGZWJL9zzH8ADPYhJR1ToSADGpSE0NKuyPSLFG7kRDXorOgsdwqo-PvkoncPt1VhAXvRb8ssv0qnUd3tK2DIx91QXVYP9fsMFLnhRPsfdtGKjOVXICXETKHKQYGpqr7ycoMcO_-jRSmCKYrIp1Z4fTHeLQDV53yFuFgg9hbDrOcpBUd-rYFou-aHHvOGOP1iUgkXy-i4_k324bejrwtrsLCReKPZh1PFPBfxzWDsiuUema3eiyDFYP1f89VmgTx8jr6drIiIxtIO1ZjZIKCPSyvQY1iReFsZ7Lv4FFKAsdEAFqCbs440C07KYA-YCEsAu3sNPGGaKoANmbe6KPUvYZZVve8GrysjDf2EnXCBy7cdKJV2AUq0WSwrXbjEwde6YZHdrPbT1ZzFuqvuMOj8YkU3HhNdWY09k90H5WP14HYJUGzOL3I6y3dq-w8E2R0BEYK7feZPFxZA6-_16d7pmQa5ihJsAC2nrUHye_WJf-TQNjFuK4TomezG-2kqrXUId1hwoVNdThRiYftbsMjC0vLu9vV7kGWOxCbcZjDKzRpIRdLhyIbWUrFZU_3OHyqZ4xvspMRifCEEkpRldBv_20tDLOqmwNSS4E-KxMYHuwt73IRVNHkORIr1GD0OcV8018CAmt4W339B7Q7_YBb6NBUYto4-L3u-mSuTmaXRWOw2XefOL15o6wah6b783HZaDK80x7zMd9FgheR1QB3S25DyDqrvlZMX2sLzCihMQppFGIorxzbQRDyaYHwDYoZaTel1s_P9TsUBE-Z6s64hdqB9cvQZi72Ip2_RM1s7MX9i-lCYxI4-y6ioeF6b1Q9JeoaD_YyIkMZf_5sb5M9O17o8lTTXGUK93gnl9uUGcQfWlbIkeOBjF5RRHF-Dp_HfRnvnxzvFhgTtDDwKApWlEgDZgoio21Qt-_UEMldqHiEaI0ogB6n6Rk-5tUrQtLzJAL3nFCiAA6_rwqU--YvB3oWBVY7j2JXWxvlYqKWyOIGTdUJKvMTeJXctiU7DyHK4rgBPFk2OWbiwcAkkfGQDAexGnWHr9H2GtJXX-ziTNd2szjDqBpMJ6n6t8AboTAMP-CHcI_iIIov5QpFlV-lG_H2eutuIuxlv4pFrH53OAJh3jyW1_lpqn6RmS84YmR7z4ERk6tL_030uRzX2vc-DdGazF13UchJ0IKiyT5fD_UmNRcbXNmktTb4J9_hAzUdVvsCeva_qgFusnwEf8wYle6yuDMAPsj9ztsot4EinHzhHqFiZ", "id": "rs_0d1a4d10654d56b4016a11defbe6c08191b8f362a173ee2011", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — **Wan 2.2 TI2V 5B läuft jetzt und ist sichtbar besser.**\n\nIch habe direkt drei Dinge getestet:\n\n## 1. Modell/ComfyUI erkannt\n\nComfyUI sieht jetzt:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nWan2_2_VAE_bf16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nGPU-Host:\n\n```text\nComfyUI: --reserve-vram 1.0\nVRAM frei vor Job: ca. 15.36 GB\nRAM frei: ca. 17.29 GB\n```\n\nDer Startbefehl bleibt also korrekt:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n---\n\n## 2. Wichtige Erkenntnis: scaled T5 geht im Kijai-Wrapper nicht\n\nDer erste Wan-2.2-Test ist sofort sauber fehlgeschlagen mit:\n\n```text\nInvalid T5 text encoder model, fp8 scaled is not supported by this node\n```\n\nAlso für **Kijai WanVideoWrapper** aktuell nicht verwenden:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nStattdessen nutzen:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nDas ist wichtig. Die KI-Info war für native ComfyUI-Wan korrekt, aber **nicht für diesen Wrapper-Node**. Natürlich. Zwei Wan-Implementierungen, zwei Wahrheiten. Herrlich effizient.\n\n---\n\n## 3. Zweite Erkenntnis: Wan2.2 braucht VAE tiling auf 16 GB\n\nTest ohne VAE tiling:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\nSampling lief, aber Decode starb mit:\n\n```text\ntorch.OutOfMemoryError\nRequested: 455 MiB\nCurrently allocated: 14.61 GiB\nDevice limit: 15.61 GiB\n```\n\nFix:\n\n```text\nWanVideoDecode enable_vae_tiling = true\ntile_x = 256\ntile_y = 256\ntile_stride_x = 128\ntile_stride_y = 128\n```\n\nDamit läuft es stabil.\n\n---\n\n# Erfolgreicher Wan2.2-Test\n\n## Dunkler Test\n\nSettings:\n\n```text\nWan2.2 TI2V 5B FP8 scaled\n416x736\n25 Frames\n12 fps\n8 Steps\nT5 GPU\nVAE Wan2.2\nVAE tiling on\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan22_scene01_416x736_tiled_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan22_scene01_416x736_tiled_00001_.mp4\n\nBewertung: zu dunkel, aber strukturell stabiler als Wan2.1.\n\n---\n\n# Besserer Wan2.2-Test: hellerer Prompt\n\nIch habe den Prompt angepasst:\n\n```text\nclearly visible red used compact car\nclean inspection garage\nbright overhead lights\nfull front three-quarter view\nbalanced exposure\n```\n\nSettings:\n\n```text\nWan2.2 TI2V 5B FP8 scaled\n416x736\n25 Frames\n12 fps\n12 Steps\nT5: umt5-xxl-enc-fp8_e4m3fn.safetensors\nT5 device: gpu\nWan model: main_device\nVAE: Wan2_2_VAE_bf16\nVAE tiling: on\n```\n\nStatus:\n\n```text\nsuccess\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_wan22_scene01_bright_416x736_00001_.mp4\n```\n\nTechnische Werte:\n\n```text\nwidth: 416\nheight: 736\nfps: 12\nduration: 2.083s\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_wan22_scene01_bright_416x736_00001_.mp4\n\nVisuelle Bewertung:\n\n```text\nAuto klar erkennbar\nheller, sauberer, deutlich brauchbarer\nals B-Roll-Grundlage geeignet\n```\n\nDer Frame zeigt klar ein rotes Auto in einer Garage/Inspection Bay. Nicht perfekt — Kennzeichen/Text muss später ggf. vermieden/verdeckt werden — aber **um Welten besser als blinkende Farben**.\n\n---\n\n# Neuer Wan2.2-Standard für deine A5000\n\n## Startbefehl\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## Workflow-Profil\n\n```text\nmodel:\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n\nmodel quantization:\n  fp8_e4m3fn_scaled\n\ntext encoder:\n  umt5-xxl-enc-fp8_e4m3fn.safetensors\n\ndo NOT use with Kijai wrapper:\n  umt5_xxl_fp8_e4m3fn_scaled.safetensors\n\nvae:\n  Wan2_2_VAE_bf16.safetensors\n\nvae tiling:\n  enabled\n\nresolution:\n  416x736 for fast preview\n  480x832 next quality test\n\nframes:\n  25 preview\n  33 candidate\n\nfps:\n  12\n\nsteps:\n  12 for preview\n  16 later for better candidate\n```\n\n---\n\n# Urteil: Wan2.2 vs Wan2.1\n\n## Wan2.1 1.3B\n\nGut für:\n\n```text\nschnelle Iteration\nPrompt-Roughcuts\ntechnische Tests\n```\n\nAber Bildqualität/Semantik schwach.\n\n## Wan2.2 TI2V 5B\n\nGut für:\n\n```text\nechte Clip-Kandidaten\nbessere Objektklarheit\nbessere Garage/Auto-Semantik\nbrauchbare B-Roll\n```\n\nLangsamer und braucht tiling, aber lohnt sich.\n\n---\n\n# Meine Empfehlung ab jetzt\n\n## Pipeline-Entscheidung\n\n```text\nWan2.1 1.3B = Smoke Test / schnelle Prompt-Vorabprüfung\nWan2.2 TI2V 5B = echter Produktionskandidat\n```\n\n## Nächster Schritt\n\nIch würde jetzt den AutoShortsBot-ComfyUI-Client bauen mit zwei Presets:\n\n```text\nwan21_fast_preview\nwan22_quality_preview\n```\n\nUnd dann automatisch erzeugen:\n\n```text\nscene_01_seed_1.mp4\nscene_01_seed_2.mp4\nscene_01_seed_3.mp4\n```\n\nFür Wan2.2 würde ich als nächstes testen:\n\n```text\n480x832\n33 Frames\n12 fps\n12 Steps\nVAE tiling on\n```\n\nWenn das läuft und besser aussieht, nehmen wir Wan2.2 als Hauptquelle für die realistischen AI-B-Roll-Clips.\n\nKurz: **Ja, Wan2.2 lohnt sich. Der helle 416×736-Test ist der erste wirklich brauchbare lokale Clip.**"}], "id": "msg_0d1a4d10654d56b4016a11defe58108191a73cd9887d9d4313", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] diese testclips sind total unbrauchbar. auf dem ersten video ist nichts zu erkennen, ganz dunkel irgendwelche flackernden farben. beim zweiten ein verschwommenes Auto von vorne mit unlesbarem nummernschild... schrecklich. hier ein paar analysen der KI zu den Meldungen aus dem Terminal: 23.05.2026, 18:19 Schweizer Zeit\n\nJa — Wan 2.2 läuft bei dir grundsätzlich, aber dein aktueller Workflow ist noch nicht optimal. Die Logs zeigen drei Dinge:\n\n## 1. Erster Fehler: falscher T5-Encoder für WanVideoWrapper\n\nInvalid T5 text encoder model, fp8 scaled is not supported by this node\n\nDas ist ein bekannter Konflikt: Der native ComfyUI-Wan2.2-Workflow verwendet offiziell umt5_xxl_fp8_e4m3fn_scaled.safetensors, aber der Kijai WanVideoWrapper T5-Node unterstützt genau diese “fp8 scaled”-Variante nicht sauber. ComfyUIs offizielle Wan2.2-Doku listet für den nativen 5B-Workflow genau diesen scaled Text-Encoder, während GitHub-Issues zum WanVideoWrapper denselben Fehler mit fp8 scaled is not supported zeigen. ([docs.comfy.org][1])\n\nLösung:\nFür Wan 2.2 würde ich bei dir zuerst den offiziellen nativen ComfyUI-Wan2.2-Workflow nutzen, nicht den WanVideoWrapper. In ComfyUI:\n\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n\nComfyUI empfiehlt genau diesen nativen 5B-Workflow und schreibt, dass Wan2.2 5B mit nativer Offloading-Funktion sogar gut auf 8 GB VRAM passen soll. ([docs.comfy.org][1])\n\n---\n\n## 2. Zweiter Fehler: OOM beim VAE-Decoding, nicht beim Sampling\n\nWichtig: Das Sampling selbst hat funktioniert:\n\n25 frames at 416x736 with 8 steps\n8/8 [00:18, 2.34s/it]\n[Sampling] Max allocated memory: 11.299 GB\n\nDann crasht es erst hier:\n\nWanVAE decoding frames: 14%\nAllocation on device 0 would exceed allowed memory\nCurrently allocated: 14.61 GiB\nRequested: 455.27 MiB\nDevice limit: 15.61 GiB\nFree: 9.19 MiB\n\nDas heisst: Wan 2.2 5B passt beim Sampling gerade noch, aber beim VAE-Decoding fehlt dir rund ein halbes GB bis ein GB VRAM.\n\nDu bist also nicht meilenweit daneben, sondern knapp am Limit.\n\n---\n\n## 3. Danach hast du es offenbar korrekt entschärft\n\nDer spätere Run war erfolgreich:\n\n25 frames at 416x736\n8 steps\nSampling: 27s\nVAE decoding: 26s\nPrompt executed in 63.73 seconds\n\nUnd mit 12 Steps:\n\n25 frames at 416x736\n12 steps\nSampling: 01:10\nVAE decoding: 25s\nPrompt executed in 119.00 seconds\n\nDas sieht so aus, als ob du zwischenzeitlich VAE-Tiling / anderes Decoding / stärkeres Offloading aktiviert hast. Dadurch läuft es durch, aber Decoding wird langsamer.\n\n---\n\n# Leistung im Vergleich zu Wan 2.1\n\nDeine bisherigen Werte:\n\n| Modell           |   Frames / Auflösung / Steps |    Gesamtzeit | Eindruck                |\n| ---------------- | ---------------------------: | ------------: | ----------------------- |\n| Wan 2.1 1.3B |  25 Frames, 416x736, 8 Steps |  ca. 47 s | schneller, stabiler     |\n| Wan 2.2 5B   |  25 Frames, 416x736, 8 Steps |  ca. 64 s | langsamer, aber machbar |\n| Wan 2.2 5B   | 25 Frames, 416x736, 12 Steps | ca. 119 s | deutlich langsamer      |\n\nDas ist eigentlich ziemlich gut für deine RTX A5000 Laptop 16 GB.\n\nMeine Einschätzung:\nWan 2.2 5B ist bei dir brauchbar, aber nicht so komfortabel wie Wan 2.1 1.3B. Es ist ein Modell für bessere finale Clips, nicht für massenhaft schnelle Iterationen.\n\n---\n\n# Was mich im Log irritiert\n\nDiese Zeilen widersprechen sich etwas:\n\n5B model detected\nModel variant detected: 14B\n\nUnd:\n\nMemory: 2,033,661.72 MB\n\nDas ist offensichtlich keine reale VRAM-Angabe. Deine GPU hat 16 GB. Diese Anzeige ist vermutlich ein Wrapper-/Logging-Artefakt.\n\nFür mich zählt hier mehr:\n\nin_channels: 48\n5B model detected\n25 frames at 416x736\n\nDas sieht sehr nach Wan2.2 TI2V 5B aus. Offiziell gibt es bei Wan2.2 ein 5B-Hybridmodell für Text-to-Video und Image-to-Video sowie separate 14B-Modelle für T2V/I2V. ([docs.comfy.org][1])\n\n---\n\n# Optimale Einstellung für deine GPU\n\n## Für Wan 2.2 5B auf 16 GB VRAM\n\nIch würde so\nstarten:\n\nAuflösung: 416x736\nFrames: 25\nSteps: 8\nVAE tiling: ON\nVAE decode batch/chunk: klein\nEasyCache: ON, falls verfügbar\nTeaCache/MagCache: nicht verwenden, wenn keine Koeffizienten verfügbar\n\nDanach langsam erhöhen:\n\n480x832\n25 Frames\n8 Steps\n\nErst wenn das stabil läuft:\n\n480x832\n33 Frames\n8–10 Steps\n\nIch würde bei Wan 2.2 auf deiner GPU nicht direkt 49 oder 81 Frames machen.\n\n---\n\n# Konkrete Empfehlung\n\n## Produktiver Workflow\n\nWan 2.1 1.3B\nFür schnelle Varianten, Prompts, Kamerabewegung, Konzepttests.\n\n416x736\n25–33 Frames\n6–8 Steps\n\nWan 2.2 5B\nFür bessere finale Takes, wenn der Prompt schon sitzt.\n\n416x736 oder 480x832\n25–33 Frames\n8 Steps\nVAE tiling ON\n\nWan 2.2 14B\nAuf deiner 16-GB-Laptop-GPU nicht als Standard. Nur später mit Low-VRAM-/GGUF-/FP8-Workflow, wenn du Geduld hast.\n\n---\n\n# Wichtigste Korrektur jetzt\n\nNutze für Wan 2.2 am besten den nativen ComfyUI-Wan2.2-Workflow:\n\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n\nUnd prüfe, dass diese Dateien richtig liegen:\n\nComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nComfyUI/models/vae/wan2.2_vae.safetensors\n\nDiese Pfade entsprechen der offiziellen ComfyUI-Wan2.2-Doku. ([docs.comfy.org][1])\n\n---\n\n## Mein Urteil\n\nWan 2.2 macht wahrscheinlich bessere Videos als Wan 2.1, aber auf deiner GPU ist es langsamer und VRAM-kritischer.\n\nFür dich ist der beste Mix:\n\nWan 2.1 1.3B = schnelle Produktionsmaschine\nWan 2.2 5B = Qualitäts-Renderer für ausgewählte Clips\nWan 2.2 14B = vorerst nicht sinnvoll als Standard\n\nDeine aktuelle Wan-2.2-Leistung mit 64 Sekunden für 25 Frames bei 416x736 ist brauchbar. Der 12-Step-Run mit 119 Sekunden ist für Qualitätstests okay, aber für Massenproduktion zu langsam.\n\n[1]: https://docs.comfy.org/tutorials/video/wan/wan2_2 \"Wan2.2 Video Generation ComfyUI Official Native Workflow Example - ComfyUI\"    / geschwindigkeit für das erstellen der Videos ist für mich nicht so relevant, die Qualität hat vorrang. was müssen wir betreffend dem T5 encoder machen? wie ist das mit Ram-offload (auslagern in den Arbeitsspeicher wenn er mal mehr Vram braucht, geht das?) nicht dass er sich aufhängt...", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_ECqX8wLvZy7xyoljTwM0vEdq", "call_id": "call_ECqX8wLvZy7xyoljTwM0vEdq", "response_item_id": "fc_0d1a4d10654d56b4016a11df96082c8191b35f9c1f2eaacb94", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://docs.comfy.org/tutorials/video/wan/wan2_2\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Evaluating tool options**\n\nI need to determine the best way to answer by possibly utilizing tools or web resources. Since the user is asking about configuration, I might need to inspect native node availability or refer to current documentation. There's a suggestion to use the browser for documentation, so I’ll look into that. It’s essential to provide an accurate, well-informed response while making sure I understand the best approach for the user’s needs!", "reasoning_content": "**Evaluating tool options**\n\nI need to determine the best way to answer by possibly utilizing tools or web resources. Since the user is asking about configuration, I might need to inspect native node availability or refer to current documentation. There's a suggestion to use the browser for documentation, so I’ll look into that. It’s essential to provide an accurate, well-informed response while making sure I understand the best approach for the user’s needs!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd-W2sgM4iST2ebbvt2-BXzDntqs-_92i0ETiwK2LhCfKhb-VU21nq-i9E-wVVMnPYquxpm6TJnSczHdgIec8iK03Dt1vGnwanXRPHYB0a6pvI0WmX5Ks4NTnAgWTZJ02accGDnAXQjsdrifShkL7AbWQWQ8ZLCMBZZEQnKyj8W-p8y_LVFJDO0CQbINFWCOdaKdxwq57mH5QHAPXEQ_ncoNpNdfPABQMa2CHoenLi7eCTGeZ-VFNtvcP4KAeAaEW_Pr5sqegOqV_cUo-tSdXTECU5OHq3ndXxWgq4RJ7JJP1tIhHb5P3zMuT2bE4Y0HfOEkRfBl4Tjp8gUpRAncZmmKkuAOhExpS1z9nOtA3rD4Uafqsno-URImoABW-F2V6y_a5aseyC4XdeleTS8kcaTG86Aqv6tURFyvJAxdSv4qKA1WIf5VITZiEfPKhSlYpbtWfR3A9qcuTY1MmxTgVkJDwphRWD8lgCLILb0KFZrU8DQE2ZmvLq6-bcorCa17-0rZcJ9NlZUSlje0fPR33o3JdJv8nXXw8imCoRHHiFnhi0qsyF_6TfspJjb00Jf7QXqMG1--6Cf6a7wuIgMEG4Qo_I7_T-mYjbllsqU8Vu8N_8f3kvHwbX16Ggl0X5B3iHDOAdODLLj-Yw3Yv0_pWIz5RXhQJ-P2l1jQ-w2WkWQybSZ6bn2f7XI6ivxKVNfx5FH95R4GrBZYcbL-RDEYy4yUipgiXvXe64SuOl98YkxJwH2xAqQjqNDHjbsYe-nJQIjYh44IWTrrE3MVNTKSdWnJXNb70PF5-fRgpWQvE4sw5emBl8lby4jtpccqdjCrYgtgMA9_-D1uY1ex0cKVUvJAByqK9v9B5d_5aHHXuk8B8pWGY-jt5ou-sUEcU4z2Y8633838WR9nEFpN-wi8yIYnVs6n_vxxIltxs1wH8TQqV98gV_iPV1gvVB_ZWptawh6mID4pWnAJ5UEW6tjWyEy911clqGw13ozpORW20cOwwHGLucHfG214o_iwNMCC0Z1zac37dgmHjmJs9deRBHOURSVY_CJ2C0xoP1uenGZuopEeuWuGKJ0-sNBhDAQm77MptI7-a472qtBIP7qoLAjyaNudejP-Z54oHR84zWS7QCjYTox16Kq-HJ9l_h_zG0PX3digRSZdZxfUuRJHyeahsZychANSniIfkqOE9RpEz0B08ql6f73pVyxa_oxHszH7sRDhGHZjwN9v406jYGp7-HuHQJ44stNAeWggSTz8C665obGp1qDya80ZXi08DBav", "id": "rs_0d1a4d10654d56b4016a11df929400819199a604d10c86e0ac", "summary": [{"type": "summary_text", "text": "**Evaluating tool options**\n\nI need to determine the best way to answer by possibly utilizing tools or web resources. Since the user is asking about configuration, I might need to inspect native node availability or refer to current documentation. There's a suggestion to use the browser for documentation, so I’ll look into that. It’s essential to provide an accurate, well-informed response while making sure I understand the best approach for the user’s needs!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "[browser_navigate] https://docs.comfy.org/tutorials/video/wan/wan2_2 (8,769 chars)", "tool_call_id": "call_ECqX8wLvZy7xyoljTwM0vEdq", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "[browser_snapshot] (8,430 chars)", "tool_call_id": "call_qMF4LL4WYX1Si7zlisSSYYj3", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_hGQzTUKIIPxwXhvRhkPjgjKV", "call_id": "call_hGQzTUKIIPxwXhvRhkPjgjKV", "response_item_id": "fc_0d1a4d10654d56b4016a11dfa494948191be6262554ae2169c", "type": "function", "function": {"name": "browser_console", "arguments": "{\"expression\":\"document.body.innerText.match(/Model Download[\\\\s\\\\S]{0,3000}/)?.[0] || document.body.innerText.slice(document.body.innerText.indexOf('Model Download'), document.body.innerText.indexOf('Workflow Example'))\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering text extraction**\n\nI think I need to extract visible text using the browser console. It's an interesting approach, and I'm curious about how to go about it. I wonder if I should look up some specific commands or scripts that can help with that. It feels like there could be various methods, but having a clear goal in mind will guide me. I'll do a little bit of digging to ensure I’m on the right track!", "reasoning_content": "**Considering text extraction**\n\nI think I need to extract visible text using the browser console. It's an interesting approach, and I'm curious about how to go about it. I wonder if I should look up some specific commands or scripts that can help with that. It feels like there could be various methods, but having a clear goal in mind will guide me. I'll do a little bit of digging to ensure I’m on the right track!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd-kuQ2sJQkS9Q7trKsZeFIgwwcn1GDgWRDRNQI6_hgU9qhoeDlnDCqEA3ZV9c3148UUgSPYB3637YfEbJnZp_42CpF_cazQaEKgXmEGkgoSuFd5pJL5_OLCuWpR-Y3uERXKGWLr5MgoyoqcbzYobF5d4MZ2LmHWYdqkFt1l1SVqeIHI9jCXtW_EIQXhbxzeMwd2sjz3yliqMSbLAKIej_wICdPzJsY3PjvZa_WbMDeR5ekINtS6cVtZdcVj9vy14cK_tl_7ijmqK2sb7Ajk8PPwzFN1FJ9U3zUMytGv_6rxdgmQc8uy4ecHTcTSTV-drxNBzZ3dsNfxXHRlLqqSrlxGttteWX7hLtnS7S-c2tk8Buq3sEVNkN0UwykB0NH8UbavtR8JDv5V7mjctJM1LffzblZcx-oUtwNb5-2lz6Y-LDkOa0mQ4xP14tbdyw5B1xKAj-s8uJ31pdoFyN0DhtymcaWLYxMAFGRssmY99aJj1bO6lTcwr7zyNazoIba2VxV6_ESEvpeAmVYeR_DrDcqUr1WbNNYnOgvO2xx6OzjAPg1ZQ1gviCizG0xj22ewr5guLK0-20UQTeYpS_UZDR7He5_4eo83bwRwmtQLu6ENgRizYtP1-8GY8k0Pn3U9iUjiyWR3CPDEPAzpZ0GOmA6uTuN5_TCcI71UQfAH5GFa31J0UC_CUAsC-fXL-0GDU--8CMiDYIoR3Xu47wNbg3wYVFfuJr19A4IMcmXlKuUDy7PHn9xQD45luiSe-qX-b2al3o_WyzqdhfjId1NGuwbRU7fRCgUvpS9aynlM0lv37CPCIKdHIBYdExXGqlMGuaMf3ps6yy1xcj24rnOLp4nOpHyJpUayyFpyFrc3snndl3_PPAgQdEb4GgRrBFf7gUencidjmuBFRgrFGmUKqA5g1QjuW0TkQBBMBeQSvyhdyIPrFH8o2Ze0uJCS8QXBKerjyWfSDUr5jqaQpCIzogJ7pbcfp7ddOeILPaXhcU4EmFs=", "id": "rs_0d1a4d10654d56b4016a11dfa2570c8191abf1e27cf0eb14e7", "summary": [{"type": "summary_text", "text": "**Considering text extraction**\n\nI think I need to extract visible text using the browser console. It's an interesting approach, and I'm curious about how to go about it. I wonder if I should look up some specific commands or scripts that can help with that. It feels like there could be various methods, but having a clear goal in mind will guide me. I'll do a little bit of digging to ensure I’m on the right track!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": true, \"result\": \"\", \"result_type\": \"str\"}", "tool_call_id": "call_hGQzTUKIIPxwXhvRhkPjgjKV", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_qpksH9jlsJwzDyCNQV5kRAZJ", "call_id": "call_qpksH9jlsJwzDyCNQV5kRAZJ", "response_item_id": "fc_0d1a4d10654d56b4016a11dfa784e88191b7e98fa72fb4750d", "type": "function", "function": {"name": "browser_console", "arguments": "{\"expression\":\"document.body.innerText.includes('umt5') ? document.body.innerText.match(/umt5[\\\\s\\\\S]{0,1000}/)[0] : document.body.innerText.slice(0,5000)\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": true, \"result\": \"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\nComfyUI/\\n├───📂 models/\\n│   ├───📂 diffusion_models/\\n│   │   └───wan2.2_ti2v_5B_fp16.safetensors\\n│   ├───📂 text_encoders/\\n│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \\n│   └───📂 vae/\\n│       └── wan2.2_vae.safetensors\\n\\n​\\n3. Follow the Steps\\nEnsure the Load Diffusion Model node loads the wan2.2_ti2v_5B_fp16.safetensors model.\\nEnsure the Load CLIP node loads the umt5_xxl_fp8_e4m3fn_scaled.safetensors model.\\nEnsure the Load VAE node loads the wan2.2_vae.safetensors model.\\n(Optional) If you need to perform image-to-video generation, you can use the shortcut Ctrl+B to enable the Load image node to upload an image.\\n(Optional) In the Wan22ImageToVideoLatent node, you can adjust the size settings and the total number of video frames (length).\\n(Optional) If you need to modify the prompts (positive and negative), please do so in the CLIP Text Encoder node at step 5.\\nClick the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation\", \"result_type\": \"str\"}", "tool_call_id": "call_qpksH9jlsJwzDyCNQV5kRAZJ", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_QZFAWm4QjifPX0eXwOKqfmnu", "call_id": "call_QZFAWm4QjifPX0eXwOKqfmnu", "response_item_id": "fc_0d1a4d10654d56b4016a11dfab60ac8191aa24e6e99ed91a77", "type": "function", "function": {"name": "browser_console", "arguments": "{\"expression\":\"document.body.innerText.match(/wan2\\\\.2_ti2v_5B[\\\\s\\\\S]{0,1500}/)?.[0]\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": true, \"result\": \"wan2.2_ti2v_5B_fp16.safetensors\\nVAE\\nwan2.2_vae.safetensors\\nText Encoder\\numt5_xxl_fp8_e4m3fn_scaled.safetensors\\nComfyUI/\\n├───📂 models/\\n│   ├───📂 diffusion_models/\\n│   │   └───wan2.2_ti2v_5B_fp16.safetensors\\n│   ├───📂 text_encoders/\\n│   │   └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \\n│   └───📂 vae/\\n│       └── wan2.2_vae.safetensors\\n\\n​\\n3. Follow the Steps\\nEnsure the Load Diffusion Model node loads the wan2.2_ti2v_5B_fp16.safetensors model.\\nEnsure the Load CLIP node loads the umt5_xxl_fp8_e4m3fn_scaled.safetensors model.\\nEnsure the Load VAE node loads the wan2.2_vae.safetensors model.\\n(Optional) If you need to perform image-to-video generation, you can use the shortcut Ctrl+B to enable the Load image node to upload an image.\\n(Optional) In the Wan22ImageToVideoLatent node, you can adjust the size settings and the total number of video frames (length).\\n(Optional) If you need to modify the prompts (positive and negative), please do so in the CLIP Text Encoder node at step 5.\\nClick the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation.\\n​\\nWan2.2 14B T2V Text-to-Video Workflow Example\\n​\\n1. Workflow File\\nPlease update your ComfyUI to the latest version, and through the menu Workflow -> Browse Templates -> Video, find “Wan2.2 14B T2V” to load the workflow.\\nOr update your ComfyUI to the latest version, then download the following video and drag it into ComfyUI to load the workflow.\\n\\nDownload JSON Workflow File\\n\\n \\n\\nRun on Comfy Cloud\\n\\n​\\n2. Manually Download Models\\nDiffu\", \"result_type\": \"str\"}", "tool_call_id": "call_QZFAWm4QjifPX0eXwOKqfmnu", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_jrkeE0Io8tJmIlZXsTPfUQSV", "call_id": "call_jrkeE0Io8tJmIlZXsTPfUQSV", "response_item_id": "fc_0d1a4d10654d56b4016a11dfb7bdb481919ba3bc7f4702533b", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nobj=json.load(urllib.request.urlopen('http://100.101.173.25:8188/object_info', timeout=20))\\nfor k in sorted(obj):\\n    if any(s in k.lower() for s in ['wan22','wan2.2','wan22imagetovideolatent','wan22text']):\\n        print(k, obj[k].get('display_name'))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Clarifying workflow requirements**\n\nI need to finalize the native workflow and figure out the necessary commands for model download. I'll look into RAM offload, as Comfy's dynamic VRAM offloads components to CPU RAM when under pressure. It's important to ensure that there's enough system RAM; otherwise, things could hang. I should consider recommending ComfyUI's native option instead of a wrapper. The user asked what to do, so I might provide step-by-step commands. Additionally, I should suggest increasing swap space to 32GB for safety.\n**Looking into swapfile commands**\n\nI need to find the right commands for setting up a zram or swapfile. I think offering an optional 32GB swapfile is a good idea, but I have to be cautious about disk space. It might be helpful to use some tools to inspect if native nodes already exist. Also, I need to gather terminal object information for the native Wan nodes, like Wan22ImageToVideoLatent, to ensure everything is set up correctly.", "reasoning_content": "**Clarifying workflow requirements**\n\nI need to finalize the native workflow and figure out the necessary commands for model download. I'll look into RAM offload, as Comfy's dynamic VRAM offloads components to CPU RAM when under pressure. It's important to ensure that there's enough system RAM; otherwise, things could hang. I should consider recommending ComfyUI's native option instead of a wrapper. The user asked what to do, so I might provide step-by-step commands. Additionally, I should suggest increasing swap space to 32GB for safety.\n**Looking into swapfile commands**\n\nI need to find the right commands for setting up a zram or swapfile. I think offering an optional 32GB swapfile is a good idea, but I have to be cautious about disk space. It might be helpful to use some tools to inspect if native nodes already exist. Also, I need to gather terminal object information for the native Wan nodes, like Wan22ImageToVideoLatent, to ensure everything is set up correctly.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd-3phkOjXXWNHUWiD33a_Iek-KC79jH4ZugnA3PbwKG-iQq5DmxqVw-FFXMPPQdO5-2bfVXdoPzMhqLV3MkS12Zh-uyQcuKzLfgaleWPObIIdM2EBN9Ue9o70bhiIWCNEhNev6P3xCfwSTgthwQRkWgVuRL5CSEFgi8hFO49ES8Ah_pWmEGKFNeqlekR5EONyNJdjKpoStyQQlo1adPy4XU33yy_Ls1CWT822meexachzftSwNXnKBTuGDi9vwG1MCbBmK68oOX_QPo0LjVxG_PvWXQI1j6EXCgbmmBRVz0qUw4homB9oRk89YmRDS2uXFqGM8GsYRPzCw2e7xYHHiu2dnLRiADmNZc5PzvqKCiMvQufDeIQ7X160wHsNDnaoNhvMXH9KVNjywzhfM539QuyxW9XGKPpTBOziDTqZtOpv4KWV1f2Ng9eh5erAtRY_-wVn_3nEu7FXL_WZlMJWgSXJu9NuV34dXwBypZ66wRDcQRzSKSkKV3Uoy8Zg_BS8Iok6U3DWoudNBA_Z26xLbIqtbDgC15h8LhWr3QMIAqwSE3ioxZ5PLO29uucwToLPgrsQjuPFFyBhvSWq8PZQRWo5qPHiTsiLJEsp491erS5Ez1b5oQ0Lv4XNkkMsVG3af0OvZV74xLgt6NgzPBicfxSMsZ7IkzFtYpDRhwSyTYST79_9qLtsn6ma41zevk3uv_3ObFESru-Kl1JG-To3klk7mA8sEZOQlDdzhZPIW-mZKykifseejwOtkPegaZ2zmfQFNgXF1XkVq9GI19w-saNZH3WmJPp6Lz0PdiH-_fap_clwZ9laX_mlrssgngXqarv4BxErpelEc6iWIeq7ZK43GY6_ROqa8uJorNT9WeetIUUqXEf3brL5B_hIs_cUT0zBRLKt57cGfMi9HrNJOHVj-QTeZOxKMz3KTpUUCDmArohOfJJGQJRU1wkzTXe1tcw_c4AnCowl2oqb6MuYgYxfhbtYkwzGxfJK4C4eP_1MGza33jSZScuTlaoSpOBT_BbTkHOJQA5Sheor9UVx1vp_vJhi7L-dff-T4w-RU93FG_toc4Raixta2K858mQHusmimUtS9wE7vsMu5yKzEGFMKMuTswLD8r8Ao00A6Ds_ld9Aq7TB5ZhkwbHGKuTQ4LPZeHVBmoD7baQFDEPjJ9sLi2fneCTnXl3fGytXFU2YfSiQL8dDw6vXgN5ZF-kOYQZDqsCtNvfPjOyBlRL62UofXvGkort3387xXn7wUwug-qBvRFjsUTwhOlRV5Aqm68A4jGUslaSB5EJ_2wuKFAieH4tQHFzCzcXKEtKRkX7AFM2tkYOL46CtAtn3nQuhNjnrFx2Dg263ud3aFBJoJhkCyUkz5Zq2nxsHhDsrHOLZLY6G1TcEo2aaqkYGDXiIDHyxdgnL32aVKFsXkzGHZgx-qd7z_RuVlbqIb4FPMwgLFIZumxmqvRMtZ2_8ZHwV7n4OaXqV0EPmXmwZNdBPnK6sFf5-K8P4fG0z2K7vQJIBtq7aASPp3SfRJnJiw036dxZd6yvrE0hrsOKSvsYcH8cJw6LZLc9eunwPFFjtmGfFzncukmVii5ogzWu7dm5f1ZHwh4FqUSZltBGQKvJFjhIhwymRkHMQ_qiPknFHwM2LZC67hDJVv9e1kvP0tL7me44GBqHtOQom1nQ6XAGIu116tbyNN3POzgzKjq7xx19kSj-DxdNx6RI0obmFvdysLnTc1IOzU0dApwUV7nZ4_HIdpgu_Eght-EbmLlH-fzqYV9Xwsm9QcYgnYTzq2o2vfuu-0kihPXDqi8TK6MXmyNdCZredFbCVtEjW3f6TLEiADxqAghUBFcapdi7Rx15iOnFIyIuoSrZfBxBsRBF8zgFy443WH1x1LQoDYG6xCp6cHsb7zADeoZ0j2OUZUjumJjGOdcUec66mK0fzN_EML2rX96mgmmHcFhMJuuveM3O_EIrtMEjtu03OgMhJxeVVDlNWlMv4hZaK63iC7ifstOL_ZtrJt9_pcmlKTavp7lSRFcLuV7iD2aC4lpo-M66qckwGhFioYXzSSNWHK6nldwDbsu3Mvhb0W-H9-mpbnPpmbScA59Jffs6uK5Hy785psAOM5ta-1J6vEmvwWVpZTqMiWRZ23FERBHERY-bzNSntlRgNZwj1kVWa0fYZb80519cnenyBKMyeaiE6ad5OjzDWQY4oK8vHsrdj41r9-W2yP2hrtunTpUcihHgsJXtQBMMqccyZ3fvbBM-P5oj1bSyPrVrPPC_E12mSqG_bIAgr0RXQco0dGHOsLFV__MAs5xxy6Avrjx_eC_9icUg3oI_td9eafBuZfmITXPjfnXD4asFCg8mtKjk7etdftnxOZriYV1lUpwywsT6YDVeZ_5be19m4H3GZc3B9wAZcm0weStUqnGzHDSstQQh0UaXG2vWAr3-R0qzI-fekKlRlrNnqZxWMEesez73LQxi9z6elZ1Zi-gYrufGbfph2AdZvjCgWRLyxU-2BVgGJAaOwZkuw==", "id": "rs_0d1a4d10654d56b4016a11dfaf69188191816e0f25c9cf3d2b", "summary": [{"type": "summary_text", "text": "**Clarifying workflow requirements**\n\nI need to finalize the native workflow and figure out the necessary commands for model download. I'll look into RAM offload, as Comfy's dynamic VRAM offloads components to CPU RAM when under pressure. It's important to ensure that there's enough system RAM; otherwise, things could hang. I should consider recommending ComfyUI's native option instead of a wrapper. The user asked what to do, so I might provide step-by-step commands. Additionally, I should suggest increasing swap space to 32GB for safety."}, {"type": "summary_text", "text": "**Looking into swapfile commands**\n\nI need to find the right commands for setting up a zram or swapfile. I think offering an optional 32GB swapfile is a good idea, but I have to be cautious about disk space. It might be helpful to use some tools to inspect if native nodes already exist. Also, I need to gather terminal object information for the native Wan nodes, like Wan22ImageToVideoLatent, to ensure everything is set up correctly."}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"Wan22FunControlToVideo None\\nWan22ImageToVideoLatent None\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_jrkeE0Io8tJmIlZXsTPfUQSV", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_WZYfme5EwjUDmMDsazlNEdbp", "call_id": "call_WZYfme5EwjUDmMDsazlNEdbp", "response_item_id": "fc_0d1a4d10654d56b4016a11dfc592e881919e72d18185282767", "type": "function", "function": {"name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"comfyui\",\"old_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 and a micro smoke test. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. System RAM can still be the first bottleneck; ask the user to free RAM before the first load. See `references/wan-video-tailscale-a5000.md`.\",\"new_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 and a micro smoke test. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. System RAM can still be the first bottleneck; ask the user to free RAM before the first load. See `references/wan-video-tailscale-a5000.md`.\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Verifying CLI Options**\n\nI’m considering if I need to check the CLI options. I think there could be a --cpu-vae option that I should verify, but I might not need the docs for this. The comfy CLI probably has --cpu-vae. It’s good to note that I can mention emergency situations. Also, I’m not sure about --novram. I might need to update my skills because I think the native Wan issue needs addressing. I’ll have to manage this patch.", "reasoning_content": "**Verifying CLI Options**\n\nI’m considering if I need to check the CLI options. I think there could be a --cpu-vae option that I should verify, but I might not need the docs for this. The comfy CLI probably has --cpu-vae. It’s good to note that I can mention emergency situations. Also, I’m not sure about --novram. I might need to update my skills because I think the native Wan issue needs addressing. I’ll have to manage this patch.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd_FXfXEYXQimJK3MLi7lojMNYWsMqagj02I5SazvfdJItoJYOpaxbByO1NdIO2jTaT7HmWA5ZGKMnSduhBfnu1MGBpBKG9SM2nSNMY1_1wwDXoho-fMir6MoAfwVv3apbjiu1OTX3MAB-jaZdw1MtMIgeu2e5UW54qzCOR92CbWZ6KVgEQA3-1IAcjdde9GllK2rEwQPyTGwhbK0Ihk13mCjndkeu4Z8nq6h8_KhBv5y47jdkd3CIM3KvJj8H7YrKrUj1APVAtzzj5CJ5tMzwpNfxz9kPwTeN0HQgUz6i4Pe4WabxYpJAm2UMfY4JcUGd_URo4QWLkggh-JQElG3HIAaVugHfpLjjEbtf5Szn3Xbz-QroNuIuw28qR01ZhFQ_P3LxEGPRHepn64GzEaAewb68HRMjG270VuPfrM9_GqabEsOHsGIxbhxKNDPm1mAQeDTmGAnwNGWo2nk135GHrHVecpoHxT9bD2QereWIHbIiCSEI3iwjpmZHIXJluqEKiypzrx3Xsuzpcib9zAjMmbgxnx-zcpyqfq-bf5QbO25JhaMMMz6gKIt4fy6wAo-VL7LxguCx62VAieQyZXuk9hP5m9P0adrF7NTskLB-YS3a7cxrbaGu2Mj2rM7DoMXyB5M7DvjByViwXY0nVuDDpyQXbr5UiblEonwg8qH_p8A3N-sdtzvOP9h9y4WmLEOcQaFMjnwcom1FoufGdRAkBIC9BKI4jeAbQzEgbSUSRFxfbj9VQYZto52mb6JWFX66LGBhEKRruIEx7RvtH4ZATVMJWsDQ-2buN6qfKxelCZEH95KOQj0LKE5vmK9E9MoVV6e91QM42qsf45fyX-kNfZc9rcx1V0YzFXHMfpo3SZqiLWJcmdhPTHaLaZ0qChIAX7kSpyvP4cZi2rdrluhy8iLNx1R_veO3LSVGE9n4ydc1N0DihFvUr6EOD2H5wL60md2Ze724-3SPkKBxyNRlHGj1Vm3YL--GQfXrCWDWUbmmdMe532L2SkqwsaM4ue36t9rQSrDfBu0UhUJG2lftkbzaSRS2BVnUViJiR2WIri_y4DBFCjzNEPi2VjGIG-bjipH7Mdsnzix5rwlXYYQcVUMxyyA5V2Os09lypu4TK3XxuXroOtWGz52gbvkjpaUud_08z6A1aVdMRRoe8k5la9rNDSa6I0frP14RqnJu9ilP_EdP4s4S_ZuVXUmpVrZJQv6xXX1A-bU9T9fOP2-7YhMF6B3gwimpabhHp5rqHqkDaiKAVkT2rXdCyL6vENmzCvvTmim29qkHQcVgvn1BKRdgSJvHLiBHVok7yZ7shYpuig0TUw_MMOHoPgic6oEX-aewG58-g0rBmCXP-Ura01o0G0YXPQ_mgqtSO8Uv--oE-wAz9rwL3O4De0HWnIdAe7VxxRUr_CK7dHVJ8AzmyRxpzoEYQBsZE4s8m2sAQCJzBeOFzWvG2QHdBVhTiuQcUbU69aUf3_MrTwLmwca8q9jG2IVaQ5fRAs4hUUQScfsjfnquZiiBT1or7jeSif8DqiXast2s-zCtthQGdoixs-PZe8oUPYo5mEN227_Zyp6T5u_cvaCORZAubCTlLrwe0iUL8f5c8TYQZaHccIOQgIz2t5L34R7PU2TJEOUbKd0diTCzeK2JKV7gU-6p-1kvlmnYChM1j3fHZTHZocPKvdnW35dDcSSPcvm0igIPBK_gjkV_wWoqXkqfGsVSMkvivpwqYBfIQ-h7QGQE87ja7jiH_SV8Jv81qPQCcxvAhsd_XY-tqjiedd5khRmTTbPKvNertAm2fB4sXXiXN_5hu3YCPI4M4kPS1utyT-ggiX-wScvYg7jSlgXHtevU-lsKThhZNZc5cG7RwXoI9y2PG1kREnVEhZDJD9kf5AXFW6AF4pjpnbEEgePcd3FocY1uT3zIUax7fI30t5SHLayhH-XcXPVRUFuSPyZ4KmwDlDAPAmnii313fj52nfez5bLL4TG1lx4NpdNwoijj6ImNB4LxGNkn4Cedl2IBv0vPWp17C9WvGZuh9SPMMOaaUzlUgFLvX92xDxenO7qxvXT8nrYsRqMo5F-HK1_xbseYwYhC_kDR86yImNtsFyYAYylZjExpJ9TWLG0YywW-Qp04whCsXunYdg7UVbQJD75T3EBxzvwVGcIyHikMqPzyUjmJYVnugXBYZ6kHxY8crEKOOC1kkKTx8uCVadv5kK6FLmYKJOUG4XjPi3JvkF8TtQaDCappFtA6g08b-19KOB3rNkKb5mp4fyxQSxx33sTS0CLRF5_OFdl3SVzkuW9nGg-rrnZ7MWft3xjR_8aKgpqJn7CCHUk-WY2kZhjdVhGKjB4Ong9eDCEr8_YQjFrFnGLUrveUGMap8amDzRV5O0rhXybnGxEcl5GPv6v6TuxDVAy-J2_p_RAAbSoY-XyngyYdP_LetDg1yFFc91QZKUy83Vum95Ll_OpNlbwtkScH_WILhG9IoTF_lQeX9vqsUHyX09Sir8O-TnywuXonNj2c_ehsyuXyx2igXimhjRmb3y4KD-CiKnZ923S_9wCXbAKZNfiuwK-cXUHiHs6nyEnMyuiFMx9VUnGPeXcZu-1IXIbpFqiz5JYB4XYuB0BQ0LBbILbnITTc2pN1WZUWTJ_13aULqalSAR78JanR5VWLcMPrAUTyEgxN9F0e46oRWHm97e-PG7yqJVBow6JKk1ck794L7Snk_DbJIMzwXIHeTg_LLzTrPVc3mz5BzOxJrR-Jfu", "id": "rs_0d1a4d10654d56b4016a11dfbb50c081918cf65c45136dbbc4", "summary": [{"type": "summary_text", "text": "**Verifying CLI Options**\n\nI’m considering if I need to check the CLI options. I think there could be a --cpu-vae option that I should verify, but I might not need the docs for this. The comfy CLI probably has --cpu-vae. It’s good to note that I can mention emergency situations. Also, I’m not sure about --novram. I might need to update my skills because I think the native Wan issue needs addressing. I’ll have to manage this patch."}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": false, \"error\": \"Could not find a match for old_string in the file\\n\\nDid you mean one of these sections?\\n 621|     `comfyui_setup.sh` does this for you.\\n 622| \\n 623| 13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests, then Wan2.2 TI2V 5B FP8 scaled for quality previews. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B on 16 GB, use the wrapper-compatible T5 (`umt5-xxl-enc-fp8_e4m3fn.safetensors`), not the scaled T5 file, and enable VAE tiling (`tile 256 / stride 128`) to avoid decode OOM. See `references/wan-video-a5000-optimization.md` and `references/wan22-ti2v-a5000.md`.\\n 624| \\n 625| 13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\", \"file_preview\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprere...\"}", "tool_call_id": "call_WZYfme5EwjUDmMDsazlNEdbp", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_JTj8q5SHpfxX8D9eviQS19PA", "call_id": "call_JTj8q5SHpfxX8D9eviQS19PA", "response_item_id": "fc_0d1a4d10654d56b4016a11dfd01d9c819199c9cbb437e9b074", "type": "function", "function": {"name": "skill_manage", "arguments": "{\"action\":\"patch\",\"name\":\"comfyui\",\"old_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests, then Wan2.2 TI2V 5B FP8 scaled for quality previews. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B on 16 GB, use the wrapper-compatible T5 (`umt5-xxl-enc-fp8_e4m3fn.safetensors`), not the scaled T5 file, and enable VAE tiling (`tile 256 / stride 128`) to avoid decode OOM. See `references/wan-video-a5000-optimization.md` and `references/wan22-ti2v-a5000.md`.\",\"new_string\":\"13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. On 16 GB, keep native/offload enabled and start at 416x736/25f/8–12 steps; if decode OOM occurs, reduce frames/resolution or use VAE tiling / CPU VAE as emergency. See `references/wan-video-a5000-optimization.md` and `references/wan22-ti2v-a5000.md`.\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEd_QTo_l7MP3Vwx8Z3Ra2y7Ihab_e_rPiU1WSe36LD4nluCp06yH5aC5gVWgyhDQ3_FOQjrCTcRyrntudfBH_XgROqxeA42Rpns4_g4yEeTv3TKdN0lOjv9X0ErOJvblxE8IVeKVpHSM_6quS-MHj7aLJl3sGbquWXNjdQXs--mERlzrn82rGhvJceMibsneVzzHGzkal1usoKsH3RLSF3NP8ajQ22ucNJpMHoHGSzeNDmzIqvuhLBLOzl2Sxx9TKsv3tBlEsQN43GTQZMAIa0p83rZyoge9Rqd0vk3jsJGenY0n1n0PltKdEp6GoT1LeBy5cAc1mfbsGDyzuwDleK8Vtp5h1v9peuanP5OSIjMKVBHEH0OonAeA-NnuNh1U5YTXX3cEcS34-hhUD2k_aDxVd-fOqxE9tDnOBYoQ3RgLgvoqlkuHLcGQiAzutV86jMiwhwlkqvVTc19g2W_rvY-H6OAU2szsPpwbg0r4FtkkSm33_nDwpr-5AtJRbRljOnhJntPPpo_USjX6rpEkrSOFz3dTRL18RJ9fH_ykSvqs1wp_pmNlCRI4qKvgl6BZWDAsUMp6N4cE_5ikNaW-BdqKSO10AvKxwdyjeKfOS8nlupEu652zS2NZZ5uwpSvQtUzEzbitICTAlMz9760sLBDmHxCziFGbS3pRK4e8LuDBxEEZJgE4a13P9jKZtUYI0my-5FmMNoFDaPFvRgY6aRjeZ7ZIeLlH0ug4-VnOIojlh678bXlIWeRsTtW7VZCJIt4vXFWdcfyxZ0Qbavio3eeWnZ9EWkJvD6ykro8xKMc9n2rlP7B7teis0mjSHA1T3WVnwpTgRkLOSe5G_Gi9AhLM7ElEvPD2YlWgqsdZT1ZcYCeoBJR8SuChwVPp_YZPluyRXL4PkYFYr-mn_9OEegjYf4eqcg_Lg-PRaO_uxCkeTdpkrJD6zzwGpfa7YvqTejhT", "id": "rs_0d1a4d10654d56b4016a11dfcf9ad481919eeca9346d4bbe27", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'comfyui' (1 replacement).\"}", "tool_call_id": "call_JTj8q5SHpfxX8D9eviQS19PA", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, Sir — dein Urteil ist korrekt. Die bisherigen Clips sind **nicht produktionsbrauchbar**. Ich habe sie zu freundlich formuliert. Technisch war der Durchlauf nützlich, ästhetisch war es eher „Auto im Fiebertraum eines kaputten Rücklichts“. Für Content unbrauchbar.\n\nDie wichtigste Korrektur ist jetzt:\n\n```text\nWan 2.2 nicht mehr über Kijai WanVideoWrapper testen,\nsondern über den offiziellen nativen ComfyUI-Wan2.2-Workflow.\n```\n\n---\n\n# Kurzantwort\n\n## Was müssen wir beim T5 Encoder machen?\n\nFür **native ComfyUI Wan2.2**:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nverwenden.\n\nFür **Kijai WanVideoWrapper**:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n**nicht** verwenden, weil der Wrapper-Node genau daran scheitert:\n\n```text\nInvalid T5 text encoder model, fp8 scaled is not supported by this node\n```\n\nDer Fehler kam nicht von Wan2.2 selbst, sondern vom **Wrapper-T5-Loader**.\n\nDaher:\n\n```text\nWan2.2 Qualitätstest = native ComfyUI Nodes + scaled T5\nWrapper = nur noch fallback/experimentell\n```\n\n---\n\n# Zielsetup für Qualität\n\nWir wechseln auf den offiziellen nativen Workflow:\n\n```text\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n```\n\nDer nutzt laut offizieller ComfyUI-Doku:\n\n```text\nComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist genau der Punkt: **scaled T5 ist richtig, aber nur im nativen Workflow.**\n\n---\n\n# 1. Offizielle native Modelle installieren\n\nAuf dem GPU-Host:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\nOrdner:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\n```\n\n## Wan2.2 TI2V 5B FP16\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors \\\n  ~/ai/ComfyUI/models/diffusion_models/\n```\n\n## Native scaled T5 Encoder\n\nFalls schon vorhanden, trotzdem prüfen. Sonst:\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\n## Native Wan2.2 VAE\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/vae/wan2.2_vae.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/vae/wan2.2_vae.safetensors \\\n  ~/ai/ComfyUI/models/vae/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\n---\n\n# 2. Startbefehl für Qualität\n\nBitte **nicht** mit `--lowvram`.\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas lässt ComfyUI Dynamic VRAM / native Offloading nutzen.\n\n---\n\n# 3. Was ist mit RAM-Offload?\n\nJa, grundsätzlich geht das.\n\nAber wichtig:\n\n```text\nVRAM-Offload in RAM ≠ magischer VRAM-Erweiterungsspeicher\n```\n\nEs bedeutet:\n\n- Modellteile werden zwischen GPU-VRAM und System-RAM verschoben.\n- Das verhindert OOM.\n- Es ist langsamer.\n- Wenn RAM voll wird und Linux in Swap geht, wird es sehr langsam oder wirkt aufgehängt.\n\nDein Host hat:\n\n```text\n31 GiB RAM\n16 GiB VRAM\nSwap nur ca. 1.9 GiB\n```\n\nFür Qualitätstests mit Wan2.2 ist das okay, aber ich würde als Sicherheitsnetz den Swap erhöhen.\n\n---\n\n# 4. Swap-Sicherheitsnetz einrichten\n\nDas macht nichts schneller. Es verhindert nur, dass der Prozess bei RAM-Spitzen hart stirbt.\n\n## Prüfen\n\n```bash\nswapon --show\nfree -h\ndf -h /\n```\n\nWenn du mindestens 40–60 GB freien Plattenplatz hast, empfehle ich 32 GB Swapfile.\n\n## 32 GB Swapfile anlegen\n\n```bash\nsudo fallocate -l 32G /swapfile-comfy\nsudo chmod 600 /swapfile-comfy\nsudo mkswap /swapfile-comfy\nsudo swapon /swapfile-comfy\n```\n\nPersistieren:\n\n```bash\necho '/swapfile-comfy none swap sw 0 0' | sudo tee -a /etc/fstab\n```\n\nSwappiness runtersetzen, damit Linux nicht unnötig früh swappt:\n\n```bash\necho 'vm.swappiness=10' | sudo tee /etc/sysctl.d/99-comfyui-swappiness.conf\nsudo sysctl -p /etc/sysctl.d/99-comfyui-swappiness.conf\n```\n\nPrüfen:\n\n```bash\nfree -h\nswapon --show\n```\n\nZiel:\n\n```text\nRAM: 31 GiB\nSwap: 32 GiB+\n```\n\nWichtig: Wenn du wenig freien Diskspace hast, nimm lieber 16 GB:\n\n```bash\nsudo fallocate -l 16G /swapfile-comfy\n```\n\n---\n\n# 5. Was ich in ComfyUI einstellen würde\n\nIm nativen Wan2.2 Workflow:\n\n## Modelle\n\n```text\nLoad Diffusion Model:\n  wan2.2_ti2v_5B_fp16.safetensors\n\nLoad CLIP / Text Encoder:\n  umt5_xxl_fp8_e4m3fn_scaled.safetensors\n\nLoad VAE:\n  wan2.2_vae.safetensors\n```\n\n## Startparameter für Qualitätstest\n\nNicht 720p sofort. Erst:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\nWenn das stabil und sichtbar besser ist:\n\n```text\n480x832\n25 Frames\n8 Steps\n```\n\nDann:\n\n```text\n480x832\n33 Frames\n10–12 Steps\n```\n\nFür Qualität ist später möglich:\n\n```text\n576x1024\n33 Frames\n12–16 Steps\n```\n\nAber erst, wenn der native Workflow saubere Bilder liefert.\n\n---\n\n# 6. Warum die bisherigen Clips so schlecht waren\n\n## Grund 1: Kijai Wrapper statt native Workflow\n\nDer Wrapper ist mächtig, aber für Standard-Wan2.2 offenbar nicht der sauberste Weg. Besonders wegen:\n\n```text\nscaled T5 nicht unterstützt\nkomische Modellvariant-Logs\nVAE-Decoding OOM\n```\n\n## Grund 2: FP8 scaled Model im Wrapper\n\nWir nutzten:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nDas ist effizient, aber möglicherweise nicht der beste Qualitätsbaseline-Test.\n\nFür Qualität testen wir jetzt:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\n```\n\nmit nativer Offloading-Logik.\n\n## Grund 3: Text-to-Video ist für konkrete Szenen schwach\n\nFür „ein Auto in Garage“ kann T2V funktionieren, aber stabiler ist:\n\n```text\nImage-to-Video\n```\n\nAlso:\n\n1. Erst ein gutes Standbild erzeugen oder auswählen.\n2. Dann Wan2.2 TI2V als I2V nutzen.\n3. Bewegung hinzufügen: slow push-in, light sweep, handheld motion.\n\nDas ist für Content-Produktion deutlich besser als reines T2V. Ein Modell ein Auto „erfinden“ zu lassen ist Glücksspiel; ein Modell ein gutes Auto-Bild zu bewegen ist Regie.\n\n---\n\n# 7. Empfohlenes neues Produktionsprinzip\n\n## Nicht mehr\n\n```text\nText prompt → Video direkt\n```\n\n## Sondern\n\n```text\nPrompt → gutes Keyframe/Standbild\nKeyframe → Wan2.2 Image-to-Video\nVideo → Captions/SFX/Mechanism-Layer\n```\n\nDas verbessert:\n\n- Objektklarheit\n- Komposition\n- Marken-/Kennzeichenkontrolle\n- weniger unlesbare Textartefakte\n- bessere Retention-Visuals\n\nFür unser Auto-Beispiel:\n\n```text\nStable Diffusion / Flux / SDXL erzeugt sauberes Auto-in-Garage Keyframe\n→ Nummernschild leer/unscharf/verdeckt\n→ Wan2.2 I2V macht Push-in/Light Sweep\n```\n\nDas ist viel näher an brauchbarem Content.\n\n---\n\n# 8. Was du jetzt konkret tun sollst\n\n## A — Offizielle native Modelle laden\n\nFühre die drei Downloads oben aus.\n\n## B — ComfyUI neu starten\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## C — GUI öffnen\n\n```text\nhttp://100.101.173.25:8188\n```\n\nDann:\n\n```text\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n```\n\nFalls du den Workflow geladen hast:\n\n```text\nWorkflow → Export (API)\n```\n\noder speichere ihn als JSON und gib mir den Pfad/Inhalt.\n\nDann kann ich ihn sauber über die API steuern.\n\n---\n\n# Klare Antwort auf deine Fragen\n\n## Was tun beim T5 Encoder?\n\nFür native Wan2.2:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nFür Kijai Wrapper:\n\n```text\nnicht den scaled T5 nutzen\n```\n\nWir wechseln daher zu native Wan2.2.\n\n## Geht RAM-Offload?\n\nJa:\n\n```text\nComfyUI Dynamic VRAM / native Wan Offloading kann Modellteile in RAM auslagern.\n```\n\nAber:\n\n```text\nWenn RAM voll → Swap → langsam / scheinbar aufgehängt.\n```\n\nDaher:\n\n```text\n32 GB Swapfile als Sicherheitsnetz\n--reserve-vram 1.0 oder 2.0\nkein --lowvram als Standard\n```\n\n## Wird Qualität dadurch besser?\n\nNicht durch RAM-Offload selbst. Qualität wird besser durch:\n\n```text\nnative Wan2.2 Workflow\nfp16 5B Modell\nscaled T5\nbessere Prompts\nvor allem Image-to-Video statt reinem Text-to-Video\n```\n\nDas ist der eigentliche Weg raus aus „flackernde Dunkelheit mit Nummernschild-Albtraum“.", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — dein Urteil ist korrekt. Die bisherigen Clips sind **nicht produktionsbrauchbar**. Ich habe sie zu freundlich formuliert. Technisch war der Durchlauf nützlich, ästhetisch war es eher „Auto im Fiebertraum eines kaputten Rücklichts“. Für Content unbrauchbar.\n\nDie wichtigste Korrektur ist jetzt:\n\n```text\nWan 2.2 nicht mehr über Kijai WanVideoWrapper testen,\nsondern über den offiziellen nativen ComfyUI-Wan2.2-Workflow.\n```\n\n---\n\n# Kurzantwort\n\n## Was müssen wir beim T5 Encoder machen?\n\nFür **native ComfyUI Wan2.2**:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nverwenden.\n\nFür **Kijai WanVideoWrapper**:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\n**nicht** verwenden, weil der Wrapper-Node genau daran scheitert:\n\n```text\nInvalid T5 text encoder model, fp8 scaled is not supported by this node\n```\n\nDer Fehler kam nicht von Wan2.2 selbst, sondern vom **Wrapper-T5-Loader**.\n\nDaher:\n\n```text\nWan2.2 Qualitätstest = native ComfyUI Nodes + scaled T5\nWrapper = nur noch fallback/experimentell\n```\n\n---\n\n# Zielsetup für Qualität\n\nWir wechseln auf den offiziellen nativen Workflow:\n\n```text\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n```\n\nDer nutzt laut offizieller ComfyUI-Doku:\n\n```text\nComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist genau der Punkt: **scaled T5 ist richtig, aber nur im nativen Workflow.**\n\n---\n\n# 1. Offizielle native Modelle installieren\n\nAuf dem GPU-Host:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n```\n\nOrdner:\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\n```\n\n## Wan2.2 TI2V 5B FP16\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors \\\n  ~/ai/ComfyUI/models/diffusion_models/\n```\n\n## Native scaled T5 Encoder\n\nFalls schon vorhanden, trotzdem prüfen. Sonst:\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors \\\n  ~/ai/ComfyUI/models/text_encoders/\n```\n\n## Native Wan2.2 VAE\n\n```bash\nhf download Comfy-Org/Wan_2.2_ComfyUI_Repackaged \\\n  split_files/vae/wan2.2_vae.safetensors \\\n  --local-dir /tmp/wan22_native\n```\n\nKopieren:\n\n```bash\ncp /tmp/wan22_native/split_files/vae/wan2.2_vae.safetensors \\\n  ~/ai/ComfyUI/models/vae/\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\n---\n\n# 2. Startbefehl für Qualität\n\nBitte **nicht** mit `--lowvram`.\n\n```bash\npkill -f \"python main.py\"\n\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\"\n[ -z \"$TS_IP\" ] && TS_IP=\"127.0.0.1\"\n\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\nDas lässt ComfyUI Dynamic VRAM / native Offloading nutzen.\n\n---\n\n# 3. Was ist mit RAM-Offload?\n\nJa, grundsätzlich geht das.\n\nAber wichtig:\n\n```text\nVRAM-Offload in RAM ≠ magischer VRAM-Erweiterungsspeicher\n```\n\nEs bedeutet:\n\n- Modellteile werden zwischen GPU-VRAM und System-RAM verschoben.\n- Das verhindert OOM.\n- Es ist langsamer.\n- Wenn RAM voll wird und Linux in Swap geht, wird es sehr langsam oder wirkt aufgehängt.\n\nDein Host hat:\n\n```text\n31 GiB RAM\n16 GiB VRAM\nSwap nur ca. 1.9 GiB\n```\n\nFür Qualitätstests mit Wan2.2 ist das okay, aber ich würde als Sicherheitsnetz den Swap erhöhen.\n\n---\n\n# 4. Swap-Sicherheitsnetz einrichten\n\nDas macht nichts schneller. Es verhindert nur, dass der Prozess bei RAM-Spitzen hart stirbt.\n\n## Prüfen\n\n```bash\nswapon --show\nfree -h\ndf -h /\n```\n\nWenn du mindestens 40–60 GB freien Plattenplatz hast, empfehle ich 32 GB Swapfile.\n\n## 32 GB Swapfile anlegen\n\n```bash\nsudo fallocate -l 32G /swapfile-comfy\nsudo chmod 600 /swapfile-comfy\nsudo mkswap /swapfile-comfy\nsudo swapon /swapfile-comfy\n```\n\nPersistieren:\n\n```bash\necho '/swapfile-comfy none swap sw 0 0' | sudo tee -a /etc/fstab\n```\n\nSwappiness runtersetzen, damit Linux nicht unnötig früh swappt:\n\n```bash\necho 'vm.swappiness=10' | sudo tee /etc/sysctl.d/99-comfyui-swappiness.conf\nsudo sysctl -p /etc/sysctl.d/99-comfyui-swappiness.conf\n```\n\nPrüfen:\n\n```bash\nfree -h\nswapon --show\n```\n\nZiel:\n\n```text\nRAM: 31 GiB\nSwap: 32 GiB+\n```\n\nWichtig: Wenn du wenig freien Diskspace hast, nimm lieber 16 GB:\n\n```bash\nsudo fallocate -l 16G /swapfile-comfy\n```\n\n---\n\n# 5. Was ich in ComfyUI einstellen würde\n\nIm nativen Wan2.2 Workflow:\n\n## Modelle\n\n```text\nLoad Diffusion Model:\n  wan2.2_ti2v_5B_fp16.safetensors\n\nLoad CLIP / Text Encoder:\n  umt5_xxl_fp8_e4m3fn_scaled.safetensors\n\nLoad VAE:\n  wan2.2_vae.safetensors\n```\n\n## Startparameter für Qualitätstest\n\nNicht 720p sofort. Erst:\n\n```text\n416x736\n25 Frames\n8 Steps\n```\n\nWenn das stabil und sichtbar besser ist:\n\n```text\n480x832\n25 Frames\n8 Steps\n```\n\nDann:\n\n```text\n480x832\n33 Frames\n10–12 Steps\n```\n\nFür Qualität ist später möglich:\n\n```text\n576x1024\n33 Frames\n12–16 Steps\n```\n\nAber erst, wenn der native Workflow saubere Bilder liefert.\n\n---\n\n# 6. Warum die bisherigen Clips so schlecht waren\n\n## Grund 1: Kijai Wrapper statt native Workflow\n\nDer Wrapper ist mächtig, aber für Standard-Wan2.2 offenbar nicht der sauberste Weg. Besonders wegen:\n\n```text\nscaled T5 nicht unterstützt\nkomische Modellvariant-Logs\nVAE-Decoding OOM\n```\n\n## Grund 2: FP8 scaled Model im Wrapper\n\nWir nutzten:\n\n```text\nTI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n```\n\nDas ist effizient, aber möglicherweise nicht der beste Qualitätsbaseline-Test.\n\nFür Qualität testen wir jetzt:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\n```\n\nmit nativer Offloading-Logik.\n\n## Grund 3: Text-to-Video ist für konkrete Szenen schwach\n\nFür „ein Auto in Garage“ kann T2V funktionieren, aber stabiler ist:\n\n```text\nImage-to-Video\n```\n\nAlso:\n\n1. Erst ein gutes Standbild erzeugen oder auswählen.\n2. Dann Wan2.2 TI2V als I2V nutzen.\n3. Bewegung hinzufügen: slow push-in, light sweep, handheld motion.\n\nDas ist für Content-Produktion deutlich besser als reines T2V. Ein Modell ein Auto „erfinden“ zu lassen ist Glücksspiel; ein Modell ein gutes Auto-Bild zu bewegen ist Regie.\n\n---\n\n# 7. Empfohlenes neues Produktionsprinzip\n\n## Nicht mehr\n\n```text\nText prompt → Video direkt\n```\n\n## Sondern\n\n```text\nPrompt → gutes Keyframe/Standbild\nKeyframe → Wan2.2 Image-to-Video\nVideo → Captions/SFX/Mechanism-Layer\n```\n\nDas verbessert:\n\n- Objektklarheit\n- Komposition\n- Marken-/Kennzeichenkontrolle\n- weniger unlesbare Textartefakte\n- bessere Retention-Visuals\n\nFür unser Auto-Beispiel:\n\n```text\nStable Diffusion / Flux / SDXL erzeugt sauberes Auto-in-Garage Keyframe\n→ Nummernschild leer/unscharf/verdeckt\n→ Wan2.2 I2V macht Push-in/Light Sweep\n```\n\nDas ist viel näher an brauchbarem Content.\n\n---\n\n# 8. Was du jetzt konkret tun sollst\n\n## A — Offizielle native Modelle laden\n\nFühre die drei Downloads oben aus.\n\n## B — ComfyUI neu starten\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --reserve-vram 1.0\n```\n\n## C — GUI öffnen\n\n```text\nhttp://100.101.173.25:8188\n```\n\nDann:\n\n```text\nWorkflow → Browse Templates → Video → Wan2.2 5B video generation\n```\n\nFalls du den Workflow geladen hast:\n\n```text\nWorkflow → Export (API)\n```\n\noder speichere ihn als JSON und gib mir den Pfad/Inhalt.\n\nDann kann ich ihn sauber über die API steuern.\n\n---\n\n# Klare Antwort auf deine Fragen\n\n## Was tun beim T5 Encoder?\n\nFür native Wan2.2:\n\n```text\numt5_xxl_fp8_e4m3fn_scaled.safetensors\n```\n\nFür Kijai Wrapper:\n\n```text\nnicht den scaled T5 nutzen\n```\n\nWir wechseln daher zu native Wan2.2.\n\n## Geht RAM-Offload?\n\nJa:\n\n```text\nComfyUI Dynamic VRAM / native Wan Offloading kann Modellteile in RAM auslagern.\n```\n\nAber:\n\n```text\nWenn RAM voll → Swap → langsam / scheinbar aufgehängt.\n```\n\nDaher:\n\n```text\n32 GB Swapfile als Sicherheitsnetz\n--reserve-vram 1.0 oder 2.0\nkein --lowvram als Standard\n```\n\n## Wird Qualität dadurch besser?\n\nNicht durch RAM-Offload selbst. Qualität wird besser durch:\n\n```text\nnative Wan2.2 Workflow\nfp16 5B Modell\nscaled T5\nbessere Prompts\nvor allem Image-to-Video statt reinem Text-to-Video\n```\n\nDas ist der eigentliche Weg raus aus „flackernde Dunkelheit mit Nummernschild-Albtraum“."}], "id": "msg_0d1a4d10654d56b4016a11dfe04ab0819187802e278f71cf66", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] jetzt haben wir aber auch modelle und bestandteile runtergeladen, welche ic wieder löschen kann, oder? ich habe nämlich nicht mehr viel speicherplatz...", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n  commands: [\\\"python3\\\"]\\nsetup:\\n  help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n  hermes:\\n    tags:\\n      - comfyui\\n      - image-generation\\n      - stable-diffusion\\n      - flux\\n      - sd3\\n      - wan-video\\n      - hunyuan-video\\n      - creative\\n      - generative-ai\\n      - video-generation\\n    related_skills: [stable-diffusion-image-generation, image_gen]\\n    category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n  editor format to API format: Reroute bypass, dotted dynamic-input keys\\n  (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n  free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n  Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n  you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool)        │\\n│   Setup, server lifecycle, custom nodes, models     │\\n│   → comfy install / launch / stop / node / model    │\\n└─────────────────────────┬───────────────────────────┘\\n                          │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts         │\\n│   Workflow execution, param injection, monitoring   │\\n│   POST /api/prompt, GET /api/view, WS /ws           │\\n│   → run_workflow.py, run_batch.py, ws_monitor.py    │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n  the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n  must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflow_api.json \\\\\\n  --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n  --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflow_api.json \\\\\\n  --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n  --host https://cloud.comfy.org \\\\\\n  --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n  --workflow flux_dev.json \\\\\\n  --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n  --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n  --workflow sdxl_img2img.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n  --workflow sdxl.json \\\\\\n  --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n  --count 8 --randomize-seed --parallel 3 \\\\\\n  --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n  \\\"status\\\": \\\"success\\\",\\n  \\\"prompt_id\\\": \\\"abc-123\\\",\\n  \\\"outputs\\\": [\\n    {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n     \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n  ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install <name>` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url <url> --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id <id>` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py <prompt_id>` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n>   zero setup. Requires an API key (paid subscription required to actually run\\n>   workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n>   - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n>   - AMD GPU with ROCm support (Linux), OR\\n>   - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n>   - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict    | Meaning                                                       | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok`       | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon)       | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely                  | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud`    | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n   ```bash\\n   export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n   ```\\n4. Run workflows:\\n   ```bash\\n   python3 scripts/run_workflow.py \\\\\\n     --workflow workflows/flux_dev_txt2img.json \\\\\\n     --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n     --host https://cloud.comfy.org \\\\\\n     --output-dir ./outputs\\n   ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia              # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd                 # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series            # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu                 # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps  # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background                       # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190    # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats      # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n  --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n  --relative-path models/checkpoints \\\\\\n  --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack             # popular utility pack\\ncomfy node install comfyui-animatediff-evolved     # video generation\\ncomfy node install comfyui-controlnet-aux          # ControlNet preprocessors\\ncomfy node install comfyui-essentials              # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json   # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sd15_txt2img.json \\\\\\n  --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n  --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sdxl_img2img.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sdxl_inpaint.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --input-image mask_image=./mask.png \\\\\\n  --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n  -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n  -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n  -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n  follow it and strip `X-API-Key` before fetching from the storage backend\\n  (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n  - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n    paid only.\\n  - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n    automatically).\\n  - `/models/<folder>` is renamed to `/experiment/models/<folder>` on cloud\\n    (the scripts route automatically).\\n  - `clientId` in WebSocket is currently ignored — all connections for a\\n    user receive the same broadcast. Filter by `prompt_id` client-side.\\n  - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n  automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}'    # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt                      # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n   API-format workflow JSON. The scripts detect editor format (top-level\\n   `nodes` and `links` arrays) and tell you to re-export via\\n   \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n   `comfy launch --background` starts one. Verify with\\n   `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n   `check_deps.py` does fuzzy matching (with/without extension and folder\\n   prefix), but the workflow itself must use the canonical name. Use\\n   `comfy model list` to discover what's installed. For WanVideoWrapper,\\n   `/object_info` is especially useful: inspect loader dropdowns to verify\\n   files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n   and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n   If it warns that it is deprecated, use `hf download <repo> <file> --local-dir <dir>`\\n   instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n   prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n   Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n   Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n   a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n   ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n   GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n   `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n   See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n   isn't installed. `check_deps.py` reports which package to install;\\n   `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n   If commands fail with \\\"no workspace found\\\", use\\n   `comfy --workspace /path/to/ComfyUI <command>` or\\n   `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n   `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n   `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n   is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n   900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n   passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n   Keep this protection on — workflows with custom save nodes can produce\\n   arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n   submitting an unknown workflow has the same trust profile as `eval`.\\n   Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n    `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n    The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n    Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n    `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. On 16 GB, keep native/offload enabled and start at 416x736/25f/8–12 steps; if decode OOM occurs, reduce frames/resolution or use VAE tiling / CPU VAE as emergency. See `references/wan-video-a5000-optimization.md` and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n      `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n      on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/rest-api.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}", "tool_call_id": "call_yNzc8VNOMAQ7IcTmw7huWALJ", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Ja, Sir. Da liegt inzwischen einiges herum, was wir **gefahrlos aufräumen** können — besonders die Wrapper-/Test-Modelle, wenn wir jetzt auf den **offiziellen nativen Wan2.2-Workflow** wechseln.\n\nWichtig: Ich kann den Speicher auf deinem GPU-Host nicht direkt per Shell sehen, aber anhand unserer Downloads kann ich dir sehr klar sagen, was weg kann.\n\n---\n\n# Kurzfassung\n\n## Behalten für native Wan2.2 Qualitätstests\n\nDiese drei bitte behalten:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\n~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist der offizielle native ComfyUI-Wan2.2-Stack.\n\n---\n\n## Kannst du löschen, wenn wir Kijai/WanVideoWrapper nicht weiter testen\n\nDiese Dateien sind für unsere nächsten Schritte nicht mehr nötig:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nOptional auch:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nAber diese drei würde ich nur löschen, wenn du wirklich Platz brauchst. Sie sind unser schneller Wan2.1-Fallback.\n\n---\n\n# Erst prüfen: Was ist groß?\n\nBitte auf dem GPU-Host ausführen:\n\n```bash\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\n```\n\nUnd für alle Safetensors:\n\n```bash\nfind ~/ai/ComfyUI/models -type f -name \"*.safetensors\" -exec ls -lh {} \\; | sort -k5 -h\n```\n\nFalls du HuggingFace-Cache prüfen willst:\n\n```bash\ndu -h -d 2 ~/.cache/huggingface | sort -h\n```\n\nDer Cache kann riesig sein, weil `hf download` Dateien teils zusätzlich im Cache hält. Charmant, wie immer: einmal Modell laden, zweimal Speicher verlieren. Fortschritt.\n\n---\n\n# Sichere Löschliste: Wrapper-Wan2.2 entfernen\n\nWenn wir jetzt offiziell native Wan2.2 nutzen, kannst du löschen:\n\n```bash\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nDas spart ungefähr:\n\n```text\n~4.9 GB + ~1.3 GB = ~6.2 GB\n```\n\nFalls der Ordner danach leer ist:\n\n```bash\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n```\n\n---\n\n# Temporäre Download-Ordner löschen\n\nWir haben native Modelle nach `/tmp/wan22_native` geladen und dann kopiert. Den temporären Ordner kannst du löschen:\n\n```bash\nrm -rf /tmp/wan22_native\nrm -rf /tmp/wan22_text_encoder\n```\n\nJe nachdem, was noch dort liegt, spart das mehrere GB.\n\nPrüfen vorher:\n\n```bash\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\n```\n\nDann löschen:\n\n```bash\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n```\n\n---\n\n# HuggingFace Cache bereinigen\n\nDas kann viel bringen.\n\n## Cache-Größe anzeigen\n\n```bash\ndu -sh ~/.cache/huggingface 2>/dev/null\n```\n\n## Variante A — interaktiv aufräumen\n\n```bash\nhf cache scan\nhf cache prune\n```\n\n## Variante B — wenn du aggressiv Platz brauchst\n\nNur machen, wenn alle benötigten Modelle bereits in `~/ai/ComfyUI/models/...` liegen:\n\n```bash\nrm -rf ~/.cache/huggingface/hub\n```\n\nDanach sind Downloads nicht weg aus ComfyUI, sofern du sie kopiert hast. Nur der Download-Cache ist weg.\n\nVorher prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nWenn die drei existieren: Cache darf weg.\n\n---\n\n# Optional: alte Testvideos löschen\n\nComfyUI-Outputs können ebenfalls wachsen:\n\n```bash\ndu -sh ~/ai/ComfyUI/output\n```\n\nWenn du die Testvideos nicht mehr brauchst:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -print\n```\n\nDann löschen:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -delete\n```\n\nFalls du wirklich alle Outputs löschen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Optional: Wrapper Custom Node behalten oder löschen?\n\nIch würde den Code erstmal behalten:\n\n```text\n~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nDer Ordner selbst ist wahrscheinlich viel kleiner als die Modelle. Nutzen wir vielleicht später für Spezialfeatures.\n\nNur wenn du radikal aufräumen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nAber: Dann funktionieren die Kijai-Wrapper-Workflows nicht mehr. Für native ComfyUI-Wan2.2 ist das okay.\n\nMeine Empfehlung:\n\n```text\nCustom node behalten\nWrapper-Modelle löschen\n```\n\n---\n\n# Minimaler sauberer Bestand für unseren nächsten Test\n\nWenn du wirklich maximal aufräumen willst und nur native Wan2.2 behalten möchtest, sollte am Ende mindestens das hier existieren:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nOptional behalten für Wan2.1-Fallback:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n# Meine empfohlene Aufräum-Reihenfolge\n\nBitte so:\n\n```bash\n# 1. Größen ansehen\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\ndu -sh ~/.cache/huggingface 2>/dev/null\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\ndu -sh ~/ai/ComfyUI/output 2>/dev/null\n\n# 2. Temporäre Download-Ordner weg\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n\n# 3. Wrapper-Wan2.2-Modelle weg\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n\n# 4. HF Cache leeren, falls Modelle in ComfyUI/models vorhanden sind\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n\nrm -rf ~/.cache/huggingface/hub\n\n# 5. Optional Outputs löschen\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Nicht löschen\n\nDiese bitte **nicht** löschen, wenn wir gleich native Wan2.2 testen:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\nwan2.2_vae.safetensors\n```\n\n---\n\n# Geschätztes Einsparpotenzial\n\nJe nachdem, wie `hf download` gecached hat:\n\n```text\nWrapper-Wan2.2 löschen:       ~6 GB\n/tmp Downloads löschen:       ~10–17 GB möglich\nHF Cache löschen:             ~10–25+ GB möglich\nComfyUI outputs löschen:      variabel\n```\n\nRealistisch kannst du wahrscheinlich:\n\n```text\n20–40 GB\n```\n\nzurückholen.\n\nKurz: **Ja, wir können einiges löschen. Behalte nur den offiziellen nativen Wan2.2-Stack; die Kijai-Wan2.2-Testmodelle und Download-Caches können weg.**", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir. Da liegt inzwischen einiges herum, was wir **gefahrlos aufräumen** können — besonders die Wrapper-/Test-Modelle, wenn wir jetzt auf den **offiziellen nativen Wan2.2-Workflow** wechseln.\n\nWichtig: Ich kann den Speicher auf deinem GPU-Host nicht direkt per Shell sehen, aber anhand unserer Downloads kann ich dir sehr klar sagen, was weg kann.\n\n---\n\n# Kurzfassung\n\n## Behalten für native Wan2.2 Qualitätstests\n\nDiese drei bitte behalten:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\n~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nDas ist der offizielle native ComfyUI-Wan2.2-Stack.\n\n---\n\n## Kannst du löschen, wenn wir Kijai/WanVideoWrapper nicht weiter testen\n\nDiese Dateien sind für unsere nächsten Schritte nicht mehr nötig:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\n~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nOptional auch:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nAber diese drei würde ich nur löschen, wenn du wirklich Platz brauchst. Sie sind unser schneller Wan2.1-Fallback.\n\n---\n\n# Erst prüfen: Was ist groß?\n\nBitte auf dem GPU-Host ausführen:\n\n```bash\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\n```\n\nUnd für alle Safetensors:\n\n```bash\nfind ~/ai/ComfyUI/models -type f -name \"*.safetensors\" -exec ls -lh {} \\; | sort -k5 -h\n```\n\nFalls du HuggingFace-Cache prüfen willst:\n\n```bash\ndu -h -d 2 ~/.cache/huggingface | sort -h\n```\n\nDer Cache kann riesig sein, weil `hf download` Dateien teils zusätzlich im Cache hält. Charmant, wie immer: einmal Modell laden, zweimal Speicher verlieren. Fortschritt.\n\n---\n\n# Sichere Löschliste: Wrapper-Wan2.2 entfernen\n\nWenn wir jetzt offiziell native Wan2.2 nutzen, kannst du löschen:\n\n```bash\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\n```\n\nDas spart ungefähr:\n\n```text\n~4.9 GB + ~1.3 GB = ~6.2 GB\n```\n\nFalls der Ordner danach leer ist:\n\n```bash\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n```\n\n---\n\n# Temporäre Download-Ordner löschen\n\nWir haben native Modelle nach `/tmp/wan22_native` geladen und dann kopiert. Den temporären Ordner kannst du löschen:\n\n```bash\nrm -rf /tmp/wan22_native\nrm -rf /tmp/wan22_text_encoder\n```\n\nJe nachdem, was noch dort liegt, spart das mehrere GB.\n\nPrüfen vorher:\n\n```bash\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\n```\n\nDann löschen:\n\n```bash\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n```\n\n---\n\n# HuggingFace Cache bereinigen\n\nDas kann viel bringen.\n\n## Cache-Größe anzeigen\n\n```bash\ndu -sh ~/.cache/huggingface 2>/dev/null\n```\n\n## Variante A — interaktiv aufräumen\n\n```bash\nhf cache scan\nhf cache prune\n```\n\n## Variante B — wenn du aggressiv Platz brauchst\n\nNur machen, wenn alle benötigten Modelle bereits in `~/ai/ComfyUI/models/...` liegen:\n\n```bash\nrm -rf ~/.cache/huggingface/hub\n```\n\nDanach sind Downloads nicht weg aus ComfyUI, sofern du sie kopiert hast. Nur der Download-Cache ist weg.\n\nVorher prüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nWenn die drei existieren: Cache darf weg.\n\n---\n\n# Optional: alte Testvideos löschen\n\nComfyUI-Outputs können ebenfalls wachsen:\n\n```bash\ndu -sh ~/ai/ComfyUI/output\n```\n\nWenn du die Testvideos nicht mehr brauchst:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -print\n```\n\nDann löschen:\n\n```bash\nfind ~/ai/ComfyUI/output -type f \\( -name \"*.mp4\" -o -name \"*.png\" -o -name \"*.webp\" \\) -mtime +0 -delete\n```\n\nFalls du wirklich alle Outputs löschen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Optional: Wrapper Custom Node behalten oder löschen?\n\nIch würde den Code erstmal behalten:\n\n```text\n~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nDer Ordner selbst ist wahrscheinlich viel kleiner als die Modelle. Nutzen wir vielleicht später für Spezialfeatures.\n\nNur wenn du radikal aufräumen willst:\n\n```bash\nrm -rf ~/ai/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper\n```\n\nAber: Dann funktionieren die Kijai-Wrapper-Workflows nicht mehr. Für native ComfyUI-Wan2.2 ist das okay.\n\nMeine Empfehlung:\n\n```text\nCustom node behalten\nWrapper-Modelle löschen\n```\n\n---\n\n# Minimaler sauberer Bestand für unseren nächsten Test\n\nWenn du wirklich maximal aufräumen willst und nur native Wan2.2 behalten möchtest, sollte am Ende mindestens das hier existieren:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n```\n\nOptional behalten für Wan2.1-Fallback:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\nls -lh ~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n# Meine empfohlene Aufräum-Reihenfolge\n\nBitte so:\n\n```bash\n# 1. Größen ansehen\ndu -h -d 2 ~/ai/ComfyUI/models | sort -h\ndu -sh ~/.cache/huggingface 2>/dev/null\ndu -sh /tmp/wan22_native /tmp/wan22_text_encoder 2>/dev/null\ndu -sh ~/ai/ComfyUI/output 2>/dev/null\n\n# 2. Temporäre Download-Ordner weg\nrm -rf /tmp/wan22_native /tmp/wan22_text_encoder\n\n# 3. Wrapper-Wan2.2-Modelle weg\nrm -i ~/ai/ComfyUI/models/diffusion_models/TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors\nrm -i ~/ai/ComfyUI/models/vae/Wan2_2_VAE_bf16.safetensors\nrmdir ~/ai/ComfyUI/models/diffusion_models/TI2V 2>/dev/null || true\n\n# 4. HF Cache leeren, falls Modelle in ComfyUI/models vorhanden sind\nls -lh ~/ai/ComfyUI/models/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors\nls -lh ~/ai/ComfyUI/models/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors\nls -lh ~/ai/ComfyUI/models/vae/wan2.2_vae.safetensors\n\nrm -rf ~/.cache/huggingface/hub\n\n# 5. Optional Outputs löschen\nrm -rf ~/ai/ComfyUI/output/*\n```\n\n---\n\n# Nicht löschen\n\nDiese bitte **nicht** löschen, wenn wir gleich native Wan2.2 testen:\n\n```text\nwan2.2_ti2v_5B_fp16.safetensors\numt5_xxl_fp8_e4m3fn_scaled.safetensors\nwan2.2_vae.safetensors\n```\n\n---\n\n# Geschätztes Einsparpotenzial\n\nJe nachdem, wie `hf download` gecached hat:\n\n```text\nWrapper-Wan2.2 löschen:       ~6 GB\n/tmp Downloads löschen:       ~10–17 GB möglich\nHF Cache löschen:             ~10–25+ GB möglich\nComfyUI outputs löschen:      variabel\n```\n\nRealistisch kannst du wahrscheinlich:\n\n```text\n20–40 GB\n```\n\nzurückholen.\n\nKurz: **Ja, wir können einiges löschen. Behalte nur den offiziellen nativen Wan2.2-Stack; die Kijai-Wan2.2-Testmodelle und Download-Caches können weg.**"}], "id": "msg_0d1a4d10654d56b4016a11e3c0b0f881919f0d3413117f61f7", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] ok, erstelle nun nochmals ein Testvideo. Ein Hause hüpft über eine Wiese. 5sekunden lang.", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_WZ5kGbLMD3CdNjZMaWJNHWdy", "call_id": "call_WZ5kGbLMD3CdNjZMaWJNHWdy", "response_item_id": "fc_0d1a4d10654d56b4016a11e51a1e648191ba1bb75e6b0d35c2", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\nobj=json.load(urllib.request.urlopen(base+'/object_info', timeout=20))\\nprint('server ok nodes', len(obj))\\nfor name in ['UNETLoader','CLIPLoader','VAELoader','CLIPTextEncode','Wan22ImageToVideoLatent','KSampler','SamplerCustomAdvanced','BasicScheduler','BasicGuider','RandomNoise','VAEDecode','CreateVideo','SaveVideo','EmptySD3LatentImage','EmptyLatentImage']:\\n    if name in obj:\\n        print('\\\\n##', name, obj[name].get('display_name'))\\n        for group in ['required','optional']:\\n            print(group)\\n            for k,v in obj[name]['input'].get(group,{}).items(): print(' ',k, str(v)[:300])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Deciding on video creation**\n\nI need to figure out the best approach for creating a test video with ComfyUI. I'll have to check if we have a native model available and consider using the current server setup. It looks like I might need to work with the Kijai wrapper for this, especially for the house jumping scene. Since the video is short (5 seconds at 12 fps, giving 61 frames), I also need to ensure the frame size fits well. Maybe vertical will work best, given the context!\n**Considering video parameters**\n\nI could use a resolution of 416x736 with 61 frames and 8 steps. If that doesn't work, I might try a fallback of 49 frames at 10 fps, which gives about 4.9 seconds. Since the user requested 5 seconds, the 49 frames would be close enough. I'll check if the Wan2.2 5B wrapper is suitable. I need to inspect the native nodes for any models that could fit into this specific workflow, like those for loading diffusion or CLIP processes. Let’s see what options I have!", "reasoning_content": "**Deciding on video creation**\n\nI need to figure out the best approach for creating a test video with ComfyUI. I'll have to check if we have a native model available and consider using the current server setup. It looks like I might need to work with the Kijai wrapper for this, especially for the house jumping scene. Since the video is short (5 seconds at 12 fps, giving 61 frames), I also need to ensure the frame size fits well. Maybe vertical will work best, given the context!\n**Considering video parameters**\n\nI could use a resolution of 416x736 with 61 frames and 8 steps. If that doesn't work, I might try a fallback of 49 frames at 10 fps, which gives about 4.9 seconds. Since the user requested 5 seconds, the 49 frames would be close enough. I'll check if the Wan2.2 5B wrapper is suitable. I need to inspect the native nodes for any models that could fit into this specific workflow, like those for loading diffusion or CLIP processes. Let’s see what options I have!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEeUaizhN3r-6rw8NIlK1yi2u9vzjvvAsa1vWfZX3MoN4YYkTgnVy510PE4wQ2dDGTB8fWEC-nTvd-kh8f7VmUQRqqCIIi2_Gb3UpWnhMdMAPHyzvczai3fWAB6rpxSUAkhWfoYiNdoy9UXTuSErlzPuQ040X7z1G1-CDCYxDXH0fwee02pM5TBKKYydKxjsCg4GZwmfQ7pMrAUlOJAGPtnpf0I8b4c9bpo9tTkPhzScQT4UXwADT-4O8t9HgvDpVJvF16IvkIJswSLrHCxkPGx5l1QqWXFMI5fW_0OIS1JhgiYMgZuixpNA97s0D_kWQi2WroYayjzReVrNK0I2HEN_gzBYZIqnrrJA0jG11j4tbNA2Rd-MyiUmRvNr4HSczvdNweO6L83LGlldJD9ewIplLWcIb95iN7pSJexrcGdTUryu20DMfsM99gEo3xYEKu_cTYuz4bW1hXbMYbY4K-YRIfJybiUT0nIia_C_kSIJAhpVtqmjTXIO0EZnYxkRY6QrUcnyV3r_HRj9odKyDJRjf43pWR6UqDzet_Au4KlyqSZjjksG9nXL7ZBi2O6py32lJqC_sgUurWgX-n_SoNDkdlXJcvyovM0GvdOnRY0PIyOVBIjyKEm_hpXeIDdOR9ik-fPFEDvBIh4gUk7jXk42TSBl9pZYZebbJEWVnwbbpySuQX4S-oc3-ry72ZoaHdypOTDNUoFof3-NqhVi7xC2xcAfQ5HZMyCmMw_whFs8S8orjs5JMhhKSGtIGSBuzfKro34ey8Uy7IeoNGq4diTPy77RbVZtQv6yGqHBo18k3fj3RGoyq8dhkB4EI-t8dN0pTjHuanMXzqhbUBcidQW5nceEPVLcMZHsQQQTLinIZvTHOmbhDJ6gUPElCmOwKyzjVGjtrRhAAburETE7p0nLqfeSPtJMaEGpFVH-Z9mc-ZTcjAMPsGICkyiJFx7sJR73LqRIyk8b0smpDKfipVmkgE1brTV5eVlFIbelVjV6FVc9wZfGfq0TQ-UqQV1fYLWcqJlAubpucWRYiLvEDSNbnG8Fl8ry-XC0GTJEPMH_3GNuuKkEQ37tlnwv84gDrhJi6NR_uKuvHOiY8TCrb8BbDfYCg_P7QFAUOnRFkkdhLEaDR65x2iB0737SMNMDNyHIC8AfhSZei-xcUruLuTa47AgZmX-KWwL56Kq7Byxgp6BlTYz-A4q137jQFYC5EFktLpc2uB0g99uz0dn1vak8rDpEMmwA4_OhA9-kh5MGnqh3RDMbkpv9UaHcOeQrFtJoVg94pxVCRfvgr2uyHmheOpNYv4-k5EnxVcT5GKdyaHEDyi1_vMCs8c5rSrGNmn9Ub5ebjmAk-hxJSGUe5gGI39O2HnQrU9nBsKNQ1h9XbubRur-6Woka7lVKOdYK58l5X0JbfbKw0n3m3qHhfyala_S1BDIHQ73FUPSAh4Pp7aqCD9fkxFBZoqIwYCuEytF3cYXDut648e9dAE_IjL7SR5rYbtMzYnFuUsd_MSSoSScP3h2jO-LFiQ-jHEb3h_QQtemoSevTD_ry3yfSbLJpSfCWDoM2-euiFgItxI023zEu5yKYNoQzw-IO0VJwbrw9GPhWhFwLiS2g6bzODvJ3l5LDJPAvrhqgCJOGJr66KsKv7-xHIgsb6O1PyVb0jqorIhl2d5SrOZFd_KXkmZT2iaBXLNQYRQdH9dbda76cUFR7r1uEjCtfpDV8X7DuwViV6iR75NypOi77kmsOxkZE4qXyl5juOYCD-xZGQ0FGUjYr3rHRtLRWhByUtyBVN2H1jN814dku1aEKMv8ojnlxAsHh6SKFiKpn4imUJTupw7G4XEp7BJAId7FAhj6CpHkiuDgbXDNl7iTLIrunSthERkEhHInSJ8B_6X7FQ2-93lls1YvCuyLB2ethvh8SXlg4CkWnGMiljyNG9LFKi0oqa7DpJsDg2BEjyGTDXD3WTdGJLKyW1RuZPT-BoqKMsaAMIuNeVNbEd0T9h3TIt-03z8NCtOIUrUhWUSgbnSjXLkzvRXIIcFNdGNex3NFsXj8m0eHYGrqP8kK4udEJSQ6Fr86DQBdgMkSDVRBj6p0Fib8P0Jyl6fyDJSKSBx5VsToiI5s2bV3aThDG00avlAcsHgKc_Gf1d1AwEu0fPsDkkJCNW4B6Xm2Pekvuvt8YWyg7JU58AsjdrV3TyUlO_PpOPRTYrXZ8L1KG1Y0_79uMHkxFW3NCrd6H-EXWbr9k8x2vQxuwMgrpne5SzDbt1ReZrh706gwkaYf6tcqyB1NsMoASwLwjr70zqPeIA56Ola0Ftn8Mf0fcxB5WtIQPzFSX0j9IJTTjyd5M5KKqH96YzLJzPShh7u7uPlORbVFWvyMLhzJsE_iSb66R46PLb0uDkFjOJiqG3Yx_MlC_cTKk96L5Q3Sg0ki0z-JR3S54ZHbXmxIXGu9FjLXrNVBcO1l5LwJhJ7re0H-GXGoU0BOxknvcJX6mJL0LL20FK1Rq8kThPcs-kr-acg8M5DXfoPOJbeLDgrEqnlUm6W-zUwH1kCQyAEjJK5B_6vf3FzpC5jROyzoeuTymdegTDwkhDPh7pAlJrHbIU3Y10FuYgxuFDp_RVRL-R9zQnO5PphmMPEOwSDR3jnMrffJq69oyD_nHVbQ1jhYuAHfmV6-Zs988FKja_jyC63NRTkoXnykOdZQuhF_uZzN2ARgCfsuyMMVFcL1dYLxFrNObkzK_Fqp59QmThPMCeIL7RQ3Ilfi46FWgq695dhIFZWlIonDdPbz2DtbBpb4oDGBX412yn3PUTw0x_gmEHolshND-eDzkFWiQ6Id4ivde6lSQDcfWkXV9md9jVCk_uglqaKobxHrH4umKgsho9_spSw3znv8DsI03DiQvQlBL0PSLLZdlUBARlTHNGX36yYvgk9ziBViJ5CS0HLVw-M0qACelqbPe_J12uFVh1pBd1PgygMFBs0ykDrT4FRkNk-jblk8AYAbb2iVPG33JtbZww0wGBdoVN4rSK", "id": "rs_0d1a4d10654d56b4016a11e50f71088191a6446d2a1d4a4842", "summary": [{"type": "summary_text", "text": "**Deciding on video creation**\n\nI need to figure out the best approach for creating a test video with ComfyUI. I'll have to check if we have a native model available and consider using the current server setup. It looks like I might need to work with the Kijai wrapper for this, especially for the house jumping scene. Since the video is short (5 seconds at 12 fps, giving 61 frames), I also need to ensure the frame size fits well. Maybe vertical will work best, given the context!"}, {"type": "summary_text", "text": "**Considering video parameters**\n\nI could use a resolution of 416x736 with 61 frames and 8 steps. If that doesn't work, I might try a fallback of 49 frames at 10 fps, which gives about 4.9 seconds. Since the user requested 5 seconds, the 49 frames would be close enough. I'll check if the Wan2.2 5B wrapper is suitable. I need to inspect the native nodes for any models that could fit into this specific workflow, like those for loading diffusion or CLIP processes. Let’s see what options I have!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"server ok nodes 875\\n\\n## UNETLoader Load Diffusion Model\\nrequired\\n  unet_name [['Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors', 'wan2.2_ti2v_5B_fp16.safetensors']]\\n  weight_dtype [['default', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e5m2'], {'advanced': True}]\\noptional\\n\\n## CLIPLoader Load CLIP\\nrequired\\n  clip_name [['umt5-xxl-enc-fp8_e4m3fn.safetensors', 'umt5_xxl_fp8_e4m3fn_scaled.safetensors']]\\n  type [['stable_diffusion', 'stable_cascade', 'sd3', 'stable_audio', 'mochi', 'ltxv', 'pixart', 'cosmos', 'lumina2', 'wan', 'hidream', 'chroma', 'ace', 'omnigen2', 'qwen_image', 'hunyuan_image', 'flux2', 'ovis', 'longcat_image', 'cogvideox']]\\noptional\\n  device [['default', 'cpu'], {'advanced': True}]\\n\\n## VAELoader Load VAE\\nrequired\\n  vae_name [['Wan2_1_VAE_bf16.safetensors', 'wan2.2_vae.safetensors', 'pixel_space']]\\noptional\\n\\n## CLIPTextEncode CLIP Text Encode (Prompt)\\nrequired\\n  text ['STRING', {'multiline': True, 'dynamicPrompts': True, 'tooltip': 'The text to be encoded.'}]\\n  clip ['CLIP', {'tooltip': 'The CLIP model used for encoding the text.'}]\\noptional\\n\\n## Wan22ImageToVideoLatent None\\nrequired\\n  vae ['VAE', {}]\\n  width ['INT', {'default': 1280, 'min': 32, 'max': 16384, 'step': 32}]\\n  height ['INT', {'default': 704, 'min': 32, 'max': 16384, 'step': 32}]\\n  length ['INT', {'default': 49, 'min': 1, 'max': 16384, 'step': 4}]\\n  batch_size ['INT', {'default': 1, 'min': 1, 'max': 4096}]\\noptional\\n  start_image ['IMAGE', {}]\\n\\n## KSampler KSampler\\nrequired\\n  model ['MODEL', {'tooltip': 'The model used for denoising the input latent.'}]\\n  seed ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615, 'control_after_generate': True, 'tooltip': 'The random seed used for creating the noise.'}]\\n  steps ['INT', {'default': 20, 'min': 1, 'max': 10000, 'tooltip': 'The number of steps used in the denoising process.'}]\\n  cfg ['FLOAT', {'default': 8.0, 'min': 0.0, 'max': 100.0, 'step': 0.1, 'round': 0.01, 'tooltip': 'The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality.'}]\\n  sampler_name [['euler', 'euler_cfg_pp', 'euler_ancestral', 'euler_ancestral_cfg_pp', 'heun', 'heunpp2', 'exp_heun_2_x0', 'exp_heun_2_x0_sde', 'dpm_2', 'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive', 'dpmpp_2s_ancestral', 'dpmpp_2s_ancestral_cfg_pp', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m', 'dpmpp_2m_cfg_p\\n  scheduler [['simple', 'sgm_uniform', 'karras', 'exponential', 'ddim_uniform', 'beta', 'normal', 'linear_quadratic', 'kl_optimal'], {'tooltip': 'The scheduler controls how noise is gradually removed to form the image.'}]\\n  positive ['CONDITIONING', {'tooltip': 'The conditioning describing the attributes you want to include in the image.'}]\\n  negative ['CONDITIONING', {'tooltip': 'The conditioning describing the attributes you want to exclude from the image.'}]\\n  latent_image ['LATENT', {'tooltip': 'The latent image to denoise.'}]\\n  denoise ['FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01, 'tooltip': 'The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling.'}]\\noptional\\n\\n## SamplerCustomAdvanced None\\nrequired\\n  noise ['NOISE', {}]\\n  guider ['GUIDER', {}]\\n  sampler ['SAMPLER', {}]\\n  sigmas ['SIGMAS', {}]\\n  latent_image ['LATENT', {}]\\noptional\\n\\n## BasicScheduler None\\nrequired\\n  model ['MODEL', {}]\\n  scheduler ['COMBO', {'multiselect': False, 'options': ['simple', 'sgm_uniform', 'karras', 'exponential', 'ddim_uniform', 'beta', 'normal', 'linear_quadratic', 'kl_optimal']}]\\n  steps ['INT', {'default': 20, 'min': 1, 'max': 10000}]\\n  denoise ['FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}]\\noptional\\n\\n## BasicGuider Basic Guider\\nrequired\\n  model ['MODEL', {}]\\n  conditioning ['CONDITIONING', {}]\\noptional\\n\\n## RandomNoise None\\nrequired\\n  noise_seed ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615, 'control_after_generate': True}]\\noptional\\n\\n## VAEDecode VAE Decode\\nrequired\\n  samples ['LATENT', {'tooltip': 'The latent to be decoded.'}]\\n  vae ['VAE', {'tooltip': 'The VAE model used for decoding the latent.'}]\\noptional\\n\\n## CreateVideo Create Video\\nrequired\\n  images ['IMAGE', {'tooltip': 'The images to create a video from.'}]\\n  fps ['FLOAT', {'default': 30.0, 'min': 1.0, 'max': 120.0, 'step': 1.0}]\\noptional\\n  audio ['AUDIO', {'tooltip': 'The audio to add to the video.'}]\\n\\n## SaveVideo Save Video\\nrequired\\n  video ['VIDEO', {'tooltip': 'The video to save.'}]\\n  filename_prefix ['STRING', {'tooltip': 'The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.', 'default': 'video/ComfyUI', 'multiline': False}]\\n  format ['COMBO', {'tooltip': 'The format to save the video as.', 'default': 'auto', 'multiselect': False, 'options': ['auto', 'mp4']}]\\n  codec ['COMBO', {'tooltip': 'The codec to use for the video.', 'default': 'auto', 'multiselect': False, 'options': ['auto', 'h264']}]\\noptional\\n\\n## EmptySD3LatentImage None\\nrequired\\n  width ['INT', {'default': 1024, 'min': 16, 'max': 16384, 'step': 16}]\\n  height ['INT', {'default': 1024, 'min': 16, 'max': 16384, 'step': 16}]\\n  batch_size ['INT', {'default': 1, 'min': 1, 'max': 4096}]\\noptional\\n\\n## EmptyLatentImage Empty Latent Image\\nrequired\\n  width ['INT', {'default': 512, 'min': 16, 'max': 16384, 'step': 8, 'tooltip': 'The width of the latent images in pixels.'}]\\n  height ['INT', {'default': 512, 'min': 16, 'max': 16384, 'step': 8, 'tooltip': 'The height of the latent images in pixels.'}]\\n  batch_size ['INT', {'default': 1, 'min': 1, 'max': 4096, 'tooltip': 'The number of latent images in the batch.'}]\\noptional\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_WZ5kGbLMD3CdNjZMaWJNHWdy", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_sCtacb5VgVmB8sUIQPHcRxqT", "call_id": "call_sCtacb5VgVmB8sUIQPHcRxqT", "response_item_id": "fc_0d1a4d10654d56b4016a11e5214e348191b4a70309685e1692", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nobj=json.load(urllib.request.urlopen('http://100.101.173.25:8188/object_info', timeout=20))\\nfor name in ['Wan22ImageToVideoLatent','UNETLoader','CLIPLoader','VAELoader','KSampler','VAEDecode','CreateVideo','SaveVideo']:\\n print('\\\\n##', name)\\n print('output', obj[name].get('output'))\\n print('output_name', obj[name].get('output_name'))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEeUhS2TRfg9rXzy-BXxUpoE4Vo8SOgMf-8_2VUNXhKsPN5weZWsa3u0L3OnDZqGkEMq1KZ-MBFRLLtggrEPWdfMPE6EGMtvlOy2SMOsBgXOTOyDcTdzFVoOQLEKL-I3EU9Bo0rVL05v9q8ECZ8P9Dy1gPwoY8lE6k3rK8BXdQxYwstpXNMrqiOlNUNbfCX6ELmW4sQtg50dFLTpIDl1_AAmeFh4V8It_kvIb0M7OqgnTkWY4vLuOyc6oaBxu9iO-NFT4eYq-oxH-0k8WyU4qJHpwxwAhWCURSSPuTOk-2N4C4uXdN0ciW4Kk6WifnYgza0veZyiuVIp1jEutXFnp-kywS_isgcgxp7OgKh2PFtUAkY-4nr-QY-rtecH43OSKZqinnrXLPrJvx0vHjONOpR1mUSxNdEXEbKPQNShFRkrIyXECCfxChD5na1Gph5HnERwh-Hajg8V1eWid7JCvxyhMjT7mw6HS6JwHbCPX2ykIEccitl5E3d9lC65AdpM0PZ4dmlnkBwZWs7BZIjfrxJlhLE5lZeVZtrrhdvvyCsalY-pn5CJ2JS35M532IKEqkP2W15lVi8escaEFJ8P_yCfywiyWbhxO6ANPwfCoBUnXLJxhCUUgNU_r9zoDByiQvEeF-mzIwS_Qt0XOgobw7_m0sPIfZfrUmJ8G0I2sWsIJqSNWM5DfKoV9mbBttPPSsBqVB1zuavyW-clHzW7r4O3Ffs5P3X0PvIauQHhNB-ueI6u4RLOWXntQkpdrqEjOrKpbVVPxwaJJ-C676zVymLkF85ul3baeoGIESLzIeJL8bZZ_Y1d7bSiWySD-gpPkAdDiNDXMmG2hE4a6DMghOHTctEB9lsV_bV86-RAxZ2OZzS3BCJplJ9IdRwnRIcTVoyKAV5Ze4eww14DVt3mVL3ut8LxLMAVgo1ZMkv1vP77-5h1wL-GPWLo3iX-j63bGNZygtCGIZMqdm3HpthwJSxyZ_-Cn_QjSMZBJzrsQpq_uwQMaC3f91T37mAVODJFWCF_oU4vJt1Tzw99SnvTOUUa9ug==", "id": "rs_0d1a4d10654d56b4016a11e51f0d908191930fd68217d544a2", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"## Wan22ImageToVideoLatent\\noutput ['LATENT']\\noutput_name ['LATENT']\\n\\n## UNETLoader\\noutput ['MODEL']\\noutput_name ['MODEL']\\n\\n## CLIPLoader\\noutput ['CLIP']\\noutput_name ['CLIP']\\n\\n## VAELoader\\noutput ['VAE']\\noutput_name ['VAE']\\n\\n## KSampler\\noutput ['LATENT']\\noutput_name ['LATENT']\\n\\n## VAEDecode\\noutput ['IMAGE']\\noutput_name ['IMAGE']\\n\\n## CreateVideo\\noutput ['VIDEO']\\noutput_name ['VIDEO']\\n\\n## SaveVideo\\noutput []\\noutput_name []\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_sCtacb5VgVmB8sUIQPHcRxqT", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_q0dQZI9nseCEJ0Fop6KnHv5g", "call_id": "call_q0dQZI9nseCEJ0Fop6KnHv5g", "response_item_id": "fc_0d1a4d10654d56b4016a11e5303dd881918755f67ebfdf5658", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_native_wan22_house_jump_5s_workflow.json\",\"content\":\"{\\n  \\\"1\\\": {\\n    \\\"class_type\\\": \\\"UNETLoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"unet_name\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n      \\\"weight_dtype\\\": \\\"default\\\"\\n    }\\n  },\\n  \\\"2\\\": {\\n    \\\"class_type\\\": \\\"CLIPLoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip_name\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n      \\\"type\\\": \\\"wan\\\",\\n      \\\"device\\\": \\\"default\\\"\\n    }\\n  },\\n  \\\"3\\\": {\\n    \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip\\\": [\\\"2\\\", 0],\\n      \\\"text\\\": \\\"A whimsical small cozy house with windows and a roof hops joyfully across a bright green meadow, clear blue sky, sunny daylight, playful cinematic animation, the house bends slightly and jumps forward like a cartoon object but rendered with realistic materials, grass moves under each landing, smooth camera tracking, sharp focus, charming, high quality, no text, no logo, no watermark\\\"\\n    }\\n  },\\n  \\\"4\\\": {\\n    \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip\\\": [\\\"2\\\", 0],\\n      \\\"text\\\": \\\"dark, night, blurry, low detail, unreadable text, subtitles, watermark, logo, distorted house, deformed roof, melting shapes, flickering colors, abstract, static image, bad anatomy, extra buildings, camera shake, overexposed\\\"\\n    }\\n  },\\n  \\\"5\\\": {\\n    \\\"class_type\\\": \\\"VAELoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"vae_name\\\": \\\"wan2.2_vae.safetensors\\\"\\n    }\\n  },\\n  \\\"6\\\": {\\n    \\\"class_type\\\": \\\"Wan22ImageToVideoLatent\\\",\\n    \\\"inputs\\\": {\\n      \\\"vae\\\": [\\\"5\\\", 0],\\n      \\\"width\\\": 416,\\n      \\\"height\\\": 736,\\n      \\\"length\\\": 49,\\n      \\\"batch_size\\\": 1\\n    }\\n  },\\n  \\\"7\\\": {\\n    \\\"class_type\\\": \\\"KSampler\\\",\\n    \\\"inputs\\\": {\\n      \\\"model\\\": [\\\"1\\\", 0],\\n      \\\"seed\\\": 2305202601,\\n      \\\"steps\\\": 12,\\n      \\\"cfg\\\": 5.5,\\n      \\\"sampler_name\\\": \\\"uni_pc\\\",\\n      \\\"scheduler\\\": \\\"simple\\\",\\n      \\\"positive\\\": [\\\"3\\\", 0],\\n      \\\"negative\\\": [\\\"4\\\", 0],\\n      \\\"latent_image\\\": [\\\"6\\\", 0],\\n      \\\"denoise\\\": 1.0\\n    }\\n  },\\n  \\\"8\\\": {\\n    \\\"class_type\\\": \\\"VAEDecode\\\",\\n    \\\"inputs\\\": {\\n      \\\"samples\\\": [\\\"7\\\", 0],\\n      \\\"vae\\\": [\\\"5\\\", 0]\\n    }\\n  },\\n  \\\"9\\\": {\\n    \\\"class_type\\\": \\\"CreateVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"images\\\": [\\\"8\\\", 0],\\n      \\\"fps\\\": 10.0\\n    }\\n  },\\n  \\\"10\\\": {\\n    \\\"class_type\\\": \\\"SaveVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"video\\\": [\\\"9\\\", 0],\\n      \\\"filename_prefix\\\": \\\"autoshorts_native_wan22_house_jump_5s\\\",\\n      \\\"format\\\": \\\"mp4\\\",\\n      \\\"codec\\\": \\\"h264\\\"\\n    }\\n  }\\n}\\n\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering workflow setup**\n\nI’m thinking about using \"uni_pc\" with a simple scheduler, 16 steps, and a cfg of 6 for video diffusion. I wonder if I need to avoid swapping. I should also make a workflow and remember to free up memory with /free unload true before submitting. If I run out of memory (OOM), I might need to lower the parameters and consider using background processing. Starting with 49 frames, 12 steps, and a resolution of 416x736 could be a good plan, but I need to have a fallback in case. Let's queue it up!", "reasoning_content": "**Considering workflow setup**\n\nI’m thinking about using \"uni_pc\" with a simple scheduler, 16 steps, and a cfg of 6 for video diffusion. I wonder if I need to avoid swapping. I should also make a workflow and remember to free up memory with /free unload true before submitting. If I run out of memory (OOM), I might need to lower the parameters and consider using background processing. Starting with 49 frames, 12 steps, and a resolution of 416x736 could be a good plan, but I need to have a fallback in case. Let's queue it up!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEeUw0OzeRNTQ17kHIN10iuQ7m2QbPyq_NojPgXSvvjScSCnBx63fYsoUFhN-e8FhkGC2Y9NOTZMtvHvWhtVoxc2-BpNfV3udmmYifRfjHwXQbaQyqRInQIwSowVqQs-LupPX1FT-veZL3px0H3cTlHNU3oqw3rvRLOtauGIOLmJy2T3iCMja8lPEfMxBnNssqYyDcwfGHwgTFbhDLbbYTmEDtnCQIChkFM6OFYBfKkdO2wAixXYEk3H2bS-XS4Uu1CgfGVgaTW0Zrg9FGzAwf_rWQ9IHRoFCOJlAVaSK_kznzdjn8u8ZRzvXTvQ1rG_rn9m66hzPoiq6QPLalSzMcvEXJ08Z1xm7gCepxipEjNxaOIxBMCfZEEz6Z1gqjPjh2gLlHxUJvmfUZ4M8PRumYR1NoDYNUkS0bVx9v3AsesQm5EQ0hTVKSSZV0R9kZ10Eize5OtskEF6Iq9QCd_miTIMonhTFgGtJw68QTfqTDeChu-qqyWKUdzd8DiqHnAgQF76CurPORqtLW3PxxcKx8XftUeidKE-FRxiDkGa7Xix_Wj79gYR497KmomRkXPf9KnxRKn4tawYeUGRCOelc0GXHq9gObAwa5oFFQFJiRuZAz9FCcEeslxnb69NEqvJUljQlqoCgDpR5wUi3hPuI53AZ7j2xvqjbKYxONRYnEVeYMt71UBEEsx8zY_odE53HH7DCcC2rviQ6ptNulP7ZKqUVLDLFEUZHtJpGjLmN4XXObEFmCpAAYwexBhQAueD5mWMbvhuGWBR43IAnz4QlOHfWccAbr1kUA5tUseY5EMbbrIcMQW14Wgfk0wlwcIwB3qtHGIbvCqqKxrOnBxLKKOV3Xzi46CEePHkfPuY3KfIbB7p5gmeo8ZgZEXW6WT8retq2HLN5g7A8KGHOyIeFkRm4mK6PmZAiAs-lIK3mhnlw6GJGKMjhcfjUY-nyprYsCtCiroZRrg9CGd1SyAMVpMltuOOjry3l36MAyS1a5VQOvZwkdSTCK6fINukS8_3V-VX7GkwhR--qShQhnTZ9c4TgKw1O8k5gt-ajEewqY0521PBNI-AkkbE3Lt4etAvNcsPlYPXHGjJ9ISThOEOcf4SNY1e7R6vHenMnu0wk1-uIROZK4LO46Bk99eXiR6cOwH2f_RDJXtnOYNTpLOgXY9IyY45Ww2m6_P_ldpUe4XCSY-eysNYgqB7X7hMVvovffer0fe1i-B2wj0kQ69h4g2jDqXjbedmpucPwKZTSzV1d0FhYwYJiDAoLtHym47h7uBzDRqx8EDB4xOV5gGYwi-dsaz5oMxFNXYcv-GAe-B1Tim-IPVuY9FVbqLuVEb_Ec8nk6YqCSubPALrP6Eb5M_ryZusPb79Qtwqi-Tva7Onj5huIIiF7fRoaP2IioGGkFpadmVO6b9vdo--Gc1uQhI5RebmVfmGbdHDJ7v1YbcGOSAzcSUe4nNvvxoUo12NP7fHIzE8qOivbXHLFuWAkIew1_R9Z-Ndkd9tH15gl84HlM3JfvoWv9prEenMKZqEPT8GybFicRQfEPXNdE6yn-wK7BMs6o37pr9mrTQfPn1C3v-5tH5R-IaMBGvU8H19tvOwTM1a8BwK7Czm-UGpoFidXJ5LM8dbN3BoRKU3pKbAXZKbUKudKvME-aX8amKW60v4o0b51eIjtPIr8Prbp6QbiI68pnHxeekl7T52Pc1FcFF53qFcbK1bBHVG1qcGD64Dvs3sfqkuAS3xZIvaZeMXP3xR5LV7jniSW_LsrafK2oK05UsATV3fH6bbTnAGQGyjNTcXtRH5HiHr3bJE8CkWLNsjXbl7SZwSK0Pgc-uEGlD0QJGWiq3CZCz80jZf8JNmceYjotcC8XXw614ShoNo9YA6_Fb9euOgY1VHJn9V0_Gb1n4Zg0NC7lJpHVQHu6NhwvpAj9yeUMI7mK1w6kQScx9QOyKDB9X8Bgkcx-R_jJHRBLmSPfP5tu5Cmn5XyKFBK5-V67lngfZRRndREtjvQnm7emdsKuE937u2ITdjCuEYD4IOW7aTTTTfwOYWoX-ci-c387LTeisrlsRMKtZ7k6MEqTkNE8ZLkV40CSbxnY2UAcq6e1-1ObESaLX5i7wKpOuZKrMjJFNv6zxoi6eXbT7RJEcjx_9BkBRpldSt1almh8tGGCHH7IVCCUVKW2Qtr4IgijhGCiurzzT3rTttYIXpJpbLUdvZe6RKXfbp7zwL_VO4TilycBraHQZQLatq-UeZscwJz8E0RhgQIZ0dOTWcTftIQPu3UOarSbX0eZcQpk8kyLTFbXuhdGGiFutMtDUeUP57uspXTI-9Gg3frpz14IpTidvfJG9lrA_qgUKXepFU-Mu8j7ZxxP92ZF2nxr_lPwoCpWAS7OjeH-cYKRDXdlD0VKnidgqAr2lmmjUESOGEdmNQ23X30GuIqCx7LKpetKtfAK07W8NDSYKQlmaDXaUQbd8VP-cZKg31MCkEfqoRJF10jRu7wOLoy6-4z6wBAPK0IZGsRYbHac1AyIiNPCI0mIS_KaAf81_w5xxeh_bh-UykeRfCcbD6N2OEXoDq_trGrKdgPNDYi8xb1QA6tUYeeig67j_ymonv3DPZsj0VTGgv-c8XdyqivBmemuudiIIFZRCy_wjQDvLdHVgkQJ50SGWG710UwZMFqZR7sOY8HeZq6t_vZhVvCTXf9NMuOOQJpS9SmwNqa7GNAYTSYrrV2SHPVHUYWRcW2rrqtSczlApIkwJpxUNKdJw9oHGDno_1qvj5zAiLjLcmpALthaTafgHbweXnj0h8jCqFzueF30R4XfSQV7GpFFfW3TcBB9PT59Se0NKKm42NdXI5aQiFXzOpYTJpPqIZp_xbilcP2MWLgFEROWM98hpvNN0TRu-A18ra1rLek-01yQ1IZeHHZnF5HJ0BoK26zmrJyRyWC1YH9FvI_YQFNkp8JtPv9q1mWiDvKdC16bMdjoN9dj32p64Aq17HVBWwomcvEwbHHKUcoxPtD5ZiV65n1nNR7m9ofyI5CXlL88JuR2PO6AguroUq9kJUroVq1qWS5xHBPjycT2yqIJM_HaqLjtT-4s1t1tshOD9nq3Hz2PkNUnaCm4UwqPkCxHjQaIuY4Wp6iIUoVhkQvZdFmE720", "id": "rs_0d1a4d10654d56b4016a11e5256c7c8191b4afaa91ec92c715", "summary": [{"type": "summary_text", "text": "**Considering workflow setup**\n\nI’m thinking about using \"uni_pc\" with a simple scheduler, 16 steps, and a cfg of 6 for video diffusion. I wonder if I need to avoid swapping. I should also make a workflow and remember to free up memory with /free unload true before submitting. If I run out of memory (OOM), I might need to lower the parameters and consider using background processing. Starting with 49 frames, 12 steps, and a resolution of 416x736 could be a good plan, but I need to have a fallback in case. Let's queue it up!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"bytes_written\": 2222, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_q0dQZI9nseCEJ0Fop6KnHv5g", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_VbCUziT2TXfqGnYuluuJU1XU", "call_id": "call_VbCUziT2TXfqGnYuluuJU1XU", "response_item_id": "fc_0d1a4d10654d56b4016a11e542d9708191baf3c0cb87f1275a", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\n# clear/free memory, unload old wrapper models\\ntry:\\n    req=urllib.request.Request(base+'/free', data=json.dumps({'unload_models': True, 'free_memory': True}).encode(), headers={'Content-Type':'application/json'})\\n    urllib.request.urlopen(req, timeout=20).read()\\nexcept Exception as e:\\n    print('free warning', type(e).__name__, e)\\nworkflow=json.load(open('data/comfyui_native_wan22_house_jump_5s_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-native-wan22-house-jump-5s'}).encode()\\nreq=urllib.request.Request(base+'/prompt', data=payload, headers={'Content-Type':'application/json'})\\ntry:\\n    print(urllib.request.urlopen(req, timeout=30).read().decode())\\nexcept Exception as e:\\n    print('ERROR', type(e).__name__, e)\\n    if hasattr(e, 'read'):\\n        print(e.read().decode())\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"857930e6-e187-4959-8d9d-af78cd8f04b6\\\", \\\"number\\\": 0, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_VbCUziT2TXfqGnYuluuJU1XU", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Need monitor.", "tool_calls": [{"id": "call_hbK0ALkTKywYhmn36SJFOepu", "call_id": "call_hbK0ALkTKywYhmn36SJFOepu", "response_item_id": "fc_0d1a4d10654d56b4016a11e54d106081919b92a345c4b45be5", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request, sys\\nbase='http://100.101.173.25:8188'\\npid='857930e6-e187-4959-8d9d-af78cd8f04b6'\\nfor i in range(220):\\n    q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n    s=json.load(urllib.request.urlopen(base+'/system_stats', timeout=10))\\n    running=len(q.get('queue_running', [])); pending=len(q.get('queue_pending', []))\\n    d=s['devices'][0]\\n    vused=(d['vram_total']-d['vram_free'])/1024**3\\n    print(f'poll {i:03d}: running={running} pending={pending} vram_used_gb={vused:.2f} ram_free_gb={s[\\\"system\\\"][\\\"ram_free\\\"]/1024**3:.2f}', flush=True)\\n    if not running and not pending:\\n        break\\n    time.sleep(10)\\nh=json.load(urllib.request.urlopen(base+'/history/'+pid, timeout=20))\\nprint('STATUS')\\nprint(json.dumps(h.get(pid, {}).get('status',{}), indent=2)[:8000])\\nprint('OUTPUTS')\\nprint(json.dumps(h.get(pid, {}).get('outputs',{}), indent=2))\\nPY\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need monitor."}], "id": "msg_0d1a4d10654d56b4016a11e54cbb048191aa689f05f7c6f789", "phase": "commentary"}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"poll 000: running=1 pending=0 vram_used_gb=8.79 ram_free_gb=16.62\\npoll 001: running=1 pending=0 vram_used_gb=14.22 ram_free_gb=17.07\\npoll 002: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.06\\npoll 003: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.03\\npoll 004: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.02\\npoll 005: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.00\\npoll 006: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.11\\npoll 007: running=1 pending=0 vram_used_gb=13.81 ram_free_gb=17.08\\npoll 008: running=1 pending=0 vram_used_gb=13.80 ram_free_gb=17.10\\npoll 009: running=1 pending=0 vram_used_gb=12.42 ram_free_gb=16.93\\npoll 010: running=1 pending=0 vram_used_gb=10.80 ram_free_gb=16.93\\npoll 011: running=1 pending=0 vram_used_gb=10.55 ram_free_gb=16.91\\npoll 012: running=0 pending=0 vram_used_gb=9.24 ram_free_gb=16.75\\nSTATUS\\n{\\n  \\\"status_str\\\": \\\"success\\\",\\n  \\\"completed\\\": true,\\n  \\\"messages\\\": [\\n    [\\n      \\\"execution_start\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"857930e6-e187-4959-8d9d-af78cd8f04b6\\\",\\n        \\\"timestamp\\\": 1779557704851\\n      }\\n    ],\\n    [\\n      \\\"execution_cached\\\",\\n      {\\n        \\\"nodes\\\": [],\\n        \\\"prompt_id\\\": \\\"857930e6-e187-4959-8d9d-af78cd8f04b6\\\",\\n        \\\"timestamp\\\": 1779557704853\\n      }\\n    ],\\n    [\\n      \\\"execution_success\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"857930e6-e187-4959-8d9d-af78cd8f04b6\\\",\\n        \\\"timestamp\\\": 1779557837105\\n      }\\n    ]\\n  ]\\n}\\nOUTPUTS\\n{\\n  \\\"10\\\": {\\n    \\\"images\\\": [\\n      {\\n        \\\"filename\\\": \\\"autoshorts_native_wan22_house_jump_5s_00001_.mp4\\\",\\n        \\\"subfolder\\\": \\\"\\\",\\n        \\\"type\\\": \\\"output\\\"\\n      }\\n    ],\\n    \\\"animated\\\": [\\n      true\\n    ]\\n  }\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_hbK0ALkTKywYhmn36SJFOepu", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Need download, inspect.", "tool_calls": [{"id": "call_IfrWUl2l1Dl8evn9QTISwZjs", "call_id": "call_IfrWUl2l1Dl8evn9QTISwZjs", "response_item_id": "fc_0d1a4d10654d56b4016a11e5d1c3388191bc6b7381d5b2964b", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_native_wan22_house_jump_5s_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'subfolder':'','type':'output'})\\nout=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/'+fn)\\nout.parent.mkdir(parents=True, exist_ok=True)\\nout.write_bytes(urllib.request.urlopen(base+'/view?'+params, timeout=180).read())\\nprint(out, out.stat().st_size)\\nPY\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=nw=1:nk=1 data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\\nmkdir -p /tmp/autoshorts_comfy_check && ffmpeg -y -i data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4 -vf \\\"select=eq(n\\\\,24)\\\" -frames:v 1 /tmp/autoshorts_comfy_check/house_jump_mid.jpg >/dev/null 2>&1 && ls -lh /tmp/autoshorts_comfy_check/house_jump_mid.jpg\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need download, inspect."}], "id": "msg_0d1a4d10654d56b4016a11e5d119b48191a53d45ba79765e96", "phase": "commentary"}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4 120676\\n416\\n736\\n10/1\\n4.900000\\n-rw-rw-r-- 1 agent agent 15K Mai 23 19:37 /tmp/autoshorts_comfy_check/house_jump_mid.jpg\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_IfrWUl2l1Dl8evn9QTISwZjs", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_9eveZ2vZBMQbv5Su9Cd5IQuF", "call_id": "call_9eveZ2vZBMQbv5Su9Cd5IQuF", "response_item_id": "fc_0d1a4d10654d56b4016a11e5dafa1c819192c4d55aeefbf00f", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/house_jump_mid.jpg\",\"question\":\"Beurteile den Frame knapp: ist ein Haus auf/über einer Wiese erkennbar? Wirkt es qualitativ brauchbar oder noch unbrauchbar? Nenne sichtbare Probleme.\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beurteile den Frame knapp: ist ein Haus auf/über einer Wiese erkennbar? Wirkt es qualitativ brauchbar oder noch unbrauchbar? Nenne sichtbare Probleme.\n[screenshot]", "tool_call_id": "call_9eveZ2vZBMQbv5Su9Cd5IQuF", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Erledigt, Sir.\n\nIch habe jetzt **nochmals ein Testvideo mit dem nativen Wan2.2-Workflow** erzeugt — nicht mehr über den Kijai Wrapper.\n\n## Testvideo\n\nPrompt:\n\n```text\nEin kleines gemütliches Haus hüpft fröhlich über eine helle grüne Wiese.\n```\n\nTechnik:\n\n```text\nModell: native Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nAuflösung: 416x736\nFrames: 49\nFPS: 10\nDauer: 4.9s\nSteps: 12\n```\n\nWarum 4.9s statt exakt 5.0s:\n\n```text\nWan arbeitet hier sauber mit 49 Frames.\n49 Frames / 10 fps = 4.9 Sekunden\n```\n\nDas ist praktisch 5 Sekunden.\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n\n---\n\n## Ergebnisbewertung\n\nBesser als die Wrapper-Testclips.\n\nMan erkennt:\n\n- helle Wiese\n- hüpfende kleine Figur / hausähnliches Objekt\n- klare Bewegung\n- cartoonhafter Stil\n\nAber ehrlich:\n\n```text\nEs ist noch kein sauber erkennbares “Haus hüpft über Wiese”-Video.\n```\n\nDas Modell hat daraus eher eine **niedliche Cartoon-Hausfigur** gemacht. Außerdem ist der Himmel stark überbelichtet. Nicht katastrophal, aber noch nicht kontrolliert genug.\n\nDer entscheidende technische Fortschritt ist:\n\n```text\nnative Wan2.2 läuft\nscaled T5 funktioniert\nVideo kommt sauber zurück\nGPU wurde genutzt\nkein Wrapper-T5-Fehler\nkein Crash\n```\n\n---\n\n## Laufzeit / GPU\n\nJob lief erfolgreich durch.\n\nPeak laut Monitoring:\n\n```text\nVRAM used: ca. 14.2 GB\nRAM frei: ca. 17 GB\n```\n\nDas bestätigt:\n\n```text\nNative Wan2.2 nutzt deine A5000 deutlich besser.\n```\n\n---\n\n## Nächste Qualitätsverbesserung\n\nFür bessere Kontrolle sollten wir jetzt nicht weiter reines Text-to-Video würfeln, sondern:\n\n```text\n1. Erst ein gutes Keyframe erzeugen:\n   klares kleines Haus auf Wiese\n\n2. Dann Wan2.2 Image-to-Video:\n   Haus hüpft 5 Sekunden über Wiese\n```\n\nDas wird sehr wahrscheinlich deutlich besser, weil das Modell dann nicht erst raten muss, wie das Haus aussieht.\n\nKurz:\n\n```text\nT2V = Glücksspiel\nI2V = Regie\n```\n\nUnd wir wollen Regie, nicht GPU-Lotto.", "reasoning": "**Finalizing media details**\n\nI need to finalize the media details and mention that it rendered in 4.9 seconds instead of exactly 5 due to a 49-frame step constraint. It seems the user might want to create something recognizable, but the quality looks more like a cute cartoon object with a house-like appearance, complete with a roof and legs. The meadow is clear, but the sky is overexposed. Maybe I should suggest upscaling or clarify what the user is looking for in the final output.", "reasoning_content": "**Finalizing media details**\n\nI need to finalize the media details and mention that it rendered in 4.9 seconds instead of exactly 5 due to a 49-frame step constraint. It seems the user might want to create something recognizable, but the quality looks more like a cute cartoon object with a house-like appearance, complete with a roof and legs. The meadow is clear, but the sky is overexposed. Maybe I should suggest upscaling or clarify what the user is looking for in the final output.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEeXnMJus9biJLHMwbsDqz_mn4NoD3RUbNha0dTLy5ZYysjPYc_6HbCALJJ0ZpdxxvKwf8vZ21mil8s7u9WAepDFhMsyQsoL92AbfHJunZx0Gc1GlXolPF7_63hatx0MW8k9eM9fpSMLEnGZ8PDVGW-Ld1GiHjw4t2M1MbUcYYlWMhAJKW-LqfMT-A7afoVRDdBl6CaR4_jy9xJyX7MXY7Pm3ZXZ2he-aUuzVj227GJd4o9KngJQkot1NOOTxBBtMYb-bBQ3zf7CyIKjUIJ46IM8aNJjxsEczoyh6QN0lPmA0PjTHg5N-wFoHkXpFkP6kd3B7T1MHx04ulzbuBgfqp2gGAe9quUAT2qP4WvzFKuAoTK9qshGyBv_nwvZtu-_LLvDROMjDHWnt60i3IkYcFKs19kbUQez9wH_A5SUW--EV4926P_ymTlaEKPdnripfNDG1bSi3j538xPvDdzx9RChDKcMvsHRADzZZ589YpuVYvhTc2X9Jif5oIBM3Yy_xfJTP7-Wc1aVdktQe9FS-lO39FXYuYhAOktW4nkc7ODX8cVqqC3jTI5Iczbp-B3jRDmgQPAqTNJ4blXmEpKaUpdarUudEizzU-ezvnJ1pTfFRg1J8svMwLgVRF6dNKN02HrVBhJvjrXe0p45wEWQzqgRaf21KLupHA0kFcjqtEV2VzrrRs9i3j4ovQM3h19CzCNyyI6jc-8NXinGvqSg_ghRIrUEp2r1VxEFg3SGpcKt6UR5vL6h5Jpgsfd9SOPgVlpl9FQ9p_XiVbHJ23K2d0iJjyusEH-ZBxbs_R9FbiRnM8Rt3m86-skxM0cZaa-1WewRQe0S4lJTi57rNMwi-8rqfcJ566iQRG5t2NfLBqt4KEE3wiSbVNh0wJPuR3INoDXaJ0VXjb1h_87Lov49RTkvUcyOMYA_oD8X1lK6Igbhc6TN5PZo51J2XDKQYAjp1Gwuw--1iIo4BqpWKZ0L5kbT7DgPkeyAHkF7k-lrTiR_Go2i18vNlSqPE1m0iqp1pLlf1n0kCo9bn80YZS0YddSSbO4xGEycms1giMdxkZb9q75ljiviH1Nz1KDm76yclDsX6edXNvw8V73lXCnrzLRoFLIXAu3AvwHgwrGIxsdsYYoSNOZ2yaVv36_mV-l6KJgY-RAx_W5blZqR1ijgkH0wIX92gMq4N9PDfYSPKuWzHBm8v6bFBUVf2-JaWsZA7Lim8MpPTkV5im9zdhtn2OmIMgElIAX11wFtr91QCV6Tzvhx0QXIj_SbElMF3y46zmUby98i1E8kqWy1uS4AWiOPqWUoLBTndpi6CTEAwGS9IriDAvgjXEWq0USpNN3PrsK9-MqdjKH-cPrjUoTrGGlgmuZSqJcWOV8JPAzdMV34oXoEVLpdvV9KYjI_2y4roR9jCIx_gs265zdzg-XcJvg93n1YGn1bZbk3zzbiK-6OK51K1VSzKuR9dEbbBd45EEyrEWF1EuC3jrj311KVfH53nU1yJTeJKHWlHdrC2S2dP7QtGmaGSVleLSMOONk-lyF-7dFrBDTVCAe2Xfu5lOsi19DPH01QXFUtGkvsAhX4uSrxOb-JmBJwLQaQ_KOEsydDl", "id": "rs_0d1a4d10654d56b4016a11e5e37c94819194f147ae17fe0962", "summary": [{"type": "summary_text", "text": "**Finalizing media details**\n\nI need to finalize the media details and mention that it rendered in 4.9 seconds instead of exactly 5 due to a 49-frame step constraint. It seems the user might want to create something recognizable, but the quality looks more like a cute cartoon object with a house-like appearance, complete with a roof and legs. The meadow is clear, but the sky is overexposed. Maybe I should suggest upscaling or clarify what the user is looking for in the final output."}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\nIch habe jetzt **nochmals ein Testvideo mit dem nativen Wan2.2-Workflow** erzeugt — nicht mehr über den Kijai Wrapper.\n\n## Testvideo\n\nPrompt:\n\n```text\nEin kleines gemütliches Haus hüpft fröhlich über eine helle grüne Wiese.\n```\n\nTechnik:\n\n```text\nModell: native Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nAuflösung: 416x736\nFrames: 49\nFPS: 10\nDauer: 4.9s\nSteps: 12\n```\n\nWarum 4.9s statt exakt 5.0s:\n\n```text\nWan arbeitet hier sauber mit 49 Frames.\n49 Frames / 10 fps = 4.9 Sekunden\n```\n\nDas ist praktisch 5 Sekunden.\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_house_jump_5s_00001_.mp4\n\n---\n\n## Ergebnisbewertung\n\nBesser als die Wrapper-Testclips.\n\nMan erkennt:\n\n- helle Wiese\n- hüpfende kleine Figur / hausähnliches Objekt\n- klare Bewegung\n- cartoonhafter Stil\n\nAber ehrlich:\n\n```text\nEs ist noch kein sauber erkennbares “Haus hüpft über Wiese”-Video.\n```\n\nDas Modell hat daraus eher eine **niedliche Cartoon-Hausfigur** gemacht. Außerdem ist der Himmel stark überbelichtet. Nicht katastrophal, aber noch nicht kontrolliert genug.\n\nDer entscheidende technische Fortschritt ist:\n\n```text\nnative Wan2.2 läuft\nscaled T5 funktioniert\nVideo kommt sauber zurück\nGPU wurde genutzt\nkein Wrapper-T5-Fehler\nkein Crash\n```\n\n---\n\n## Laufzeit / GPU\n\nJob lief erfolgreich durch.\n\nPeak laut Monitoring:\n\n```text\nVRAM used: ca. 14.2 GB\nRAM frei: ca. 17 GB\n```\n\nDas bestätigt:\n\n```text\nNative Wan2.2 nutzt deine A5000 deutlich besser.\n```\n\n---\n\n## Nächste Qualitätsverbesserung\n\nFür bessere Kontrolle sollten wir jetzt nicht weiter reines Text-to-Video würfeln, sondern:\n\n```text\n1. Erst ein gutes Keyframe erzeugen:\n   klares kleines Haus auf Wiese\n\n2. Dann Wan2.2 Image-to-Video:\n   Haus hüpft 5 Sekunden über Wiese\n```\n\nDas wird sehr wahrscheinlich deutlich besser, weil das Modell dann nicht erst raten muss, wie das Haus aussieht.\n\nKurz:\n\n```text\nT2V = Glücksspiel\nI2V = Regie\n```\n\nUnd wir wollen Regie, nicht GPU-Lotto."}], "id": "msg_0d1a4d10654d56b4016a11e5e763dc819185c2ee331d82ed46", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] Nutze es nun per ,image to video‘. Image kannst du per chatgpt pro erstellen lassen. Es soll ein niedlicher haase über das feld hüpfen. Photorealistisch!", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_nvH0SkN4zjZS9ewEtbimt4t6", "call_id": "call_nvH0SkN4zjZS9ewEtbimt4t6", "response_item_id": "fc_0d1a4d10654d56b4016a11eb092a488191a34138a8b84949ed", "type": "function", "function": {"name": "image_generate", "arguments": "{\"aspect_ratio\":\"portrait\",\"prompt\":\"Photorealistic vertical 9:16 image of a cute small wild rabbit in a lush green meadow, standing in mid-hop pose with ears up, soft golden daylight, shallow depth of field, realistic fur detail, natural grass and tiny wildflowers, clear bright sky, cinematic nature photography, sharp focus on rabbit, clean background, no text, no watermark, no logo\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEesJZ8mLLfGkNuooTfBPNNMZafJk9EnnLs9xsE9xWxNYxUsQ8eEgWs2truNmIqqYYvTIio07D_JUxkHROs_W7jj4sl3ig2rdV4uS4p0EoxyNSKASW9qTf1np1kpkJQm5xUZAS0UdsvvnN3CaNpz1ke1FeMDiIEhfcs_8leCnzhIi4A2qqgeto0hikGc2I7AkqnL3fnB_yBQTELOeOBIWc7n0dD3n4DGwRqQdJbx7_K_SoxvPjUAHuQIYukNMXV8CSLQMrFAts7ElLgV6rZOKXn2WVplqGZuuqx0ohJhKkty2jSK6gm7630CGwzrxSYeNdmMOlw_n4deUsUh0f0j-CvuLGNaWgjS9bBHHrmxh9xEMgFqsnoAxa_tlKUGhTUyRKGYucbshUvEUnr5f2GV4wjt0xjbOG6YIAwiqph6pWi69DGaeobXS2yVixAANbngB4WDCEYvci_ZcqZlE-GdV_4CRVuD5gcDTLgsk7hcgBHLjjV5ic-Y_gXw4f16PLZtnSyopH9HeusnQJEQSZMvm7oRcRfvRvB3VJEclR_gjvoVYApum0I_L85OgD9OZYOGMqKL_Yaln3kjWn5Fwp4Rkr8Zi4ZFNbGEHJ-rUrINhtenyWm_YFwfwsBFuZMoPWQ9p7CIwDodQPVZz8CEX2A86nMezvpqguTzK3YkmlfhDZondmOJKhHOUU_QDE6M5aff52pqgLkmFijyu4JGFci5Cw-9JFTP8pWQj5yZjwQbQupGGlHSneL78Zl3I97yZcVCumr2DntereosGed6X9ZntRdTLHLa5F42nGRfhttqMshrPEtfAL6Zs5YWt_fWZOKXolRVRjrb-1ncBKT0u-kKmASy6tWOS4uDADQTf80xoQqL2cNDiWpuZtsgvNGnLRbJxW-VJ9MmIOKZ3mhGlxJs6zmRLJLh7F0MWSLFhmjK8nmLpr4GhjPEKmZ1eB6BjT4nRVFKSAb8s7II31KOulKe07OgaPKxpexl_6fmOU1YkUnFLpwCtx9OsZq_fU7v9MqrJXld0PSg7Ody4vY7M3HhCOrnvnPL4I9oQH2gkTB1aFr3VUDbERGgSog75SjAbw9EAk2cOMQuGb23WuSoBpXVl7SKxhRDSflWcVZ9oL_13GoYmQbVZIj-i7wc6HGhmP0abbqq0NXgFt9-kzeljZj0gj2s6Ji9AmUcmjqGJFXAteiYn33ifDDWdK3iIu6saXjdtquwmafAKpvwiQ2oPPD2njGjF5-QRNIjiIl8VWYR-nxTQrU4ay-imcDP1V9Ws_K5r5Kr8NIytCdFKgwZlSGr9GLdwskd5uiJFD-_v9_n5pDSlX_A0U2u-Nlviy-VGJzWD-IruzAFnNEnvhS62zCiID4NosdHCc5mqRDZyt1nHsicBUdgTDO2Jwx84CRLbH70pDLYLD1mgrbPWKWIZsbezrqov2K-2XmrOzpAwV0fol9WYPWVWPZA6cY9XGdRaqlf8-USdQgCZQFRN5H3fJumrn_wcAoQgtP-BLpRmoMK7vIJmq_7T13qLO-AsTQr-L_ToV2bK_Co7p9szCeMvPyBOJV2dg5U89wCiqDypM7eytJovvbbnZlA4xdVo7WdUQeDT9d-VTh5S0xJlYzFaTI_HKGmK5xwmL1qU2PMbrkbG9tjYx7fk25u9McXugNQ02qcHUS68StcEClW7RsXZlwRPTW7mN8ewxF0Cv0iUYN9f2j3blGNYE59dY2pUYXO0-JKJZI2ytS4ZizJ_MJD0hzXVSXK3hCTseTKIHZBdsWJeGJ95PuGjWmkbyjY2mwZaTjuJ1A7BqhumNXvuluvFlrZeJERTiVMSbi-JcVQ74IjaRpKDnfSEier6CT4e4q13Nk6P7q7xuzy97nQd-hUhd4OKaYGwFLDWTniHEPr0vnPmanmPDiiG5FDMoDEn6HAXe5cFhCVlWPBoy--cGysPxrVc57If5BWRjE4ZH4KembkZtXV6uTu1jIJgcHuO5NyCV01rxfaMQubteAr5YXr4b9HWdn7xv0OsK3gLRowYN84yzx0ioCo=", "id": "rs_0d1a4d10654d56b4016a11eb053d9c8191859fac8cf25a60d4", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\n  \"success\": false,\n  \"image\": null,\n  \"error\": \"Image generation is unavailable in this environment.\\n\\nMissing requirements:\\n  - FAL_KEY environment variable is not set\\n\\nTo enable image generation, do one of:\\n  1. Get a free API key at https://fal.ai and set FAL_KEY=<your-key> (then restart the session)\\n  3. Configure a different image_gen provider via `hermes tools` → Image Generation (run `hermes plugins list` to see installed backends)\",\n  \"error_type\": \"ValueError\"\n}", "tool_call_id": "call_nvH0SkN4zjZS9ewEtbimt4t6", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_yVTKqSrLTYZtM4RFZ9uBT9M7", "call_id": "call_yVTKqSrLTYZtM4RFZ9uBT9M7", "response_item_id": "fc_0d1a4d10654d56b4016a11eb13fe908191a6c4712a8bbc415e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, json\\nquery='photograph rabbit meadow grass'\\nurl='https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch='+urllib.parse.quote(query)+'&gsrnamespace=6&gsrlimit=10&prop=imageinfo&iiprop=url|extmetadata&format=json'\\nprint(url)\\ndata=json.load(urllib.request.urlopen(url,timeout=20))\\nfor page in data.get('query',{}).get('pages',{}).values():\\n    title=page.get('title')\\n    info=(page.get('imageinfo') or [{}])[0]\\n    print('\\\\n',title)\\n    print(info.get('url'))\\n    meta=info.get('extmetadata',{})\\n    print(meta.get('LicenseShortName',{}).get('value'), meta.get('Artist',{}).get('value','')[:80])\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Exploring image generation options**\n\nI'm considering a fallback since the user wants an image, but I can't create one directly. I might think about local image generation using ComfyUI, but I need to check for the right models. I could also look for public domain images, like a cute rabbit photo from Wikimedia Commons, which seems like the safest option. It’s best if I search for it via the API to find something fitting!", "reasoning_content": "**Exploring image generation options**\n\nI'm considering a fallback since the user wants an image, but I can't create one directly. I might think about local image generation using ComfyUI, but I need to check for the right models. I could also look for public domain images, like a cute rabbit photo from Wikimedia Commons, which seems like the safest option. It’s best if I search for it via the API to find something fitting!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEesUB3jIoJLoBhJlOR4-qNI0hjPOSUpPsWJcIkj2qfiomDLn9BZ7_56pV68oQpdSklfpBtznhLfws01zwp0fFPHKDXYvh5mxeSVksawa_zNx3-q5ERT3FuZChlDgRuvAOxtnUWG9v__DEm73sAKQSz7zRx-MtOSY07KVZFmIKUqoCzG44kRNVmgjdf_WWgAC-RIU0owOp5DgXzlnBkjvIdUXAV-D9P3FLvCBXth_DvMsjPpZd03n5gTvbunaEUzCyuC0h6c7uM3ewPTPRfR4965eeHhjVv5_nHgaOpL8u1Ll5TFP1K3h0WfD4koFTe2ytnamzSl308-fQKnZVSyGe3B79QEUUPf7rsZgQhRbsRXELcNQX8CnBJZ8JBTa851WJVDlZPtwVQYhT1y0zlhKhl0ryoWRvnHEq8Mhi0CM8LnKLRdRElsom8XtujTHMhIjEGaXTmOKfL2KybZ45758m63fKHuj6-BgaBr7TL257ZeOl-anpbcuF1wVy5jxA9Dne8ieQYttZj2z5m56mlCNgbG0yahDQGWg2djZoByx8KGVR2OY-nbzwvPf9vPYw2ZwyKdIQTueTOdgYo0mcNNWDZFO04Iz677L3PWx4mY5kHuQk845JaNUWzFKfX7c6h2YouC5VGCCi7xANa91oNG8SV1_NLCcL7ITw_hgOY3AZQmsJYbMqQTDrO8BvH_80XCh3txD7WsbjrYG3pZX6gvegf8S2lyEHGaxKcDn6oqH9S0T_Otl-bLgfjKFnXIf3gurqYw571EdNhwwj4ubKQXpZHewDPnmiV5IrfAS0gsBR30-NLTbY7YA0IosnyuuimuE1AIDAp8jDEZg5n-HtgkKqN-IBIeCXbgqfuaRhGq1fyNiYI-8r8sYEVJE10pmphn6YSgxRRxBncqVOeo4cNC0VquaxIWJhdp9BWuyM--H_vNQZykmXCKZXi0ZjKkc0cNmR1l5Rpen_WqLK_yF1133e6NPd06eftF33eeZknrHRFlGz3jL8Sl5OFbAkXQ6GRrYIYwBmClvMq5KZkaMHcbJdMBspdVN-oI1fxfbUm0i2JuUKdv0_Y1l0Knwh7ERxmbUEl1g_1awIzaum5_ZbTjKlyq3giZXVtERcmUPzx0SkWUdK1VNiMlwf6HKMyDSZcwbw3ifqbbX5AAFyVZMn_gprP1GBr9g4qUVEzPJwE3YaRuDxPgph6T5aJvkLv1fBAYotoGVTydko4iQYRykuHlVoF_JiCypykJWLxSI3QOImokdtZ1ZlQINn6cjtnMjPqVqulZn3DcdTz8ArHblspyLHGEFg_UTZ2vtJI6DmUIN78AmGNcEq6YKEyuCv5vmnAnHAMfCo1G_-fQ8CJfGTWVGfYtjHa9Y63GC9zrrPbzk-xa6B3b7w6-E0nOTmHx5KNvrZcuyH76Ih06RrG9V_nSuhcFEhL0gmDyfoEl4RmzfCIVRutnTr-zew9kDVLs0EBnaZOMRcSLGuHvjAIu_hqrkS2-4RlSFUqylMygufplQ4N_KN71hkeXFd46GVCIzl7KNiLvY9dcT_ejcM1Rl_rL_cVAPc-jQfRPYwZoBTNYAsX70wafQKyv6yB615k2r_YyiLLZlum-dY21Vnt0yJP63r5KA6GDuL-oX2IVl9mcW3vsq2llcb88-RkviNdjb4HcQFUf7QvaU3MvEjYZ68cWlr6MLb0NwiSwvZJNKt3BwO8VbjGjsUV3qIdME9S2Kleco8VmmFg440J2HRQvPOvM2BnhSSo9aw7XN-E1fuam5ayZ548RC-x7vGxr7ZnRZaLi4S5zkHUVvd7EClLT_xZujHksXQ1cFwxrZv49vClUTpxVIYppXnO3JtLB5ThNgMpQXtqNbcsoiAjzetp7Z9RLN62dHMBGVvwkwjSTvXfC4KBfPuXNHQ3SQRDAe3gR0SSLg_imeu_HyZ-BBQslcIL3T8C7Tnis45RxrSO2UezhR_EyYH0LVApyVAvnq0z6kxmq4djI0szufi8kEBuEIzYVdsuFuUJ-axEhZYvDOhbo96YO4qo-4WfY7R1i_oP_vrqjVmaJoS_94k0EpM0NLGikeIl1XWqM3VCpkJYxeUBC1964xQX0=", "id": "rs_0d1a4d10654d56b4016a11eb0de4b0819182b02f7ea11fdd18", "summary": [{"type": "summary_text", "text": "**Exploring image generation options**\n\nI'm considering a fallback since the user wants an image, but I can't create one directly. I might think about local image generation using ComfyUI, but I need to check for the right models. I could also look for public domain images, like a cute rabbit photo from Wikimedia Commons, which seems like the safest option. It’s best if I search for it via the API to find something fitting!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch=photograph%20rabbit%20meadow%20grass&gsrnamespace=6&gsrlimit=10&prop=imageinfo&iiprop=url|extmetadata&format=json\\nTraceback (most recent call last):\\n  File \\\"<stdin>\\\", line 5, in <module>\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 216, in urlopen\\n    return opener.open(url, data, timeout)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 525, in open\\n    response = meth(req, response)\\n               ^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 634, in http_response\\n    response = self.parent.error(\\n               ^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 563, in error\\n    return self._call_chain(*args)\\n           ^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 496, in _call_chain\\n    result = func(*args)\\n             ^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 643, in http_error_default\\n    raise HTTPError(req.full_url, code, msg, hdrs, fp)\\nurllib.error.HTTPError: HTTP Error 403: Forbidden\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_yVTKqSrLTYZtM4RFZ9uBT9M7", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_ofcTuwzrByUdBEoaCuC9CSSh", "call_id": "call_ofcTuwzrByUdBEoaCuC9CSSh", "response_item_id": "fc_0d1a4d10654d56b4016a11eb31f958819193defa0a23964e98", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, json\\nquery='rabbit grass meadow photo'\\nurl='https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch='+urllib.parse.quote(query)+'&gsrnamespace=6&gsrlimit=15&prop=imageinfo&iiprop=url|extmetadata&format=json'\\nreq=urllib.request.Request(url, headers={'User-Agent':'HermesAutoShorts/1.0 (research)'})\\ndata=json.load(urllib.request.urlopen(req,timeout=20))\\nfor page in data.get('query',{}).get('pages',{}).values():\\n    title=page.get('title')\\n    info=(page.get('imageinfo') or [{}])[0]\\n    meta=info.get('extmetadata',{})\\n    print('\\\\n',title)\\n    print(info.get('url'))\\n    print(meta.get('LicenseShortName',{}).get('value'), meta.get('ImageDescription',{}).get('value','')[:120].replace('\\\\n',' '))\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEesxaHoBfVSX5qQ2ouOSCdolZ5kBFHm2l567JtpnfSDwGKJZJeDf5butuEX5HDdaRe3xF6nXGAdbBUbcBHc_Y3RDdgmGS5m-jvcXAomo7Ecu5gt5x_YnAxURf9Xxl--7yIYIdYeoWehIGkJGhzORzYqMejEydaDKVP9VS2_vEDV8wmngPlX1C66vQvji3044TXqg3cd3J8rcFH-oa89A9ou8J1XY9SLZls0Oi2EaWFzv1wZhk8yCT61naF429-KyTS29wKFEukmGcVSj-xp1tng_Caj4ojvV8qHk8GEqKCOffyjT8dccfioUDCr6xGmoORuMVveFCEZ0sJEqFsZm4De66oUjZVZCLL7g5CpB2diK9dn_BIfFPBuA-VsL8hpOYzc8G5CDHmrugESf_eX74bnht1JZMLrGdN2pTr6-Grk8N0U1Xt0EBE6nzMEK8RNOYW1LsOJEdH9OA2YUdZ295mfF1XMeurpCVAnaGW_4wOXAGtAZtibzAM6ESlVePNuqE0f6Zqb0F87lNxYI5cJPFNwsrJpeHuAbJ5mqsEd0CI0V64gss6yg1v3MwBYdVY7MeEf5TjCQ5DUkQ22iC_yMGGSTcYmnia0BkZQFPHla4dmBzxks7tbjPefTqXn3O0Ukqo0E_Kvh7NppC8AHO94l5bQcH5uvuc5ahJM2DBy3wshZU_OHdK8YCpS6TGr_vnXTmE0A6NpPdGQnM8FN06k_iLw9RmDC3S4Hz8dt2n6nNMBhVUQCZ9Aq0XvHA273cmD6T3LTJCct16gdqRhBhJwh-LDADWKJrQmTkpneVZWFWBr9uF2loirTLmdLZHQrOaHLs4ttceTK9HsDZuZcyxq62DmuenndIDzT4Q8-jjRCrVZYXOt5pc9puqC-SHn1rZ3JRMwa3UweVHG_RQJWdmmmaqBy7OLl8xJ89P9MdQHMN_McI7k=", "id": "rs_0d1a4d10654d56b4016a11eb318e84819197390402900fd6a9", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"File:Blue-eyed grass (Sisyrinchium bellum) (53042926012).jpg\\nhttps://upload.wikimedia.org/wikipedia/commons/f/f5/Blue-eyed_grass_%28Sisyrinchium_bellum%29_%2853042926012%29.jpg\\nPublic domain In the heart of California's Central Coast, the Fort Ord National Monument encompasses a sweeping landscape of vivid bea\\n\\n File:Blue-eyed grass (Sisyrinchium bellum) (53043561329).jpg\\nhttps://upload.wikimedia.org/wikipedia/commons/c/c3/Blue-eyed_grass_%28Sisyrinchium_bellum%29_%2853043561329%29.jpg\\nPublic domain In the heart of California's Central Coast, the Fort Ord National Monument encompasses a sweeping landscape of vivid bea\\n\\n File:Catalog of Copyright Entries, 1918 Works of Art, Photos (IA catalogofcopyrig134libr).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/e/e3/Catalog_of_Copyright_Entries%2C_1918_Works_of_Art%2C_Photos_%28IA_catalogofcopyrig134libr%29.pdf\\nPublic domain  :<b>Title</b>: Catalog of Copyright Entries, 1918 Works of Art, Photos <dl><dd><b>Description</b>: The Catalogs of Copy\\n\\n File:Deák Ferenc Straße 17, Hausrind, Reh, 2023 Szeged.jpg\\nhttps://upload.wikimedia.org/wikipedia/commons/6/60/De%C3%A1k_Ferenc_Stra%C3%9Fe_17%2C_Hausrind%2C_Reh%2C_2023_Szeged.jpg\\nCC BY-SA 4.0 <br> <dl><dd>Two storey modern building. Former? Csongrád County Agriculture Office building. - On its facade: 'Agricult\\n\\n File:Food & fur breeders of America .. (IA foodfurbreederso00food).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/3/38/Food_%26_fur_breeders_of_America_.._%28IA_foodfurbreederso00food%29.pdf\\nPublic domain <div class=\\\"description\\\"> Subjects: Fur-bearing animals; Rabbits; Fur trade</div>\\n\\n File:Gateway to Long Meadow, near Offham - geograph.org.uk - 32946.jpg\\nhttps://upload.wikimedia.org/wikipedia/commons/d/df/Gateway_to_Long_Meadow%2C_near_Offham_-_geograph.org.uk_-_32946.jpg\\nCC BY-SA 2.0 Gateway to Long Meadow, near Offham. Numerous rabbits could be seen grazing in the evening sunshine on this field. They \\n\\n File:Hickman's popcorn flower (Plagiobothrys chorisianus var hickmanii) and Blue-eyed grass (Sisyrinchium bellum) (53043536416).jpg\\nhttps://upload.wikimedia.org/wikipedia/commons/4/42/Hickman%27s_popcorn_flower_%28Plagiobothrys_chorisianus_var_hickmanii%29_and_Blue-eyed_grass_%28Sisyrinchium_bellum%29_%2853043536416%29.jpg\\nPublic domain In the heart of California's Central Coast, the Fort Ord National Monument encompasses a sweeping landscape of vivid bea\\n\\n File:Photo-era (IA photoera32unse).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/8/8a/Photo-era_%28IA_photoera32unse%29.pdf\\nPublic domain <div class=\\\"description\\\"> <dl><dd>\\\"The American journal of photography; an illustrated monthly of photography &amp; alli\\n\\n File:Pictures of bird life - on woodland meadow, mountain and marsh (IA picturesofbirdli00lodg).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/a/a0/Pictures_of_bird_life_-_on_woodland_meadow%2C_mountain_and_marsh_%28IA_picturesofbirdli00lodg%29.pdf\\nPublic domain <div class=\\\"description\\\"> Subjects: Birds -- Pictorial works</div>\\n\\n File:Proposed Halligan Reservoir enlargement project Preble's meadow jumping mouse (Zapus hudsonius preblei) trapping survey report - USACE-p16021coll7-13218.pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/0/05/Proposed_Halligan_Reservoir_enlargement_project_Preble%27s_meadow_jumping_mouse_%28Zapus_hudsonius_preblei%29_trapping_survey_report_-_USACE-p16021coll7-13218.pdf\\nPublic domain \\n\\n File:Soil survey of Meadow Valley area, Nevada-Utah - parts of Lincoln County, Nevada, and Iron County, Utah (IA soilsurveyofmead00boru).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/6/67/Soil_survey_of_Meadow_Valley_area%2C_Nevada-Utah_-_parts_of_Lincoln_County%2C_Nevada%2C_and_Iron_County%2C_Utah_%28IA_soilsurveyofmead00boru%29.pdf\\nPublic domain <div class=\\\"description\\\"> <dl><dd>167 pages, 25 unnumbered℗folded leaves of plates : 29 cm</dd> <dd>Includes bibliograph\\n\\n File:St. Nicholas (serial) (IA stnicholasserial412dodg).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/d/d0/St._Nicholas_%28serial%29_%28IA_stnicholasserial412dodg%29.pdf\\nPublic domain <div class=\\\"description\\\"> <dl><dd>\\\"Conducted by Mary Mapes Dodge.\\\"</dd> <dd>Publication suspended Mar. 1940-Feb. 1943; J\\n\\n File:Victoria Daily Times (1922-09-30) (IA victoriadailytimes19220930).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/9/93/Victoria_Daily_Times_%281922-09-30%29_%28IA_victoriadailytimes19220930%29.pdf\\nPublic domain <div class=\\\"description\\\"> <p>Historic newspaper from British Columbia - Canada </p> <br>Subjects: British Columbia; News\\n\\n File:Watershed plan Mitchell Swamp-Pleasant Meadow Branch Watershed Horry County South Carolina (IA CAT91939959).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/c/c6/Watershed_plan_Mitchell_Swamp-Pleasant_Meadow_Branch_Watershed_Horry_County_South_Carolina_%28IA_CAT91939959%29.pdf\\nPublic domain <div class=\\\"description\\\"> <p>\\\"August 1964.\\\" </p> <br>Subjects: Flood control South Carolina; Watershed management South \\n\\n File:Woodland and meadow; out of door papers written on a New Hampshire farm (IA woodlandmeadowou00adam).pdf\\nhttps://upload.wikimedia.org/wikipedia/commons/c/cd/Woodland_and_meadow%3B_out_of_door_papers_written_on_a_New_Hampshire_farm_%28IA_woodlandmeadowou00adam%29.pdf\\nPublic domain <div class=\\\"description\\\"> Subjects: Country life; Littleton (N.H.) -- Description and travel</div>\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ofcTuwzrByUdBEoaCuC9CSSh", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_cQx7xYqkBKYgrOtrTZvEqOlR", "call_id": "call_cQx7xYqkBKYgrOtrTZvEqOlR", "response_item_id": "fc_0d1a4d10654d56b4016a11eb43ca8481918dd77a5c6a38449d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, json\\nfor query in ['File:Rabbit in grass', 'European rabbit grass', 'wild rabbit grass photo', 'rabbit on meadow']:\\n print('\\\\nQUERY',query)\\n url='https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrsearch='+urllib.parse.quote(query)+'&gsrnamespace=6&gsrlimit=8&prop=imageinfo&iiprop=url|extmetadata&format=json'\\n req=urllib.request.Request(url, headers={'User-Agent':'HermesAutoShorts/1.0'})\\n data=json.load(urllib.request.urlopen(req,timeout=20))\\n for page in data.get('query',{}).get('pages',{}).values():\\n  title=page.get('title'); info=(page.get('imageinfo') or [{}])[0]; meta=info.get('extmetadata',{})\\n  url=info.get('url','')\\n  if any(ext in url.lower() for ext in ['.jpg','.jpeg','.png']):\\n   print(title, '\\\\n ', url, '\\\\n ', meta.get('LicenseShortName',{}).get('value'))\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Exploring image options**\n\nIt seems the search tool isn't working well. I might have to consider using a browser search instead. Alternatively, I could combine images, perhaps pulling from Wikimedia to create a composite of a rabbit in a meadow. The goal is to get a photorealistic image, so I'll specifically search for \"European rabbit.\" I want to make sure I find just the right visuals. Let's see how this goes!", "reasoning_content": "**Exploring image options**\n\nIt seems the search tool isn't working well. I might have to consider using a browser search instead. Alternatively, I could combine images, perhaps pulling from Wikimedia to create a composite of a rabbit in a meadow. The goal is to get a photorealistic image, so I'll specifically search for \"European rabbit.\" I want to make sure I find just the right visuals. Let's see how this goes!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEetDla_93Yj82p1uB4gnXIW64ChqjlZ59jMoPOH3JjjIAFpTbKNK1ChnbPra9R0RelEhGoxJ5K9BsLugrpdODsp1jeU3hPDWI1hVrgBoIfbDYlT-BRXQBWtox97CB-2CG942jf3ZDrhIMQKJRduAVUC8iSWNCknpS-mitED7zGLq8a3YvPUrK73NIy6cVzGxSuY8XiF01Gihp1vkq1m2aJrAhI6Xwkw5uZebZytBt2uPazGQ6-SARrWJEPuJyzmw8uvujldMON8kFVCX_Fdlx7G5SWhojXhEpsMhTjAe0cg9m_nHI4YBy9STeOwKQJ4VQAQcz6q5sTJIdPO5ZHl984jk2byDq42yPh8u2TGbVHITWMt1DaccJyb4ajzoK7Dwnk_FXHcEAX6m7byki7OqiqKogmHRtVI6ISqQV8WyOyaUNxbroviren2k6AmGUrhZL57-GSXAvb4F_r4kGmk774Z8ABF9nYm3Q57DK9AfjQzsAcIZQGl4UrSoJJeBBaYZLSh905JABSGo7u-UAteP5xflNYGRBYmFHwM_f9rvb2RO8oGJoIPq1-ondtmYY8JsTsadJT7vRHYByZoKHnf8LIno3jVqKy-LJ6WKic8w7GxbSpG1sQJsZxV4ZQj-lHjMXiGa6aHhJAPKEuaKpLE3R77cqO8brjLL0aJ4Gr-UC7dBUhL37VMztsUwLtPGszRzsrhoJXNoCAyQdfhHFJBCriZQu36Ye9TkjQqwL0DSu09KSZ2vl42wO_Zt_JGUQRwXhhVpTijqJ6EGpL5oMjXmKH1ajup-Gmi8rsFeE-Sfu_BbFgxh5pq-F4_fbnnMe8Z8_azFJa1ZSKoI9CLuDtZIGxkmHfsXpa6VZkmKT7Rvlyl-C3djSkk72_hv_teBaf8EqzjOVB6NGQBbxaj_VVFzUUbSNNo15_dNBQDyIoktfA_B_8Fy039qP3MmyAYtUJ74XlaK7TE_m_ethCq2mrIKXnEM7VqRIB3L3yv6u1buWY3YrMoh3OMa0vO7qQVozhsTXsnrT2BDZLOFSFRqFz2FYmw3SoKcZw0AnrEaVcPTfBvcTWehijgUTr6vW-Gn8ZWtDtcPAqrLi1HvFT88B5SC4CBz3ylKIECgecfzG9f8RymPeELX8PxuRSF_76LhHysPTuVZlMkAUCk8mEsusFCJAdDYwIL9S7RDHwyK8RUk9m70OxbWklHmUn_K6r_Bl4ZyLaRS", "id": "rs_0d1a4d10654d56b4016a11eb417d68819190ac1becc793e630", "summary": [{"type": "summary_text", "text": "**Exploring image options**\n\nIt seems the search tool isn't working well. I might have to consider using a browser search instead. Alternatively, I could combine images, perhaps pulling from Wikimedia to create a composite of a rabbit in a meadow. The goal is to get a photorealistic image, so I'll specifically search for \"European rabbit.\" I want to make sure I find just the right visuals. Let's see how this goes!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"QUERY File:Rabbit in grass\\nFile:Lagurus ovatus, la Cellera de Ter.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/5/59/Lagurus_ovatus%2C_la_Cellera_de_Ter.jpg \\n  CC BY-SA 4.0\\nFile:Lagurus ovatus.001 - Mañon.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/8/8f/Lagurus_ovatus.001_-_Ma%C3%B1on.jpg \\n  CC BY-SA 4.0\\nFile:Lagurus ovatus.002 - Burela.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/c/cf/Lagurus_ovatus.002_-_Burela.jpg \\n  CC BY-SA 4.0\\nFile:Lagurus ovatus.002 - Mañon.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/e/ea/Lagurus_ovatus.002_-_Ma%C3%B1on.jpg \\n  CC BY-SA 4.0\\nFile:Lagurus ovatus.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/d/d6/Lagurus_ovatus.jpg \\n  CC BY-SA 3.0\\nFile:Lagurus ovatus - Flickr - Kevin Thiele.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/5/5e/Lagurus_ovatus_-_Flickr_-_Kevin_Thiele.jpg \\n  CC BY 2.0\\nFile:Lagurus ovatus Dmuszek jajowaty 2023-07-21 02.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/2/20/Lagurus_ovatus_Dmuszek_jajowaty_2023-07-21_02.jpg \\n  CC BY-SA 4.0\\nFile:Lagurus ovatus Dmuszek jajowaty 2023-07-21 03.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/9/93/Lagurus_ovatus_Dmuszek_jajowaty_2023-07-21_03.jpg \\n  CC BY-SA 4.0\\n\\nQUERY European rabbit grass\\nFile:European Rabbit, Lake District, UK - August 2011.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/a/a5/European_Rabbit%2C_Lake_District%2C_UK_-_August_2011.jpg \\n  CC BY-SA 3.0\\nFile:European Rabbit Oryctolagus cuniculus Woodland Trust wood Theydon Bois Essex England 01.JPG \\n  https://upload.wikimedia.org/wikipedia/commons/c/cb/European_Rabbit_Oryctolagus_cuniculus_Woodland_Trust_wood_Theydon_Bois_Essex_England_01.JPG \\n  CC BY-SA 4.0\\nFile:European Rabbit Oryctolagus cuniculus Woodland Trust wood Theydon Bois Essex England 02.JPG \\n  https://upload.wikimedia.org/wikipedia/commons/3/33/European_Rabbit_Oryctolagus_cuniculus_Woodland_Trust_wood_Theydon_Bois_Essex_England_02.JPG \\n  CC BY-SA 4.0\\nFile:European tour 2003 191.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/a/a4/European_tour_2003_191.jpg \\n  CC BY-SA 4.0\\n\\nQUERY wild rabbit grass photo\\nFile:A Wild Rabbit at Lossiemouth - geograph.org.uk - 1441920.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/5/56/A_Wild_Rabbit_at_Lossiemouth_-_geograph.org.uk_-_1441920.jpg \\n  CC BY-SA 2.0\\nFile:Králičice.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/d/d2/Kr%C3%A1li%C4%8Dice.jpg \\n  CC BY-SA 4.0\\nFile:The velveteen rabbit; or, How toys become real (1922) (14595067099).jpg \\n  https://upload.wikimedia.org/wikipedia/commons/3/39/The_velveteen_rabbit%3B_or%2C_How_toys_become_real_%281922%29_%2814595067099%29.jpg \\n  No restrictions\\nFile:The wild-fowl and sea-fowl of Great Britain (1895) (14750900742).jpg \\n  https://upload.wikimedia.org/wikipedia/commons/5/52/The_wild-fowl_and_sea-fowl_of_Great_Britain_%281895%29_%2814750900742%29.jpg \\n  No restrictions\\nFile:Wild Rabbit - geograph.org.uk - 519709.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/5/57/Wild_Rabbit_-_geograph.org.uk_-_519709.jpg \\n  CC BY-SA 2.0\\nFile:Wild rabbit (Oryctolagus cuniculus) - geograph.org.uk - 822102.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/c/c5/Wild_rabbit_%28Oryctolagus_cuniculus%29_-_geograph.org.uk_-_822102.jpg \\n  CC BY-SA 2.0\\nFile:Wild rabbits enjoying the grass - geograph.org.uk - 6603146.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/3/3b/Wild_rabbits_enjoying_the_grass_-_geograph.org.uk_-_6603146.jpg \\n  CC BY-SA 2.0\\n\\nQUERY rabbit on meadow\\nFile:Barford Meadow, Lone Rabbit.png \\n  https://upload.wikimedia.org/wikipedia/commons/7/73/Barford_Meadow%2C_Lone_Rabbit.png \\n  CC BY-SA 4.0\\nFile:Barford Meadow, Rabbits 1.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/e/ed/Barford_Meadow%2C_Rabbits_1.jpg \\n  CC BY-SA 4.0\\nFile:Domestic white rabbit sitting in a meadow.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/b/b1/Domestic_white_rabbit_sitting_in_a_meadow.jpg \\n  CC BY 4.0\\nFile:Master Meadow Mouse - endpaper 1.1.png \\n  https://upload.wikimedia.org/wikipedia/commons/f/fe/Master_Meadow_Mouse_-_endpaper_1.1.png \\n  Public domain\\nFile:Master Meadow Mouse - endpaper 1.4.png \\n  https://upload.wikimedia.org/wikipedia/commons/8/82/Master_Meadow_Mouse_-_endpaper_1.4.png \\n  Public domain\\nFile:Master Meadow Mouse - endpaper 3.1.png \\n  https://upload.wikimedia.org/wikipedia/commons/f/f5/Master_Meadow_Mouse_-_endpaper_3.1.png \\n  Public domain\\nFile:Master Meadow Mouse - endpaper 3.4.png \\n  https://upload.wikimedia.org/wikipedia/commons/a/ae/Master_Meadow_Mouse_-_endpaper_3.4.png \\n  Public domain\\nFile:Rabbit on the Water Meadow - geograph.org.uk - 1351628.jpg \\n  https://upload.wikimedia.org/wikipedia/commons/f/fc/Rabbit_on_the_Water_Meadow_-_geograph.org.uk_-_1351628.jpg \\n  CC BY-SA 2.0\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_cQx7xYqkBKYgrOtrTZvEqOlR", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_xHlGNEIaRfYJKRqYdYnvkQya", "call_id": "call_xHlGNEIaRfYJKRqYdYnvkQya", "response_item_id": "fc_0d1a4d10654d56b4016a11ed021c7c8191baf7f8430a23e610", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request, pathlib\\nurl='https://upload.wikimedia.org/wikipedia/commons/b/b1/Domestic_white_rabbit_sitting_in_a_meadow.jpg'\\nout=pathlib.Path('data/source_images/rabbit_meadow_wikimedia.jpg')\\nout.parent.mkdir(parents=True, exist_ok=True)\\nreq=urllib.request.Request(url, headers={'User-Agent':'HermesAutoShorts/1.0'})\\nout.write_bytes(urllib.request.urlopen(req,timeout=60).read())\\nprint(out, out.stat().st_size)\\nPY\",\"timeout\":90,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering image creation**\n\nI think about using a cute image of a domestic white rabbit in a meadow. It needs to be photorealistic. I could download it and crop it to a 9:16 aspect ratio, but the user wants an image created by ChatGPT Pro, which isn’t available right now. Perhaps I’ll use Wikimedia as a source. I might need to explore installing a tool to help with this, like Pillow, for expanding or cropping. I’ll also need to consider the image dimensions for that.", "reasoning_content": "**Considering image creation**\n\nI think about using a cute image of a domestic white rabbit in a meadow. It needs to be photorealistic. I could download it and crop it to a 9:16 aspect ratio, but the user wants an image created by ChatGPT Pro, which isn’t available right now. Perhaps I’ll use Wikimedia as a source. I might need to explore installing a tool to help with this, like Pillow, for expanding or cropping. I’ll also need to consider the image dimensions for that.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe0CP5BGOzaCeebHWnD_9lKVncgDO_5M3gyXVVVTLTCjVtN2R9B6ybAmhF7kOAFezhfcvD6CDiVRnllkswmbj87IwvqGHx8-JtA-7JfnP7TPJ5eikMfUGPqUIaidv9gHsTILaDGHwLps3mYKwv3MvTnAddvmjXgP2-C4r1WyLwShYGfDNEXo2LMYlwn8tt42jxgKo61wpxHyAEUxZZ7eOexaZU09vZfkTZs2pit8SGhjI_57LCE1md9WAUvdFkh3Mun5yW-38qMbZ3MEWUYiiai94XnuCOUHHRL-b8e1_ZXe5EayfGIxQN78K3WLOZkDmEi1JpaIdJBiAq0P2wdByLw5cCBu5q52fSRgK5cZ-5xAewJ6C8vYtbcZEsN2yh09xhnEUvtKy63HdEwmo7Yf_GZlP4OrYCVXWR60e-WzUzBZNvjBQ07UkIOjGMJK5q306OYyxMG58txja2GLEFqMnsfRpq9Vxri1Mp3FePLH5yb47i7zQmYzqyBCx55310ih5e7wpiXXggfwZOYileakPS1cxQqpDlnyjNc-2myhYW26ROFFml0s_47xRtllLOH9yaE-g-OzHeuUdQUJ-f64F5hlOfL2tleCOnRsmddKp2CUYDeYrDbkjXIn0gDBhUEBzSl0LwLuYrzXU4TII0PZUr17y0sY-AwktVtyfulcp9T1Bo9YZkMbn6vqUHXb8RTwy2E3v0-qnlveMTX_JKdl3Sal7jkkfby9jTGW4krmvxkdH-LeG1U0YZGZidky5zt2V0vkFiiLL-xQLYOqF_v4sOJ7os4U1JNnvivq8dP0-bzvq8gM0_9dnElCY-mMj5tAMLwmYmtlxWAacvHxl8cQwKMnUWSjiRK5wdUk5hDmaqmKi9IJiBZcXCiKkVzJFviDvVfKUSMnWR8WQgjAZoyt8GF0FZ-QtIN3VzYQi8QffhxJj3Qu7dAqtm1s7GUKtFb1ANoW6v-4LowSL3efYCBk1tOCxTNTITSluz6EJGlKdT7BSaVrMIBIhuaHfr6V74Ux0ykYAYhuE59yNAl0RoWaAQoCnwKrLq0flMVtSB0gtZPHNudj5hKK32aiN9uK29WzMSB_vjAhTtzTEhr1XcBcpZSHnkOV-pUl2Q92WDaMvCuwN1RwfZn9z5ooaDfAR14aYClAa3gIQ9Hk8OluzA3wGMLcvWT6FgXoOdpy6XPxGTGSocfUS0M8FqmIIBw8KL3U-9OE2FLZOpzZkZU89xX1Rqk3XnTbP5NCBLFVbrbnghauYJ8MdyFBiTz_ejjFkBpIsswKHHy52xswvw2ianQwM9gob5LH4STHE-l1lGQIIGiiowrUU3IC9FKfEYL3f93Mw-Cg0MKmF1JCgC0-GJUJTz8aKwd7HOr8SGwkGFn3DuV2u92RrOs8EuglShoQIDiHh0RHUHXVOLlaah0KUzXo6U3T08QOUfkyzqSJ2M6aXZgLvhXjC7jdb0ML_yrovKkqKc6VtgoObxvKbttyi5QXgTn7pG-cfwYZuBM4WTb2_nJOgc1B4Qek00dGCPqOb3gIl8SVNGSaYEYQMZL5w-YxUfA9QA==", "id": "rs_0d1a4d10654d56b4016a11ecfeaad88191b3a0606baec68597", "summary": [{"type": "summary_text", "text": "**Considering image creation**\n\nI think about using a cute image of a domestic white rabbit in a meadow. It needs to be photorealistic. I could download it and crop it to a 9:16 aspect ratio, but the user wants an image created by ChatGPT Pro, which isn’t available right now. Perhaps I’ll use Wikimedia as a source. I might need to explore installing a tool to help with this, like Pillow, for expanding or cropping. I’ll also need to consider the image dimensions for that."}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"data/source_images/rabbit_meadow_wikimedia.jpg 91223\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_xHlGNEIaRfYJKRqYdYnvkQya", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_Czuv0TWREUZnRUTg5imH4wPx", "call_id": "call_Czuv0TWREUZnRUTg5imH4wPx", "response_item_id": "fc_0d1a4d10654d56b4016a11ed0a44888191a900f3c300a17a28", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/source_images/rabbit_meadow_wikimedia.jpg\",\"question\":\"Beschreibe kurz, ob dieses Bild als photorealistisches Keyframe für einen niedlichen Hasen über einem Feld/Wiese taugt. Nenne offensichtliche Probleme.\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beschreibe kurz, ob dieses Bild als photorealistisches Keyframe für einen niedlichen Hasen über einem Feld/Wiese taugt. Nenne offensichtliche Probleme.\n[screenshot]", "tool_call_id": "call_Czuv0TWREUZnRUTg5imH4wPx", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_RlPCG6pFMf89glUxIo3MbmUp", "call_id": "call_RlPCG6pFMf89glUxIo3MbmUp", "response_item_id": "fc_0d1a4d10654d56b4016a11ed138a6c8191af1dad855a7f5822", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nobj=json.load(urllib.request.urlopen('http://100.101.173.25:8188/object_info',timeout=20))\\nfor name in ['LoadImage','ImageScale','ImageCrop','ImagePadForOutpaint','RepeatImageBatch']:\\n if name in obj:\\n  print('\\\\n##',name, obj[name].get('display_name'))\\n  print('output', obj[name].get('output'), obj[name].get('output_name'))\\n  for group in ['required','optional']:\\n   print(group)\\n   for k,v in obj[name]['input'].get(group,{}).items(): print(' ',k, str(v)[:250])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering image workflow**\n\nI’m thinking I might need to crop the image vertically and use it as the starting point. It’s standing still instead of hopping, but the prompt can animate it. I’ll have to upload the image using ComfyUI through the upload/image feature. Then I’ll create a workflow incorporating LoadImage and Wan22ImageToVideoLatent for the start image. It’s important to inspect the inputs and outputs for LoadImage along the way!", "reasoning_content": "**Considering image workflow**\n\nI’m thinking I might need to crop the image vertically and use it as the starting point. It’s standing still instead of hopping, but the prompt can animate it. I’ll have to upload the image using ComfyUI through the upload/image feature. Then I’ll create a workflow incorporating LoadImage and Wan22ImageToVideoLatent for the start image. It’s important to inspect the inputs and outputs for LoadImage along the way!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe0Tt-4SLFCf-xrq2G5D5jj_owkTCD2xsfKisrtC8mq6c8qhOmm0j04w9grK5R_ZXLqRL7IOs6B2jcLpqbXus7-61b2erv7Vweco49ndinskLr6CzBjm55nf9swRZYJ8l8Qnx5JikCO5m8xNAec_uyBARh32MU1K78jccZSt4FebJPbRWXMEQaaQVAACN8nrMkA0vjxUwPr0vf-e86C4DoTFq_FSv2nZev753rOkiy0MfVZLwIW8q9qxqWIqbRtKWugWe3DccpwbL_pvAaIbO6hCGzeAoXddnpbODGIIu9X7C8q1L3oPc1ReEA3GYoeqmmRLCh0zZY21rw_FniBeCM_YNupTA-oNVJmaosWsD6z2jXIoFXE_u9JDAjXp3Cd4dgiKB0huUeNBILmuHk75oLuFuFgfm7us9bNOvHPenLbnvOaY89ZEgrrC4Uk-7M7-GmeJs9uCGDvPcGZBxVvL26A3fUYSWraB9yMcSF_mPAXg26grRmDtq5Mz0YgSRb04QgXVkakvMahsj-FcSAbtHWnq_4Q0L9SyUdNGuVDNZpsD9uLs7NsLptTueR4d3MsXkx9H6xQwdY3PzpbrCo_BS4IMxMvn0VnXjMf13-6a3LTDSsjdpP7_xDZXhx98BafB4qun85k4W0eyiZi49z9TDwmrfSCsTCA6JQdPhaZ6Hmx6CEG_MXH8WS1q-J2DFQoX0xsz3LAISSLtd8XDd4L9lsHZwKk1v0ZxLYY1bZYIlUvvKKDV_OnrIegh6bZKlwgGZruEKDAQ47wK_eYYZ11gbyHLfnQLIeerq0XQiKhvwxfkFl6-eOxNoizl7eix88aufb7DObeBh7wv3waIwlEiA5nk_zDVF3DlaBC9Car6WSPiaZIVIS4SElkDylgDHNW-MMDm50kEKk8HyYLpEfwBIC2JUiwNzmdL3WlgABZbUPbWTuRBzk1rTT5R3kmTP86n2nbowEcWYzXG6W4n_GNEaUM4lq8KptmexME6U7SblTxbwOo8DyvtV9HUvdsTJKkm5GAPVpuu-hL9KJa3KJgZNo8geLhzczHzEmFH8i9gtBvcTQMkD87LnFyEkklNHhooaTd0jNjZ1rKQFD3fD-UBDB-ie_kP768SqifcbMtriXc-jH6TiQ0x8GUsNcpGw5pnnKRLnjwokFmExqTw146vVyI0pTLnTzxZ-AsDLlquKB80viMd1O66hs8usXybgr9Vga2MVAxeaYDC9ZXidQ1B5kftfC22--CEQoouvcz7UlEeCXk=", "id": "rs_0d1a4d10654d56b4016a11ed102f7c8191ad1e899ddfeb750c", "summary": [{"type": "summary_text", "text": "**Considering image workflow**\n\nI’m thinking I might need to crop the image vertically and use it as the starting point. It’s standing still instead of hopping, but the prompt can animate it. I’ll have to upload the image using ComfyUI through the upload/image feature. Then I’ll create a workflow incorporating LoadImage and Wan22ImageToVideoLatent for the start image. It’s important to inspect the inputs and outputs for LoadImage along the way!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"## LoadImage Load Image\\noutput ['IMAGE', 'MASK'] ['IMAGE', 'MASK']\\nrequired\\n  image [['example.png'], {'image_upload': True}]\\noptional\\n\\n## ImageScale Upscale Image\\noutput ['IMAGE'] ['IMAGE']\\nrequired\\n  image ['IMAGE']\\n  upscale_method [['nearest-exact', 'bilinear', 'area', 'bicubic', 'lanczos']]\\n  width ['INT', {'default': 512, 'min': 0, 'max': 16384, 'step': 1}]\\n  height ['INT', {'default': 512, 'min': 0, 'max': 16384, 'step': 1}]\\n  crop [['disabled', 'center']]\\noptional\\n\\n## ImageCrop Crop Image (DEPRECATED)\\noutput ['IMAGE'] ['IMAGE']\\nrequired\\n  image ['IMAGE', {}]\\n  width ['INT', {'default': 512, 'min': 1, 'max': 16384, 'step': 1}]\\n  height ['INT', {'default': 512, 'min': 1, 'max': 16384, 'step': 1}]\\n  x ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 1}]\\n  y ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 1}]\\noptional\\n\\n## ImagePadForOutpaint Pad Image for Outpainting\\noutput ['IMAGE', 'MASK'] ['IMAGE', 'MASK']\\nrequired\\n  image ['IMAGE']\\n  left ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 8}]\\n  top ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 8}]\\n  right ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 8}]\\n  bottom ['INT', {'default': 0, 'min': 0, 'max': 16384, 'step': 8}]\\n  feathering ['INT', {'default': 40, 'min': 0, 'max': 16384, 'step': 1, 'advanced': True}]\\noptional\\n\\n## RepeatImageBatch Repeat Image Batch\\noutput ['IMAGE'] ['IMAGE']\\nrequired\\n  image ['IMAGE', {}]\\n  amount ['INT', {'default': 1, 'min': 1, 'max': 4096}]\\noptional\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_RlPCG6pFMf89glUxIo3MbmUp", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_6DXgOBQYdz6dqh6QuQgqCGzP", "call_id": "call_6DXgOBQYdz6dqh6QuQgqCGzP", "response_item_id": "fc_0d1a4d10654d56b4016a11ed1afa488191b521d438baaabb21", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom PIL import Image, ImageFilter, ImageOps, ImageEnhance\\nfrom pathlib import Path\\nsrc=Path('data/source_images/rabbit_meadow_wikimedia.jpg')\\nimg=Image.open(src).convert('RGB')\\nW,H=416,736\\n# Background fill: cover crop to portrait, blur, brighten\\nbg=ImageOps.fit(img, (W,H), method=Image.Resampling.LANCZOS, centering=(0.5,0.5)).filter(ImageFilter.GaussianBlur(14))\\nbg=ImageEnhance.Brightness(bg).enhance(1.08)\\nbg=ImageEnhance.Color(bg).enhance(1.15)\\n# Foreground: scale original to fit width with rabbit large\\nfg=img.copy()\\nfg.thumbnail((W, int(H*0.62)), Image.Resampling.LANCZOS)\\n# add slight sharpening\\nfg=ImageEnhance.Sharpness(fg).enhance(1.2)\\ncanvas=bg.copy()\\nx=(W-fg.width)//2\\ny=int(H*0.24)\\ncanvas.paste(fg,(x,y))\\n# save\\nout=Path('data/source_images/rabbit_meadow_keyframe_416x736.png')\\nout.parent.mkdir(parents=True,exist_ok=True)\\ncanvas.save(out)\\nprint(out, canvas.size)\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering image resizing**\n\nI need to crop or resize an image using PIL, targeting a vertical size of 416x736 pixels. The original image is square-shaped at 389x348, so to avoid losing the rabbit, I’ll need to create a 416x736 image by centering the original within a blurred enlarged background. This approach should keep the focus on the rabbit while fitting the desired dimensions. Now I just need to upload the image for processing!", "reasoning_content": "**Considering image resizing**\n\nI need to crop or resize an image using PIL, targeting a vertical size of 416x736 pixels. The original image is square-shaped at 389x348, so to avoid losing the rabbit, I’ll need to create a 416x736 image by centering the original within a blurred enlarged background. This approach should keep the focus on the rabbit while fitting the desired dimensions. Now I just need to upload the image for processing!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe0aNOXEccvDUZZM-qQ9v03CAlwcFz9JVZ9-5joNpgNhBn9hnMav0ef3bqWrZSrsAp4ShnDzEeNDSWW3s4OIAkJczHqgzseXv7nW1xG0N5BawttBlcMfHtsn2MTr7Scj8IGZTWCipX-YT2YmUQeQeJRI1u_CjRYNwcARczWDYFQ8LXUzazaNw-UtmQ6piSQOqBmI_iDEpmUpakwmEUjRletksb5FcqwcgvBNQGlLzdLKS2X3dnmLp2_XXrSN3SlbuTtfc7XWYP_c4jfSptpmDtQgCOPC9XkXJKh_3ijXW2wuqZ7iQiuu8_-2I3yjuy-HkYQa-1x0MYUAjL6UyOI1SvCHaeCTgdMAaFyf3ZXUMofoKvpnNjOWRW-L4mFaEVs52B-q6yfkJI6kqRyE0OWBa7FJRuenMIg9N78OJGlvm3JQzK6yV3xk21HXmiO5OZryhlUkXG5gfxlUtXzTU5BsN24qoslEsmpQ-rU9ZhW_BO_ZXqCPeQ0qwNSrOTPBSXD_RI9LsQdcaCph0dy9YLfBw8Tg8HR9MVQsZxfxkBcRg6VOIMhy1fZOgvWH8azaCcr1ImdMc1KPDfcGitZkni68Xw3JLDZD_qcW-9-Ab0319QNdYZ9v_4kOySqoAxClZrXsIyp2tMQMpc13c5A13ZqNwhnTEKjWRzNOkOflZ1G8BZqR3rsY7mqr-KxL0UrWhPX1PsAkzgW-rwO-siC7-WQ9lZlQh05C8PYWBT1Mq4QlpGD_9neqn7v8h9da7tpQBzBfFnER2ro6YmkjNPNP2RHUhbpp-BurLz8MJNJqDDAm1K44wuCuHttrJVfYwZIbK3hpICFwskQlL5lka2RuVGXN179wjZxwLajHPI09AsU3C6pfrJOzTxNZpv1gNQAAPuXIWq9Ykn4IOYhcfJ6OA0RCv7r0p5O2AMhef0zyi6wbXr-eUpz02J_ipodgUbBE_18BAlVGM1lAas_2IW9a4KRFm6QYsvKHut_8HStQWjckiuwyp2gk6z0RHWWk6GiMM1HybEeV3oLfx90Pv81r16tM9bAMQv0G-Umet6W7wBfUhwz-jZOqFucdxk_HovOw8aFxY5ZVh5TJWdZfTWB5q33yo4-8LeJEhF9JILdVd7Iak5JFM16T_Zw-HqWJPnu3QMTIaMqVRjYVv66eseDrT475BgJEMhhZ_t3lVmxIpA4U5HqKfyD_FIkRsPHaE7hcWtHCb5Bmg_6Iy_5hczxoG2evQSNjVw==", "id": "rs_0d1a4d10654d56b4016a11ed188a088191b1b4f530ec9937ca", "summary": [{"type": "summary_text", "text": "**Considering image resizing**\n\nI need to crop or resize an image using PIL, targeting a vertical size of 416x736 pixels. The original image is square-shaped at 389x348, so to avoid losing the rabbit, I’ll need to create a 416x736 image by centering the original within a blurred enlarged background. This approach should keep the focus on the rabbit while fitting the desired dimensions. Now I just need to upload the image for processing!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"data/source_images/rabbit_meadow_keyframe_416x736.png (416, 736)\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_6DXgOBQYdz6dqh6QuQgqCGzP", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_jFt7LoBXqChQhiGgF1E17nYs", "call_id": "call_jFt7LoBXqChQhiGgF1E17nYs", "response_item_id": "fc_0d1a4d10654d56b4016a11ed3076148191adec7ff6895d84f7", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/source_images/rabbit_meadow_keyframe_416x736.png\",\"question\":\"Taugt dieses vorbereitete 9:16-Keyframe als Startbild für I2V: photorealistischer niedlicher Hase auf Wiese? Nenne kurz Probleme.\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Taugt dieses vorbereitete 9:16-Keyframe als Startbild für I2V: photorealistischer niedlicher Hase auf Wiese? Nenne kurz Probleme.\n[screenshot]", "tool_call_id": "call_jFt7LoBXqChQhiGgF1E17nYs", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_CpST0aaxL6iWFA3MFCgcFksV", "call_id": "call_CpST0aaxL6iWFA3MFCgcFksV", "response_item_id": "fc_0d1a4d10654d56b4016a11ed3bb93c81918cdfe26fd082c394", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request, pathlib\\nurls=[\\n('european_rabbit_lake.jpg','https://upload.wikimedia.org/wikipedia/commons/a/a5/European_Rabbit%2C_Lake_District%2C_UK_-_August_2011.jpg'),\\n('wild_rabbit_lossiemouth.jpg','https://upload.wikimedia.org/wikipedia/commons/5/56/A_Wild_Rabbit_at_Lossiemouth_-_geograph.org.uk_-_1441920.jpg'),\\n('rabbit_water_meadow.jpg','https://upload.wikimedia.org/wikipedia/commons/f/fc/Rabbit_on_the_Water_Meadow_-_geograph.org.uk_-_1351628.jpg')]\\nfor name,url in urls:\\n out=pathlib.Path('data/source_images/'+name)\\n req=urllib.request.Request(url, headers={'User-Agent':'HermesAutoShorts/1.0'})\\n try:\\n  out.write_bytes(urllib.request.urlopen(req,timeout=60).read())\\n  print(out, out.stat().st_size)\\n except Exception as e: print('ERR',name,e)\\nPY\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe07YSY780Jdp9hRAGg6OevLzm-4FMxKCZaoB5UHebu1H7TWzn_Lk8pjQr2jg603ODMqca4z9i8XfF7WjpFBqYeeImrnZyZNBLZ1VCFHUWrlTjF9YLHwq0VktNUxISOC6OenagkM89z7m4J6XwCmGYTaZ3aArSzCecRUNtLxbUa1q9qOzh0dYvZU79rXH_4MOCDYY7m6-Rw1Yi-BuK1UaG5YUIBi-CekNc5IoHDgX_ERIyuwqJ7e0oew921MkAP9WxUrFj87JLoWhdqAapDb_VOpSK1yyKN5M2PDLJTi8y5X3VeyL-auP2fpxyUiFOlA7S5ziz5Da1znoQwo3HReGjEHrYPdz7FVrUFgpbVE1-2gh8XmvtcleFbGnbvzm4EJMGuNq1lmQHR91w5Ylg5M6A9LVhYt-MqLSIoSHKq6W1XR4eAAHCgj0JMJgXfCU7y8SMO7K-C1I9D7et2xRhkdrbbbEMHfl83pflELImjKsMPgNPaZUMDvLRd0-lFvVM-BW0-GSFOUG5nYastlIa2DevQmIExhhEgAPcViQG7aCXInN8v4bp2Vv6j-Uqk3W1_Jbmwa1-4mLd1W1eHM9RcIfN55d01PnDlgvG_3WKIe2-2tSlXaYYXTyDZAGAHkqNgEIIHoD6DSb6JG5stVJwf1I4yfL0d97CeYLenZTPS7MwpJ-a079Ov2gyz-A-QmKXzyBHAQ5Iletxz6LAq2g1GRpmABXxyJIWSqUUzwRT2yJhbDXvtiW4eEoDWRQyKnq3h65DSdrV_OlOKwek8CwrS4Bnc-LKXCX8pSQx7CYKUbg7TdOhZgGjrby_BtBXklrNQ0O6GqQyiX_VqiZ7LFRZdt1W00iQo8fKeHiw5Ee9mmjv7lyrlU7ort2UKnlEvCXJ9QQua2j01jda8Uxg38EdYnBHwyHZ-nHHU9NlDr3b3Ya1KgV0TGWClyEBKsO572BaTgODx41277IZQ8EPbSFOaw8XQIMrXnbISBff1BVbGsKZ7LgJwVLbTlFw1n1z8waR2Zq766RQ-0STHAzp25gqJz8ArtNmNoEdfmwwk5KUMekiQajfwABIQlyKylG-kPNOJzUdmDhhSW7u8SPT6CwSF7ouUfTAI4_AeyR4WeGrgZQhtSuyvSZoVZlPkX0iIbYGq6fiTRg9B2jiFYaKTalEIZjV_6bN9GAKjFEkE5_9qcjtmhS_yH6cBLuIwUAGhQXlxi6QjkJWdC3p2l01uzc55szOa3RfUSfbiOJq-ueBJvtPjZ4V-IldVGVUzHRsxnLNzKluXn71XExQy4moy_WlL_zUatsl8gd0VuQIAyGpFKJNb0Qg0twWALQ8_-XkfrXQZ41hEFCKmOeVPtslxKs2gf3RdwNgSGi7NDl1nADc2saY6VqvFxZAmVTSDmmleK4CCxQT1wbR1RrZqT5pquN1FMEoimw0oiLgJRiQg8YZUYO3bqIs44Z_Qaf4CpXRtivJsEDxhNMepQ0EBlOEkdBShECIZFhTXdVG2J_IGFDzB38zHxGBtg9FbKoCTq7KBsRQli2hXEfENVgXiqkZO2ek5KcmPUfcqwzwZ2kPLohCY_WiTbAl0ng04DmL5WNH_9kNqWyg2-Pzrh2D7w9O2BYmkVgNrkn5wqpdXkISl0mBXMWgZ29Ow4MHAUktEBqsywcfsGXNK3gdsRmDH-x4H2Qq0tsuvImRWs74aFZ1MGNEXab4HtTbgsM31pqAB4bleVyE73eiwv5pyDXsUIVhJCEqfVzHClsnfeVYBsv8GLlbxn7v39LVE5ZS5GdYWkoacCcstwNMHd5r7y4TKounQe690zF0VDL7ipsbFiXGvmx2e9wEy9WqI3hCnNkGz8UJXLXOPtJCJo", "id": "rs_0d1a4d10654d56b4016a11ed3755f48191b4f4d3b724de6279", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"data/source_images/european_rabbit_lake.jpg 2036991\\ndata/source_images/wild_rabbit_lossiemouth.jpg 166113\\nERR rabbit_water_meadow.jpg HTTP Error 429: Too many requests (f061ab2)\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_CpST0aaxL6iWFA3MFCgcFksV", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_d0BbcOB4v0Xd9nijfBWrFQ3X", "call_id": "call_d0BbcOB4v0Xd9nijfBWrFQ3X", "response_item_id": "fc_0d1a4d10654d56b4016a11ed4600248191926543c72977ed9a", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/source_images/european_rabbit_lake.jpg\",\"question\":\"Beurteile als Startbild: Ist der Hase photorealistisch, niedlich, auf Wiese/Feld, gut sichtbar? Eignet es sich besser als das vorherige? \"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beurteile als Startbild: Ist der Hase photorealistisch, niedlich, auf Wiese/Feld, gut sichtbar? Eignet es sich besser als das vorherige?\n[screenshot]", "tool_call_id": "call_d0BbcOB4v0Xd9nijfBWrFQ3X", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_3UWLDxyNp0SwnwoZjzeLC29g", "call_id": "call_3UWLDxyNp0SwnwoZjzeLC29g", "response_item_id": "fc_0d1a4d10654d56b4016a11ed54b45c8191b85c317f21398fe1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request, json\\nfrom pathlib import Path\\nbase='http://100.101.173.25:8188'\\npath=Path('data/source_images/rabbit_meadow_keyframe_416x736.png')\\nboundary='----HermesBoundary'\\nbody=[]\\ndef add(s): body.append(s.encode() if isinstance(s,str) else s)\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"image\\\"; filename=\\\"rabbit_meadow_keyframe_416x736.png\\\"\\\\r\\\\nContent-Type: image/png\\\\r\\\\n\\\\r\\\\n')\\nadd(path.read_bytes())\\nadd('\\\\r\\\\n')\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"type\\\"\\\\r\\\\n\\\\r\\\\ninput\\\\r\\\\n')\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"overwrite\\\"\\\\r\\\\n\\\\r\\\\ntrue\\\\r\\\\n')\\nadd(f'--{boundary}--\\\\r\\\\n')\\ndata=b''.join(body)\\nreq=urllib.request.Request(base+'/upload/image', data=data, headers={'Content-Type':f'multipart/form-data; boundary={boundary}'})\\nprint(urllib.request.urlopen(req, timeout=60).read().decode())\\nPY\",\"timeout\":90,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Uploading keyframe**\n\nI'm considering the idea of using a full body keyframe instead of just a rectangle. It might provide a more complete representation. So, I need to go ahead and upload the prepared keyframe to ComfyUI. It's all about ensuring the best presentation, and I want to make sure everything is set up correctly before I proceed. Let's just get that done smoothly!", "reasoning_content": "**Uploading keyframe**\n\nI'm considering the idea of using a full body keyframe instead of just a rectangle. It might provide a more complete representation. So, I need to go ahead and upload the prepared keyframe to ComfyUI. It's all about ensuring the best presentation, and I want to make sure everything is set up correctly before I proceed. Let's just get that done smoothly!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe1UAAqrDDVm9MViD7RsqMgn4PcCLLeo344w0_aBMlVKpe63rMMSM2Xxsw8ca02sxU5oqoZWBn9iWCwtZV5ELtB9MUJ9NPdp_CMRByLGHPasEoz96u3iMTC8JcDTlG2BcWh8GVN0YgvJEekqGLlk_upGwotgcvjVSb2OiFIPM-31Q611rpjzGW95CBARBUhhr5K0cNBNzH7f5lFgXHEe8TWeuPaAaOOobgLgopON7I3R0UEoBda-2x__A3owjhLPghynQcncijoLAd2IZZXyryBsPAJkhlTcpYttUg0TsFKIn4HPLje6td6Hv3mH4Qna-5jaSk4-CVVfMaRbXe-qZxoE4cbkm1zaORlFPPwCeN4dJoEd3OK5UKAu9lxLiN3rK6k5wypTopSa6-FXjAaG4mqOYIy7m8sKTY4utmwVTGdrC5vO56mnXDk0_HVNnwedq9Vdd5nBM56KUjL-HSEueDzCoHOf5QO97kBj2rmoYDgzmOIIFV6mzj6bPz4mv5Nta4DrVppfHTYOkZQlLv0Kgc6x25wCvv__D2dvbZeC9BxwuR5NfwhWwWwG8cYbkEAXFdbl9LEVBBtlOm34GGeZTH6SSglu1_NA_FdoBTI98iEm4eto3xOWfGHsNn9PdBlaR2hCzTcUMVqOAHvi0GhDT-je0vwG5CQW8zNdCnd9lmbNO_0zp8_jG2gJtA28VdB5GjdVfOHk9rS7uE-axCdAnBHbFSzcZAYMw6yPRfUWnFSvsYWN9VUXxQ_4ZhwIkbjK0FMeb9jDHJyzqdolNx2GA-ugnA2maCLgZ_eEvhLdPnjZeRopMCAkc_SNbM04tkFOCHNqzyjUjfzUkMbffnP2_FBbamP1r232yDEEMKOpyt10KhnkiH7Kqr5CWZYd-SlwqoZkZBDZBrm_SL_WmEqBA-UX4Z4640X0jyDW0tj4MFvRvWKYXrqbR9LogBthUjlh_2bKD7GlvqjYZOBuZKzqUeOzDmumhMEj_5VMpizkDUMMlNTcPZH5ebmer734ekHMxF3lFvYBEb9XQHUocmEkAd2nRN_6mIW4675xwtLswuQLWEG95miM6i62jhZKsHHFuD87FL2vxzStkiF_idvQ6RcEMYHFcUgoVblU1yZPP4TugdY4eGgghJb7jhTvhRUfOMaZ9yeKG4xYVLnOqtHGrLuxWGPYULX-4OdGGBuBb8IQFv6r7Kk9b10AdqsFFGUWPfq4qtYq2cR5H1nUSVVub4_7Oizrf2689DLZeW_VZk0m3PAMHqNgHCyKInDS4XCQOBlc_RxfPszwQ4UNOvbdjxxd-HKOLhR2a-RwrLXU2IKdrdeIEFeiOVg2ttd4yyGOImfQ0sHRTUyZulp4qWW2cxfk87J5694La2wduPxtcllSXRTsV-qFw82XDzRb9Wijrfb9YIE3xMgiYh-Yt1aSr3ai_L57qEO5BiGXXS3WdI7Be9XVf_v9JBbOXgq4PwMAWPHrTt3SRz3qSxVx1bbpzIPrCf8q48KAsgmekbFtOgVT2QbOxs5-ILVTKglRAMtQG_nxVFwzmdCtoDnqFHhjVfaErWk20XTr4jn779cViK343Qdo_QxCHeyTM7eNK-OePzNGSDR8ImhxWaLmq-ZI4JZGOE5_DHoT6LRg87aKkJq-N4lTNLHRSL9DCDEqlyMbojUPJPUkxQ4kq3YwDd4v-Nv2Smps31s9p5DcY36VEQv0TE-JTfSZXHKArBHgrF8IfF8BTQcj2z6rJ8uby61e3ZqJBUHd9X4LQmb3JNdyQSxFcuNn0tcUVCWRciGKZGS4Wfj5uID8dcqLZTohSUqIYHkNbY6WYcPLFIkZENi6990o3oxLiz7ivKz5ANG0FUdilFGqPrtn9NzEBo9QTagM6P0bB_SCGf-YTo6qg7loDFhmb7BuNGbEQtSXUyREWwEA0iYQDh00Rb30GRNOMW2zybYROfbCyOfj_Tns5cme5XUeUJ_NUyPml_bEprpsPINyhrY-YNky3JXBYyU1PfY0hBhjRKZyqS-XGm5zHLOhyZaIP6SmgGHO51lLLroELUF6MBSq3q7nRFLTgfybdHcTycfyROsx0I8UueosyywgpH3BJ5uVcYm0QPIirpM4njzXXwMrcWfaxwmQt1Wd0cdBaH5JTIEBYNWTMPzY7KVTAco-CAO4ngdfGJJdgemNw0bDBVd7wAkUxjmZSCGqDkSDBqeyrBO39BLANy-lIp5b_VkKWbSqg0JOXRd9sYv0Ax0iZT4hwfE3WO8-z_lSo0Y3CthvDqmIZQ0oDeSLOmVxcgSCwSrJwpH_ix_-ZyX5hVf4aAqDoOgFuhhWiQJR1GyypO5tiA==", "id": "rs_0d1a4d10654d56b4016a11ed4ed0f48191b3da93175d5c4f9a", "summary": [{"type": "summary_text", "text": "**Uploading keyframe**\n\nI'm considering the idea of using a full body keyframe instead of just a rectangle. It might provide a more complete representation. So, I need to go ahead and upload the prepared keyframe to ComfyUI. It's all about ensuring the best presentation, and I want to make sure everything is set up correctly before I proceed. Let's just get that done smoothly!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"{\\\"name\\\": \\\"rabbit_meadow_keyframe_416x736.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_3UWLDxyNp0SwnwoZjzeLC29g", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_aTOJnnqwxuEvAHZ8q9QDxt9w", "call_id": "call_aTOJnnqwxuEvAHZ8q9QDxt9w", "response_item_id": "fc_0d1a4d10654d56b4016a11ed6320d4819192c421c9a7cf75b4", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"{\\n  \\\"1\\\": {\\n    \\\"class_type\\\": \\\"UNETLoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"unet_name\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n      \\\"weight_dtype\\\": \\\"default\\\"\\n    }\\n  },\\n  \\\"2\\\": {\\n    \\\"class_type\\\": \\\"CLIPLoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip_name\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n      \\\"type\\\": \\\"wan\\\",\\n      \\\"device\\\": \\\"default\\\"\\n    }\\n  },\\n  \\\"3\\\": {\\n    \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip\\\": [\\\"2\\\", 0],\\n      \\\"text\\\": \\\"Photorealistic vertical nature video of a cute rabbit hopping across a sunny green meadow. The rabbit makes small realistic hops forward through grass, ears bouncing naturally, fur detailed and realistic, gentle camera tracking, bright daylight, shallow depth of field, natural colors, high quality wildlife footage, no text, no logo, no watermark. Keep the rabbit anatomically correct and preserve the same rabbit from the starting image.\\\"\\n    }\\n  },\\n  \\\"4\\\": {\\n    \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n    \\\"inputs\\\": {\\n      \\\"clip\\\": [\\\"2\\\", 0],\\n      \\\"text\\\": \\\"cartoon, animation, deformed rabbit, extra ears, extra legs, melting body, distorted face, blurry, low detail, flickering colors, abstract, static image, text, subtitles, watermark, logo, bad anatomy, duplicated rabbit, disappearing rabbit, unrealistic jump, camera shake\\\"\\n    }\\n  },\\n  \\\"5\\\": {\\n    \\\"class_type\\\": \\\"VAELoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"vae_name\\\": \\\"wan2.2_vae.safetensors\\\"\\n    }\\n  },\\n  \\\"6\\\": {\\n    \\\"class_type\\\": \\\"LoadImage\\\",\\n    \\\"inputs\\\": {\\n      \\\"image\\\": \\\"rabbit_meadow_keyframe_416x736.png\\\"\\n    }\\n  },\\n  \\\"7\\\": {\\n    \\\"class_type\\\": \\\"ImageScale\\\",\\n    \\\"inputs\\\": {\\n      \\\"image\\\": [\\\"6\\\", 0],\\n      \\\"upscale_method\\\": \\\"lanczos\\\",\\n      \\\"width\\\": 416,\\n      \\\"height\\\": 736,\\n      \\\"crop\\\": \\\"disabled\\\"\\n    }\\n  },\\n  \\\"8\\\": {\\n    \\\"class_type\\\": \\\"Wan22ImageToVideoLatent\\\",\\n    \\\"inputs\\\": {\\n      \\\"vae\\\": [\\\"5\\\", 0],\\n      \\\"width\\\": 416,\\n      \\\"height\\\": 736,\\n      \\\"length\\\": 49,\\n      \\\"batch_size\\\": 1,\\n      \\\"start_image\\\": [\\\"7\\\", 0]\\n    }\\n  },\\n  \\\"9\\\": {\\n    \\\"class_type\\\": \\\"KSampler\\\",\\n    \\\"inputs\\\": {\\n      \\\"model\\\": [\\\"1\\\", 0],\\n      \\\"seed\\\": 2305202602,\\n      \\\"steps\\\": 16,\\n      \\\"cfg\\\": 5.0,\\n      \\\"sampler_name\\\": \\\"uni_pc\\\",\\n      \\\"scheduler\\\": \\\"simple\\\",\\n      \\\"positive\\\": [\\\"3\\\", 0],\\n      \\\"negative\\\": [\\\"4\\\", 0],\\n      \\\"latent_image\\\": [\\\"8\\\", 0],\\n      \\\"denoise\\\": 1.0\\n    }\\n  },\\n  \\\"10\\\": {\\n    \\\"class_type\\\": \\\"VAEDecode\\\",\\n    \\\"inputs\\\": {\\n      \\\"samples\\\": [\\\"9\\\", 0],\\n      \\\"vae\\\": [\\\"5\\\", 0]\\n    }\\n  },\\n  \\\"11\\\": {\\n    \\\"class_type\\\": \\\"CreateVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"images\\\": [\\\"10\\\", 0],\\n      \\\"fps\\\": 10.0\\n    }\\n  },\\n  \\\"12\\\": {\\n    \\\"class_type\\\": \\\"SaveVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"video\\\": [\\\"11\\\", 0],\\n      \\\"filename_prefix\\\": \\\"autoshorts_native_wan22_rabbit_i2v_5s\\\",\\n      \\\"format\\\": \\\"mp4\\\",\\n      \\\"codec\\\": \\\"h264\\\"\\n    }\\n  }\\n}\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_native_wan22_rabbit_i2v_5s_workflow.json\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Creating a workflow**\n\nI’m looking to create a workflow called I2V, starting with LoadImage. I’m considering using ImageScale to 416x736, with the crop disabled and centered. With Wan22ImageToVideoLatent, I’ll input start_image from LoadImage or the scaled image. I want the length to be 49 frames at 10 fps for a total of 4.9 seconds. If I use native fp16, that could be fine. Maybe 16 steps? Quality matters more, even if 49 frames seem a bit long. I need to run it!", "reasoning_content": "**Creating a workflow**\n\nI’m looking to create a workflow called I2V, starting with LoadImage. I’m considering using ImageScale to 416x736, with the crop disabled and centered. With Wan22ImageToVideoLatent, I’ll input start_image from LoadImage or the scaled image. I want the length to be 49 frames at 10 fps for a total of 4.9 seconds. If I use native fp16, that could be fine. Maybe 16 steps? Quality matters more, even if 49 frames seem a bit long. I need to run it!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe1jXlHNvZY6emOcfOuxm3u8sNlDEbhgSLjm5fu8i4QF0hNBDFUa7obg1b8Xfpy8Q4PwRGhUlleKxz-pXisVwxejFIKmQ-s9ebddNirmQ2cvSh5Ji0NVSQ0oamxkaI7cWr9jwzK5WvwYpqMobsr8mdKsB8AgfO68BD3ICv0uhdbsXBxLEf7zKb0WZFXgs-ZM6K3_rColgB7UibMW5S-_Z0PaSOjIUXXk1VLA9wGGEBMgjwJAFs8O0XfTe8DCdLlkEpauw1vzi_z_zsjXay3R7MqWt0susW328ffUz5Fkpy5ugU-z4F8s2PWVOj3LDggNrHwUFatP5TubfMW589CN4PxPSLv9hdRgJLJUhbuo1L7IoQNsF9hq7ZFH7U02GkyIHr3Gez__4rgrXAq-jQs2dHjxNT2sjlf3WgEKObgdJAp16-LAVx0cp3oeqm-CVNZSfjWHAivre3gcMX8e2rBFgrKHi9XNmAsHQgkcYpIsgOX7fGeo3zJ7F5d-QgbW6LpfvEErGSFCTAS6qdwK6K7gMD2pT2h5-utHW7mDGL0p8LHdJXUx5FxBr8MMXQHPwLWlFBheC5Vtu5HaBqQGq6SWThG9KRDWGzDG1d3T3_1NbhqgsJgRV1hAxzJgsqStBuR85T6TcW4DtfzBvutmiyHu61nfygNf1qhCrBjBf8OuoEam9wOE6684cRh_JWpGvHSyuvdbYkyU0jUw4SrPuTybJhrTp5gQoJ9ISvUb1dEJeTQhwoINBv0cCtQoxHtQp6zUDErNR5nxeDirHk2XXhkfTVkRLUgjesJg_BMd5FIg8qVRr6hgxVCSCB2-4-sePDoCN7ZBYLj1zXkeWUZ7BJQFMSgkIxeBtFBhRuUNeIx5bwbyfuasuKW16DIM1Jxlase-3qRg1wIk6DMQ13WHQB5pNN8TJk6UUXqAHza2Bz2v52aS0OtjNZQzhK1AsfEK2T5Z5dun7GBiGfYDkX8Q9TJaGwqGA6kddGWntIIuOw1K6DdUAk2qzeSsfx1KA1F0X3vb18rTEnYkrinUZXOCEyHZQ0BP7ZvaVkZVudAyhBoCCRWB1fvJOVYqRtIwvlGydsznlFK3DQxnJhwoFdWYL3NNsU_6UjawGC2yfINw5zV9IQtrTH4iQQJUv7KlpyRYvebNmB9FNKWkaQ-WpRoSKCOHkGh162_Mx5Nn-TNzyD6hLu_ngr6ygCHtvblpEqYOsesxM-c0yT0OzAIXxHJNO_YrCZUDCjCFwltkhVTGg95BuNrZP-XeKzBGL2hx16Dl8rPvDMgyECjDYiRJZxPfhnKUx8X1aub6sP1jwjShYxqOCiole6o=", "id": "rs_0d1a4d10654d56b4016a11ed5e82888191ad961ab6c2ef793a", "summary": [{"type": "summary_text", "text": "**Creating a workflow**\n\nI’m looking to create a workflow called I2V, starting with LoadImage. I’m considering using ImageScale to 416x736, with the crop disabled and centered. With Wan22ImageToVideoLatent, I’ll input start_image from LoadImage or the scaled image. I want the length to be 49 frames at 10 fps for a total of 4.9 seconds. If I use native fp16, that could be fine. Maybe 16 steps? Quality matters more, even if 49 frames seem a bit long. I need to run it!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"bytes_written\": 2671, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_aTOJnnqwxuEvAHZ8q9QDxt9w", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_jNQwG02xWFgvlOKpB4S7QSYe", "call_id": "call_jNQwG02xWFgvlOKpB4S7QSYe", "response_item_id": "fc_0d1a4d10654d56b4016a11ed791cac8191abbd8ea785a5fc35", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\ntry:\\n    req=urllib.request.Request(base+'/free', data=json.dumps({'unload_models': True, 'free_memory': True}).encode(), headers={'Content-Type':'application/json'})\\n    urllib.request.urlopen(req, timeout=20).read()\\nexcept Exception as e:\\n    print('free warning', type(e).__name__, e)\\nworkflow=json.load(open('data/comfyui_native_wan22_rabbit_i2v_5s_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-native-wan22-rabbit-i2v-5s'}).encode()\\nreq=urllib.request.Request(base+'/prompt', data=payload, headers={'Content-Type':'application/json'})\\ntry:\\n    print(urllib.request.urlopen(req, timeout=30).read().decode())\\nexcept Exception as e:\\n    print('ERROR', type(e).__name__, e)\\n    if hasattr(e, 'read'):\\n        print(e.read().decode())\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"76cb91e6-b0d9-4c49-863c-09578c59a8db\\\", \\\"number\\\": 1, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_jNQwG02xWFgvlOKpB4S7QSYe", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_MiccWPwPhDY8K3kEmsHJjdTG", "call_id": "call_MiccWPwPhDY8K3kEmsHJjdTG", "response_item_id": "fc_0d1a4d10654d56b4016a11ed875560819198c94a8cb500214d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request\\nbase='http://100.101.173.25:8188'\\npid='76cb91e6-b0d9-4c49-863c-09578c59a8db'\\nfor i in range(240):\\n    q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n    s=json.load(urllib.request.urlopen(base+'/system_stats', timeout=10))\\n    running=len(q.get('queue_running', [])); pending=len(q.get('queue_pending', []))\\n    d=s['devices'][0]\\n    vused=(d['vram_total']-d['vram_free'])/1024**3\\n    print(f'poll {i:03d}: running={running} pending={pending} vram_used_gb={vused:.2f} ram_free_gb={s[\\\"system\\\"][\\\"ram_free\\\"]/1024**3:.2f}', flush=True)\\n    if not running and not pending:\\n        break\\n    time.sleep(10)\\nh=json.load(urllib.request.urlopen(base+'/history/'+pid, timeout=20))\\nprint('STATUS')\\nprint(json.dumps(h.get(pid, {}).get('status',{}), indent=2)[:8000])\\nprint('OUTPUTS')\\nprint(json.dumps(h.get(pid, {}).get('outputs',{}), indent=2))\\nPY\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"poll 000: running=1 pending=0 vram_used_gb=13.87 ram_free_gb=15.59\\npoll 001: running=1 pending=0 vram_used_gb=13.76 ram_free_gb=15.50\\npoll 002: running=1 pending=0 vram_used_gb=13.77 ram_free_gb=15.51\\npoll 003: running=1 pending=0 vram_used_gb=13.76 ram_free_gb=15.53\\npoll 004: running=1 pending=0 vram_used_gb=14.16 ram_free_gb=15.49\\npoll 005: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=15.51\\npoll 006: running=1 pending=0 vram_used_gb=13.98 ram_free_gb=15.50\\npoll 007: running=1 pending=0 vram_used_gb=14.02 ram_free_gb=15.52\\npoll 008: running=1 pending=0 vram_used_gb=14.05 ram_free_gb=15.51\\npoll 009: running=1 pending=0 vram_used_gb=13.45 ram_free_gb=15.69\\npoll 010: running=1 pending=0 vram_used_gb=12.32 ram_free_gb=15.60\\npoll 011: running=1 pending=0 vram_used_gb=11.00 ram_free_gb=15.59\\npoll 012: running=1 pending=0 vram_used_gb=9.64 ram_free_gb=15.59\\npoll 013: running=0 pending=0 vram_used_gb=9.43 ram_free_gb=15.46\\nSTATUS\\n{\\n  \\\"status_str\\\": \\\"success\\\",\\n  \\\"completed\\\": true,\\n  \\\"messages\\\": [\\n    [\\n      \\\"execution_start\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"76cb91e6-b0d9-4c49-863c-09578c59a8db\\\",\\n        \\\"timestamp\\\": 1779559812049\\n      }\\n    ],\\n    [\\n      \\\"execution_cached\\\",\\n      {\\n        \\\"nodes\\\": [],\\n        \\\"prompt_id\\\": \\\"76cb91e6-b0d9-4c49-863c-09578c59a8db\\\",\\n        \\\"timestamp\\\": 1779559812052\\n      }\\n    ],\\n    [\\n      \\\"execution_success\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"76cb91e6-b0d9-4c49-863c-09578c59a8db\\\",\\n        \\\"timestamp\\\": 1779559950557\\n      }\\n    ]\\n  ]\\n}\\nOUTPUTS\\n{\\n  \\\"12\\\": {\\n    \\\"images\\\": [\\n      {\\n        \\\"filename\\\": \\\"autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\\\",\\n        \\\"subfolder\\\": \\\"\\\",\\n        \\\"type\\\": \\\"output\\\"\\n      }\\n    ],\\n    \\\"animated\\\": [\\n      true\\n    ]\\n  }\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_MiccWPwPhDY8K3kEmsHJjdTG", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Need download and inspect.", "tool_calls": [{"id": "call_eWg4p2jZnHqHOpAqPCRpb5eJ", "call_id": "call_eWg4p2jZnHqHOpAqPCRpb5eJ", "response_item_id": "fc_0d1a4d10654d56b4016a11ee192dd88191871227e9ffd7b7f3", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'subfolder':'','type':'output'})\\nout=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/'+fn)\\nout.parent.mkdir(parents=True, exist_ok=True)\\nout.write_bytes(urllib.request.urlopen(base+'/view?'+params, timeout=180).read())\\nprint(out, out.stat().st_size)\\nPY\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=nw=1:nk=1 data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\\nffmpeg -y -i data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4 -vf \\\"select=eq(n\\\\,24)\\\" -frames:v 1 /tmp/autoshorts_comfy_check/rabbit_i2v_mid.jpg >/dev/null 2>&1 && ls -lh /tmp/autoshorts_comfy_check/rabbit_i2v_mid.jpg\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need download and inspect."}], "id": "msg_0d1a4d10654d56b4016a11ee18dc488191874f15edb1954f1c", "phase": "commentary"}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4 875454\\n416\\n736\\n10/1\\n4.900000\\n-rw-rw-r-- 1 agent agent 34K Mai 23 20:12 /tmp/autoshorts_comfy_check/rabbit_i2v_mid.jpg\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_eWg4p2jZnHqHOpAqPCRpb5eJ", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_R6wNbPOoZ9eATEDpIFU51HQX", "call_id": "call_R6wNbPOoZ9eATEDpIFU51HQX", "response_item_id": "fc_0d1a4d10654d56b4016a11ee2414c88191b058e378bb1c8cc3", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/rabbit_i2v_mid.jpg\",\"question\":\"Beurteile knapp: Ist ein photorealistischer Hase auf einer Wiese erkennbar? Gibt es sichtbare Probleme wie Box/Artefakte? Eignet sich das als I2V-Testclip?\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beurteile knapp: Ist ein photorealistischer Hase auf einer Wiese erkennbar? Gibt es sichtbare Probleme wie Box/Artefakte? Eignet sich das als I2V-Testclip?\n[screenshot]", "tool_call_id": "call_R6wNbPOoZ9eATEDpIFU51HQX", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_pUqFIatvG9pxibs3HOQItQYE", "call_id": "call_pUqFIatvG9pxibs3HOQItQYE", "response_item_id": "fc_0d1a4d10654d56b4016a11ee309bb88191bd2778ee90e9602d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nfrom PIL import Image, ImageOps, ImageEnhance\\nfrom pathlib import Path\\nsrc=Path('data/source_images/rabbit_meadow_wikimedia.jpg')\\nimg=Image.open(src).convert('RGB')\\n# Crop to portrait without inset rectangle, centered on rabbit face/body.\\n# Use crop box shifted slightly right because rabbit is centered/right.\\nw,h=img.size\\ntarget_aspect=416/736\\ncrop_w=int(h*target_aspect)\\nx0=max(0, int((w-crop_w)*0.58))\\nbox=(x0,0,x0+crop_w,h)\\ncropped=img.crop(box)\\nout_img=cropped.resize((416,736), Image.Resampling.LANCZOS)\\nout_img=ImageEnhance.Sharpness(out_img).enhance(1.25)\\nout=Path('data/source_images/rabbit_meadow_keyframe_crop_416x736.png')\\nout_img.save(out)\\nprint('source', img.size, 'crop', box, 'out', out, out_img.size)\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering image adjustments**\n\nI’m looking at the target aspect ratio of .565 and a source of 1.118. I think cropping the width to about 196 px from the center might work. I might keep the rabbit in view, but cutting the sides isn’t ideal, though it’s not terrible. There’ll be a significant upscale, but thankfully, there won’t be a box. Okay, let’s go ahead and generate that image!", "reasoning_content": "**Considering image adjustments**\n\nI’m looking at the target aspect ratio of .565 and a source of 1.118. I think cropping the width to about 196 px from the center might work. I might keep the rabbit in view, but cutting the sides isn’t ideal, though it’s not terrible. There’ll be a significant upscale, but thankfully, there won’t be a box. Okay, let’s go ahead and generate that image!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe4w-7JMUlnZkxFm6W7GitqFTZ8f0g6RgXzkTBxcx4VEa0jUD6SPQbB-0xt0vwUTXYQWe7QBX-YV73V4mPH_99hFzgaooRy74_jvzE2CQcmZvDy7pvGImCQQJlMgYnv3Hxju5f8uihI92q7lbrtp7f9LTy86G7BAkII8NCp_Ix_ngcB3YlXDlbWy2ykugQq6Tn9Sf9nUhZHQRYQuSGxwfdyb7sf52r-tDv0WvEr1bUWL5TcxvjeVUYCoEvNFv1bDWqBMVf3tlT-EUs03UDEr92j7okIohS9w376c4OYDebeYY17oUvddT2mMadmcC25nBBpmo7acrDL46UHRYdXADq_SA_TXltcVP0hpf8M_1fxrlzCAfa-BZpZt6VH1SOELtXEnzKAu7GcYKfBP904gLksX97VSp0RTfen2o_vMTR4Ip9IWYqbxrDOicEzLueakUGjsxS-VZW2aOdWxsk_wHehCzH360O9WE4fu-i2opUJ_A7em5g_5iCb1-gRjdPssc4u7AuK9pH8QjKYEXGw-so61F6ijKZ5iZi6F24mM0fTKuIsug8NayFHgwLqIm3fGttSgFvHbdUN3bqwD-mZDzgsoNgNBrXsrLP4WCK_9deiy1eDKWgvkRdgKKkg-OTF61PRczexXr3f6NU1C-jJ439rR1_Pnif_4iVVhH7eR7qzJD07PVd4ok_V5HfmzQwGBvXSr5N25wTjFqPzYeox45lE9i6oK-3HxEXtOtdBMuo7stPdBJDeOvJLeG4Fq_VNHYvdZp7b6w4J73rzRKL3MvtZSEqbs3PulLVPQaYmUm3OyijbhL8g4JlVki0Ob1a2Bk1Fiu3wQeo7DKfZ1ximFiQeP0njyghYJ5Ig4ChBSNk7v9-Qd1dvQAsExeJSaRwUOEbCRH05AFsUxqL_J6fxV9QAl4M3asbFepKA0SLAh9IVxStqNfRg4iFRwObXVdoImWArDgP-JnNPJyGDbE1eNberX_9m3Go7TcgPOLt7AVenNK5uzpm2p9lcI4a40AuA-AHn7T3Nm0jChe729NyGqJqGwxGwBKiK_Uc_7t-Xrd6wPUoQTpZW7s-AefJI7JghquUj0Cc54nh5ua5TII9xafnxjT5CRsQjHpc5P8j_U6OX8rXq0zSqA4CRvofoiJflPwYZTcvI2-YgW66sessLl3f3piUOZbAvyAyYtHWRiV7bPPmD_eHp-1d9BdYZnK2YcJSt0DjZbX9biTqUeCcSwsOMRkk95d6JK0FcToJ95QDr19WMqaLoW3h44CpBVSHCLBpRRh1v2XlS242qrEC-N_HfdbhsgPl7BnRYHKiwyiTEu-8-mcdKucaP36I2pf5Uq7hWk1fpehKgfwx8v1uuqO8tJlv3GjD4DQwJDiIQMxFUAgkGXO0eDWKud6zdgtbvDbS4TxqTYDhXeaWdIfiDB6sXXOJZmrGqxqLWlSBif4oF2vMjW4vLyZ5PTnwRR_BaCy6pXYMgDhCHyBJotxY2lSB-kYR6Bmm7vwxYXWiXdyzenrMlbhhQiBI43ZQ0B8v74GWHTBdlB41Q-8KNQ3-kbcCUsA8eVpSWm_L2Taz2v9p26YgQGmAQQmLFGGgW4_1kjqMsBlfk4UKKq_DtixgFDGcWblv-jNR1Qf8MNFSjTFc4jN7dLrp2D8Qc_hV2X0Ljgdbbg8w7vwENIAKrgogQzWesI3Ii_3VK-sxUNQIwqiGqnNLmQQisS_MyNG9-mMU_oL92_KR9enSvgq68_GjUCxFHE8D0dEFMB2BnnhqaeILs7H_cNBFTbjgJKYwBAwMr6adIJWntzV5uz9hL9Uq8rzlTg150HBXMz7foh732Bb6EZ4SdKqywr564QulhXH324D2KqOfzYsU_maHnb6mTjWfqngeO6Ls0B0F7aBUAI17vp5BcdjwHJ5J8m5yMsDWcFEPZch5471cvYOeRs3ZxDOQ_XDv8hr_6iFZ-OW01T8Fc5eEOyQS_ZHR84fb6BkS60qp3n6MPumfAYpueNjQKmFuNGkTyIh5469p8OehC0g4aeCOS1QLPBJxO9WSglr05pxvxpEFquuujXj6CwWxXgqyZ3Xxvt0MNcLZjVmZ9IzpbWJUlVcu5KRRR1Cz8HHtRSl9i4cYJx80SFj0i3R71Tn2VhW8gZ-QDdLrEYWEtEDJwk5kuFsNA7L3iyXL-ZgQcRRhLFlaDbUDc3YdDLVRaE-SzYGDAwGT4bLUedQ4YJ8T7xk8r8wrcy1XmqlYimSdcpvE5aw9NrV7oRmYznZQ6CGOsYL_sFviP8EPegPmgVqmC7L35wN3UCQccK-tZgkAtgn4roladsqTEJg59tOQAyNCC_t8CYYCdwLON_KfloueRusZOA_fs0e3bJLZt1m2BuSTEiCAAtHJUNVgpjVyAshZUp-PqlbR2OxklB1ey2P0gZ-ItyNxsLtTZXai-VJrralxTK", "id": "rs_0d1a4d10654d56b4016a11ee29eb608191a7b1674b1a034de1", "summary": [{"type": "summary_text", "text": "**Considering image adjustments**\n\nI’m looking at the target aspect ratio of .565 and a source of 1.118. I think cropping the width to about 196 px from the center might work. I might keep the rabbit in view, but cutting the sides isn’t ideal, though it’s not terrible. There’ll be a significant upscale, but thankfully, there won’t be a box. Okay, let’s go ahead and generate that image!"}]}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"source (387, 348) crop (110, 0, 306, 348) out data/source_images/rabbit_meadow_keyframe_crop_416x736.png (416, 736)\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_pUqFIatvG9pxibs3HOQItQYE", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_gmFAVGVEZORchLporRIBdrE3", "call_id": "call_gmFAVGVEZORchLporRIBdrE3", "response_item_id": "fc_0d1a4d10654d56b4016a11ee3c7ae88191b8d6166ed63f6b8d", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/source_images/rabbit_meadow_keyframe_crop_416x736.png\",\"question\":\"Ist dieses alternative 9:16-Keyframe ohne Rechteck besser? Ist der Hase gut sichtbar oder zu stark beschnitten/unscharf?\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Ist dieses alternative 9:16-Keyframe ohne Rechteck besser? Ist der Hase gut sichtbar oder zu stark beschnitten/unscharf?\n[screenshot]", "tool_call_id": "call_gmFAVGVEZORchLporRIBdrE3", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_W7NfUbWm1nfzXtCpgI3jlxPs", "call_id": "call_W7NfUbWm1nfzXtCpgI3jlxPs", "response_item_id": "fc_0d1a4d10654d56b4016a11ee47327481919702653a9addb3cd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request\\nfrom pathlib import Path\\nbase='http://100.101.173.25:8188'\\npath=Path('data/source_images/rabbit_meadow_keyframe_crop_416x736.png')\\nboundary='----HermesBoundary2'\\nparts=[]\\ndef add(x): parts.append(x.encode() if isinstance(x,str) else x)\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"image\\\"; filename=\\\"rabbit_meadow_keyframe_crop_416x736.png\\\"\\\\r\\\\nContent-Type: image/png\\\\r\\\\n\\\\r\\\\n')\\nadd(path.read_bytes()); add('\\\\r\\\\n')\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"type\\\"\\\\r\\\\n\\\\r\\\\ninput\\\\r\\\\n')\\nadd(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"overwrite\\\"\\\\r\\\\n\\\\r\\\\ntrue\\\\r\\\\n')\\nadd(f'--{boundary}--\\\\r\\\\n')\\nreq=urllib.request.Request(base+'/upload/image', data=b''.join(parts), headers={'Content-Type':f'multipart/form-data; boundary={boundary}'})\\nprint(urllib.request.urlopen(req, timeout=60).read().decode())\\nPY\",\"timeout\":90,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe5HVb_rKPREy7uCOrH0Qqhy81YAxcuWQ7SFtpNEPTM15p43qAXrbHTxPltnpMNUVJ65027-R6626dcb8YA7s6BCKN-0gkJA4bgBp8XyMSqGdjpDh9o8MQV0xxOdF1lfYN-AUqxBzPgB5k4oG-bkq5Yegq10aefT2KjFhiZl1SBGmh1YdHW4uec1TlORF_CqPRTTBXlCVwtgqK-moqO39ZlZ40Ph9Q5rRRZdV-4QNU4ZynOF7SeQdtuTBK2SNL13SdsnS2fo5TTpkTVuodwQEsIP4y-s0eN1zJI8d1jOf84s1iDbXPZO5jeAPKVr4n0hSh8rJ8LahMo_rKTwehMDSwVsbDT5hRB7smqrt_jWWEiaskEwXhURfRCyylqV5YhSRb4k9Op5CRz1g8n9ks53HxJphmZT38UcJXQWuSmzxpxuJkaH5i6eKlG8cALk-Un8HNsRk8FqE-sY3Ks4_ROfgbeWs7RjVF1CFRafniGj8yo4UQDpdfpfEmVKrRdxRQlKxiptnFsSKhfSGmWA7DMMMJa6W64C8_40jclySSYgSAVZznfLghMOScOes2axf3NpzcDlEPeUEUf5j8VpzMEX8NOuVHXvFUAibWPno54eW8maLNpiNda8bfoIIwSdbk0F9TTtRMIsv8Pe4Q0epv3qNNNuC4qFmUDVDz3FwU9_B4xD-5yLqp_T2xZEONofrpRjPgrWNdAY0cFX-QNZhoBfz4lt42eEk905VIFncqX5LrCSbu2KEBedF7ePsni8yVjB7AykIgAiklSuG5ohyDkeBcE-F2u9uJXcTl85zY10J1EYqbkaUbEo2VLCmdqaZ0Ncym4MJjAWVKm1DVz_OpaRC-_LNySkU0J677ayiOPTegyfKAlBAdvg8tnDXVAE5F-bR3caEJLDWGimOyb2g919v7Uvx-pyrNKsdZr5-EAHzCq5u5JgbTyXKMfmnR8aeLNT2-5eFH6x5eQW8F7EvrQrPJje0qba9XgfsDFjtJDeVb_GcyToI7wI8830WTE0Je26-mBnRgM-HK3FRBtcLuECCFFSJA==", "id": "rs_0d1a4d10654d56b4016a11ee44c2ec819186a98cfe4f58a84f", "summary": []}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"{\\\"name\\\": \\\"rabbit_meadow_keyframe_crop_416x736.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_W7NfUbWm1nfzXtCpgI3jlxPs", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_yi8ekh7PeL4PXtltDFPQWk9t", "call_id": "call_yi8ekh7PeL4PXtltDFPQWk9t", "response_item_id": "fc_0d1a4d10654d56b4016a11ee52a3248191aba6f4cbb3355a61", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json\\nfrom pathlib import Path\\np=Path('data/comfyui_native_wan22_rabbit_i2v_5s_workflow.json')\\nw=json.loads(p.read_text())\\nw['6']['inputs']['image']='rabbit_meadow_keyframe_crop_416x736.png'\\nw['12']['inputs']['filename_prefix']='autoshorts_native_wan22_rabbit_i2v_crop_5s'\\nw['9']['inputs']['seed']=2305202603\\nout=Path('data/comfyui_native_wan22_rabbit_i2v_crop_5s_workflow.json')\\nout.write_text(json.dumps(w, indent=2))\\nprint(out)\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"data/comfyui_native_wan22_rabbit_i2v_crop_5s_workflow.json\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_yi8ekh7PeL4PXtltDFPQWk9t", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_2IjasLwU8dYNFs6mHZKst9tL", "call_id": "call_2IjasLwU8dYNFs6mHZKst9tL", "response_item_id": "fc_0d1a4d10654d56b4016a11ee6037488191ae8bca2414754eb9", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\ntry:\\n    req=urllib.request.Request(base+'/free', data=json.dumps({'unload_models': False, 'free_memory': True}).encode(), headers={'Content-Type':'application/json'})\\n    urllib.request.urlopen(req, timeout=20).read()\\nexcept Exception as e: print('free warning', type(e).__name__, e)\\nworkflow=json.load(open('data/comfyui_native_wan22_rabbit_i2v_crop_5s_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-native-wan22-rabbit-i2v-crop-5s'}).encode()\\nreq=urllib.request.Request(base+'/prompt', data=payload, headers={'Content-Type':'application/json'})\\nprint(urllib.request.urlopen(req, timeout=30).read().decode())\\nPY\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"7fbcb6b5-8526-44f8-a127-e4b6b6f0cc0b\\\", \\\"number\\\": 2, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_2IjasLwU8dYNFs6mHZKst9tL", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_eOEi4yM9jajRgZT7UySQ97VW", "call_id": "call_eOEi4yM9jajRgZT7UySQ97VW", "response_item_id": "fc_0d1a4d10654d56b4016a11ee6a1a9c8191bb111e1608b0babb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request\\nbase='http://100.101.173.25:8188'\\npid='7fbcb6b5-8526-44f8-a127-e4b6b6f0cc0b'\\nfor i in range(220):\\n    q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n    s=json.load(urllib.request.urlopen(base+'/system_stats', timeout=10))\\n    running=len(q.get('queue_running', [])); pending=len(q.get('queue_pending', []))\\n    d=s['devices'][0]\\n    vused=(d['vram_total']-d['vram_free'])/1024**3\\n    print(f'poll {i:03d}: running={running} pending={pending} vram_used_gb={vused:.2f} ram_free_gb={s[\\\"system\\\"][\\\"ram_free\\\"]/1024**3:.2f}', flush=True)\\n    if not running and not pending:\\n        break\\n    time.sleep(10)\\nh=json.load(urllib.request.urlopen(base+'/history/'+pid, timeout=20))\\nprint('STATUS')\\nprint(json.dumps(h.get(pid, {}).get('status',{}), indent=2)[:5000])\\nprint('OUTPUTS')\\nprint(json.dumps(h.get(pid, {}).get('outputs',{}), indent=2))\\nPY\",\"timeout\":600,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"poll 000: running=1 pending=0 vram_used_gb=13.09 ram_free_gb=14.59\\npoll 001: running=1 pending=0 vram_used_gb=13.76 ram_free_gb=14.43\\npoll 002: running=1 pending=0 vram_used_gb=13.76 ram_free_gb=14.45\\npoll 003: running=1 pending=0 vram_used_gb=13.98 ram_free_gb=14.45\\npoll 004: running=1 pending=0 vram_used_gb=14.02 ram_free_gb=14.46\\npoll 005: running=1 pending=0 vram_used_gb=14.08 ram_free_gb=14.49\\npoll 006: running=1 pending=0 vram_used_gb=14.07 ram_free_gb=14.52\\npoll 007: running=1 pending=0 vram_used_gb=13.94 ram_free_gb=14.56\\npoll 008: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.48\\npoll 009: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.49\\npoll 010: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.51\\npoll 011: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.50\\npoll 012: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.47\\npoll 013: running=1 pending=0 vram_used_gb=13.73 ram_free_gb=14.60\\npoll 014: running=1 pending=0 vram_used_gb=13.69 ram_free_gb=14.64\\npoll 015: running=1 pending=0 vram_used_gb=13.51 ram_free_gb=14.62\\npoll 016: running=1 pending=0 vram_used_gb=11.85 ram_free_gb=14.60\\npoll 017: running=1 pending=0 vram_used_gb=11.01 ram_free_gb=14.62\\npoll 018: running=1 pending=0 vram_used_gb=9.80 ram_free_gb=14.58\\npoll 019: running=0 pending=0 vram_used_gb=9.59 ram_free_gb=14.45\\nSTATUS\\n{\\n  \\\"status_str\\\": \\\"success\\\",\\n  \\\"completed\\\": true,\\n  \\\"messages\\\": [\\n    [\\n      \\\"execution_start\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"7fbcb6b5-8526-44f8-a127-e4b6b6f0cc0b\\\",\\n        \\\"timestamp\\\": 1779560038761\\n      }\\n    ],\\n    [\\n      \\\"execution_cached\\\",\\n      {\\n        \\\"nodes\\\": [],\\n        \\\"prompt_id\\\": \\\"7fbcb6b5-8526-44f8-a127-e4b6b6f0cc0b\\\",\\n        \\\"timestamp\\\": 1779560038763\\n      }\\n    ],\\n    [\\n      \\\"execution_success\\\",\\n      {\\n        \\\"prompt_id\\\": \\\"7fbcb6b5-8526-44f8-a127-e4b6b6f0cc0b\\\",\\n        \\\"timestamp\\\": 1779560237518\\n      }\\n    ]\\n  ]\\n}\\nOUTPUTS\\n{\\n  \\\"12\\\": {\\n    \\\"images\\\": [\\n      {\\n        \\\"filename\\\": \\\"autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\\\",\\n        \\\"subfolder\\\": \\\"\\\",\\n        \\\"type\\\": \\\"output\\\"\\n      }\\n    ],\\n    \\\"animated\\\": [\\n      true\\n    ]\\n  }\\n}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_eOEi4yM9jajRgZT7UySQ97VW", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_BAioiEeqDfdvZRUcTM86BGxv", "call_id": "call_BAioiEeqDfdvZRUcTM86BGxv", "response_item_id": "fc_0d1a4d10654d56b4016a11ef399b708191897d64d690b25ad6", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.parse, urllib.request, pathlib\\nbase='http://100.101.173.25:8188'\\nfn='autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4'\\nparams=urllib.parse.urlencode({'filename':fn,'subfolder':'','type':'output'})\\nout=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/'+fn)\\nout.parent.mkdir(parents=True, exist_ok=True)\\nout.write_bytes(urllib.request.urlopen(base+'/view?'+params, timeout=180).read())\\nprint(out, out.stat().st_size)\\nPY\\nffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=nw=1:nk=1 data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\\nffmpeg -y -i data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4 -vf \\\"select=eq(n\\\\,24)\\\" -frames:v 1 /tmp/autoshorts_comfy_check/rabbit_i2v_crop_mid.jpg >/dev/null 2>&1 && ls -lh /tmp/autoshorts_comfy_check/rabbit_i2v_crop_mid.jpg\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "{\"output\": \"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4 551187\\n416\\n736\\n10/1\\n4.900000\\n-rw-rw-r-- 1 agent agent 27K Mai 23 20:17 /tmp/autoshorts_comfy_check/rabbit_i2v_crop_mid.jpg\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_BAioiEeqDfdvZRUcTM86BGxv", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_UT6llYuktLuklsa7ofzjX0wE", "call_id": "call_UT6llYuktLuklsa7ofzjX0wE", "response_item_id": "fc_0d1a4d10654d56b4016a11ef4693288191b9cd160e39657133", "type": "function", "function": {"name": "vision_analyze", "arguments": "{\"image_url\":\"/tmp/autoshorts_comfy_check/rabbit_i2v_crop_mid.jpg\",\"question\":\"Beurteile knapp den zweiten I2V-Test: photorealistischer Hase auf Wiese? Keine Box? Qualität/Probleme?\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "tool", "content": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beurteile knapp den zweiten I2V-Test: photorealistischer Hase auf Wiese? Keine Box? Qualität/Probleme?\n[screenshot]", "tool_call_id": "call_UT6llYuktLuklsa7ofzjX0wE", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "assistant", "content": "Erledigt, Sir.\n\nIch habe jetzt **Image-to-Video** mit dem nativen Wan2.2-Workflow getestet.\n\n## Wichtiger Hinweis\n\nDie eingebaute Image-Generation hier war nicht verfügbar:\n\n```text\nFAL_KEY fehlt\n```\n\nIch konnte also **kein ChatGPT/FAL-Bild direkt selbst erzeugen**. Als Ersatz habe ich ein photorealistisches Kaninchenbild aus Wikimedia Commons genommen, als 9:16-Keyframe vorbereitet und dann per Wan2.2 I2V animiert.\n\nNicht ideal, aber für den technischen Test sauber genug.\n\n---\n\n## Test 1 — verworfen\n\nIch hatte zuerst ein 9:16-Keyframe mit blurred background + eingefügtem Hasenbild gebaut.\n\nProblem:\n\n```text\nsichtbarer rechteckiger Bildkasten\n```\n\nOutput existiert, aber ist nicht empfehlenswert:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n\n---\n\n## Test 2 — besserer I2V-Test ohne Bildkasten\n\nIch habe danach ein alternatives Keyframe erstellt:\n\n```text\nrabbit_meadow_keyframe_crop_416x736.png\n```\n\nDas ist ein echter 9:16-Crop ohne sichtbaren Rahmen.\n\nDann I2V:\n\n```text\nNative Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nStart image: rabbit_meadow_keyframe_crop_416x736.png\nResolution: 416x736\nFrames: 49\nFPS: 10\nDuration: 4.9s\nSteps: 16\nCFG: 5.0\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n\n---\n\n## Bewertung\n\nDieser zweite Clip ist deutlich besser:\n\n- photorealistischer Hase erkennbar\n- Wiese erkennbar\n- kein sichtbarer Rechteck-Kasten\n- native Wan2.2 I2V läuft stabil\n- 4.9 Sekunden, also praktisch dein gewünschter 5s-Test\n\nAber:\n\n```text\nDer Hase “hüpft” noch nicht überzeugend über das Feld.\n```\n\nEr bewegt sich eher leicht / morphend im Bild. Das liegt am Startbild: Der Hase sitzt sehr frontal und ist nicht in Sprungpose. Wan hält das Startbild stark fest — gut für Identität, schlecht für große Bewegung.\n\n---\n\n# Erkenntnis\n\nI2V ist technisch klar besser als T2V, aber für „Hase hüpft über Feld“ brauchen wir ein besseres Keyframe:\n\n```text\nHase seitlich sichtbar\nganzer Körper\nSprung-/Laufpose\nmehr freie Wiese vor ihm\nkeine frontale Sitzpose\n```\n\nDas aktuelle Bild ist niedlich, aber als Startpose für „hüpfen“ ungeeignet. Ein sitzender Hase wird nicht plötzlich elegant zum BBC-Nature-Sprinter. Leider keine Magie, nur Diffusion.\n\n---\n\n# Nächster sinnvoller Schritt\n\nFür einen wirklich guten Clip:\n\n1. In ChatGPT Pro / Bildgenerator ein Keyframe erzeugen:\n\n```text\nPhotorealistic vertical 9:16 wildlife photo of a cute brown rabbit mid-hop across a sunny green meadow, full body side view, all four legs visible, ears up, natural grass, shallow depth of field, bright daylight, sharp realistic fur, lots of open meadow in front of the rabbit, no text, no watermark\n```\n\n2. Dann dieses Bild hier verwenden als:\n\n```text\nstart_image\n```\n\n3. Wan2.2 I2V erneut mit Prompt:\n\n```text\nThe rabbit hops forward across the meadow with small realistic jumps, ears bouncing naturally, grass moving under its feet, smooth wildlife camera tracking.\n```\n\nKurz: **I2V funktioniert. Für überzeugendes Hüpfen brauchen wir jetzt ein gutes seitliches Mid-Hop-Keyframe statt eines sitzenden frontalen Kaninchens.**", "reasoning": "**Finalizing media output**\n\nI need to wrap up by mentioning that image generation is currently unavailable, so I've used an external Wikimedia keyframe and cropped it. The user wanted something photorealistic, which we achieved. I should probably note that I have two outputs: the first included a box (which I don’t recommend), and the second does not have one. It makes sense to deliver the second option since it's cleaner. That sounds good to me!", "reasoning_content": "**Finalizing media output**\n\nI need to wrap up by mentioning that image generation is currently unavailable, so I've used an external Wikimedia keyframe and cropped it. The user wanted something photorealistic, which we achieved. I should probably note that I have two outputs: the first included a box (which I don’t recommend), and the second does not have one. It makes sense to deliver the second option since it's cleaner. That sounds good to me!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEe9Q-ENdPHw9fJ_KRD_TEl0bx7WsBxqNVV-AWm9_PV3--gU0uy6eWtYV-LyjvXmwUFvsEIH4AY-yIyz64MrxE6PRSw0tYEZrFDE_JCBTAExCO0t4wNNYkfmtlCkO8rst2m0C930hYQEfus64Bz7thz76d32HYA7B4PwTb4TJS3ZkaxAOLfC4Bf4J7gmI5RM7AnBBuJTbehAYKCDn-Dw8iExy08DjJ2sGR0zKYDLwrzQjMTam9m2i7Lc_IqdgG3JRRE77Ni63-BBlVQ1HOmPK6fNJgEGo7-HWNf80Poj-g_af6RTsgAu2dQxvdWwOcwNdO-W-yreEsMAUcvPvdNLWb19Weu4Nout1D0ALnURvGb1jQkEQCu438XnZvS3y8apSz6C9VlCMGW0FeNpt99px1Arb0I1sBwXZLGxI56CC57yjNcdjXjiw7-MstxA_gPKCB-NebCWn3Qz0BIDUi01wDcJXqd7cILAiR7gBbgK-1cGaTtEYAMYp7nYX68YpXQ8O8JHkO4mEKOE7PCh2E-iwZFVgOYFF8YIdxm9G0LmG8ewpE6mupad5OwxW1asK9L-rMwvzBk5pB05YHN_cp2UC3Zxt4z9SaCSAsDmiNs67XUFq_9yrsil2yM-yDW6nLTppKXJtl3vNOrj-RYU7SEBIhrVUIUZLy7O6GsyxECbH2KbmsitYu8KoVPNXlxD8ifqLTOB1ejeJ1_Md7QHi-K7Q5PAvpaToLSApmB-BFbRWo-YV6xenB1jygvqSFBRVln8vHBSY3788rU4m00y1WCN4lo7pY4p-8bTuKYmNkdMO9baz0ERi9XYOX9Jty3EsKkF8TVlozuxpMCwU6R16QCt0AUECeiNWTgGFBBVwsvoSSpHrSSDLmRxgUGkfru2LwPOAzG90drN8eV2aIpKS1wn6pwxCDybmaXM16mNGo0tEkExhilWJ_6h0MOQDtbKnvQpOpvgsvSdlgs8yLB89wrL3PGOArIn5qMduoGecGMHz79aE8o5fh1tK2t08FDELUNVu51Kh9dGPZ7kTNKqjD3Vhnmo_1q23h9G0ETEsWClGXcOJf1Zac1z3iLvO2Vpf3HriCdF04TvlKGxbbXf6XAxJ61P7uDBzZTeUsEwXEfYoQuAqU3ZsqNmeUJzgTKj_xzI2GwR46ESSBxgEraMWqOR5vIiW9KrxHzjzoB_v2ZxCWDknNUm7X2SqYqahJSNozQui5cJzbvTO7ucyhMp2h0hTYwxK1AJ9GOd_Hg4yq0l4fF-5pJk1OM4_p-cobLfGX5dyQUj8", "id": "rs_0d1a4d10654d56b4016a11ef4d5d3c819183e5c9388a14829b", "summary": [{"type": "summary_text", "text": "**Finalizing media output**\n\nI need to wrap up by mentioning that image generation is currently unavailable, so I've used an external Wikimedia keyframe and cropped it. The user wanted something photorealistic, which we achieved. I should probably note that I have two outputs: the first included a box (which I don’t recommend), and the second does not have one. It makes sense to deliver the second option since it's cleaner. That sounds good to me!"}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\nIch habe jetzt **Image-to-Video** mit dem nativen Wan2.2-Workflow getestet.\n\n## Wichtiger Hinweis\n\nDie eingebaute Image-Generation hier war nicht verfügbar:\n\n```text\nFAL_KEY fehlt\n```\n\nIch konnte also **kein ChatGPT/FAL-Bild direkt selbst erzeugen**. Als Ersatz habe ich ein photorealistisches Kaninchenbild aus Wikimedia Commons genommen, als 9:16-Keyframe vorbereitet und dann per Wan2.2 I2V animiert.\n\nNicht ideal, aber für den technischen Test sauber genug.\n\n---\n\n## Test 1 — verworfen\n\nIch hatte zuerst ein 9:16-Keyframe mit blurred background + eingefügtem Hasenbild gebaut.\n\nProblem:\n\n```text\nsichtbarer rechteckiger Bildkasten\n```\n\nOutput existiert, aber ist nicht empfehlenswert:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_5s_00001_.mp4\n\n---\n\n## Test 2 — besserer I2V-Test ohne Bildkasten\n\nIch habe danach ein alternatives Keyframe erstellt:\n\n```text\nrabbit_meadow_keyframe_crop_416x736.png\n```\n\nDas ist ein echter 9:16-Crop ohne sichtbaren Rahmen.\n\nDann I2V:\n\n```text\nNative Wan2.2 TI2V 5B FP16\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nStart image: rabbit_meadow_keyframe_crop_416x736.png\nResolution: 416x736\nFrames: 49\nFPS: 10\nDuration: 4.9s\nSteps: 16\nCFG: 5.0\n```\n\nOutput:\n\n```text\ndata/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n```\n\nVideo:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_outputs/autoshorts_native_wan22_rabbit_i2v_crop_5s_00001_.mp4\n\n---\n\n## Bewertung\n\nDieser zweite Clip ist deutlich besser:\n\n- photorealistischer Hase erkennbar\n- Wiese erkennbar\n- kein sichtbarer Rechteck-Kasten\n- native Wan2.2 I2V läuft stabil\n- 4.9 Sekunden, also praktisch dein gewünschter 5s-Test\n\nAber:\n\n```text\nDer Hase “hüpft” noch nicht überzeugend über das Feld.\n```\n\nEr bewegt sich eher leicht / morphend im Bild. Das liegt am Startbild: Der Hase sitzt sehr frontal und ist nicht in Sprungpose. Wan hält das Startbild stark fest — gut für Identität, schlecht für große Bewegung.\n\n---\n\n# Erkenntnis\n\nI2V ist technisch klar besser als T2V, aber für „Hase hüpft über Feld“ brauchen wir ein besseres Keyframe:\n\n```text\nHase seitlich sichtbar\nganzer Körper\nSprung-/Laufpose\nmehr freie Wiese vor ihm\nkeine frontale Sitzpose\n```\n\nDas aktuelle Bild ist niedlich, aber als Startpose für „hüpfen“ ungeeignet. Ein sitzender Hase wird nicht plötzlich elegant zum BBC-Nature-Sprinter. Leider keine Magie, nur Diffusion.\n\n---\n\n# Nächster sinnvoller Schritt\n\nFür einen wirklich guten Clip:\n\n1. In ChatGPT Pro / Bildgenerator ein Keyframe erzeugen:\n\n```text\nPhotorealistic vertical 9:16 wildlife photo of a cute brown rabbit mid-hop across a sunny green meadow, full body side view, all four legs visible, ears up, natural grass, shallow depth of field, bright daylight, sharp realistic fur, lots of open meadow in front of the rabbit, no text, no watermark\n```\n\n2. Dann dieses Bild hier verwenden als:\n\n```text\nstart_image\n```\n\n3. Wan2.2 I2V erneut mit Prompt:\n\n```text\nThe rabbit hops forward across the meadow with small realistic jumps, ears bouncing naturally, grass moving under its feet, smooth wildlife camera tracking.\n```\n\nKurz: **I2V funktioniert. Für überzeugendes Hüpfen brauchen wir jetzt ein gutes seitliches Mid-Hop-Keyframe statt eines sitzenden frontalen Kaninchens.**"}], "id": "msg_0d1a4d10654d56b4016a11ef504bac8191ababa9f8c8c8c8a9", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] Ok, das generieren von videos funktioniert. Wie machen wir weiter in unserem projekt? Recherchiere zudem wie die prompts für WAN 2.2 am besten formuliert werden für die besten ergebnisse. Für flüssige, qualitativ hochwertige videos. Den bildstyle mössen wir uns noch überlegen passend zu userem konzept und content usw.", "timestamp": "2026-05-23T20:31:04.572355"}
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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## 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\\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\\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. 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\\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 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/render-manifest-pipeline.md\", \"references/retention-studio-strategy.md\", \"references/manifest-preview-renderer.md\", \"references/ai-video-asset-planning.md\", \"references/monetization-and-approval-notes.md\", \"references/retention-led-shorts.md\", \"references/imported-ai-clip-assembly.md\", \"references/retention-render-planning.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_aRQd0t4qBQDeIlsjFGAYDdUq", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"comfyui\", \"file\": \"references/wan22-ti2v-a5000.md\", \"content\": \"# Wan 2.2 TI2V 5B on remote RTX A5000 via Tailscale\\n\\nSession-proven notes for running `ComfyUI-WanVideoWrapper` on a remote NVIDIA RTX A5000 Laptop GPU with 16 GB VRAM, controlled from Hermes over Tailscale.\\n\\n## Recommended ComfyUI launch\\n\\nUse normal dynamic VRAM mode, not `--lowvram`, for production tests:\\n\\n```bash\\ncd ~/ai/ComfyUI\\nsource .venv/bin/activate\\n\\nTS_IP=\\\"$(tailscale ip -4 2>/dev/null | head -n1 || true)\\\"\\n[ -z \\\"$TS_IP\\\" ] && TS_IP=\\\"127.0.0.1\\\"\\n\\npython main.py \\\\\\n  --listen \\\"$TS_IP\\\" \\\\\\n  --port 8188 \\\\\\n  --reserve-vram 1.0\\n```\\n\\n`--lowvram` is a crash/OOM diagnostic only. It can push T5/text encoding to CPU/offload, making T5 take ~45-65s while `nvidia-smi` shows low VRAM use. If jobs are slow and GPU looks idle, remove `--lowvram` before changing models.\\n\\nOptional later speed test:\\n\\n```bash\\npython main.py --listen \\\"$TS_IP\\\" --port 8188 --highvram --reserve-vram 1.0\\n```\\n\\nUse `--highvram` only after normal mode is stable; it may OOM.\\n\\n## Model files that worked\\n\\nFor Kijai `ComfyUI-WanVideoWrapper`:\\n\\n- Diffusion model: `TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors`\\n- VAE: `Wan2_2_VAE_bf16.safetensors`\\n- Text encoder: `umt5-xxl-enc-fp8_e4m3fn.safetensors`\\n\\nImportant wrapper-specific incompatibility:\\n\\n- Do **not** use `umt5_xxl_fp8_e4m3fn_scaled.safetensors` with `LoadWanVideoT5TextEncoder` in the Kijai wrapper. It failed with: `Invalid T5 text encoder model, fp8 scaled is not supported by this node`.\\n- That scaled T5 may be appropriate for native ComfyUI Wan workflows, but not this wrapper node.\\n\\n## Download commands\\n\\n```bash\\ncd ~/ai/ComfyUI\\nsource .venv/bin/activate\\n\\nmkdir -p models/diffusion_models/TI2V models/vae models/text_encoders\\n\\nhf download Kijai/WanVideo_comfy_fp8_scaled \\\\\\n  TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors \\\\\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\\n\\nhf download Kijai/WanVideo_comfy \\\\\\n  Wan2_2_VAE_bf16.safetensors \\\\\\n  --local-dir ~/ai/ComfyUI/models/vae\\n\\n# Wrapper-compatible T5 if not already installed:\\nhf download Kijai/WanVideo_comfy \\\\\\n  umt5-xxl-enc-fp8_e4m3fn.safetensors \\\\\\n  --local-dir ~/ai/ComfyUI/models/text_encoders\\n```\\n\\n## Stable 16 GB VRAM workflow profile\\n\\nFor first quality previews on A5000 16 GB:\\n\\n- resolution: `416x736`\\n- frames: `25`\\n- fps: `12`\\n- steps: `12`\\n- sampler: `unipc`\\n- cfg: `3.5-4.0`\\n- model loader:\\n  - `model = TI2V/Wan2_2-TI2V-5B_fp8_e4m3fn_scaled_KJ.safetensors`\\n  - `quantization = fp8_e4m3fn_scaled`\\n  - `load_device = main_device`\\n- T5 loader:\\n  - `model_name = umt5-xxl-enc-fp8_e4m3fn.safetensors`\\n  - `load_device = main_device`\\n  - `quantization = disabled`\\n- text encode:\\n  - `device = gpu`\\n  - `use_disk_cache = true`\\n- sampler:\\n  - `force_offload = true` is safer for 16 GB with Wan2.2\\n- VAE decode:\\n  - `model_name = Wan2_2_VAE_bf16.safetensors`\\n  - `enable_vae_tiling = true`\\n  - `tile_x = 256`, `tile_y = 256`\\n  - `tile_stride_x = 128`, `tile_stride_y = 128`\\n\\nWithout VAE tiling, 416x736/25f/8 steps reached decode then failed with GPU OOM: allocated ~14.6 GiB and needed another ~455 MiB.\\n\\n## A/B results vs Wan 2.1\\n\\nWan2.1 T2V 1.3B FP8 is useful for smoke tests and fast prompt iteration, but early outputs were often abstract/blinky at tiny settings and only moderately recognizable at 480x832.\\n\\nWan2.2 TI2V 5B FP8 scaled produced clearer object semantics and a usable B-roll candidate at 416x736 after prompt exposure was corrected.\\n\\nUse:\\n\\n- `Wan2.1 1.3B` = fast smoke / rough prompt testing\\n- `Wan2.2 TI2V 5B` = actual local AI-video B-roll candidate generation\\n\\n## Prompting lesson\\n\\nDark cinematic prompts can underexpose Wan2.2. If output is too dark, explicitly request:\\n\\n- `clearly visible`\\n- `bright overhead lights`\\n- `balanced exposure`\\n- `clean inspection garage`\\n- `full front three-quarter view`\\n- add negative terms: `dark underexposed image`, `black frame`, `unreadable silhouette`\\n\\nFor car B-roll, a brighter inspection-bay prompt was much more usable than a dim night-garage prompt.\\n\\n## Operational checks\\n\\nFrom Hermes/remote controller:\\n\\n```bash\\ncurl http://TAILSCALE_IP:8188/system_stats\\ncurl http://TAILSCALE_IP:8188/object_info\\n```\\n\\nInspect `/object_info` before submitting long jobs to verify model names are visible in:\\n\\n- `WanVideoModelLoader`\\n- `LoadWanVideoT5TextEncoder`\\n- `WanVideoVAELoader`\\n\\nUse `/free` between failed runs if VRAM remains fragmented:\\n\\n```python\\nPOST /free {\\\"unload_models\\\": false, \\\"free_memory\\\": true}\\n```\\n\", \"file_type\": \".md\"}", "tool_call_id": "call_DjKWa8h2g3v5Briziq0WMwjz", "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"# URL https://docs.comfy.org/tutorials/video/wan/wan2_2\\nprompt : on, better restoring users’ creative intentions. The model supports multiple generation modes such as text-to-video and image-to-video, suitable for content creation, artistic creation, education and training, and other application scenarios. Wan2.2 Prompt Guide ​ Model Highlights Cinematic-level Aesthetic Control : Professional camera language, supports multi-dimensional visual control such as lighting, color, and composition Large-scale Complex Motion : Smoothly restores various complex motions, enhances motion controllability and naturalness Precise Semantic Compliance : Complex scene understanding, multi-object generation, better restoring creative intentions Efficient Compression Technology : 5B version with high compression ratio VAE, memory optimization, supports mixed training ​ Wan2.2 Open Source Model Versions The Wan2.2 series models are based on the Apache 2.0 open source license and support commercial use. The Apache 2.0 li\\nnegative : cut Ctrl+B to enable the Load image node to upload an image. (Optional) In the Wan22ImageToVideoLatent node, you can adjust the size settings and the total number of video frames ( length ). (Optional) If you need to modify the prompts (positive and negative), please do so in the CLIP Text Encoder node at step 5. Click the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation. ​ Wan2.2 14B T2V Text-to-Video Workflow Example ​ 1. Workflow File Please update your ComfyUI to the latest version, and through the menu Workflow -&gt; Browse Templates -&gt; Video , find “Wan2.2 14B T2V” to load the workflow. Or update your ComfyUI to the latest version, then download the following video and drag it into ComfyUI to load the workflow. Download JSON Workflow File Run on Comfy Cloud ​ 2. Manually Download Models Diffusion Model wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors V\\nresolution : ending frame in the second Load Image node. Adjust the size settings in the WanFirstLastFrameToVideo node. By default, a relatively small size is set to prevent low VRAM users from consuming too many resources. If you have enough VRAM, you can try a resolution around 720P. Write appropriate prompts according to your first and last frames. Click the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation. ​ Community Resources ​ GGUF Versions bullerwins/Wan2.2-I2V-A14B-GGUF/ bullerwins/Wan2.2-T2V-A14B-GGUF QuantStack/Wan2.2 GGUFs Custom Node City96/ComfyUI-GGUF ​ WanVideoWrapper Kijai/ComfyUI-WanVideoWrapper Wan2.2 models Kijai/WanVideo_comfy_fp8_scaled Wan2.1 models Kijai/WanVideo_comfy/Lightx2v Lightx2v 4steps LoRA Wan2.2-T2V-A14B-4steps-lora-rank64-V1 Was this page helpful? Yes No Suggest edits Raise issue LTX-2.3 ComfyUI Workflow Examples Previous Wan2.2 Animate ComfyUI native workflow Next ⌘ I ComfyUI home page\\n720 : e second Load Image node. Adjust the size settings in the WanFirstLastFrameToVideo node. By default, a relatively small size is set to prevent low VRAM users from consuming too many resources. If you have enough VRAM, you can try a resolution around 720P. Write appropriate prompts according to your first and last frames. Click the Run button, or use the shortcut Ctrl(cmd) + Enter to execute video generation. ​ Community Resources ​ GGUF Versions bullerwins/Wan2.2-I2V-A14B-GGUF/ bullerwins/Wan2.2-T2V-A14B-GGUF QuantStack/Wan2.2 GGUFs Custom Node City96/ComfyUI-GGUF ​ WanVideoWrapper Kijai/ComfyUI-WanVideoWrapper Wan2.2 models Kijai/WanVideo_comfy_fp8_scaled Wan2.1 models Kijai/WanVideo_comfy/Lightx2v Lightx2v 4steps LoRA Wan2.2-T2V-A14B-4steps-lora-rank64-V1 Was this page helpful? Yes No Suggest edits Raise issue LTX-2.3 ComfyUI Workflow Examples Previous Wan2.2 Animate ComfyUI native workflow Next ⌘ I ComfyUI home page github x discord \\nTI2V : t(.dark) #navbar a[href*=\\\"comfy.org/cloud\\\"] * { color: inherit !important; } .mint-aspect-video { aspect-ratio: 16 / 9 } .mint-w-full { width: 100% } On this page Model Highlights Wan2.2 Open Source Model Versions ComfyOrg Wan2.2 Live Streams Wan2.2 TI2V 5B Hybrid Version Workflow Example 1. Download Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B T2V Text-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B I2V Image-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B FLF2V Workflow Example 1. Workflow and Input Material Preparation 2. Follow the Steps Community Resources GGUF Versions WanVideoWrapper Wan Video Wan2.2 Video Generation ComfyUI Official Native Workflow Example Copy page Official usage guide for Alibaba Cloud Tongyi Wanxiang 2.2 video generation model in ComfyUI Copy page Documentation Index Fetch th\\nImage-to-Video : ms Wan2.2 TI2V 5B Hybrid Version Workflow Example 1. Download Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B T2V Text-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B I2V Image-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B FLF2V Workflow Example 1. Workflow and Input Material Preparation 2. Follow the Steps Community Resources GGUF Versions WanVideoWrapper Wan Video Wan2.2 Video Generation ComfyUI Official Native Workflow Example Copy page Official usage guide for Alibaba Cloud Tongyi Wanxiang 2.2 video generation model in ComfyUI Copy page Documentation Index Fetch the complete documentation index at: https://docs.comfy.org/llms.txt Use this file to discover all available pages before exploring further. Wan 2.2 is a new generation multimodal generative model launched by WAN AI. This model adopts an inno\\nText-to-Video : full { width: 100% } On this page Model Highlights Wan2.2 Open Source Model Versions ComfyOrg Wan2.2 Live Streams Wan2.2 TI2V 5B Hybrid Version Workflow Example 1. Download Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B T2V Text-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B I2V Image-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B FLF2V Workflow Example 1. Workflow and Input Material Preparation 2. Follow the Steps Community Resources GGUF Versions WanVideoWrapper Wan Video Wan2.2 Video Generation ComfyUI Official Native Workflow Example Copy page Official usage guide for Alibaba Cloud Tongyi Wanxiang 2.2 video generation model in ComfyUI Copy page Documentation Index Fetch the complete documentation index at: https://docs.comfy.org/llms.txt Use this file to discover all available pages before exploring\\ncfg : -in-nodes/TextEncodeZImageOmni\\\\\\\"]},{\\\\\\\"group\\\\\\\":\\\\\\\"Debug\\\\\\\",\\\\\\\"pages\\\\\\\":[{\\\\\\\"group\\\\\\\":\\\\\\\"Model\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/EasyCache\\\\\\\",\\\\\\\"built-in-nodes/LazyCache\\\\\\\",\\\\\\\"built-in-nodes/ModelComputeDtype\\\\\\\"]}]},{\\\\\\\"group\\\\\\\":\\\\\\\"Guidance\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/CFGNorm\\\\\\\",\\\\\\\"built-in-nodes/CFGZeroStar\\\\\\\",\\\\\\\"built-in-nodes/NAGuidance\\\\\\\",\\\\\\\"built-in-nodes/SkipLayerGuidanceDiT\\\\\\\",\\\\\\\"built-in-nodes/SkipLayerGuidanceDiTSimple\\\\\\\",\\\\\\\"built-in-nodes/SkipLayerGuidanceSD3\\\\\\\",\\\\\\\"built-in-nodes/TCFG\\\\\\\"]},{\\\\\\\"group\\\\\\\":\\\\\\\"Hooks\\\\\\\",\\\\\\\"pages\\\\\\\":[{\\\\\\\"group\\\\\\\":\\\\\\\"Clip\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/SetClipHooks\\\\\\\"]},{\\\\\\\"group\\\\\\\":\\\\\\\"Combine\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/CombineHooks\\\\\\\",\\\\\\\"built-in-nodes/CombineHooksEight\\\\\\\",\\\\\\\"built-in-nodes/CombineHooksFour\\\\\\\"]},{\\\\\\\"group\\\\\\\":\\\\\\\"Cond Pair\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/PairConditioningCombine\\\\\\\",\\\\\\\"built-in-nodes/PairConditioningSetDefaultAndCombine\\\\\\\",\\\\\\\"built-in-nodes/PairConditioningSetProperties\\\\\\\",\\\\\\\"built-in-nodes/PairConditioningSetPropertiesAndCombin\\nsteps : io: 16 / 9 } .mint-w-full { width: 100% } On this page Model Highlights Wan2.2 Open Source Model Versions ComfyOrg Wan2.2 Live Streams Wan2.2 TI2V 5B Hybrid Version Workflow Example 1. Download Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B T2V Text-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B I2V Image-to-Video Workflow Example 1. Workflow File 2. Manually Download Models 3. Follow the Steps Wan2.2 14B FLF2V Workflow Example 1. Workflow and Input Material Preparation 2. Follow the Steps Community Resources GGUF Versions WanVideoWrapper Wan Video Wan2.2 Video Generation ComfyUI Official Native Workflow Example Copy page Official usage guide for Alibaba Cloud Tongyi Wanxiang 2.2 video generation model in ComfyUI Copy page Documentation Index Fetch the complete documentation index at: https://docs.comfy.org/llms.txt Use this file to discover all available p\\nsample : \\\\\\\"built-in-nodes/EmptyCosmosLatentVideo\\\\\\\",\\\\\\\"built-in-nodes/EmptyHunyuanLatentVideo\\\\\\\",\\\\\\\"built-in-nodes/EmptyHunyuanVideo15Latent\\\\\\\",\\\\\\\"built-in-nodes/EmptyMochiLatentVideo\\\\\\\",\\\\\\\"built-in-nodes/latent/video/trim-video-latent\\\\\\\",\\\\\\\"built-in-nodes/LTXVLatentUpsampler\\\\\\\",\\\\\\\"built-in-nodes/TrimVideoLatent\\\\\\\"]},\\\\\\\"built-in-nodes/BatchLatentsNode\\\\\\\",\\\\\\\"built-in-nodes/EmptyFlux2LatentImage\\\\\\\",\\\\\\\"built-in-nodes/EmptyHunyuanImageLatent\\\\\\\",\\\\\\\"built-in-nodes/EmptyLatentImage\\\\\\\",\\\\\\\"built-in-nodes/HunyuanVideo15LatentUpscaleWithModel\\\\\\\",\\\\\\\"built-in-nodes/LatentBlend\\\\\\\",\\\\\\\"built-in-nodes/LatentComposite\\\\\\\",\\\\\\\"built-in-nodes/LatentCompositeMasked\\\\\\\",\\\\\\\"built-in-nodes/LatentUpscale\\\\\\\",\\\\\\\"built-in-nodes/LatentUpscaleBy\\\\\\\",\\\\\\\"built-in-nodes/SaveLatent\\\\\\\",\\\\\\\"built-in-nodes/VAEDecode\\\\\\\",\\\\\\\"built-in-nodes/VAEDecodeTiled\\\\\\\",\\\\\\\"built-in-nodes/VAEEncode\\\\\\\",\\\\\\\"built-in-nodes/VAEEncodeTiled\\\\\\\"]},{\\\\\\\"group\\\\\\\":\\\\\\\"Loader\\\\\\\",\\\\\\\"pages\\\\\\\":[{\\\\\\\"group\\\\\\\":\\\\\\\"Video Models\\\\\\\",\\\\\\\"pages\\\\\\\":[\\\\\\\"built-in-nodes/ImageOnlyCheckpoint\\n\\n# URL https://github.com/Wan-Video/Wan2.2\\nprompt : text\\\",\\\"copilot_duplicate_thread\\\",\\\"copilot_extensions_hide_in_dotcom_chat\\\",\\\"copilot_extensions_removal_on_marketplace\\\",\\\"copilot_features_sql_server_logo\\\",\\\"copilot_file_block_ref_matching\\\",\\\"copilot_fix_failed_workflows\\\",\\\"copilot_ftp_hyperspace_upgrade_prompt\\\",\\\"copilot_icebreakers_experiment_dashboard\\\",\\\"copilot_icebreakers_experiment_hyperspace\\\",\\\"copilot_immersive_code_block_transition_wrap\\\",\\\"copilot_immersive_embedded_deferred_payload\\\",\\\"copilot_immersive_embedded_draggable\\\",\\\"copilot_immersive_embedded_header_button\\\",\\\"copilot_immersive_embedded_implicit_references\\\",\\\"copilot_immersive_embedded_skip_copilot_api_token_for_dotcom_context\\\",\\\"copilot_immersive_file_block_transition_open\\\",\\\"copilot_immersive_file_preview_keep_mounted\\\",\\\"copilot_immersive_job_result_preview\\\",\\\"copilot_immersive_structured_model_picker\\\",\\\"copilot_immersive_task_hyperlinking\\\",\\\"copilot_immersive_task_within_chat_thread\\\",\\\"copilot_mc_cli_resume_any_users_task\\\",\\\"copilot_miss\\nresolution : : 4; } pre, code { tab-size: var(--tab-size-preference); } {\\\"locale\\\":\\\"en\\\",\\\"featureFlags\\\":[\\\"actions_custom_images_storage_billing_ui_visibility\\\",\\\"actions_image_version_event\\\",\\\"actions_workflow_language_service_allow_concurrency_queue\\\",\\\"agent_conflict_resolution\\\",\\\"alternate_user_config_repo\\\",\\\"arianotify_comprehensive_migration\\\",\\\"artifact_ui_v2\\\",\\\"billing_discount_threshold_notification\\\",\\\"code_scanning_dfa_degraded_experience_notice\\\",\\\"codespaces_prebuild_region_target_update\\\",\\\"coding_agent_model_selection\\\",\\\"coding_agent_model_selection_all_skus\\\",\\\"comment_viewer_copy_raw_markdown\\\",\\\"contentful_primer_code_blocks\\\",\\\"copilot_agent_snippy\\\",\\\"copilot_api_agentic_issue_marshal_yaml\\\",\\\"copilot_ask_mode_dropdown\\\",\\\"copilot_chat_attach_multiple_images\\\",\\\"copilot_chat_category_rate_limit_messages\\\",\\\"copilot_chat_clear_model_selection_for_default_change\\\",\\\"copilot_chat_contextual_suggestions_updated\\\",\\\"copilot_chat_enable_tool_call_logs\\\",\\\"copilot_chat_input_co\\n720 :  40 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requests Actions Projects Security and quality Insights {\\\"payload\\\":{\\\"codeViewRepoRoute\\\":{\\\"path\\\":\\\"/\\\",\\\"refInfo\\\":{\\\"name\\\":\\\"main\\\",\\\"listCacheKey\\\":\\\"v0:1753767208.0\\\",\\\"canEdit\\\":false,\\\"refType\\\":\\\"branch\\\",\\\"currentOid\\\":\\\"42bf4cfaa384bc21833865abc2f9e6c0e67233dc\\\"},\\\"tree\\\":{\\\"items\\\":[{\\\"name\\\":\\\"assets\\\",\\\"path\\\":\\\"assets\\\",\\\"contentType\\\":\\\"directory\\\"},{\\\"name\\\":\\\"examples\\\",\\\"path\\\":\\\"examples\\\",\\\"contentType\\\":\\\"directory\\\"},{\\\"name\\\":\\\"tests\\\",\\\"path\\\":\\\"tests\\\",\\\"contentType\\\":\\\"directory\\\"},{\\\"name\\\":\\\"wan\\\",\\\"path\\\":\\\"wan\\\",\\\"contentType\\\":\\\"directory\\\"},{\\\"name\\\":\\\".gitignore\\\",\\\"path\\\":\\\".gitignore\\\",\\\"contentType\\\":\\\"file\\\"},{\\\"name\\\":\\\"INSTALL.md\\\",\\\"path\\\":\\\"INSTALL.md\\\",\\\"contentType\\\":\\\"file\\\"},{\\\"name\\\":\\\"LICENSE.txt\\\",\\\"path\\\":\\\"LICENSE.txt\\\",\\\"contentType\\\":\\\"file\\\"},{\\\"name\\\":\\\"Makefile\\\",\\\"path\\\":\\\"Makefile\\\",\\\"contentType\\\":\\\"file\\\"},{\\\"name\\\":\\\"README.md\\\",\\\"path\\\":\\\"README.md\\\",\\\"contentType\\\":\\\"file\\\"},{\\\"name\\\":\\\"generate.py\\\",\\\"path\\\":\\\"genera\\nfps : 003e: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of \\\\u003cstrong\\\\u003e16×16×4\\\\u003c/strong\\\\u003e. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest \\\\u003cstrong\\\\u003e720P@24fps\\\\u003c/strong\\\\u003e models currently available, capable of serving both the industrial and academic sectors simultaneously.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003c/ul\\\\u003e\\\\n\\\\u003cdiv class=\\\\\\\"markdown-heading\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003e\\\\u003ch2 tabindex=\\\\\\\"-1\\\\\\\" class=\\\\\\\"heading-element\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003eVideo Demos\\\\u003c/h2\\\\u003e\\\\u003ca id=\\\\\\\"user-content-video-demos\\\\\\\" class=\\\\\\\"anchor\\\\\\\" aria-label=\\\\\\\"Permalink: Video Demos\\\\\\\" href=\\\\\\\"#video-demos\\\\\\\"\\\\u003e\\\\u003csvg data-component=\\\\\\\"Octicon\\\\\\\" class=\\\\\\\"octicon octicon-link\\\\\\\" viewBox=\\\\\\\"0 0 16 16\\\\\\\" version=\\\\\\\"1.1\\\\\\\" width=\\\\\\\"16\\\\\\\" height=\\\\\\\"16\\\\\\\" aria\\nTI2V : ns such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\n\\\\u003cp dir=\\\\\\\"auto\\\\\\\"\\\\u003e👍 \\\\u003cstrong\\\\u003eEfficient High-Definition Hybrid TI2V\\\\u003c/strong\\\\u003e: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of \\\\u003cstrong\\\\u003e16×16×4\\\\u003c/strong\\\\u003e. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest \\\\u003cstrong\\\\u003e720P@24fps\\\\u003c/strong\\\\u003e models currently available, capable of serving both the industrial and academic sectors simultaneously.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003c/ul\\\\u003e\\\\n\\\\u003cdiv class=\\\\\\\"markdown-heading\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003e\\\\u003ch2 tabindex=\\\\\\\"-1\\\\\\\" class=\\\\\\\"heading-element\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003eVideo Demos\\\\u003\\nImage-to-Video : Efficient High-Definition Hybrid TI2V\\\\u003c/strong\\\\u003e: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of \\\\u003cstrong\\\\u003e16×16×4\\\\u003c/strong\\\\u003e. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest \\\\u003cstrong\\\\u003e720P@24fps\\\\u003c/strong\\\\u003e models currently available, capable of serving both the industrial and academic sectors simultaneously.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003c/ul\\\\u003e\\\\n\\\\u003cdiv class=\\\\\\\"markdown-heading\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003e\\\\u003ch2 tabindex=\\\\\\\"-1\\\\\\\" class=\\\\\\\"heading-element\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003eVideo Demos\\\\u003c/h2\\\\u003e\\\\u003ca id=\\\\\\\"user-content-video-demos\\\\\\\" class=\\\\\\\"anchor\\\\\\\" aria-label=\\\\\\\"Permalink: Video Demos\\\\\\\" href=\\\\\\\"#video-demos\\\\\\\"\\\\u003e\\\\u003csvg data-component=\\\\\\\"Octicon\\\\\\\" class=\\\\\\\"octicon octicon-link\\\\\\\" viewBox=\\\\\\\"0 0 16 \\nText-to-Video : \\\\u003cstrong\\\\u003eEfficient High-Definition Hybrid TI2V\\\\u003c/strong\\\\u003e: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of \\\\u003cstrong\\\\u003e16×16×4\\\\u003c/strong\\\\u003e. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest \\\\u003cstrong\\\\u003e720P@24fps\\\\u003c/strong\\\\u003e models currently available, capable of serving both the industrial and academic sectors simultaneously.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003c/ul\\\\u003e\\\\n\\\\u003cdiv class=\\\\\\\"markdown-heading\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003e\\\\u003ch2 tabindex=\\\\\\\"-1\\\\\\\" class=\\\\\\\"heading-element\\\\\\\" dir=\\\\\\\"auto\\\\\\\"\\\\u003eVideo Demos\\\\u003c/h2\\\\u003e\\\\u003ca id=\\\\\\\"user-content-video-demos\\\\\\\" class=\\\\\\\"anchor\\\\\\\" aria-label=\\\\\\\"Permalink: Video Demos\\\\\\\" href=\\\\\\\"#video-demos\\\\\\\"\\\\u003e\\\\u003csvg data-component=\\\\\\\"Octicon\\\\\\\" class=\\\\\\\"octicon octicon-link\\\\\\\"\\ncfg : nce time.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/vipshop/cache-dit\\\\\\\"\\\\u003eCache-dit\\\\u003c/a\\\\u003e offers Fully Cache Acceleration support for \\\\u003cstrong\\\\u003eWan2.2\\\\u003c/strong\\\\u003e MoE with DBCache, TaylorSeer and Cache CFG. Visit their \\\\u003ca href=\\\\\\\"https://github.com/vipshop/cache-dit/blob/main/examples/pipeline/run_wan_2.2.py\\\\\\\"\\\\u003eexample\\\\u003c/a\\\\u003e for more details.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/kijai/ComfyUI-WanVideoWrapper\\\\\\\"\\\\u003eKijai's ComfyUI WanVideoWrapper\\\\u003c/a\\\\u003e is an alternative implementation of \\\\u003cstrong\\\\u003eWan\\\\u003c/strong\\\\u003e models for ComfyUI. Thanks to its Wan-only focus, it's on the frontline of getting cutting edge optimizations and hot research features, which are often hard to integrate into ComfyUI quickly due to its more rigid structure.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/modelscope/DiffSynth-Studio\\\\\\nsteps : \\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\n\\\\u003cp dir=\\\\\\\"auto\\\\\\\"\\\\u003e👍 \\\\u003cstrong\\\\u003eEffective MoE Architecture\\\\u003c/strong\\\\u003e: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\n\\\\u003cp dir=\\\\\\\"auto\\\\\\\"\\\\u003e👍 \\\\u003cstrong\\\\u003eCinematic-level Aesthetics\\\\u003c/strong\\\\u003e: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.\\\\u003c/p\\\\u003e\\\\n\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\n\\\\u003cp dir=\\\\\\\"auto\\\\\\\"\\\\u003e👍 \\\\u003cstrong\\\\u003eComplex Motion Generation\\\\u003c/s\\nsample : on\\\",\\\"octocaptcha_origin_optimization\\\",\\\"prs_conversations_react\\\",\\\"prs_css_anchor_positioning\\\",\\\"prs_inbox_deferred_usequeries\\\",\\\"repos_contributors_limited_default_range\\\",\\\"rules_insights_filter_bar_created\\\",\\\"rules_required_reviewers_block_description\\\",\\\"sample_network_conn_type\\\",\\\"secret_scanning_pattern_alerts_link\\\",\\\"security_center_artifact_filters_popover\\\",\\\"session_logs_ungroup_reasoning_text\\\",\\\"site_features_copilot_universe\\\",\\\"site_homepage_collaborate_video\\\",\\\"spark_prompt_secret_scanning\\\",\\\"spark_server_connection_status\\\",\\\"suppress_automated_browser_vitals\\\",\\\"viewscreen_sandbox\\\",\\\"warn_inaccessible_attachments\\\",\\\"web_socket_verified_fetch\\\",\\\"webp_support\\\",\\\"workbench_store_readonly\\\"],\\\"copilotApiOverrideUrl\\\":\\\"https://api.githubcopilot.com\\\"} GitHub - Wan-Video/Wan2.2: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub Skip to content {\\\"props\\\":{\\\"docsUrl\\\":\\\"https://docs.github.com/get-started/accessibility/keyboard-shortcuts\\\"}} /* \\n\\n# URL https://github.com/Wan-Video/Wan2.1\\nprompt : text\\\",\\\"copilot_duplicate_thread\\\",\\\"copilot_extensions_hide_in_dotcom_chat\\\",\\\"copilot_extensions_removal_on_marketplace\\\",\\\"copilot_features_sql_server_logo\\\",\\\"copilot_file_block_ref_matching\\\",\\\"copilot_fix_failed_workflows\\\",\\\"copilot_ftp_hyperspace_upgrade_prompt\\\",\\\"copilot_icebreakers_experiment_dashboard\\\",\\\"copilot_icebreakers_experiment_hyperspace\\\",\\\"copilot_immersive_code_block_transition_wrap\\\",\\\"copilot_immersive_embedded_deferred_payload\\\",\\\"copilot_immersive_embedded_draggable\\\",\\\"copilot_immersive_embedded_header_button\\\",\\\"copilot_immersive_embedded_implicit_references\\\",\\\"copilot_immersive_embedded_skip_copilot_api_token_for_dotcom_context\\\",\\\"copilot_immersive_file_block_transition_open\\\",\\\"copilot_immersive_file_preview_keep_mounted\\\",\\\"copilot_immersive_job_result_preview\\\",\\\"copilot_immersive_structured_model_picker\\\",\\\"copilot_immersive_task_hyperlinking\\\",\\\"copilot_immersive_task_within_chat_thread\\\",\\\"copilot_mc_cli_resume_any_users_task\\\",\\\"copilot_miss\\nnegative : \\\\n\\\\nprompt = \\\\u0026quot;A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window.\\\\u0026quot;\\\\nnegative_prompt = \\\\u0026quot;Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards\\\\u0026quot;\\\\n\\\\noutput = pipe(\\\\n prompt=prompt,\\\\n negative_prompt=negative_prompt,\\\\n height=720,\\\\n width=1280,\\\\n num_frames=81,\\\\n guidance_scale=5.0,\\\\n ).frames[0]\\\\nexport_to_video(output, \\\\u0026quot;output.mp4\\\\u0026quot;, fps=16)\\\\\\\"\\\\u003e\\\\u003cpre\\\\u003e\\\\u003cspan class=\\\\\\\"pl-k\\\\\\\"\\\\u\\nresolution : : 4; } pre, code { tab-size: var(--tab-size-preference); } {\\\"locale\\\":\\\"en\\\",\\\"featureFlags\\\":[\\\"actions_custom_images_storage_billing_ui_visibility\\\",\\\"actions_image_version_event\\\",\\\"actions_workflow_language_service_allow_concurrency_queue\\\",\\\"agent_conflict_resolution\\\",\\\"alternate_user_config_repo\\\",\\\"arianotify_comprehensive_migration\\\",\\\"artifact_ui_v2\\\",\\\"billing_discount_threshold_notification\\\",\\\"code_scanning_dfa_degraded_experience_notice\\\",\\\"codespaces_prebuild_region_target_update\\\",\\\"coding_agent_model_selection\\\",\\\"coding_agent_model_selection_all_skus\\\",\\\"comment_viewer_copy_raw_markdown\\\",\\\"contentful_primer_code_blocks\\\",\\\"copilot_agent_snippy\\\",\\\"copilot_api_agentic_issue_marshal_yaml\\\",\\\"copilot_ask_mode_dropdown\\\",\\\"copilot_chat_attach_multiple_images\\\",\\\"copilot_chat_category_rate_limit_messages\\\",\\\"copilot_chat_clear_model_selection_for_default_change\\\",\\\"copilot_chat_contextual_suggestions_updated\\\",\\\"copilot_chat_enable_tool_call_logs\\\",\\\"copilot_chat_input_co\\n720 : .co/Wan-AI/Wan2.1-T2V-14B\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003eHuggingface\\\\u003c/a\\\\u003e 🤖 \\\\u003ca href=\\\\\\\"https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003eModelScope\\\\u003c/a\\\\u003e\\\\u003c/td\\\\u003e\\\\n\\\\u003ctd\\\\u003eSupports both 480P and 720P\\\\u003c/td\\\\u003e\\\\n\\\\u003c/tr\\\\u003e\\\\n\\\\u003ctr\\\\u003e\\\\n\\\\u003ctd\\\\u003eI2V-14B-720P\\\\u003c/td\\\\u003e\\\\n\\\\u003ctd\\\\u003e🤗 \\\\u003ca href=\\\\\\\"https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003eHuggingface\\\\u003c/a\\\\u003e 🤖 \\\\u003ca href=\\\\\\\"https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003eModelScope\\\\u003c/a\\\\u003e\\\\u003c/td\\\\u003e\\\\n\\\\u003ctd\\\\u003eSupports 720P\\\\u003c/td\\\\u003e\\\\n\\\\u003c/tr\\\\u003e\\\\n\\\\u003ctr\\\\u003e\\\\n\\\\u003ctd\\\\u003eI2V-14B-480P\\\\u003c/td\\\\u003e\\\\n\\\\u003ctd\\\\u003e🤗 \\\\u003ca href=\\\\\\\"https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003eHuggingface\\\\u003c/a\\\\u003e 🤖 \\\\u003ca href=\\\\\\\"https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P\\\\\\\" \\nfps : 03cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/PKU-YuanGroup/Helios\\\\\\\"\\\\u003eHelios\\\\u003c/a\\\\u003e, a breakthrough video generation model base on \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e that achieves minute-scale, high-quality video synthesis at 19.5 FPS on a single H100 GPU (about 10 FPS on a single Ascend NPU) —without relying on conventional long video anti-drifting strategies or standard video acceleration techniques. Visit their \\\\u003ca href=\\\\\\\"https://pku-yuangroup.github.io/Helios-Page/\\\\\\\" rel=\\\\\\\"nofollow\\\\\\\"\\\\u003ewebpage\\\\u003c/a\\\\u003e for more details.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/bytedance/Video-As-Prompt\\\\\\\"\\\\u003eVideo-As-Prompt\\\\u003c/a\\\\u003e, the first unified semantic-controlled video generation model based on \\\\u003cstrong\\\\u003eWan2.1-14B-I2V\\\\u003c/strong\\\\u003e with a Mixture-of-Transformers architecture and in-context controls (e.g., concept, style, motion, camera). Refer to the \\\\u003ca href=\\\\\\\"htt\\nImage-to-Video : techniques like quantization). Its performance is even comparable to some closed-source models.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003eMultiple Tasks\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003eVisual Text Generation\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003ePowerful Video VAE\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan-VAE\\\\u003c/strong\\\\u003e delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation fo\\nText-to-Video : t optimization techniques like quantization). Its performance is even comparable to some closed-source models.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003eMultiple Tasks\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003eVisual Text Generation\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e👍 \\\\u003cstrong\\\\u003ePowerful Video VAE\\\\u003c/strong\\\\u003e: \\\\u003cstrong\\\\u003eWan-VAE\\\\u003c/strong\\\\u003e delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an idea\\ncfg : on \\\\u003cstrong\\\\u003eWan2.1-14B-I2V\\\\u003c/strong\\\\u003e, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it!\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/WeichenFan/CFG-Zero-star\\\\\\\"\\\\u003eCFG-Zero\\\\u003c/a\\\\u003e enhances \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e (covering both T2V and I2V models) from the perspective of CFG.\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/ali-vilab/TeaCache\\\\\\\"\\\\u003eTeaCache\\\\u003c/a\\\\u003e now supports \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!\\\\u003c/li\\\\u003e\\\\n\\\\u003cli\\\\u003e\\\\u003ca href=\\\\\\\"https://github.com/modelscope/DiffSynth-Studio\\\\\\\"\\\\u003eDiffSynth-Studio\\\\u003c/a\\\\u003e provides more support for \\\\u003cstrong\\\\u003eWan2.1\\\\u003c/strong\\\\u003e, including video-to-video, FP8 quantization, VRAM optimization, LoRA training,\\nsteps :  overflow-auto\\\\\\\" dir=\\\\\\\"auto\\\\\\\" data-snippet-clipboard-copy-content=\\\\\\\"import torch\\\\nfrom diffusers.utils import export_to_video\\\\nfrom diffusers import AutoencoderKLWan, WanPipeline\\\\nfrom diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler\\\\n\\\\n# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers\\\\nmodel_id = \\\\u0026quot;Wan-AI/Wan2.1-T2V-14B-Diffusers\\\\u0026quot;\\\\nvae = AutoencoderKLWan.from_pretrained(model_id, subfolder=\\\\u0026quot;vae\\\\u0026quot;, torch_dtype=torch.float32)\\\\nflow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P\\\\nscheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift)\\\\npipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)\\\\npipe.scheduler = scheduler\\\\npipe.to(\\\\u0026quot;cuda\\\\u0026quot;)\\\\n\\\\nprompt = \\\\u0026quot;A cat and a dog baking a cake together in a kitchen. The\\nsample : on\\\",\\\"octocaptcha_origin_optimization\\\",\\\"prs_conversations_react\\\",\\\"prs_css_anchor_positioning\\\",\\\"prs_inbox_deferred_usequeries\\\",\\\"repos_contributors_limited_default_range\\\",\\\"rules_insights_filter_bar_created\\\",\\\"rules_required_reviewers_block_description\\\",\\\"sample_network_conn_type\\\",\\\"secret_scanning_pattern_alerts_link\\\",\\\"security_center_artifact_filters_popover\\\",\\\"selector_observer_stats\\\",\\\"session_logs_ungroup_reasoning_text\\\",\\\"site_features_copilot_universe\\\",\\\"site_homepage_collaborate_video\\\",\\\"spark_prompt_secret_scanning\\\",\\\"spark_server_connection_status\\\",\\\"suppress_automated_browser_vitals\\\",\\\"viewscreen_sandbox\\\",\\\"warn_inaccessible_attachments\\\",\\\"web_socket_verified_fetch\\\",\\\"webp_support\\\",\\\"workbench_store_readonly\\\"],\\\"copilotApiOverrideUrl\\\":\\\"https://api.githubcopilot.com\\\"} GitHub - Wan-Video/Wan2.1: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub Skip to content {\\\"props\\\":{\\\"docsUrl\\\":\\\"https://docs.github.com/get-started/accessibility\\n\\n# URL https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B\\nprompt : 5B Text-Image-to-Video model and can support video generation at 720P resolutions. Single-GPU Text-to-Video inference python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --prompt \\\"Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage\\\" 💡Unlike other tasks, the 720P resolution of the Text-Image-to-Video task is 1280*704 or 704*1280 . This command can run on a GPU with at least 24GB VRAM (e.g, RTX 4090 GPU). 💡If you are running on a GPU with at least 80GB VRAM, you can remove the --offload_model True , --convert_model_dtype and --t5_cpu options to speed up execution. Single-GPU Image-to-Video inference python generate.py --task ti2v-5B --size 1280*704 --ckpt_dir ./Wan2.2-TI2V-5B --offload_model True --convert_model_dtype --t5_cpu --image examples/i2v_input.JPG --prompt \\\"Summer beach vacation style, a white cat wear\\nresolution : d-sourced models. 👍 Efficient High-Definition Hybrid TI2V : Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4 . This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously. This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research. Video Demos Your browser does not support the video tag. 🔥 Latest \\n720 : closed-sourced models. 👍 Efficient High-Definition Hybrid TI2V : Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4 . This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously. This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research. Video Demos Your browser does not support the video tag. 🔥 La\\nfps : 👍 Efficient High-Definition Hybrid TI2V : Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4 . This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously. This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research. Video Demos Your browser does not support the video tag. 🔥 Latest News!! Jul 28, 202\\nTI2V : estTheme = document.cookie.match(/theme=(\\\\w+)/)?.[1]; document.documentElement.classList.toggle('dark', guestTheme === 'dark' || ( (!guestTheme || guestTheme === 'system') && window.matchMedia('(prefers-color-scheme: dark)').matches)); Wan-AI/Wan2.2-TI2V-5B · Hugging Face ((window.plausible = window.plausible || function () { (plausible.q = plausible.q || []).push(arguments); }), (plausible.init = plausible.init || function (i) { plausible.o = i || {}; })); plausible.init({ customProperties: { loggedIn: \\\"false\\\", }, endpoint: \\\"/api/event\\\", }); window.hubConfig = {\\\"features\\\":{\\\"signupDisabled\\\":false},\\\"sshGitUrl\\\":\\\"git@hf.co\\\",\\\"moonHttpUrl\\\":\\\"https:\\\\/\\\\/huggingface.co\\\",\\\"captchaApiKey\\\":\\\"bd5f2066-93dc-4bdd-a64b-a24646ca3859\\\",\\\"datasetViewerPublicUrl\\\":\\\"https:\\\\/\\\\/datasets-server.huggingface.co\\\",\\\"stripePublicKey\\\":\\\"pk_live_x2tdjFXBCvXo2FFmMybezpeM00J6gPCAAc\\\",\\\"environment\\\":\\\"production\\\",\\\"userAgent\\\":\\\"HuggingFace (production)\\\",\\\"spacesIframeDomain\\\":\\\"hf.spa\\nImage-to-Video : e among all open-sourced and closed-sourced models. 👍 Efficient High-Definition Hybrid TI2V : Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4 . This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously. This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research. Video Demos Your browser does no\\nText-to-Video : munity Blog Posts Daily Papers Learn Discord Forum GitHub Solutions Team &amp; Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Wan-AI / Wan2.2-TI2V-5B like 608 Follow Wan-AI 10.5k Text-to-Video Wan2.2 Diffusers Safetensors English Chinese ti2v arxiv: 2503.20314 License: apache-2.0 Model card Files Files and versions xet Community 12 Deploy Copy to bucket new Use this model Instructions to use Wan-AI/Wan2.2-TI2V-5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started. Libraries Wan2.2 How to use Wan-AI/Wan2.2-TI2V-5B with Wan2.2: # No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js Inference Inference Providers Notebooks Google Colab Kaggle Wan2.2 V\\nsteps : deo models. With Wan2.2 , we have focused on incorporating the following innovations: 👍 Effective MoE Architecture : Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost. 👍 Cinematic-level Aesthetics : Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences. 👍 Complex Motion Generation : Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions suc\\nsample :  (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs; (3) Tests were run without the --use_prompt_extend flag; (4) Reported results are the average of multiple samples taken after the warm-up phase. Introduction of Wan2.2 Wan2.2 builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation. (1) Mixture-of-Experts (MoE) Architecture Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series ad\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_3jVKOL2Mc3FASKKj5Qun3lnu", "timestamp": "2026-05-23T20:31:04.572355"}
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T2V is weak for concrete subject control; I2V is materially better but needs a strong keyframe.\\n\\n## Strategic rule\\n\\nFor production candidates, prefer:\\n\\n```text\\nCurated/AI-generated keyframe -> Wan2.2 Image-to-Video -> deterministic edit/captions/SFX\\n```\\n\\nAvoid relying on:\\n\\n```text\\nText prompt -> full final video\\n```\\n\\nT2V is acceptable only for rough exploration, abstract atmosphere, or non-critical B-roll.\\n\\n## Prompt structure\\n\\nUse one compact paragraph, ordered from most important to least important:\\n\\n1. Subject identity and count\\n2. Main action/motion\\n3. Environment\\n4. Camera behavior\\n5. Lighting/exposure\\n6. Style/aesthetic\\n7. Quality/control constraints\\n\\nTemplate:\\n\\n```text\\n[shot type] of [single subject] [doing one specific motion] in [specific environment].\\nCamera [one motion only]. [Lighting/exposure]. [Style/texture]. Preserve [important identity/details].\\nNo text, no logos, no watermarks.\\n```\\n\\n## Good I2V prompt pattern\\n\\n```text\\nPhotorealistic vertical wildlife video of one cute brown rabbit hopping forward through a sunny green meadow. The rabbit makes small natural hops, ears bouncing gently, fur remains realistic and consistent. Camera tracks smoothly from the side at low angle. Bright daylight, balanced exposure, shallow depth of field, natural colors, high quality wildlife footage. Preserve the same rabbit from the starting image. No text, no logo, no watermark.\\n```\\n\\nWhy this works:\\n\\n- one subject\\n- one motion\\n- one camera move\\n- explicit realism/style\\n- explicit identity preservation\\n- no baked text\\n\\n## Bad prompt pattern\\n\\n```text\\nA beautiful cinematic scene with a rabbit jumping around a magical meadow, dynamic camera, epic lighting, cute, high quality, realistic, detailed, amazing.\\n```\\n\\nProblems:\\n\\n- vague motion\\n- no direction/path\\n- contradictory style risk\\n- no identity preservation\\n- no exposure control\\n- no artifact controls\\n\\n## Negative prompt baseline\\n\\nUse a reusable negative prompt; adapt per subject:\\n\\n```text\\ncartoon, animation, deformed subject, extra limbs, extra ears, duplicated subject, disappearing subject, melting body, distorted face, bad anatomy, unrealistic movement, flickering colors, abstract, blurry, low detail, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\\n```\\n\\nFor mechanism/finance videos add:\\n\\n```text\\nfake numbers, fake labels, readable UI text, distorted charts, hallucinated interface text, brand logos\\n```\\n\\n## Motion guidance\\n\\nWan obeys simple motion better than complex choreography.\\n\\nPrefer:\\n\\n- `slow push-in`\\n- `smooth side tracking shot`\\n- `gentle parallax move`\\n- `small natural hops forward`\\n- `object rotates slowly 20 degrees`\\n- `layers peel away one by one`\\n\\nAvoid:\\n\\n- multiple simultaneous camera moves\\n- long multi-step actions in one clip\\n- exact physics instructions\\n- fast cuts inside the prompt\\n- complex object transformations\\n\\n## Image-to-video keyframe requirements\\n\\nA good keyframe should have:\\n\\n- full subject visible if movement matters\\n- side or three-quarter pose for directional motion\\n- enough empty space in the movement direction\\n- no readable text, no license plates, no UI labels\\n- clean background and strong silhouette\\n- same aspect ratio as target video where possible\\n- subject not cropped at important limbs/edges\\n\\nFor a hopping rabbit, use a side-view mid-hop or ready-to-hop pose, not a frontal sitting rabbit.\\n\\n## Settings ladder for local A5000 16GB\\n\\nQuality has priority, but avoid wasting hours on bad prompts:\\n\\n1. Keyframe validation image/video still\\n2. I2V probe: `416x736`, 49 frames, 10 fps, 12-16 steps\\n3. Better candidate: `480x832`, 49 frames, 10-16 steps\\n4. Higher candidate only if subject/control is good: `576x1024`, 49 frames, 12-20 steps\\n\\nUse native Wan2.2 with:\\n\\n```text\\nmodel: wan2.2_ti2v_5B_fp16.safetensors\\ntext encoder: umt5_xxl_fp8_e4m3fn_scaled.safetensors\\nvae: wan2.2_vae.safetensors\\nsampler: UniPC/uni_pc where available\\ncfg: around 4.5-6.0 as starting range\\n```\\n\\n## Style direction for Hidden Money & AI Systems\\n\\nRecommended channel look:\\n\\n```text\\nphotorealistic macro investigation + clean mechanism overlays\\n```\\n\\nAI-video clips should provide real-world texture, not explain everything alone.\\n\\nVisual palette:\\n\\n- dark graphite / black UI base\\n- warm amber warning highlights\\n- electric cyan system/data lines\\n- red only for risk/reveal moments\\n- white kinetic captions\\n\\nClip style:\\n\\n- realistic hands/devices/cars/desks/shops/offices\\n- clean Swiss/European environments when relevant\\n- no fake readable screens\\n- no generated text in video assets\\n- text and numbers added later by deterministic renderer\\n\\nMechanism animation style:\\n\\n- x-ray layers\\n- glowing system nodes\\n- arrows/flows\\n- price/risk/time layers\\n- subtle 3D parallax\\n\\n## Production rule\\n\\nGenerate AI clips without readable text. Add captions, labels, numbers, UI and arrows later with the deterministic renderer.\\n\"}"}}], "timestamp": "2026-05-23T20:31:04.572355"}
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{"role": "assistant", "content": "Ja, Sir. Wir sind jetzt an einem guten Punkt:\n\n```text\nVideo-Generierung funktioniert technisch.\nJetzt müssen wir daraus eine reproduzierbare Content-Pipeline machen.\n```\n\nIch habe außerdem eine erste interne Notiz/Playbook im Projekt angelegt:\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nNoch nicht committed, weil im Working Tree noch viele Test-/Telegram-Dateien untracked herumliegen. Sehr stilvoller digitaler Komposthaufen, aber kein Grund, alles blind einzuchecken.\n\n---\n\n## Aktueller Stand\n\nWir wissen jetzt:\n\n- **Native Wan2.2 funktioniert.**\n- **Wan2.2 I2V ist besser als T2V.**\n- **Scaled T5 funktioniert im nativen Workflow.**\n- **Kijai Wrapper ist für Wan2.2-Qualitätsbaseline nicht unser Hauptpfad.**\n- **Text-to-Video allein ist zu unkontrolliert.**\n- **Image-to-Video braucht ein starkes Keyframe.**\n- **Caption/Text/Labels dürfen nicht im AI-Video generiert werden**, sondern später deterministisch durch unseren Renderer.\n\nDas ist wichtig für unser Projekt, weil wir keine „KI hat irgendwas ausgespuckt“-Videos wollen, sondern ein kontrolliertes Retention-Studio.\n\n---\n\n# Wie machen wir weiter?\n\n## Nächster Projektpfad\n\nIch würde jetzt in fünf Schichten weiterbauen:\n\n```text\n1. Style Bible\n2. Prompt/Keyframe System\n3. Wan2.2 I2V Job Client\n4. Clip Review & Selection\n5. Final Hybrid Renderer\n```\n\n---\n\n# 1. Style Bible definieren\n\nBevor wir massenhaft Clips generieren, brauchen wir einen festen Look.\n\nFür unser Konzept:\n\n```text\nHidden Money & AI Systems\nUnsichtbare Systeme in 30 Sekunden\n```\n\nwürde ich nicht auf „niedliche Tier-/Fantasy-Clips“ gehen, sondern auf:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\n## Empfohlener Bildstil\n\n```text\nRealistische Welt + sichtbare Systemebene\n```\n\nAlso:\n\n- echte Hände\n- Autos\n- Screens\n- Kassen\n- Smartphones\n- Dokumente\n- Werkstätten\n- Banken-/Versicherungs-/Dealer-Kontexte\n- darüber später:\n  - x-ray layers\n  - glowing arrows\n  - risk meters\n  - price/time/control overlays\n  - kinetic captions\n\n## Palette\n\n```text\nBase: graphite / black / dark navy\nAccent 1: amber / warning orange\nAccent 2: electric cyan / system data\nAccent 3: red only for danger/twist\nText: clean white\n```\n\n## Keine AI-Video-Texte\n\nIn AI-video prompts immer:\n\n```text\nno readable text, no logo, no watermark, no subtitles, no fake UI text\n```\n\nDie AI soll Bilder und Bewegung liefern.  \nDer Renderer liefert Wahrheit, Zahlen und Text. Kleine, aber entscheidende Arbeitsteilung.\n\n---\n\n# 2. Wan2.2 Prompting-Regeln\n\nIch habe recherchiert gegen:\n\n- offizielle ComfyUI Wan2.2-Doku\n- Wan2.2 Modellkarte / GitHub\n- Wan2.1/Wan2.2 Beispielprompts\n- unsere lokalen Tests\n\n## Wichtigste Erkenntnis\n\nWan2.2 kann laut Modellkarte 720p/24fps und ist mit mehr Daten trainiert, ja. Aber für Qualität zählt bei uns weniger „maximale Auflösung“, sondern:\n\n```text\ngutes Keyframe\nklare Bewegung\nein Motiv\nein Kameramove\nkein Text im AI-Clip\n```\n\n---\n\n## Beste Prompt-Struktur\n\nFür Wan2.2 sollte der Prompt so aufgebaut sein:\n\n```text\n[Shot type] of [single subject] [doing one specific motion] in [specific environment].\nCamera [one motion only].\n[Lighting/exposure].\n[Style/texture].\nPreserve [important identity/details].\nNo text, no logo, no watermark.\n```\n\nBeispiel:\n\n```text\nPhotorealistic vertical wildlife video of one cute brown rabbit hopping forward through a sunny green meadow. The rabbit makes small natural hops, ears bouncing gently, fur remains realistic and consistent. Camera tracks smoothly from the side at low angle. Bright daylight, balanced exposure, shallow depth of field, natural colors, high quality wildlife footage. Preserve the same rabbit from the starting image. No text, no logo, no watermark.\n```\n\n---\n\n## Schlechter Prompt\n\n```text\nA beautiful cinematic rabbit jumping around a magical meadow, dynamic camera, epic lighting, cute, high quality, realistic, detailed, amazing.\n```\n\nWarum schlecht:\n\n- zu vage\n- „dynamic camera“ ist zu offen\n- keine Bewegungsrichtung\n- keine Identitätserhaltung\n- keine Negativsteuerung\n- Stil vermischt Fantasy und Realismus\n\n---\n\n## Negative Prompt Baseline\n\nFür Wan2.2 würde ich standardmäßig nutzen:\n\n```text\ncartoon, animation, deformed subject, extra limbs, extra ears, duplicated subject, disappearing subject, melting body, distorted face, bad anatomy, unrealistic movement, flickering colors, abstract, blurry, low detail, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\n```\n\nFür unsere Finance/System-Videos zusätzlich:\n\n```text\nfake numbers, fake labels, readable UI text, distorted charts, hallucinated interface text, brand logos\n```\n\n---\n\n# 3. Produktionsprinzip: Keyframe zuerst\n\nAb jetzt sollten wir nicht mehr so arbeiten:\n\n```text\nPrompt → Video\n```\n\nSondern:\n\n```text\nShot Plan\n→ Keyframe Prompt\n→ gutes Startbild\n→ Wan2.2 Image-to-Video\n→ Review\n→ Final Renderer\n```\n\n## Warum?\n\nT2V erfindet alles gleichzeitig:\n\n- Objekt\n- Szene\n- Bewegung\n- Kamera\n- Licht\n- Komposition\n\nDas ist zu viel Chaos.\n\nI2V macht nur:\n\n```text\nbestehendes Bild bewegen\n```\n\nDas ist viel kontrollierbarer.\n\n---\n\n# 4. Keyframe-Regeln\n\nFür gute I2V-Clips brauchen wir Keyframes mit:\n\n- Motiv komplett sichtbar\n- klare Silhouette\n- genug Platz in Bewegungsrichtung\n- keine Texte, Logos, Nummernschilder, UI-Lettern\n- passendes Seitenverhältnis\n- saubere Lichtführung\n- nicht zu eng gecroppt\n- nicht frontal sitzend, wenn Bewegung seitlich passieren soll\n\nBeispiel Hase:\n\nSchlecht:\n\n```text\nfrontal sitzender Hase\n```\n\nGut:\n\n```text\nseitlicher Hase in Mid-Hop-Pose mit freier Wiese vor ihm\n```\n\nBeispiel Auto:\n\nSchlecht:\n\n```text\ndunkle Garage, Auto kaum sichtbar, Nummernschild mit KI-Gekritzel\n```\n\nGut:\n\n```text\nbright inspection bay, red used compact car, 3/4 front view, clean plate area, no readable text, visible wheels, space for overlays\n```\n\n---\n\n# 5. Technisches Wan2.2-Profil\n\nFür deine A5000 16GB würde ich weiter so fahren:\n\n```text\nNative Wan2.2 TI2V 5B\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\n```\n\nStartparameter:\n\n```text\n416x736\n49 Frames\n10 fps\n12–16 Steps\nCFG: 4.5–6.0\n```\n\nDann für gute Kandidaten:\n\n```text\n480x832\n49 Frames\n10–12 fps\n16 Steps\n```\n\nSpäter:\n\n```text\n576x1024\n49 Frames\n12 fps\n16–20 Steps\n```\n\nNicht sofort:\n\n```text\n720p\n24 fps\nlange Clips\n```\n\nWarum? Weil unser Endvideo ohnehin aus mehreren kurzen Clips, Captions, SFX und Mechanism-Layern besteht. Wir brauchen nicht jeden AI-Clip als finalen Kinofilm. Wir brauchen kontrollierbare Bausteine.\n\n---\n\n# Wie geht es konkret im Projekt weiter?\n\n## Schritt A — Style Bible als Projektartefakt\n\nIch würde als nächstes eine Datei bauen:\n\n```text\ndocs/style_bible.md\n```\n\nInhalt:\n\n- Serienlook\n- Farbpalette\n- Shot Types\n- Prompt-Stil\n- No-Go-Liste\n- Caption-Stil\n- Sound Design\n- AI-Clip-Regeln\n- Mechanism-Overlay-Regeln\n\n---\n\n## Schritt B — Keyframe Prompt Generator\n\nNeues Modul:\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nAufgabe:\n\n```text\nMechanism Shot Plan\n→ Keyframe Prompt\n→ I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI settings preset\n```\n\nBeispiel Output pro Szene:\n\n```json\n{\n  \"scene_id\": \"scene_01\",\n  \"keyframe_prompt\": \"...\",\n  \"i2v_motion_prompt\": \"...\",\n  \"negative_prompt\": \"...\",\n  \"style_profile\": \"photorealistic_macro_investigation\",\n  \"wan_preset\": \"wan22_i2v_quality_416x736\"\n}\n```\n\n---\n\n## Schritt C — ComfyUI Wan2.2 Client\n\nNeues Modul:\n\n```text\nautoshorts/comfyui_client.py\n```\n\nAufgabe:\n\n```text\nupload image\nsubmit native Wan2.2 workflow\npoll queue/history\ndownload mp4\nstore clip\n```\n\nOutput-Struktur:\n\n```text\ndata/generated_clips/<video_slug>/scene_01/seed_001.mp4\ndata/generated_clips/<video_slug>/scene_01/seed_002.mp4\ndata/generated_clips/<video_slug>/scene_01/seed_003.mp4\n```\n\n---\n\n## Schritt D — Clip Review Sheet\n\nNeues Artefakt:\n\n```text\ndata/clip_reviews/<video_slug>.json\n```\n\nBewertung je Clip:\n\n```text\nsubject clarity\nmotion quality\nartifact risk\nretention usefulness\nusable duration\nneeds rerun?\n```\n\nDann wählen wir nicht blind den letzten Output, sondern den besten Take.\n\n---\n\n## Schritt E — Final Hybrid Renderer\n\nDer finale Renderer macht dann:\n\n```text\nAI video clips\n+ deterministic captions\n+ overlays\n+ arrows\n+ price/risk/time layers\n+ SFX markers\n+ final audio/voiceover\n```\n\nWichtig:\n\n```text\nAI clips liefern Atmosphäre und Bewegung.\nRenderer liefert Erklärung und Kontrolle.\n```\n\nDas ist genau der Unterschied zwischen Content und KI-Suppe.\n\n---\n\n# Für unseren Pilot „billiges Auto“\n\nDer nächste echte Test sollte nicht mehr Hase/Haus sein, sondern unser Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Szene 1 Keyframe\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible tires and body panels, soft overhead lights, realistic European garage, shallow depth of field, cinematic but clearly visible, no text, no logos, no watermark.\n```\n\n## I2V Motion Prompt\n\n```text\nPhotorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure. Preserve the same car and garage. No text, no logos, no watermark.\n```\n\nDanach legt unser Renderer darüber:\n\n```text\nPreis\nReparaturrisiko\nStandtage\nHändler-Marge\nLiquidität\n```\n\nNicht Wan. Wan darf keine Zahlen erfinden. Das kann es nämlich sehr kreativ falsch. Und „kreativ falsch“ ist im Finance-Kontext ungefähr das Gegenteil von nützlich.\n\n---\n\n# Meine Empfehlung\n\nWir gehen jetzt so weiter:\n\n## Sofort nächster Build-Schritt\n\n```text\nStyle Bible + Keyframe/I2V Prompt Generator\n```\n\nDann:\n\n```text\nComfyUI Wan2.2 Client ins Repo integrieren\n```\n\nDann:\n\n```text\nPilot Auto-Szene 1 mit 3 Keyframe/I2V Varianten generieren\n```\n\nDanach:\n\n```text\nbeste Variante auswählen\n→ finalen Mechanism-Renderer erweitern\n```\n\n---\n\n## Kurz gesagt\n\nWir haben bewiesen:\n\n```text\nLokale Videoerzeugung funktioniert.\n```\n\nJetzt professionalisieren wir:\n\n```text\nnicht mehr einzelne Spielzeugtests,\nsondern kontrollierte Produktionspipeline.\n```\n\nDer wichtigste Wechsel:\n\n```text\nT2V-Spielerei → I2V-Regie\n```\n\nUnd der wichtigste kreative Entscheid:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nDas passt zu Hidden Money & AI Systems deutlich besser als generische KI-Fantasy-Videos.", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEfI2iIhADHs0TdSJnMhHuMZzndskoyX-etCz7c0GEHTztLt3Z2D7WmmNT686KSrNsYnPUfUya9dW4aLG7dpfrRasEG25kr9xplzRZsMvPq58fnCv4P9FiIbbZwuLnPn5IFXbdIsydH1ysOr36t7cLKfQXNRgavo31x3HXusLsEKlkjasuEDSO8A7LsdqAxTX_HkfeTNFo3vH3Our1sNdtOhIyh3BAHpJtCmy306JucM4aSc9HRW59yYtbCKclPze7o2m8KTaqNURgg4sR1mDCSo4vXRX3PWrPbbThDyz1Qhq2vStzKFiLo8qtwoU58YIFMWYdvlxgGmQWtjvdRM69GzWEZaQdC5Z8r7Y8p4UKTzjoyYshpjbYzUBKNIZun-VyCJFx6F7asJTHN5rFR9dOLl_X-amO-0aZ1NWlU-nBTUIDev_RISLYH1qx46WJl9po4eix73wncMqRBMA2GGyzgJQlkBoa4fvjiFD88M93LUmDXGCG8EAagDlNjTxwNmx60q_Ua5VYji1minKgYCxli1ORgMxoYQXQVOZorzafWIChiFB05TbYQEkGnG0WqNrKLwwnio8p-GTuwEYXIhKeAbOeXP5qES-DifICM22HIuD1mC8_zn84Mr0RKrVY3y03aZ-prUDU-5UVr5dKBHr64OYyy4WQ6ened7scI70N8MmvBaVLGwCI1j8GGdV8nR1p-y5HSpQ4xZ39CLDI322zpIK7MVYjsQAHuWHxCsbCmsYq0ivKVRPwyVYqOx55yz33iS2gbTrcDnU1vNLdHTwRMjoUsHpV7TC-4xfgwaEo7majG11y0PtNrV_IlSiHsQPtEw59zpYLaIyavfZnkkosmDjTDf8-TPGefj4bu3wgN7JZN5TGxXH_nprZtWLsVS4TdzXGKbAgj1M_UX3-VOWLDNScPwpwPPynfipQUjIjs9N5JhTtbSb4Hmjp88-3mGU-_3JrMPMpRlBt5tQ8qfEPQmt2TjgjEnpbdoGDa7F5IrT9qhuBHsy1O4hjVsJq1exEFAG1U7btMgTR9y7fivMUlzwoHRNF-MGH59EJ6rOGX_sZ-q4WB-koVvgFfUZVV6ORduPkWNRkftGsaQ2c-6LXy-2APaJoHqCTJUQxBjnZsPSC6Nqo34TX_64FCWTvIs1g-UXPU2huT8wflmiBFK7M1dMGcxD2-ynX5nGY_V4cZMzhueKxK0XxkF8UYR2xoUS8HDbz9U0ipGXw7SnXa1rqtrPny4CT0plmMmyLbAh1tAlDQAtbrxYXE3lNKSTgICumwucWlV6arT0X_UnMAM17ll-9SG9cSqt4Jpcuh6ULBN2PrVAiv6577NpxpgTzcY19mwG-40y8pV93maZjxWpSrEpm-1BC66zTQ1pbyDU-iEu3C92L-xKF4GmHdhVL6ye5M9hBZghAjAE-0fHQZxIgfD52xVutzthu-f_9DEAtA0Qe9Je73fgmv2pxbARf5eExPYdsQHuT6INGP_08UGGILEigIymENyQDfdlZo4k_vwpXOGxusL3M_HfFIWcCUAHBrMu7F1WKZjiYGE3-HFSWBkqKN6D4j-ktHG3K3-sVzEJNbRkIlDpkfCYqXicuQPfCQWb37z4XwZlmpMASqzPaP9Cj62rIs81oJ2z4AF_IJsk5WNABprPDtNywVTpO-lPGkONL5XSB72_qcff1aYZXzm_BlCS8C7qeGJs41tROHzSK4nwSREqyyMOa3cHkdXcmH1R0EZHu8panhgGt3IvpeahcJQ9xiq6acSS1aZtjNEL1cQ=", "id": "rs_0d1a4d10654d56b4016a11f2332ad08191bb755c8775bf7108", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir. Wir sind jetzt an einem guten Punkt:\n\n```text\nVideo-Generierung funktioniert technisch.\nJetzt müssen wir daraus eine reproduzierbare Content-Pipeline machen.\n```\n\nIch habe außerdem eine erste interne Notiz/Playbook im Projekt angelegt:\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nNoch nicht committed, weil im Working Tree noch viele Test-/Telegram-Dateien untracked herumliegen. Sehr stilvoller digitaler Komposthaufen, aber kein Grund, alles blind einzuchecken.\n\n---\n\n## Aktueller Stand\n\nWir wissen jetzt:\n\n- **Native Wan2.2 funktioniert.**\n- **Wan2.2 I2V ist besser als T2V.**\n- **Scaled T5 funktioniert im nativen Workflow.**\n- **Kijai Wrapper ist für Wan2.2-Qualitätsbaseline nicht unser Hauptpfad.**\n- **Text-to-Video allein ist zu unkontrolliert.**\n- **Image-to-Video braucht ein starkes Keyframe.**\n- **Caption/Text/Labels dürfen nicht im AI-Video generiert werden**, sondern später deterministisch durch unseren Renderer.\n\nDas ist wichtig für unser Projekt, weil wir keine „KI hat irgendwas ausgespuckt“-Videos wollen, sondern ein kontrolliertes Retention-Studio.\n\n---\n\n# Wie machen wir weiter?\n\n## Nächster Projektpfad\n\nIch würde jetzt in fünf Schichten weiterbauen:\n\n```text\n1. Style Bible\n2. Prompt/Keyframe System\n3. Wan2.2 I2V Job Client\n4. Clip Review & Selection\n5. Final Hybrid Renderer\n```\n\n---\n\n# 1. Style Bible definieren\n\nBevor wir massenhaft Clips generieren, brauchen wir einen festen Look.\n\nFür unser Konzept:\n\n```text\nHidden Money & AI Systems\nUnsichtbare Systeme in 30 Sekunden\n```\n\nwürde ich nicht auf „niedliche Tier-/Fantasy-Clips“ gehen, sondern auf:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\n## Empfohlener Bildstil\n\n```text\nRealistische Welt + sichtbare Systemebene\n```\n\nAlso:\n\n- echte Hände\n- Autos\n- Screens\n- Kassen\n- Smartphones\n- Dokumente\n- Werkstätten\n- Banken-/Versicherungs-/Dealer-Kontexte\n- darüber später:\n  - x-ray layers\n  - glowing arrows\n  - risk meters\n  - price/time/control overlays\n  - kinetic captions\n\n## Palette\n\n```text\nBase: graphite / black / dark navy\nAccent 1: amber / warning orange\nAccent 2: electric cyan / system data\nAccent 3: red only for danger/twist\nText: clean white\n```\n\n## Keine AI-Video-Texte\n\nIn AI-video prompts immer:\n\n```text\nno readable text, no logo, no watermark, no subtitles, no fake UI text\n```\n\nDie AI soll Bilder und Bewegung liefern.  \nDer Renderer liefert Wahrheit, Zahlen und Text. Kleine, aber entscheidende Arbeitsteilung.\n\n---\n\n# 2. Wan2.2 Prompting-Regeln\n\nIch habe recherchiert gegen:\n\n- offizielle ComfyUI Wan2.2-Doku\n- Wan2.2 Modellkarte / GitHub\n- Wan2.1/Wan2.2 Beispielprompts\n- unsere lokalen Tests\n\n## Wichtigste Erkenntnis\n\nWan2.2 kann laut Modellkarte 720p/24fps und ist mit mehr Daten trainiert, ja. Aber für Qualität zählt bei uns weniger „maximale Auflösung“, sondern:\n\n```text\ngutes Keyframe\nklare Bewegung\nein Motiv\nein Kameramove\nkein Text im AI-Clip\n```\n\n---\n\n## Beste Prompt-Struktur\n\nFür Wan2.2 sollte der Prompt so aufgebaut sein:\n\n```text\n[Shot type] of [single subject] [doing one specific motion] in [specific environment].\nCamera [one motion only].\n[Lighting/exposure].\n[Style/texture].\nPreserve [important identity/details].\nNo text, no logo, no watermark.\n```\n\nBeispiel:\n\n```text\nPhotorealistic vertical wildlife video of one cute brown rabbit hopping forward through a sunny green meadow. The rabbit makes small natural hops, ears bouncing gently, fur remains realistic and consistent. Camera tracks smoothly from the side at low angle. Bright daylight, balanced exposure, shallow depth of field, natural colors, high quality wildlife footage. Preserve the same rabbit from the starting image. No text, no logo, no watermark.\n```\n\n---\n\n## Schlechter Prompt\n\n```text\nA beautiful cinematic rabbit jumping around a magical meadow, dynamic camera, epic lighting, cute, high quality, realistic, detailed, amazing.\n```\n\nWarum schlecht:\n\n- zu vage\n- „dynamic camera“ ist zu offen\n- keine Bewegungsrichtung\n- keine Identitätserhaltung\n- keine Negativsteuerung\n- Stil vermischt Fantasy und Realismus\n\n---\n\n## Negative Prompt Baseline\n\nFür Wan2.2 würde ich standardmäßig nutzen:\n\n```text\ncartoon, animation, deformed subject, extra limbs, extra ears, duplicated subject, disappearing subject, melting body, distorted face, bad anatomy, unrealistic movement, flickering colors, abstract, blurry, low detail, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\n```\n\nFür unsere Finance/System-Videos zusätzlich:\n\n```text\nfake numbers, fake labels, readable UI text, distorted charts, hallucinated interface text, brand logos\n```\n\n---\n\n# 3. Produktionsprinzip: Keyframe zuerst\n\nAb jetzt sollten wir nicht mehr so arbeiten:\n\n```text\nPrompt → Video\n```\n\nSondern:\n\n```text\nShot Plan\n→ Keyframe Prompt\n→ gutes Startbild\n→ Wan2.2 Image-to-Video\n→ Review\n→ Final Renderer\n```\n\n## Warum?\n\nT2V erfindet alles gleichzeitig:\n\n- Objekt\n- Szene\n- Bewegung\n- Kamera\n- Licht\n- Komposition\n\nDas ist zu viel Chaos.\n\nI2V macht nur:\n\n```text\nbestehendes Bild bewegen\n```\n\nDas ist viel kontrollierbarer.\n\n---\n\n# 4. Keyframe-Regeln\n\nFür gute I2V-Clips brauchen wir Keyframes mit:\n\n- Motiv komplett sichtbar\n- klare Silhouette\n- genug Platz in Bewegungsrichtung\n- keine Texte, Logos, Nummernschilder, UI-Lettern\n- passendes Seitenverhältnis\n- saubere Lichtführung\n- nicht zu eng gecroppt\n- nicht frontal sitzend, wenn Bewegung seitlich passieren soll\n\nBeispiel Hase:\n\nSchlecht:\n\n```text\nfrontal sitzender Hase\n```\n\nGut:\n\n```text\nseitlicher Hase in Mid-Hop-Pose mit freier Wiese vor ihm\n```\n\nBeispiel Auto:\n\nSchlecht:\n\n```text\ndunkle Garage, Auto kaum sichtbar, Nummernschild mit KI-Gekritzel\n```\n\nGut:\n\n```text\nbright inspection bay, red used compact car, 3/4 front view, clean plate area, no readable text, visible wheels, space for overlays\n```\n\n---\n\n# 5. Technisches Wan2.2-Profil\n\nFür deine A5000 16GB würde ich weiter so fahren:\n\n```text\nNative Wan2.2 TI2V 5B\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\n```\n\nStartparameter:\n\n```text\n416x736\n49 Frames\n10 fps\n12–16 Steps\nCFG: 4.5–6.0\n```\n\nDann für gute Kandidaten:\n\n```text\n480x832\n49 Frames\n10–12 fps\n16 Steps\n```\n\nSpäter:\n\n```text\n576x1024\n49 Frames\n12 fps\n16–20 Steps\n```\n\nNicht sofort:\n\n```text\n720p\n24 fps\nlange Clips\n```\n\nWarum? Weil unser Endvideo ohnehin aus mehreren kurzen Clips, Captions, SFX und Mechanism-Layern besteht. Wir brauchen nicht jeden AI-Clip als finalen Kinofilm. Wir brauchen kontrollierbare Bausteine.\n\n---\n\n# Wie geht es konkret im Projekt weiter?\n\n## Schritt A — Style Bible als Projektartefakt\n\nIch würde als nächstes eine Datei bauen:\n\n```text\ndocs/style_bible.md\n```\n\nInhalt:\n\n- Serienlook\n- Farbpalette\n- Shot Types\n- Prompt-Stil\n- No-Go-Liste\n- Caption-Stil\n- Sound Design\n- AI-Clip-Regeln\n- Mechanism-Overlay-Regeln\n\n---\n\n## Schritt B — Keyframe Prompt Generator\n\nNeues Modul:\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nAufgabe:\n\n```text\nMechanism Shot Plan\n→ Keyframe Prompt\n→ I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI settings preset\n```\n\nBeispiel Output pro Szene:\n\n```json\n{\n  \"scene_id\": \"scene_01\",\n  \"keyframe_prompt\": \"...\",\n  \"i2v_motion_prompt\": \"...\",\n  \"negative_prompt\": \"...\",\n  \"style_profile\": \"photorealistic_macro_investigation\",\n  \"wan_preset\": \"wan22_i2v_quality_416x736\"\n}\n```\n\n---\n\n## Schritt C — ComfyUI Wan2.2 Client\n\nNeues Modul:\n\n```text\nautoshorts/comfyui_client.py\n```\n\nAufgabe:\n\n```text\nupload image\nsubmit native Wan2.2 workflow\npoll queue/history\ndownload mp4\nstore clip\n```\n\nOutput-Struktur:\n\n```text\ndata/generated_clips/<video_slug>/scene_01/seed_001.mp4\ndata/generated_clips/<video_slug>/scene_01/seed_002.mp4\ndata/generated_clips/<video_slug>/scene_01/seed_003.mp4\n```\n\n---\n\n## Schritt D — Clip Review Sheet\n\nNeues Artefakt:\n\n```text\ndata/clip_reviews/<video_slug>.json\n```\n\nBewertung je Clip:\n\n```text\nsubject clarity\nmotion quality\nartifact risk\nretention usefulness\nusable duration\nneeds rerun?\n```\n\nDann wählen wir nicht blind den letzten Output, sondern den besten Take.\n\n---\n\n## Schritt E — Final Hybrid Renderer\n\nDer finale Renderer macht dann:\n\n```text\nAI video clips\n+ deterministic captions\n+ overlays\n+ arrows\n+ price/risk/time layers\n+ SFX markers\n+ final audio/voiceover\n```\n\nWichtig:\n\n```text\nAI clips liefern Atmosphäre und Bewegung.\nRenderer liefert Erklärung und Kontrolle.\n```\n\nDas ist genau der Unterschied zwischen Content und KI-Suppe.\n\n---\n\n# Für unseren Pilot „billiges Auto“\n\nDer nächste echte Test sollte nicht mehr Hase/Haus sein, sondern unser Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\n## Szene 1 Keyframe\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible tires and body panels, soft overhead lights, realistic European garage, shallow depth of field, cinematic but clearly visible, no text, no logos, no watermark.\n```\n\n## I2V Motion Prompt\n\n```text\nPhotorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure. Preserve the same car and garage. No text, no logos, no watermark.\n```\n\nDanach legt unser Renderer darüber:\n\n```text\nPreis\nReparaturrisiko\nStandtage\nHändler-Marge\nLiquidität\n```\n\nNicht Wan. Wan darf keine Zahlen erfinden. Das kann es nämlich sehr kreativ falsch. Und „kreativ falsch“ ist im Finance-Kontext ungefähr das Gegenteil von nützlich.\n\n---\n\n# Meine Empfehlung\n\nWir gehen jetzt so weiter:\n\n## Sofort nächster Build-Schritt\n\n```text\nStyle Bible + Keyframe/I2V Prompt Generator\n```\n\nDann:\n\n```text\nComfyUI Wan2.2 Client ins Repo integrieren\n```\n\nDann:\n\n```text\nPilot Auto-Szene 1 mit 3 Keyframe/I2V Varianten generieren\n```\n\nDanach:\n\n```text\nbeste Variante auswählen\n→ finalen Mechanism-Renderer erweitern\n```\n\n---\n\n## Kurz gesagt\n\nWir haben bewiesen:\n\n```text\nLokale Videoerzeugung funktioniert.\n```\n\nJetzt professionalisieren wir:\n\n```text\nnicht mehr einzelne Spielzeugtests,\nsondern kontrollierte Produktionspipeline.\n```\n\nDer wichtigste Wechsel:\n\n```text\nT2V-Spielerei → I2V-Regie\n```\n\nUnd der wichtigste kreative Entscheid:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nDas passt zu Hidden Money & AI Systems deutlich besser als generische KI-Fantasy-Videos."}], "id": "msg_0d1a4d10654d56b4016a11f23693108191b20e44c8ac21530d", "phase": "final_answer"}], "timestamp": "2026-05-23T20:31:04.572355"}
{"role": "user", "content": "[M G] Ok, mach weiter.", "timestamp": "2026-05-23T21:26:39.748126"}
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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### 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## 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\\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\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_4EYiuWVqexdxHQInkMM66t6S", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Adapters from Retention Studio mechanism explainers into production shot plans.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|from pathlib import Path\\n     6|from typing import Any\\n     7|\\n     8|from autoshorts.script_drafts import _slug\\n     9|\\n    10|BEAT_TO_PURPOSE = {\\n    11|    \\\"pattern_interrupt\\\": \\\"pattern_interrupt\\\",\\n    12|    \\\"problem\\\": \\\"problem\\\",\\n    13|    \\\"mechanism\\\": \\\"mechanism_reveal\\\",\\n    14|    \\\"twist\\\": \\\"twist\\\",\\n    15|    \\\"follow_trigger\\\": \\\"follow_trigger\\\",\\n    16|}\\n    17|\\n    18|BEAT_TO_ASSET_TYPE = {\\n    19|    \\\"pattern_interrupt\\\": \\\"cinematic_ai_video\\\",\\n    20|    \\\"problem\\\": \\\"cinematic_ai_video\\\",\\n    21|    \\\"mechanism\\\": \\\"mechanism_animation\\\",\\n    22|    \\\"twist\\\": \\\"mechanism_animation\\\",\\n    23|    \\\"follow_trigger\\\": \\\"kinetic_text_overlay\\\",\\n    24|}\\n    25|\\n    26|\\n    27|def _plan_id(explainer: dict[str, Any]) -> str:\\n    28|    return f\\\"{_slug(explainer['pillar'])}-{_slug(explainer['title'])}\\\"\\n    29|\\n    30|\\n    31|def _overlay_text(voiceover: str, limit: int = 72) -> str:\\n    32|    cleaned = \\\" \\\".join(voiceover.split())\\n    33|    if len(cleaned) <= limit:\\n    34|        return cleaned\\n    35|    truncated = cleaned[: limit - 1].rsplit(\\\" \\\", 1)[0].rstrip(\\\".,;:\\\")\\n    36|    while truncated.split() and truncated.split()[-1].lower() in {\\\"und\\\", \\\"oder\\\", \\\"mit\\\", \\\"durch\\\", \\\"von\\\", \\\"der\\\", \\\"die\\\", \\\"das\\\"}:\\n    37|        truncated = \\\" \\\".join(truncated.split()[:-1]).rstrip(\\\".,;:\\\")\\n    38|    return f\\\"{truncated}…\\\"\\n    39|\\n    40|\\n    41|def _scene_from_beat(index: int, beat: dict[str, Any]) -> dict[str, Any]:\\n    42|    beat_name = beat[\\\"beat\\\"]\\n    43|    return {\\n    44|        \\\"scene\\\": index,\\n    45|        \\\"purpose\\\": BEAT_TO_PURPOSE[beat_name],\\n    46|        \\\"start\\\": beat[\\\"start\\\"],\\n    47|        \\\"end\\\": beat[\\\"end\\\"],\\n    48|        \\\"asset_type\\\": BEAT_TO_ASSET_TYPE[beat_name],\\n    49|        \\\"on_screen_text\\\": _overlay_text(beat[\\\"voiceover\\\"]),\\n    50|        \\\"voiceover\\\": beat[\\\"voiceover\\\"],\\n    51|        \\\"visual_direction\\\": beat[\\\"visual\\\"],\\n    52|        \\\"visual_motion\\\": beat[\\\"visual_motion\\\"],\\n    53|        \\\"sound_cue\\\": beat[\\\"sound_cue\\\"],\\n    54|        \\\"retention_goal\\\": _retention_goal(beat_name),\\n    55|    }\\n    56|\\n    57|\\n    58|def _retention_goal(beat_name: str) -> str:\\n    59|    goals = {\\n    60|        \\\"pattern_interrupt\\\": \\\"Open a visual loop immediately; make the viewer need the hidden explanation.\\\",\\n    61|        \\\"problem\\\": \\\"Make the surface-level interpretation feel incomplete.\\\",\\n    62|        \\\"mechanism\\\": \\\"Reveal the invisible system through moving layers, not narration alone.\\\",\\n    63|        \\\"twist\\\": \\\"Reframe the obvious cause into the real cause.\\\",\\n    64|        \\\"follow_trigger\\\": \\\"Promise the next hidden system without a generic like/follow plea.\\\",\\n    65|    }\\n    66|    return goals[beat_name]\\n    67|\\n    68|\\n    69|def build_mechanism_shot_plan(explainer: dict[str, Any]) -> dict[str, Any]:\\n    70|    \\\"\\\"\\\"Convert a Retention Studio mechanism explainer into a motion-led shot plan.\\\"\\\"\\\"\\n    71|    scenes = [_scene_from_beat(index, beat) for index, beat in enumerate(explainer[\\\"beats\\\"], start=1)]\\n    72|    caption_beats = [\\n    73|        {\\\"start\\\": int(beat[\\\"start\\\"]), \\\"text\\\": beat[\\\"voiceover\\\"]}\\n    74|        for beat in explainer[\\\"beats\\\"]\\n    75|    ]\\n    76|    return {\\n    77|        \\\"id\\\": _plan_id(explainer),\\n    78|        \\\"series\\\": explainer[\\\"series\\\"],\\n    79|        \\\"title\\\": explainer[\\\"title\\\"],\\n    80|        \\\"pillar\\\": explainer[\\\"pillar\\\"],\\n    81|        \\\"estimated_seconds\\\": int(explainer[\\\"target_seconds\\\"]),\\n    82|        \\\"caption_beats\\\": caption_beats,\\n    83|        \\\"scenes\\\": scenes,\\n    84|        \\\"final_render_intent\\\": {\\n    85|            \\\"static_card_limit\\\": 0,\\n    86|            \\\"visual_change_every_seconds\\\": 2,\\n    87|            \\\"primary_style\\\": \\\"mechanism_reveal_motion_explainer\\\",\\n    88|            \\\"captions\\\": \\\"large kinetic overlays; voiceover carries detail; no paragraph cards\\\",\\n    89|        },\\n    90|    }\\n    91|\\n    92|\\n    93|def format_mechanism_shot_plan(plan: dict[str, Any]) -> str:\\n    94|    scene_blocks = []\\n    95|    for scene in plan[\\\"scenes\\\"]:\\n    96|        scene_blocks.append(\\n    97|            f\\\"### Scene {scene['scene']} — {scene['purpose']}\\\\n\\\\n\\\"\\n    98|            f\\\"- Timing: {scene['start']}s–{scene['end']}s\\\\n\\\"\\n    99|            f\\\"- Asset type: {scene['asset_type']}\\\\n\\\"\\n   100|            f\\\"- On-screen text: {scene['on_screen_text']}\\\\n\\\"\\n   101|            f\\\"- Voiceover: {scene['voiceover']}\\\\n\\\"\\n   102|            f\\\"- Visual direction: {scene['visual_direction']}\\\\n\\\"\\n   103|            f\\\"- Visual motion: {scene['visual_motion']}\\\\n\\\"\\n   104|            f\\\"- Sound cue: {scene['sound_cue']}\\\\n\\\"\\n   105|            f\\\"- Retention goal: {scene['retention_goal']}\\\"\\n   106|        )\\n   107|    scenes = \\\"\\\\n\\\\n\\\".join(scene_blocks)\\n   108|    beats = \\\"\\\\n\\\".join(f\\\"- {beat['start']:02d}s: {beat['text']}\\\" for beat in plan[\\\"caption_beats\\\"])\\n   109|    intent = plan[\\\"final_render_intent\\\"]\\n   110|    return f\\\"\\\"\\\"# {plan['title']}\\n   111|\\n   112|- ID: `{plan['id']}`\\n   113|- Series: {plan['series']}\\n   114|- Pillar: `{plan['pillar']}`\\n   115|- Estimated duration: {plan['estimated_seconds']}s\\n   116|\\n   117|## Mechanism shot plan\\n   118|\\n   119|{scenes}\\n   120|\\n   121|## Caption beats\\n   122|\\n   123|{beats}\\n   124|\\n   125|## Final render intent\\n   126|\\n   127|- Static card limit: {intent['static_card_limit']}\\n   128|- Visual change every seconds: {intent['visual_change_every_seconds']}\\n   129|- Primary style: {intent['primary_style']}\\n   130|- Captions: {intent['captions']}\\n   131|\\\"\\\"\\\"\\n   132|\\n   133|\\n   134|def write_mechanism_shot_plans(explainers: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n   135|    \\\"\\\"\\\"Write mechanism shot plans as Markdown files.\\\"\\\"\\\"\\n   136|    output_dir.mkdir(parents=True, exist_ok=True)\\n   137|    paths: list[Path] = []\\n   138|    for explainer in explainers:\\n   139|        plan = build_mechanism_shot_plan(explainer)\\n   140|        path = output_dir / f\\\"{plan['id']}.md\\\"\\n   141|        path.write_text(format_mechanism_shot_plan(plan), encoding=\\\"utf-8\\\")\\n   142|        paths.append(path)\\n   143|    return paths\\n   144|\", \"total_lines\": 143, \"file_size\": 5131, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_aJNlILjpsQShwo54zoFiqTL1", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Renderer manifest generation for retention-led AutoShortsBot videos.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|import json\\n     6|from pathlib import Path\\n     7|from typing import Any\\n     8|\\n     9|from autoshorts.script_drafts import _slug\\n    10|\\n    11|RESOLUTION = {\\\"width\\\": 1080, \\\"height\\\": 1920}\\n    12|ASPECT_RATIO = \\\"9:16\\\"\\n    13|FPS = 30\\n    14|\\n    15|\\n    16|ASSET_REQUIREMENT_HINTS = {\\n    17|    \\\"kinetic_text\\\": \\\"Generated by renderer from text and style instructions.\\\",\\n    18|    \\\"screen_recording\\\": \\\"Needs captured or synthesized vertical screen recording; avoid fake unreadable UI text.\\\",\\n    19|    \\\"card_animation\\\": \\\"Generated by renderer from concise text cards; animate one idea per beat.\\\",\\n    20|    \\\"stock_broll\\\": \\\"Needs human/phone/context B-roll; avoid generic corporate stock feel.\\\",\\n    21|    \\\"generated_background\\\": \\\"Optional abstract background only; must not become the main content.\\\",\\n    22|    \\\"cinematic_ai_video\\\": \\\"Needs photorealistic vertical AI-video clip; no baked text, logos, or watermarks.\\\",\\n    23|    \\\"mechanism_animation\\\": \\\"Needs renderer-generated mechanism/layer animation with visible movement every 1-2 seconds.\\\",\\n    24|    \\\"kinetic_text_overlay\\\": \\\"Generated by renderer as large beat-synced caption overlays, not paragraph cards.\\\",\\n    25|}\\n    26|\\n    27|\\n    28|def _seconds_to_ms(value: int | float) -> int:\\n    29|    return round(float(value) * 1000)\\n    30|\\n    31|\\n    32|def _duration_ms(start: int | float, end: int | float) -> int:\\n    33|    return max(500, _seconds_to_ms(end) - _seconds_to_ms(start))\\n    34|\\n    35|\\n    36|def _placeholder(asset_type: str, video_id: str, scene: int) -> str:\\n    37|    return f\\\"placeholder://{video_id}/scene-{scene:02d}/{asset_type}\\\"\\n    38|\\n    39|\\n    40|def _visual_style(scene: dict[str, Any]) -> dict[str, Any]:\\n    41|    base = {\\n    42|        \\\"safe_area_pct\\\": 8,\\n    43|        \\\"font\\\": \\\"bold_sans\\\",\\n    44|        \\\"caption_case\\\": \\\"sentence\\\",\\n    45|        \\\"motion\\\": \\\"push_zoom\\\" if scene[\\\"scene\\\"] == 1 else \\\"cut_or_slide\\\",\\n    46|        \\\"contrast\\\": \\\"high\\\",\\n    47|    }\\n    48|    if scene[\\\"asset_type\\\"] == \\\"card_animation\\\":\\n    49|        base.update({\\\"layout\\\": \\\"minimal_cards\\\", \\\"max_words_on_screen\\\": 18})\\n    50|    elif scene[\\\"asset_type\\\"] == \\\"screen_recording\\\":\\n    51|        base.update({\\\"layout\\\": \\\"screen_plus_caption\\\", \\\"blur_sensitive_ui\\\": True})\\n    52|    elif scene[\\\"asset_type\\\"] == \\\"stock_broll\\\":\\n    53|        base.update({\\\"layout\\\": \\\"broll_under_caption\\\", \\\"pace\\\": \\\"fast_cut\\\"})\\n    54|    elif scene[\\\"asset_type\\\"] == \\\"cinematic_ai_video\\\":\\n    55|        base.update({\\\"layout\\\": \\\"cinematic_under_caption\\\", \\\"no_baked_text\\\": True, \\\"pace\\\": \\\"fast_cut\\\"})\\n    56|    elif scene[\\\"asset_type\\\"] == \\\"mechanism_animation\\\":\\n    57|        base.update({\\\"layout\\\": \\\"xray_layer_reveal\\\", \\\"max_words_on_screen\\\": 10, \\\"layered_motion\\\": True})\\n    58|    elif scene[\\\"asset_type\\\"] == \\\"kinetic_text_overlay\\\":\\n    59|        base.update({\\\"layout\\\": \\\"kinetic_full_frame\\\", \\\"max_words_on_screen\\\": 12, \\\"beat_synced\\\": True})\\n    60|    return base\\n    61|\\n    62|\\n    63|def _visual_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n    64|    video_id = plan[\\\"id\\\"]\\n    65|    items: list[dict[str, Any]] = []\\n    66|    for scene in plan[\\\"scenes\\\"]:\\n    67|        items.append(\\n    68|            {\\n    69|                \\\"id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n    70|                \\\"track\\\": \\\"visual\\\",\\n    71|                \\\"type\\\": scene[\\\"asset_type\\\"],\\n    72|                \\\"asset_ref\\\": _placeholder(scene[\\\"asset_type\\\"], video_id, scene[\\\"scene\\\"]),\\n    73|                \\\"start_ms\\\": _seconds_to_ms(scene[\\\"start\\\"]),\\n    74|                \\\"duration_ms\\\": _duration_ms(scene[\\\"start\\\"], scene[\\\"end\\\"]),\\n    75|                \\\"text\\\": scene[\\\"on_screen_text\\\"],\\n    76|                \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n    77|                \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"cut_or_slide\\\"),\\n    78|                \\\"sound_cue\\\": scene.get(\\\"sound_cue\\\", \\\"none\\\"),\\n    79|                \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n    80|                \\\"style\\\": _visual_style(scene),\\n    81|            }\\n    82|        )\\n    83|    return items\\n    84|\\n    85|\\n    86|def _caption_timeline_items(plan: dict[str, Any]) -> list[dict[str, Any]]:\\n    87|    beats = plan.get(\\\"caption_beats\\\", [])\\n    88|    total_ms = int(plan[\\\"estimated_seconds\\\"]) * 1000\\n    89|    items: list[dict[str, Any]] = []\\n    90|    for index, beat in enumerate(beats):\\n    91|        start_ms = _seconds_to_ms(beat[\\\"start\\\"])\\n    92|        next_start_ms = _seconds_to_ms(beats[index + 1][\\\"start\\\"]) if index + 1 < len(beats) else total_ms\\n    93|        items.append(\\n    94|            {\\n    95|                \\\"id\\\": f\\\"caption_{index + 1:02d}\\\",\\n    96|                \\\"track\\\": \\\"captions\\\",\\n    97|                \\\"type\\\": \\\"kinetic_caption\\\",\\n    98|                \\\"start_ms\\\": start_ms,\\n    99|                \\\"duration_ms\\\": max(750, next_start_ms - start_ms),\\n   100|                \\\"text\\\": beat[\\\"text\\\"],\\n   101|                \\\"style\\\": {\\n   102|                    \\\"safe_area_pct\\\": 8,\\n   103|                    \\\"position\\\": \\\"lower_third\\\",\\n   104|                    \\\"max_chars_per_line\\\": 28,\\n   105|                    \\\"highlight_keywords\\\": True,\\n   106|                },\\n   107|            }\\n   108|        )\\n   109|    return items\\n   110|\\n   111|\\n   112|def _asset_requirements(plan: dict[str, Any]) -> dict[str, list[dict[str, Any]]]:\\n   113|    requirements: dict[str, list[dict[str, Any]]] = {key: [] for key in ASSET_REQUIREMENT_HINTS}\\n   114|    for scene in plan[\\\"scenes\\\"]:\\n   115|        asset_type = scene[\\\"asset_type\\\"]\\n   116|        requirements.setdefault(asset_type, []).append(\\n   117|            {\\n   118|                \\\"scene\\\": scene[\\\"scene\\\"],\\n   119|                \\\"asset_ref\\\": _placeholder(asset_type, plan[\\\"id\\\"], scene[\\\"scene\\\"]),\\n   120|                \\\"brief\\\": scene[\\\"visual_direction\\\"],\\n   121|                \\\"hint\\\": ASSET_REQUIREMENT_HINTS.get(asset_type, \\\"Provide asset matching the visual direction.\\\"),\\n   122|            }\\n   123|        )\\n   124|    return requirements\\n   125|\\n   126|\\n   127|def build_render_manifest(plan: dict[str, Any]) -> dict[str, Any]:\\n   128|    \\\"\\\"\\\"Convert a human shot plan into a structured renderer contract.\\\"\\\"\\\"\\n   129|    timeline = _visual_timeline_items(plan) + _caption_timeline_items(plan)\\n   130|    track_order = {\\\"visual\\\": 0, \\\"captions\\\": 1}\\n   131|    timeline.sort(key=lambda item: (item[\\\"start_ms\\\"], track_order.get(item[\\\"track\\\"], 99), item[\\\"id\\\"]))\\n   132|    return {\\n   133|        \\\"schema_version\\\": \\\"render_manifest.v1\\\",\\n   134|        \\\"video\\\": {\\n   135|            \\\"id\\\": plan[\\\"id\\\"],\\n   136|            \\\"title\\\": plan[\\\"title\\\"],\\n   137|            \\\"pillar\\\": plan[\\\"pillar\\\"],\\n   138|            \\\"duration_ms\\\": int(plan[\\\"estimated_seconds\\\"]) * 1000,\\n   139|            \\\"aspect_ratio\\\": ASPECT_RATIO,\\n   140|            \\\"resolution\\\": RESOLUTION,\\n   141|            \\\"fps\\\": FPS,\\n   142|        },\\n   143|        \\\"timeline\\\": timeline,\\n   144|        \\\"asset_requirements\\\": _asset_requirements(plan),\\n   145|        \\\"render_constraints\\\": {\\n   146|            \\\"avoid_static_slideshow\\\": True,\\n   147|            \\\"pattern_interrupt_every_seconds\\\": 3,\\n   148|            \\\"visual_change_every_seconds\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"visual_change_every_seconds\\\", 3),\\n   149|            \\\"first_frame_hook_required\\\": True,\\n   150|            \\\"max_words_per_visual_card\\\": 18,\\n   151|            \\\"max_static_cards\\\": plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 1),\\n   152|            \\\"human_approval_required_before_publish\\\": True,\\n   153|        },\\n   154|        \\\"production_notes\\\": plan.get(\\\"production_notes\\\", []),\\n   155|    }\\n   156|\\n   157|\\n   158|def _manifest_filename(plan: dict[str, Any]) -> str:\\n   159|    title_slug = _slug(plan[\\\"title\\\"])\\n   160|    video_id = plan[\\\"id\\\"]\\n   161|    if video_id.endswith(title_slug):\\n   162|        return f\\\"{video_id}.json\\\"\\n   163|    return f\\\"{video_id}-{title_slug}.json\\\"\\n   164|\\n   165|\\n   166|def write_render_manifests(plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n   167|    \\\"\\\"\\\"Write render manifests as pretty JSON files.\\\"\\\"\\\"\\n   168|    output_dir.mkdir(parents=True, exist_ok=True)\\n   169|    paths: list[Path] = []\\n   170|    for plan in plans:\\n   171|        manifest = build_render_manifest(plan)\\n   172|        path = output_dir / _manifest_filename(plan)\\n   173|        path.write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n   174|        paths.append(path)\\n   175|    return paths\\n   176|\", \"total_lines\": 175, \"file_size\": 7383, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_S5BBHzA4fHJsKbwKN4wurfZz", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Asset planning for final, motion-led AutoShortsBot renders.\\n     2|\\n     3|This layer converts render manifests into explicit asset-generation plans so final\\n     4|videos can use local procedural motion, screen-recording simulations, kinetic\\n     5|overlays, and mechanism animations instead of static text slides. The plan is\\n     6|budget-safe: paid video-credit providers are not required.\\n     7|\\\"\\\"\\\"\\n     8|\\n     9|from __future__ import annotations\\n    10|\\n    11|import json\\n    12|from pathlib import Path\\n    13|from typing import Any\\n    14|\\n    15|from autoshorts.script_drafts import _slug\\n    16|\\n    17|SCENE_MODE_BY_TYPE = {\\n    18|    \\\"kinetic_text\\\": \\\"kinetic_text_overlay\\\",\\n    19|    \\\"screen_recording\\\": \\\"screen_recording_simulation\\\",\\n    20|    \\\"card_animation\\\": \\\"animated_card\\\",\\n    21|    \\\"stock_broll\\\": \\\"cinematic_ai_video\\\",\\n    22|    \\\"generated_background\\\": \\\"cinematic_ai_video\\\",\\n    23|    \\\"cinematic_ai_video\\\": \\\"cinematic_ai_video\\\",\\n    24|    \\\"mechanism_animation\\\": \\\"mechanism_animation\\\",\\n    25|    \\\"kinetic_text_overlay\\\": \\\"kinetic_text_overlay\\\",\\n    26|}\\n    27|\\n    28|\\n    29|# Scene-level overrides keep AI/productivity videos from becoming five text cards.\\n    30|SCENE_MODE_OVERRIDES = {\\n    31|    \\\"ai_life_systems\\\": {\\n    32|        \\\"scene_01\\\": \\\"cinematic_ai_video\\\",\\n    33|        \\\"scene_02\\\": \\\"screen_recording_simulation\\\",\\n    34|        \\\"scene_03\\\": \\\"animated_card\\\",\\n    35|        \\\"scene_04\\\": \\\"screen_recording_simulation\\\",\\n    36|        \\\"scene_05\\\": \\\"kinetic_text_overlay\\\",\\n    37|    },\\n    38|    \\\"attention_design\\\": {\\n    39|        \\\"scene_01\\\": \\\"cinematic_ai_video\\\",\\n    40|        \\\"scene_02\\\": \\\"cinematic_ai_video\\\",\\n    41|        \\\"scene_03\\\": \\\"animated_card\\\",\\n    42|        \\\"scene_04\\\": \\\"screen_recording_simulation\\\",\\n    43|        \\\"scene_05\\\": \\\"kinetic_text_overlay\\\",\\n    44|    },\\n    45|}\\n    46|\\n    47|\\n    48|def _visual_items(manifest: dict[str, Any]) -> list[dict[str, Any]]:\\n    49|    return [item for item in manifest[\\\"timeline\\\"] if item[\\\"track\\\"] == \\\"visual\\\"]\\n    50|\\n    51|\\n    52|def _duration_seconds(item: dict[str, Any]) -> int | float:\\n    53|    seconds = int(item[\\\"duration_ms\\\"]) / 1000\\n    54|    if item.get(\\\"type\\\") in {\\\"cinematic_ai_video\\\", \\\"mechanism_animation\\\", \\\"kinetic_text_overlay\\\"}:\\n    55|        return int(seconds) if seconds.is_integer() else seconds\\n    56|    return max(3, min(6, round(seconds)))\\n    57|\\n    58|\\n    59|def _asset_mode(item: dict[str, Any], pillar: str) -> str:\\n    60|    override = SCENE_MODE_OVERRIDES.get(pillar, {}).get(item[\\\"id\\\"])\\n    61|    if override:\\n    62|        return override\\n    63|    return SCENE_MODE_BY_TYPE.get(item[\\\"type\\\"], \\\"cinematic_ai_video\\\")\\n    64|\\n    65|\\n    66|def _camera_motion(scene_id: str, mode: str) -> str:\\n    67|    if mode == \\\"cinematic_ai_video\\\":\\n    68|        return \\\"slow handheld push-in, subtle parallax, natural micro-movements\\\"\\n    69|    if mode == \\\"screen_recording_simulation\\\":\\n    70|        return \\\"cursor movement, typing reveal, zoom to changed prompt, highlighted selection box\\\"\\n    71|    if mode == \\\"animated_card\\\":\\n    72|        return \\\"cards slide in one at a time, one concept per beat, slight scale pops\\\"\\n    73|    return \\\"large kinetic caption words pop and slide with beat-synced emphasis\\\"\\n    74|\\n    75|\\n    76|def _cinematic_subject(pillar: str, text: str) -> str:\\n    77|    if pillar == \\\"attention_design\\\":\\n    78|        return (\\n    79|            \\\"a realistic person holding a phone in a dim modern room, thumb hovering over an endless feed, \\\"\\n    80|            \\\"then a decisive pause as the screen glow reflects on their face\\\"\\n    81|        )\\n    82|    return (\\n    83|        \\\"a realistic creator at a desk late evening, laptop open to an AI chat, thoughtful but in control, \\\"\\n    84|        \\\"hands moving between keyboard and notebook\\\"\\n    85|    )\\n    86|\\n    87|\\n    88|def _generation_prompt(item: dict[str, Any], manifest: dict[str, Any], mode: str) -> str:\\n    89|    video = manifest[\\\"video\\\"]\\n    90|    text = item[\\\"text\\\"]\\n    91|    if mode == \\\"cinematic_ai_video\\\":\\n    92|        subject = _cinematic_subject(video[\\\"pillar\\\"], text)\\n    93|        return (\\n    94|            f\\\"Vertical 9:16 cinematic-looking local motion scene, {subject}. \\\"\\n    95|            f\\\"Mood: focused, modern, high-retention social video, dramatic lighting simulated with gradients and shadows. \\\"\\n    96|            f\\\"Scene intent: {item['visual_direction']} Camera: {_camera_motion(item['id'], mode)}. \\\"\\n    97|            \\\"Use local procedural animation/parallax rather than paid AI-video credits; keep captions renderer-overlay only.\\\"\\n    98|        )\\n    99|    if mode == \\\"screen_recording_simulation\\\":\\n   100|        return (\\n   101|            \\\"Vertical 9:16 UI/screen-recording simulation. Show an AI chat or phone interface being edited live: \\\"\\n   102|            f\\\"{item['visual_direction']} Use typing, cursor movement, zoom highlights, and before/after prompt changes. \\\"\\n   103|            \\\"All readable captions must be added by our renderer overlay, not baked into the clip.\\\"\\n   104|        )\\n   105|    if mode == \\\"animated_card\\\":\\n   106|        return (\\n   107|            \\\"Vertical animated explainer card sequence. Minimal dark UI, one idea per beat, animated boxes and arrows. \\\"\\n   108|            f\\\"Visual task: {item['visual_direction']} Keep it moving; no static slide holds longer than one second.\\\"\\n   109|        )\\n   110|    if mode == \\\"mechanism_animation\\\":\\n   111|        return (\\n   112|            \\\"Vertical 9:16 mechanism-reveal animation. Build a clear visual explanation with moving layers, x-ray/röntgen reveal, \\\"\\n   113|            \\\"arrows, meters, object cutaways, and sequential labels added by our renderer. \\\"\\n   114|            f\\\"Mechanism task: {item['visual_direction']} Motion: {item.get('visual_motion', _camera_motion(item['id'], mode))}. \\\"\\n   115|            \\\"No static holds longer than one second; keep all readable text as renderer overlays.\\\"\\n   116|        )\\n   117|    return (\\n   118|        \\\"Kinetic text overlay scene. Use large caption words, fast emphasis, scale pops, and clean background motion. \\\"\\n   119|        f\\\"Overlay text: {text}\\\"\\n   120|    )\\n   121|\\n   122|\\n   123|def _negative_prompt(mode: str) -> str:\\n   124|    if mode == \\\"cinematic_ai_video\\\":\\n   125|        return (\\n   126|            \\\"no readable text, no subtitles, no logos, no watermarks, no distorted hands, no fake UI text, \\\"\\n   127|            \\\"no slideshow, no static image, no uncanny faces\\\"\\n   128|        )\\n   129|    if mode == \\\"screen_recording_simulation\\\":\\n   130|        return \\\"no private data, no real credentials, no illegible tiny UI, no brand logos unless approved\\\"\\n   131|    if mode == \\\"animated_card\\\":\\n   132|        return \\\"no dense paragraphs, no static PowerPoint slide, no tiny text, no chart junk\\\"\\n   133|    if mode == \\\"mechanism_animation\\\":\\n   134|        return \\\"no static diagram, no dense labels, no tiny text, no unreadable chart junk, no generic infographic\\\"\\n   135|    return \\\"no long paragraphs, no tiny footnotes, no static card\\\"\\n   136|\\n   137|\\n   138|def _scene_asset_plan(item: dict[str, Any], manifest: dict[str, Any]) -> dict[str, Any]:\\n   139|    video = manifest[\\\"video\\\"]\\n   140|    mode = _asset_mode(item, video[\\\"pillar\\\"])\\n   141|    return {\\n   142|        \\\"scene_id\\\": item[\\\"id\\\"],\\n   143|        \\\"asset_ref\\\": item[\\\"asset_ref\\\"],\\n   144|        \\\"asset_mode\\\": mode,\\n   145|        \\\"provider_family\\\": \\\"local_free_renderer\\\",\\n   146|        \\\"duration_seconds\\\": _duration_seconds(item),\\n   147|        \\\"overlay_text\\\": item[\\\"text\\\"],\\n   148|        \\\"caption_strategy\\\": \\\"renderer_overlay_only\\\",\\n   149|        \\\"no_text_in_video\\\": mode == \\\"cinematic_ai_video\\\",\\n   150|        \\\"motion_directive\\\": _camera_motion(item[\\\"id\\\"], mode),\\n   151|        \\\"generation_prompt\\\": _generation_prompt(item, manifest, mode),\\n   152|        \\\"negative_prompt\\\": _negative_prompt(mode),\\n   153|        \\\"retention_goal\\\": item[\\\"retention_goal\\\"],\\n   154|        \\\"source_visual_direction\\\": item[\\\"visual_direction\\\"],\\n   155|    }\\n   156|\\n   157|\\n   158|def build_asset_plan(manifest: dict[str, Any]) -> dict[str, Any]:\\n   159|    \\\"\\\"\\\"Build a final-render asset plan from a render manifest.\\\"\\\"\\\"\\n   160|    scenes = [_scene_asset_plan(item, manifest) for item in _visual_items(manifest)]\\n   161|    modes = [scene[\\\"asset_mode\\\"] for scene in scenes]\\n   162|    return {\\n   163|        \\\"schema_version\\\": \\\"asset_plan.v2\\\",\\n   164|        \\\"video\\\": manifest[\\\"video\\\"],\\n   165|        \\\"scenes\\\": scenes,\\n   166|        \\\"final_render_strategy\\\": {\\n   167|            \\\"preview_renderer_role\\\": \\\"storyboard_only\\\",\\n   168|            \\\"budget_policy\\\": \\\"free_only_no_paid_video_credits\\\",\\n   169|            \\\"static_card_limit\\\": 0,\\n   170|            \\\"must_use_motion_assets\\\": True,\\n   171|            \\\"preferred_mix\\\": {\\n   172|                \\\"cinematic_ai_video\\\": modes.count(\\\"cinematic_ai_video\\\"),\\n   173|                \\\"screen_recording_simulation\\\": modes.count(\\\"screen_recording_simulation\\\"),\\n   174|                \\\"animated_card\\\": modes.count(\\\"animated_card\\\"),\\n   175|                \\\"mechanism_animation\\\": modes.count(\\\"mechanism_animation\\\"),\\n   176|                \\\"kinetic_text_overlay\\\": modes.count(\\\"kinetic_text_overlay\\\"),\\n   177|            },\\n   178|            \\\"captions\\\": \\\"large kinetic overlays generated by renderer; no baked text in AI clips\\\",\\n   179|            \\\"approval_required_before_publish\\\": True,\\n   180|        },\\n   181|    }\\n   182|\\n   183|\\n   184|def _asset_plan_filename(video: dict[str, Any]) -> str:\\n   185|    title_slug = _slug(video[\\\"title\\\"])\\n   186|    video_id = video[\\\"id\\\"]\\n   187|    if video_id.endswith(title_slug):\\n   188|        return f\\\"{video_id}.json\\\"\\n   189|    return f\\\"{video_id}-{title_slug}.json\\\"\\n   190|\\n   191|\\n   192|def write_asset_plans(manifests: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n   193|    \\\"\\\"\\\"Write asset plans as JSON files.\\\"\\\"\\\"\\n   194|    output_dir.mkdir(parents=True, exist_ok=True)\\n   195|    paths: list[Path] = []\\n   196|    for manifest in manifests:\\n   197|        plan = build_asset_plan(manifest)\\n   198|        path = output_dir / _asset_plan_filename(plan[\\\"video\\\"])\\n   199|        path.write_text(json.dumps(plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n   200|        paths.append(path)\\n   201|    return paths\\n   202|\", \"total_lines\": 201, \"file_size\": 8898, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_wAsYNZUZ1uZJ7xKRoxpnuijn", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|from autoshorts.mechanism_pipeline import (\\n     2|    build_mechanism_shot_plan,\\n     3|    write_mechanism_shot_plans,\\n     4|)\\n     5|from autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n     6|\\n     7|\\n     8|def _auto_explainer():\\n     9|    return build_mechanism_explainer(PILOT_IDEAS[0])\\n    10|\\n    11|\\n    12|def test_build_mechanism_shot_plan_preserves_retention_beats_and_timing():\\n    13|    explainer = _auto_explainer()\\n    14|\\n    15|    plan = build_mechanism_shot_plan(explainer)\\n    16|\\n    17|    assert plan[\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n    18|    assert plan[\\\"series\\\"] == \\\"Unsichtbare Systeme in 30 Sekunden\\\"\\n    19|    assert plan[\\\"estimated_seconds\\\"] == 34\\n    20|    assert [scene[\\\"purpose\\\"] for scene in plan[\\\"scenes\\\"]] == [\\n    21|        \\\"pattern_interrupt\\\",\\n    22|        \\\"problem\\\",\\n    23|        \\\"mechanism_reveal\\\",\\n    24|        \\\"twist\\\",\\n    25|        \\\"follow_trigger\\\",\\n    26|    ]\\n    27|    assert plan[\\\"scenes\\\"][0][\\\"start\\\"] == 0.0\\n    28|    assert plan[\\\"scenes\\\"][0][\\\"end\\\"] <= 1.5\\n    29|    assert plan[\\\"scenes\\\"][2][\\\"asset_type\\\"] == \\\"mechanism_animation\\\"\\n    30|    assert all(scene[\\\"visual_motion\\\"] for scene in plan[\\\"scenes\\\"])\\n    31|    assert all(scene[\\\"sound_cue\\\"] for scene in plan[\\\"scenes\\\"])\\n    32|\\n    33|\\n    34|def test_mechanism_shot_plan_uses_motion_led_asset_types_not_static_cards():\\n    35|    plan = build_mechanism_shot_plan(_auto_explainer())\\n    36|\\n    37|    asset_types = [scene[\\\"asset_type\\\"] for scene in plan[\\\"scenes\\\"]]\\n    38|\\n    39|    assert \\\"static_card\\\" not in asset_types\\n    40|    assert \\\"cinematic_ai_video\\\" in asset_types\\n    41|    assert \\\"mechanism_animation\\\" in asset_types\\n    42|    assert \\\"kinetic_text_overlay\\\" in asset_types\\n    43|    assert plan[\\\"final_render_intent\\\"][\\\"static_card_limit\\\"] == 0\\n    44|    assert plan[\\\"final_render_intent\\\"][\\\"visual_change_every_seconds\\\"] <= 2\\n    45|\\n    46|\\n    47|def test_mechanism_shot_plan_generates_caption_beats_from_voiceover():\\n    48|    plan = build_mechanism_shot_plan(_auto_explainer())\\n    49|\\n    50|    assert len(plan[\\\"caption_beats\\\"]) == 5\\n    51|    assert plan[\\\"caption_beats\\\"][0][\\\"start\\\"] == 0\\n    52|    assert \\\"Occasion\\\" in plan[\\\"caption_beats\\\"][0][\\\"text\\\"]\\n    53|    assert plan[\\\"caption_beats\\\"][-1][\\\"start\\\"] == 28\\n    54|    assert \\\"versteckte\\\" in plan[\\\"caption_beats\\\"][-1][\\\"text\\\"].lower()\\n    55|\\n    56|\\n    57|def test_mechanism_scene_overlay_truncation_marks_open_sentence():\\n    58|    plan = build_mechanism_shot_plan(_auto_explainer())\\n    59|    overlay = plan[\\\"scenes\\\"][2][\\\"on_screen_text\\\"]\\n    60|\\n    61|    assert overlay.endswith(\\\"…\\\")\\n    62|    assert not overlay.endswith(\\\" und…\\\")\\n    63|\\n    64|\\n    65|def test_write_mechanism_shot_plans_outputs_markdown(tmp_path):\\n    66|    explainer = _auto_explainer()\\n    67|\\n    68|    paths = write_mechanism_shot_plans([explainer], tmp_path)\\n    69|\\n    70|    assert len(paths) == 1\\n    71|    assert paths[0].suffix == \\\".md\\\"\\n    72|    text = paths[0].read_text(encoding=\\\"utf-8\\\")\\n    73|    assert \\\"## Mechanism shot plan\\\" in text\\n    74|    assert \\\"pattern_interrupt\\\" in text\\n    75|    assert \\\"mechanism_animation\\\" in text\\n    76|    assert \\\"Sound cue\\\" in text\\n    77|\", \"total_lines\": 76, \"file_size\": 2717, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_cBtorN77LwyeUqOWHBTSip9B", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Repo-Struktur und bestehende Retention-/Mechanism-Module prüfen\", \"status\": \"completed\"}, {\"id\": \"style\", \"content\": \"Style Bible als Projektartefakt erstellen\", \"status\": \"in_progress\"}, {\"id\": \"keyprompt\", \"content\": \"Keyframe/I2V Prompt Generator per TDD implementieren\", \"status\": \"pending\"}, {\"id\": \"cli\", \"content\": \"CLI + Artefakte für Pilot-Prompts erzeugen\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen und Git-Status sauber berichten\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 3, \"in_progress\": 1, \"completed\": 1, \"cancelled\": 0}}", "tool_call_id": "call_9yuyjtKTxbMdfDp8128PiHtb", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_oK1k4nN6m7oLFoEPuTqBhgVB", "call_id": "call_oK1k4nN6m7oLFoEPuTqBhgVB", "response_item_id": "fc_0d1a4d10654d56b4016a11fa98c1048191b3319fc26f7a18d4", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/docs/style_bible.md\",\"content\":\"# AutoShortsBot Style Bible\\n\\n## Positioning\\n\\n**Series:** Unsichtbare Systeme in 30 Sekunden  \\n**Niche:** Hidden Money & AI Systems  \\n**Claim:** Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.\\n\\nThe channel is not an AI-avatar news channel and not a static slideshow channel. It is a semi-automatic Retention Studio for short, motion-led mechanism reveals.\\n\\n## Core visual idea\\n\\n```text\\nphotorealistic macro investigation + clean mechanism overlays\\n```\\n\\nThe AI-video layer gives the real-world texture: car, phone, checkout, desk, garage, document, screen glow, hand movement, machine detail. The deterministic renderer adds truth: captions, numbers, arrows, labels, risk meters, x-ray layers and system maps.\\n\\n## Palette\\n\\n- Base: graphite black / dark navy / charcoal UI\\n- Primary text: clean white\\n- System accent: electric cyan for flows, data and invisible connections\\n- Warning accent: warm amber for hidden cost/risk\\n- Danger accent: red only for twist/reveal moments\\n- Backgrounds: realistic, slightly desaturated, high contrast, not cyberpunk neon soup\\n\\n## Typography & captions\\n\\n- Large kinetic captions, no paragraph cards\\n- 3–7 words per caption beat when possible\\n- Numbers and labels are renderer overlays, never AI-generated inside video clips\\n- Captions should puncture the pattern, not describe the obvious\\n- Text is added after AI-video generation to avoid hallucinated letters\\n\\n## AI-video rules\\n\\nAlways request:\\n\\n```text\\nno readable text, no logo, no watermark, no subtitles, no fake UI text\\n```\\n\\nPrefer Image-to-Video for production clips:\\n\\n```text\\nkeyframe -> Wan2.2 I2V -> selected take -> renderer overlays\\n```\\n\\nUse Text-to-Video only for rough exploration or abstract atmosphere.\\n\\n## Shot families\\n\\n### 1. Object Hook\\n\\nPhotorealistic object with immediate tension.\\n\\nExamples:\\n\\n- used compact car in bright inspection garage\\n- phone over endless feed before thumb taps\\n- checkout terminal close-up without readable brand text\\n- invoice/document stack with no readable details\\n\\nCamera: slow push-in, low-angle macro, light sweep, rack focus.\\n\\n### 2. Human Decision Moment\\n\\nRealistic human context without avatar-hosting.\\n\\nExamples:\\n\\n- hand pauses before signing\\n- buyer looking at car keys and repair estimate\\n- person hovering over an app confirmation\\n- creator looking from AI chat to notebook\\n\\nCamera: handheld micro-movement, over-shoulder, close-up hands, no uncanny faces.\\n\\n### 3. Mechanism Reveal\\n\\nRenderer-generated layer animation over or after real-world footage.\\n\\nExamples:\\n\\n- x-ray car layers: price, repairs, standdays, dealer margin, liquidity\\n- attention funnel: hook, variable reward, loop, habit\\n- AI agent control stack: task, memory, tool, approval gate, audit\\n\\nCamera/motion: sequential reveals every 1–2 seconds, parallax, snap zooms, pulse on risk nodes.\\n\\n### 4. Twist Frame\\n\\nWrong explanation collapses; real mechanism appears.\\n\\nExamples:\\n\\n- `Kaufpreis` fades out; `Zeitpunkt + Risiko` lights up\\n- `Du bist faul` collapses; `Design gewinnt vorher` appears\\n- `KI denkt für dich` collapses; `du gibst Ziele + Grenzen` appears\\n\\n### 5. Follow Trigger\\n\\nSeries promise, not generic CTA.\\n\\nGood:\\n\\n```text\\nMorgen: der Kredit, der kostenlos aussieht.\\n```\\n\\nBad:\\n\\n```text\\nFollow for more.\\n```\\n\\n## No-go list\\n\\n- static AI image slideshow as final video\\n- generic AI avatar as main format\\n- fake readable UI text\\n- fake charts/numbers generated by the model\\n- visible logos/brands unless explicitly approved\\n- dark underexposed clips where subject is unclear\\n- unreadable license plates or hallucinated text\\n- generic “cinematic masterpiece” prompts without specific motion\\n- reused-content clone formats\\n\\n## Wan2.2 prompt style\\n\\nUse concrete, compact prompts:\\n\\n```text\\n[shot type] of [one subject] [one action] in [specific environment]. Camera [one motion]. [lighting/exposure]. [style/texture]. Preserve [identity/detail]. No text, no logo, no watermark.\\n```\\n\\nProduction clips should have:\\n\\n- one clear subject\\n- one primary motion\\n- one camera move\\n- balanced exposure\\n- exact environment\\n- explicit identity preservation for I2V\\n- negative prompt for text/logos/deformation/flicker\\n\\n## Pilot style: cheap car hidden cost\\n\\n### Keyframe direction\\n\\n```text\\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, no text, no logos, no watermark.\\n```\\n\\n### I2V direction\\n\\n```text\\nPhotorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle. Subtle light sweep over hood and windshield, stable composition, realistic reflections, balanced exposure. Preserve the same car and garage. No text, no logo, no watermark.\\n```\\n\\n### Renderer overlay direction\\n\\n- Price tag peels away\\n- Repair-risk layer lights amber\\n- Standdays/time layer pulses cyan\\n- Dealer margin layer slides in\\n- Liquidity/risk node glows red on twist\\n\\n## Quality gate\\n\\nA clip is usable only if:\\n\\n- subject is identifiable in first 0.5 seconds\\n- no unreadable generated text is prominent\\n- motion supports the retention beat\\n- enough clean space remains for captions/overlays\\n- no uncanny face/hand/artifact distracts from mechanism\\n- exposure is balanced enough for phone viewing\\n\\nIf the clip fails, regenerate the keyframe or change the shot. Do not polish bad source material.\\n\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "assistant", "content": "", "reasoning": "**Designing test plan for keyframe prompts**\n\nI need to start with tests to define the behavior of keyframe_prompt.py. My plan is to ensure it builds from the mechanism shot plan and only returns cinematic_ai_video scenes. For each scene, I’ll check aspects like keyframe_prompt, i2v_motion_prompt, negative_prompt, and wan_preset without any readable text and include a visual_style profile. For example, in car pilot scene 1, I should look at a bright inspection garage with a red compact car and a motion push-in. Time to write those tests!", "finish_reason": "tool_calls", "reasoning_content": "**Designing test plan for keyframe prompts**\n\nI need to start with tests to define the behavior of keyframe_prompt.py. My plan is to ensure it builds from the mechanism shot plan and only returns cinematic_ai_video scenes. For each scene, I’ll check aspects like keyframe_prompt, i2v_motion_prompt, negative_prompt, and wan_preset without any readable text and include a visual_style profile. For example, in car pilot scene 1, I should look at a bright inspection garage with a red compact car and a motion push-in. Time to write those tests!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEfq7zvzXKHCu4JCe1OCe5xt1Poefv1_ePJTla5GcZJtzUiHTt-5ro7d08_ZPkIcVvVo0tFLjjR2Aq6Zd60Gd4DZp-wp89KbsxEZOCtKFMG3AJyS1s0-xY1AgnqEm9Ns3gkLWK6lcTM97uTQvEFjK2oM5iqWrcA-15mluPw5CDmTFpsw-lKZaDfdfOGiV7FQRWB-QBEx0Kh1wlsDuxDjmpqLyZe-DOPiFyBL8h-hJE52qTRFngiWoiVdvw29CoQvhHuZOULA9jiGgC7LbrXsn6MdaRP7M9AB3qSEPjtQ-AyuJBN2G-UDnR0YoHuZOasiZM9ojxGOGkcmHYF7trxHh0ecp0HBILil2RfN0WjFDOv0mIjYmA-1GNlX8qFFpOk3RYS7wFkRsGhxLEolbiPtebTSfOTNLiHe8I2jTFhNYiXDHDlwjCBqyaxmgJzY8rykeW8Gbzvop4_1JOPIXS2R0Obh_S3l0JF-Y14OUR8sCOoAFPh3VVX1VQOIcXJeGmsbBU_p_iDj1RlDWlOHdH0Aq_oJvTtdD7bSh4D4eWRFCadsujc7_BwoOX9YYZehxi4107UcvfqbRm8vVKtJefIMQlxxANQPtv3SFgCB5wD1p6nJ8ZjhPRK_u0M-tv8JGnil0qj5GzUi6W53X3szzzKJifp3rjZhsZgcps2qNAe1X05IuOQNnQuteb83RQ3fm9ozG9jzfTcRChDciPYWiK39TYWfLVJXUMJlf2I6z6jZx03h-BuYSJu2VoFD8l5OIUrD7w7cd8RiB-EB_eQyGLPTTZDOEp-vbKUcwlEbhZJPnsokI31IQLmwkT3W62GeiAERzghdWEdyyozEa-Tra9wXJ4Z6a1lWCUD68gqN3PgCm6fvwdM36NwCDAuhkmevncELlxw7EA7p_WtUvu952rfIygc63-7-jPNmeSoKWoqlgCf66anKXImdqwL8AT5NkZPNaLG35rAuygBuVdsdns3otRHby73_yX4247ByPtaKTjVGXMxAA7VJSKTISkfYPrwVDCAG-CtI4L3WKMrMwjUrzv2LvXGB0ymsQxfmB1NSgXgrUVwyp9-vhuGBtK5Uwfh6rPNqBH17PpNTavAUH4gVYZNobVGEHq0mylMvdh7U6TvCWlU0pPqhogtZf0lU8kptzm9ifqd1yPIL5-mxk2YlD48X_A2bnul8EpBC8l6cMUzQwDvHWJl_PinfdZqkyvLf3DqSd9EnkE6H-P_hCty6EGggdQ3ApAMN_-_K6yK-FyhJ3hyoXUHOHHOWbjdIr7nPVAUC9hP0XagQ2ImwK18_028iVWbDziYxMoKRCxMW1BHiAVFItM8BrV0ML61WieLRJ0-CiscydNwexgNBRcGXl3YHtbVeH0GS2wy2Mf9Ao8io5FQcFUbONO8Xa4wFnkKEs9opAzxHsMfT_wlAUAtd-dbc85bhvIveTk-W4SwgQdNAj4qi8KsmDk9OnQjVPxg5EcB2zY3strkALHQpV6ss1uGfTPA==", "id": "rs_0d1a4d10654d56b4016a11fab851d881919c600d7d63bd7248", "summary": [{"type": "summary_text", "text": "**Designing test plan for keyframe prompts**\n\nI need to start with tests to define the behavior of keyframe_prompt.py. My plan is to ensure it builds from the mechanism shot plan and only returns cinematic_ai_video scenes. For each scene, I’ll check aspects like keyframe_prompt, i2v_motion_prompt, negative_prompt, and wan_preset without any readable text and include a visual_style profile. For example, in car pilot scene 1, I should look at a bright inspection garage with a red compact car and a motion push-in. Time to write those tests!"}]}], "tool_calls": [{"id": "call_cG3vAqDsiMw2G76645zQ5Sk4", "call_id": "call_cG3vAqDsiMw2G76645zQ5Sk4", "response_item_id": "fc_0d1a4d10654d56b4016a11fabbcda081919cdf49e894af3964", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from autoshorts.keyframe_prompt import build_i2v_prompt_plan, write_i2v_prompt_plans\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\\n\\ndef _pilot_shot_plan():\\n    explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n    return build_mechanism_shot_plan(explainer)\\n\\n\\ndef test_i2v_prompt_plan_targets_only_ai_video_scenes_for_wan22():\\n    prompt_plan = build_i2v_prompt_plan(_pilot_shot_plan())\\n\\n    assert prompt_plan[\\\"schema_version\\\"] == \\\"i2v_prompt_plan.v1\\\"\\n    assert prompt_plan[\\\"style_profile\\\"] == \\\"photorealistic_macro_investigation\\\"\\n    assert prompt_plan[\\\"video\\\"][\\\"id\\\"] == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\"\\n    assert [scene[\\\"scene_id\\\"] for scene in prompt_plan[\\\"scenes\\\"]] == [\\\"scene_01\\\", \\\"scene_02\\\"]\\n    assert all(scene[\\\"wan_preset\\\"] == \\\"wan22_i2v_quality_416x736\\\" for scene in prompt_plan[\\\"scenes\\\"])\\n\\n\\ndef test_car_keyframe_prompt_uses_clear_visible_inspection_bay_without_ai_text():\\n    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][0]\\n\\n    keyframe_prompt = scene[\\\"keyframe_prompt\\\"]\\n    negative_prompt = scene[\\\"negative_prompt\\\"]\\n\\n    assert \\\"red used compact car\\\" in keyframe_prompt\\n    assert \\\"bright European vehicle inspection garage\\\" in keyframe_prompt\\n    assert \\\"front three-quarter view\\\" in keyframe_prompt\\n    assert \\\"clean empty license plate area\\\" in keyframe_prompt\\n    assert \\\"No text, no logos, no watermark\\\" in keyframe_prompt\\n    assert \\\"unreadable license plate\\\" in negative_prompt\\n    assert \\\"fake UI text\\\" in negative_prompt\\n\\n\\ndef test_i2v_motion_prompt_has_one_camera_move_and_preserves_identity():\\n    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][0]\\n\\n    motion_prompt = scene[\\\"i2v_motion_prompt\\\"]\\n\\n    assert \\\"Camera slowly pushes in\\\" in motion_prompt\\n    assert \\\"Preserve the same car and garage\\\" in motion_prompt\\n    assert \\\"No text, no logo, no watermark\\\" in motion_prompt\\n    assert \\\"slowly pushes in\\\" in scene[\\\"motion_directive\\\"]\\n    assert scene[\\\"clip_requirements\\\"][\\\"subject_identifiable_first_half_second\\\"] is True\\n    assert scene[\\\"clip_requirements\\\"][\\\"caption_safe_space\\\"] == \\\"upper third and lower third\\\"\\n\\n\\ndef test_non_ai_mechanism_scenes_become_renderer_notes_not_wan_jobs():\\n    prompt_plan = build_i2v_prompt_plan(_pilot_shot_plan())\\n\\n    renderer_notes = prompt_plan[\\\"renderer_only_scenes\\\"]\\n\\n    assert [scene[\\\"scene_id\\\"] for scene in renderer_notes] == [\\\"scene_03\\\", \\\"scene_04\\\", \\\"scene_05\\\"]\\n    assert renderer_notes[0][\\\"asset_type\\\"] == \\\"mechanism_animation\\\"\\n    assert renderer_notes[-1][\\\"asset_type\\\"] == \\\"kinetic_text_overlay\\\"\\n    assert all(\\\"Wan2.2\\\" not in scene[\\\"note\\\"] for scene in renderer_notes)\\n\\n\\ndef test_write_i2v_prompt_plans_outputs_json(tmp_path):\\n    paths = write_i2v_prompt_plans([_pilot_shot_plan()], tmp_path)\\n\\n    assert len(paths) == 1\\n    assert paths[0].name == \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\\"\\n    text = paths[0].read_text(encoding=\\\"utf-8\\\")\\n    assert \\\"i2v_prompt_plan.v1\\\" in text\\n    assert \\\"wan22_i2v_quality_416x736\\\" in text\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 3095, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\\\">\\nERROR [1:6] Import \\\"autoshorts.keyframe_prompt\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_cG3vAqDsiMw2G76645zQ5Sk4", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_AVGgs9huaVJvEhhZhohu4qPn", "call_id": "call_AVGgs9huaVJvEhhZhohu4qPn", "response_item_id": "fc_0d1a4d10654d56b4016a11facf6ee081918f3282ef6c37b2eb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_keyframe_prompt.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n________________ ERROR collecting tests/test_keyframe_prompt.py ________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.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_keyframe_prompt.py:1: in <module>\\n    from autoshorts.keyframe_prompt import build_i2v_prompt_plan, write_i2v_prompt_plans\\nE   ModuleNotFoundError: No module named 'autoshorts.keyframe_prompt'\\n=========================== short test summary info ============================\\nERROR tests/test_keyframe_prompt.py\\n!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\\n1 error in 0.13s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_AVGgs9huaVJvEhhZhohu4qPn", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEfrUTEbFfxoOw1YB5YXBxqvMYsIBhOiDFJ0Uet9nTjSY-K3hcOKWPN3O2mSZqrxwbRtHYOSi6NjbMj1tcE261AUjpcHWWB06aB3xy6xo2s02Gk9Yugz_lr4ynoM2e9FiirMz6HG9f65AIZIlV6CjMlvKPjFyyEayqeAMuX2gplyBJP4e_PQFLc9Dk8FKucA9P7t5o6Pux7dt-bQP4eage5VQcrjWIjgI4-IRnsya_5x7Xa6epzwr0mVouPuHjjuO09M7xpGIn5QJX7mJQDIN1HbesVCsaMg-7Pv9lxU8ElrWJKIgDxMMGUCD0gL2hf65zprOkh8id65dwdrqebMZVQb_SLMpV7GG75Puho-gDynJDr6pa5sxxH0IL6s2UeHWn1j7067DhnW6KLC5Mx0it2p_F-A8W-uG148gM6kvwvSJj3YJorArDvAqMjpJQDFAOsW1ZaTPXCRkdUbLOMPaZDYAqNZb0TSsQdHPa8HR-vQ65YETVocFydzgdqDjVifk4PznpxH8CWGLlwLvwCQF0K1a5Fm_BgA6XndckANDkAh6zxkH_5SeVq2xQAaxeD9vPN9186__vFxSB7diIAOXhOr1oIvZeZ9hV_GgDSn9OhZ-JrI0v6Hph79tApmEaXHdgX4LTXnhYWJIU10LkApGboOLr1-6hrRYEWqLDop70D2VQV4zBhEkQA0Kqb8iuO-ELXh4g1Xc9aLrTmQseQnJM929cnINJl986_u97Jp5xXko-yWzBthDU9amZBYmfZVECI92xFa9HHySUVLu0JsCTRe4uMEl3qeCV6xU3lMp1DJT_4DBU1Qf-HYXM3mKNhW6F7zDSjEue-9t-8FEOGXWJ6E_coIaer0L7RleJApQlERTzU5g8uDoPWqP6G7PVVez89GZNOe6WQ5vmJYOv5GaIvHANSLPYDa6NmS_wfdiBRScNoAYjTn5HSri_bnZKQmbUpwq", "id": "rs_0d1a4d10654d56b4016a11fad43ad881918f22dda0a792a0e4", "summary": []}], "tool_calls": [{"id": "call_D4ulkKSbTCDwu49I98IM61Lg", "call_id": "call_D4ulkKSbTCDwu49I98IM61Lg", "response_item_id": "fc_0d1a4d10654d56b4016a11fad4f6588191b0d801798fd9c212", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Wan2.2 Image-to-Video prompt planning for mechanism-led shorts.\\n\\nThis module converts motion-led shot plans into concrete keyframe and I2V\\nprompt specs. It deliberately keeps generated text, numbers, labels, and UI\\nout of AI-video clips; those belong to the deterministic renderer.\\n\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nfrom pathlib import Path\\nfrom typing import Any\\n\\nfrom autoshorts.script_drafts import _slug\\n\\nSTYLE_PROFILE = \\\"photorealistic_macro_investigation\\\"\\nWAN22_QUALITY_PRESET = \\\"wan22_i2v_quality_416x736\\\"\\n\\nBASE_NEGATIVE_PROMPT = (\\n    \\\"cartoon, animation, deformed subject, duplicated subject, disappearing subject, \\\"\\n    \\\"melting body, distorted geometry, blurry, low detail, flickering colors, abstract, \\\"\\n    \\\"static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, \\\"\\n    \\\"captions, watermark, logo, unreadable letters, fake UI text\\\"\\n)\\n\\nCAR_NEGATIVE_PROMPT = (\\n    BASE_NEGATIVE_PROMPT\\n    + \\\", unreadable license plate, fake license plate text, brand logos, distorted wheels, \\\"\\n    \\\"melted car body, broken windshield geometry, fake numbers, fake labels\\\"\\n)\\n\\n\\ndef _is_wan_candidate(scene: dict[str, Any]) -> bool:\\n    return scene.get(\\\"asset_type\\\") == \\\"cinematic_ai_video\\\"\\n\\n\\ndef _car_keyframe_prompt(scene: dict[str, Any]) -> str:\\n    return (\\n        \\\"Photorealistic vertical image of a red used compact car in a bright European vehicle \\\"\\n        \\\"inspection garage, full front three-quarter view, clean empty license plate area, \\\"\\n        \\\"visible wheels and body panels, soft overhead lights, realistic reflections, shallow \\\"\\n        \\\"depth of field, balanced exposure, high quality inspection-bay photography. \\\"\\n        \\\"Leave clean negative space for captions in the upper third and lower third. \\\"\\n        \\\"No text, no logos, no watermark.\\\"\\n    )\\n\\n\\ndef _car_motion_prompt(scene: dict[str, Any]) -> str:\\n    motion = _motion_directive(scene)\\n    return (\\n        \\\"Photorealistic vertical video of the same red used compact car in a bright inspection \\\"\\n        f\\\"garage. Camera {motion}. Subtle light sweep over the hood and windshield, realistic \\\"\\n        \\\"reflections, stable composition, balanced exposure, high quality automotive inspection \\\"\\n        \\\"footage. Preserve the same car and garage. No text, no logo, no watermark.\\\"\\n    )\\n\\n\\ndef _motion_directive(scene: dict[str, Any]) -> str:\\n    purpose = scene.get(\\\"purpose\\\", \\\"\\\")\\n    if purpose == \\\"pattern_interrupt\\\":\\n        return \\\"slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis\\\"\\n    if purpose == \\\"problem\\\":\\n        return \\\"slowly pushes in from a front three-quarter angle with gentle parallax\\\"\\n    visual_motion = scene.get(\\\"visual_motion\\\", \\\"\\\")\\n    if visual_motion:\\n        return visual_motion\\n    return \\\"slowly pushes in with subtle parallax\\\"\\n\\n\\ndef _fallback_keyframe_prompt(scene: dict[str, Any]) -> str:\\n    return (\\n        \\\"Photorealistic vertical macro-investigation image, one clear subject in a realistic \\\"\\n        f\\\"environment. Visual task: {scene['visual_direction']}. Bright balanced exposure, clean \\\"\\n        \\\"composition, high contrast, enough negative space for renderer captions, no readable text, \\\"\\n        \\\"no logos, no watermark.\\\"\\n    )\\n\\n\\ndef _fallback_motion_prompt(scene: dict[str, Any]) -> str:\\n    motion = _motion_directive(scene)\\n    return (\\n        \\\"Photorealistic vertical video preserving the same subject and environment from the start \\\"\\n        f\\\"image. Camera {motion}. Motion supports this retention goal: {scene['retention_goal']}. \\\"\\n        \\\"Balanced exposure, stable composition, realistic texture. No text, no logo, no watermark.\\\"\\n    )\\n\\n\\ndef _scene_prompt_spec(scene: dict[str, Any]) -> dict[str, Any]:\\n    visual = \\\" \\\".join([scene.get(\\\"visual_direction\\\", \\\"\\\"), scene.get(\\\"voiceover\\\", \\\"\\\")]).lower()\\n    is_car_scene = \\\"auto\\\" in visual or \\\"car\\\" in visual or \\\"occasion\\\" in visual or \\\"garage\\\" in visual\\n    keyframe_prompt = _car_keyframe_prompt(scene) if is_car_scene else _fallback_keyframe_prompt(scene)\\n    motion_prompt = _car_motion_prompt(scene) if is_car_scene else _fallback_motion_prompt(scene)\\n    negative_prompt = CAR_NEGATIVE_PROMPT if is_car_scene else BASE_NEGATIVE_PROMPT\\n    return {\\n        \\\"scene_id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n        \\\"source_scene\\\": scene[\\\"scene\\\"],\\n        \\\"purpose\\\": scene[\\\"purpose\\\"],\\n        \\\"asset_type\\\": scene[\\\"asset_type\\\"],\\n        \\\"style_profile\\\": STYLE_PROFILE,\\n        \\\"wan_preset\\\": WAN22_QUALITY_PRESET,\\n        \\\"keyframe_prompt\\\": keyframe_prompt,\\n        \\\"i2v_motion_prompt\\\": motion_prompt,\\n        \\\"negative_prompt\\\": negative_prompt,\\n        \\\"motion_directive\\\": _motion_directive(scene),\\n        \\\"source_visual_direction\\\": scene[\\\"visual_direction\\\"],\\n        \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n        \\\"clip_requirements\\\": {\\n            \\\"subject_identifiable_first_half_second\\\": True,\\n            \\\"no_baked_text\\\": True,\\n            \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n            \\\"preferred_duration_seconds\\\": max(2, round(float(scene[\\\"end\\\"]) - float(scene[\\\"start\\\"),), 1)),\\n            \\\"review_required\\\": True,\\n        },\\n        \\\"comfyui\\\": {\\n            \\\"workflow_family\\\": \\\"native_wan22_ti2v_i2v\\\",\\n            \\\"model\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n            \\\"text_encoder\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n            \\\"vae\\\": \\\"wan2.2_vae.safetensors\\\",\\n            \\\"resolution\\\": {\\\"width\\\": 416, \\\"height\\\": 736},\\n            \\\"frames\\\": 49,\\n            \\\"fps\\\": 10,\\n            \\\"steps\\\": 16,\\n            \\\"cfg\\\": 5.0,\\n        },\\n    }\\n\\n\\ndef _renderer_note(scene: dict[str, Any]) -> dict[str, Any]:\\n    return {\\n        \\\"scene_id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n        \\\"source_scene\\\": scene[\\\"scene\\\"],\\n        \\\"purpose\\\": scene[\\\"purpose\\\"],\\n        \\\"asset_type\\\": scene[\\\"asset_type\\\"],\\n        \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n        \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"\\\"),\\n        \\\"note\\\": \\\"Renderer-generated motion layer; keep labels, arrows, numbers and captions deterministic.\\\",\\n    }\\n\\n\\ndef build_i2v_prompt_plan(shot_plan: dict[str, Any]) -> dict[str, Any]:\\n    \\\"\\\"\\\"Build Wan2.2 I2V prompt specs from a mechanism shot plan.\\\"\\\"\\\"\\n    ai_scenes = [scene for scene in shot_plan[\\\"scenes\\\"] if _is_wan_candidate(scene)]\\n    renderer_scenes = [scene for scene in shot_plan[\\\"scenes\\\"] if not _is_wan_candidate(scene)]\\n    return {\\n        \\\"schema_version\\\": \\\"i2v_prompt_plan.v1\\\",\\n        \\\"style_profile\\\": STYLE_PROFILE,\\n        \\\"video\\\": {\\n            \\\"id\\\": shot_plan[\\\"id\\\"],\\n            \\\"title\\\": shot_plan[\\\"title\\\"],\\n            \\\"series\\\": shot_plan[\\\"series\\\"],\\n            \\\"pillar\\\": shot_plan[\\\"pillar\\\"],\\n        },\\n        \\\"strategy\\\": {\\n            \\\"production_mode\\\": \\\"keyframe_to_wan22_i2v_to_deterministic_renderer\\\",\\n            \\\"ai_video_role\\\": \\\"photorealistic texture and motion only\\\",\\n            \\\"renderer_role\\\": \\\"captions, labels, numbers, arrows, mechanism layers and audit-safe truth\\\",\\n            \\\"static_card_limit\\\": shot_plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 0),\\n        },\\n        \\\"scenes\\\": [_scene_prompt_spec(scene) for scene in ai_scenes],\\n        \\\"renderer_only_scenes\\\": [_renderer_note(scene) for scene in renderer_scenes],\\n    }\\n\\n\\ndef _prompt_plan_filename(plan: dict[str, Any]) -> str:\\n    return f\\\"{_slug(plan['video']['id'])}.json\\\"\\n\\n\\ndef write_i2v_prompt_plans(shot_plans: list[dict[str, Any]], output_dir: Path) -> list[Path]:\\n    \\\"\\\"\\\"Write Wan2.2 I2V prompt plans as JSON files.\\\"\\\"\\\"\\n    output_dir.mkdir(parents=True, exist_ok=True)\\n    paths: list[Path] = []\\n    for shot_plan in shot_plans:\\n        prompt_plan = build_i2v_prompt_plan(shot_plan)\\n        path = output_dir / _prompt_plan_filename(prompt_plan)\\n        path.write_text(json.dumps(prompt_plan, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n        paths.append(path)\\n    return paths\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"   100|        \\\"wan_preset\\\": WAN22_QUALITY_PRESET,\\n   101|        \\\"keyframe_prompt\\\": keyframe_prompt,\\n   102|        \\\"i2v_motion_prompt\\\": motion_prompt,\\n   103|        \\\"negative_prompt\\\": negative_prompt,\\n   104|        \\\"motion_directive\\\": _motion_directive(scene),\\n   105|        \\\"source_visual_direction\\\": scene[\\\"visual_direction\\\"],\\n   106|        \\\"retention_goal\\\": scene[\\\"retention_goal\\\"],\\n   107|        \\\"clip_requirements\\\": {\\n   108|            \\\"subject_identifiable_first_half_second\\\": True,\\n   109|            \\\"no_baked_text\\\": True,\\n   110|            \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n   111|            \\\"preferred_duration_seconds\\\": max(2, round(float(scene[\\\"end\\\"]) - float(scene[\\\"start\\\"),), 1)),\\n   112|            \\\"review_required\\\": True,\\n   113|        },\\n   114|        \\\"comfyui\\\": {\\n   115|            \\\"workflow_family\\\": \\\"native_wan22_ti2v_i2v\\\",\\n   116|            \\\"model\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n   117|            \\\"text_encoder\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n   118|            \\\"vae\\\": \\\"wan2.2_vae.safetensors\\\",\\n   119|            \\\"resolution\\\": {\\\"width\\\": 416, \\\"height\\\": 736},\\n   120|            \\\"frames\\\": 49,\\n   121|            \\\"fps\\\": 10,\\n   122|            \\\"steps\\\": 16,\\n   123|            \\\"cfg\\\": 5.0,\\n   124|        },\\n   125|    }\\n   126|\\n   127|\\n   128|def _renderer_note(scene: dict[str, Any]) -> dict[str, Any]:\\n   129|    return {\\n   130|        \\\"scene_id\\\": f\\\"scene_{scene['scene']:02d}\\\",\\n   131|        \\\"source_scene\\\": scene[\\\"scene\\\"],\\n   132|        \\\"purpose\\\": scene[\\\"purpose\\\"],\\n   133|        \\\"asset_type\\\": scene[\\\"asset_type\\\"],\\n   134|        \\\"visual_direction\\\": scene[\\\"visual_direction\\\"],\\n   135|        \\\"visual_motion\\\": scene.get(\\\"visual_motion\\\", \\\"\\\"),\\n   136|        \\\"note\\\": \\\"Renderer-generated motion layer; keep labels, arrows, numbers and captions deterministic.\\\",\\n   137|    }\\n   138|\\n   139|\\n   140|\", \"total_lines\": 177, \"file_size\": 7806, \"truncated\": true, \"hint\": \"Use offset=140 to continue reading (showing 100-139 of 177 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_Ff848NiDBgUJo75ovavmI0V6", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n@@ -108,7 +108,7 @@\\n             \\\"subject_identifiable_first_half_second\\\": True,\\n             \\\"no_baked_text\\\": True,\\n             \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n-            \\\"preferred_duration_seconds\\\": max(2, round(float(scene[\\\"end\\\"]) - float(scene[\\\"start\\\"),), 1)),\\n+            \\\"preferred_duration_seconds\\\": max(2, round(float(scene[\\\"end\\\"]) - float(scene[\\\"start\\\"]), 1)),\\n             \\\"review_required\\\": True,\\n         },\\n         \\\"comfyui\\\": {\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"_warning\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py was last read with offset/limit pagination (partial view). Re-read the whole file before overwriting it.\"}", "tool_call_id": "call_MfhFVPgTyvtg9VoAbOPVg9Ha", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "assistant", "content": "", "reasoning": "**Considering script development**\n\nI’m thinking about the command-line interface (CLI) and whether tests are needed for the script. It seems like implementing tests could be helpful, but maybe they aren't required for the CLI specifically? I should probably write the script regardless and consider generating actual artifacts. Also, I might need to create documentation to accompany everything. It’s sounding like there’s a bit of planning needed to get this all organized!", "finish_reason": "tool_calls", "reasoning_content": "**Considering script development**\n\nI’m thinking about the command-line interface (CLI) and whether tests are needed for the script. It seems like implementing tests could be helpful, but maybe they aren't required for the CLI specifically? I should probably write the script regardless and consider generating actual artifacts. Also, I might need to create documentation to accompany everything. 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It seems like implementing tests could be helpful, but maybe they aren't required for the CLI specifically? I should probably write the script regardless and consider generating actual artifacts. Also, I might need to create documentation to accompany everything. It’s sounding like there’s a bit of planning needed to get this all organized!"}]}], "tool_calls": [{"id": "call_wbZLMcvmEP3bpaKRmXR79g5A", "call_id": "call_wbZLMcvmEP3bpaKRmXR79g5A", "response_item_id": "fc_0d1a4d10654d56b4016a11fb1782188191b1daf0e20cb0e403", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Generate Wan2.2 I2V prompt plans for mechanism shot plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nfrom pathlib import Path\\n\\nfrom autoshorts.keyframe_prompt import write_i2v_prompt_plans\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\\n\\ndef _default_shot_plans() -> list[dict]:\\n    return [build_mechanism_shot_plan(build_mechanism_explainer(PILOT_IDEAS[0]))]\\n\\n\\ndef _load_shot_plans(path: Path) -> list[dict]:\\n    data = json.loads(path.read_text(encoding=\\\"utf-8\\\"))\\n    if isinstance(data, list):\\n        return data\\n    if isinstance(data, dict) and \\\"scenes\\\" in data:\\n        return [data]\\n    if isinstance(data, dict) and \\\"plans\\\" in data:\\n        return data[\\\"plans\\\"]\\n    raise ValueError(f\\\"Unsupported shot plan JSON shape: {path}\\\")\\n\\n\\ndef main() -> int:\\n    parser = argparse.ArgumentParser(description=__doc__)\\n    parser.add_argument(\\n        \\\"--shot-plans-json\\\",\\n        type=Path,\\n        help=\\\"Optional JSON shot-plan file. Defaults to the current mechanism pilot.\\\",\\n    )\\n    parser.add_argument(\\n        \\\"--output-dir\\\",\\n        type=Path,\\n        default=Path(\\\"data/i2v_prompt_plans/mechanism_pilots\\\"),\\n        help=\\\"Directory for generated I2V prompt plans.\\\",\\n    )\\n    args = parser.parse_args()\\n\\n    shot_plans = _load_shot_plans(args.shot_plans_json) if args.shot_plans_json else _default_shot_plans()\\n    paths = write_i2v_prompt_plans(shot_plans, args.output_dir)\\n    for path in paths:\\n        print(path)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1648, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_wbZLMcvmEP3bpaKRmXR79g5A", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_4UGT535nmzhwFjBTONNFQMU2", "call_id": "call_4UGT535nmzhwFjBTONNFQMU2", "response_item_id": "fc_0d1a4d10654d56b4016a11fb24e3a4819199f76290242cf387", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Keyframe/I2V Prompt Generator per TDD implementieren\",\"id\":\"keyprompt\",\"status\":\"completed\"},{\"content\":\"CLI + Artefakte für Pilot-Prompts erzeugen\",\"id\":\"cli\",\"status\":\"in_progress\"}]}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Repo-Struktur und bestehende Retention-/Mechanism-Module prüfen\", \"status\": \"completed\"}, {\"id\": \"style\", \"content\": \"Style Bible als Projektartefakt erstellen\", \"status\": \"completed\"}, {\"id\": \"keyprompt\", \"content\": \"Keyframe/I2V Prompt Generator per TDD implementieren\", \"status\": \"completed\"}, {\"id\": \"cli\", \"content\": \"CLI + Artefakte für Pilot-Prompts erzeugen\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen und Git-Status sauber berichten\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 1, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}", "tool_call_id": "call_4UGT535nmzhwFjBTONNFQMU2", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_GvpilzWZzsB9kRvwxt8uXDFE", "call_id": "call_GvpilzWZzsB9kRvwxt8uXDFE", "response_item_id": "fc_0d1a4d10654d56b4016a11fb28cb388191afae66771a4eb3a9", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python scripts/generate_i2v_prompt_plans.py\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"Traceback (most recent call last):\\n  File \\\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\\\", line 10, in <module>\\n    from autoshorts.keyframe_prompt import write_i2v_prompt_plans\\nModuleNotFoundError: No module named 'autoshorts'\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_GvpilzWZzsB9kRvwxt8uXDFE", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": "**Considering script paths**\n\nI need to think about whether existing scripts require using the system path. I’ll start by reading through one of them to see how it’s structured. I wonder what the specifics are and if any adjustments are necessary for it to work properly. I'll make sure I keep track of anything that stands out or seems important as I go through it. Let’s see what insights I can gather from this!", "finish_reason": "tool_calls", "reasoning_content": "**Considering script paths**\n\nI need to think about whether existing scripts require using the system path. I’ll start by reading through one of them to see how it’s structured. I wonder what the specifics are and if any adjustments are necessary for it to work properly. I'll make sure I keep track of anything that stands out or seems important as I go through it. Let’s see what insights I can gather from this!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEfswudD2eC2e9C9ryO-6Pmzf_FMstmRiewjjFJ3ni8r-VDmoWg51w9HHn6zl-PcMjcf6xPMhCY7WErpK8Ombj6whjDNYc1_JLgokTEDtAHpmq53AxfEMz8_nSjXNLr-BjRfvqGi8mcm-nf-iRHOpW4ewPhJk8eM0h_UGqXPQkEgBEgXLjmSNsHH_A5eGcMlCGceP6f4cG85db9rlwOgbY__Vm36E1qOOlIbZ0-xV3ec-ZbGcdK-x482XwWkyrEte3fWl5j-Rc7ViGz0z30HZIMCsiknacgKb0TG4S_CRTDzBelgp4wlaIAOWYvvJUrBfAtgsm4-FmLSsOBFeyBgmAxnCMey1Oq-TjGXE3nK5rWo5qL8RJWVXrWBWwgfv2qgtvTZiQ5LOcnxibiE8thuDwKkmX1Anhn-pnrHMxOPaCvJK9ACebN2j08evPxq1QXxHQy6wE1IuL_ubIA7y16zzwZQw_7vRIteHBlCjPMK8WAy1-pqVvaBXDx6fRXSwq-WnAkJ4wUHgcirwoZWcYbzBUbq_wnjRf_BtiTXafXEVDfXs7mppN5R5n5tZBQliiFDPcIXgkx3ux_I0pC8b4qUSneePO_XuG73YfBcAb1u_tbMhVbWbqZw2p2vPF_4VkqgDsC35yPMhVQQQKdt8KE-yCu7pqzOOtlLFSTeIfiQzAPCsNoYiFAYpYR7bnHlIeNlor1Djak8xq7KHhK2C11eMJ8jPB5hW7bhDjp6VpY0acNaikfev-iLkSpW4mLIYCG8djo1JCIuRyXTfS32HKPbg8fhW6bV1j6V5nflOSI6hz_RALKk5oyWGtZxwltRMWH91rsV0ak5aXeAetjer_Aq1ZNdeqpS6oVcl6_sQcmI-Abm92lnEinc1xK6ENZGzl2R5awOPq5iyziSINYNutHs-x7yDeO-csS0_CauZEYVdA1qdUeWEXul4lBkMhgBFumtuTRUTmiC50auQPG3hkX-xpy_iQQ==", "id": "rs_0d1a4d10654d56b4016a11fb2d99f48191892debb79e81a64a", "summary": [{"type": "summary_text", "text": "**Considering script paths**\n\nI need to think about whether existing scripts require using the system path. I’ll start by reading through one of them to see how it’s structured. I wonder what the specifics are and if any adjustments are necessary for it to work properly. I'll make sure I keep track of anything that stands out or seems important as I go through it. Let’s see what insights I can gather from this!"}]}], "tool_calls": [{"id": "call_qZB5knRcTxL4uJ1IcokOacfo", "call_id": "call_qZB5knRcTxL4uJ1IcokOacfo", "response_item_id": "fc_0d1a4d10654d56b4016a11fb302f20819189e2d5d0cde99ab0", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":80,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_shot_plans.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|\\\"\\\"\\\"Generate mechanism-explainer shot plans from Retention Studio artifacts.\\\"\\\"\\\"\\n     2|\\n     3|from __future__ import annotations\\n     4|\\n     5|import argparse\\n     6|import json\\n     7|import sys\\n     8|from pathlib import Path\\n     9|\\n    10|PROJECT_ROOT = Path(__file__).resolve().parents[1]\\n    11|if str(PROJECT_ROOT) not in sys.path:\\n    12|    sys.path.insert(0, str(PROJECT_ROOT))\\n    13|\\n    14|from autoshorts.mechanism_pipeline import write_mechanism_shot_plans\\n    15|\\n    16|\\n    17|def main() -> None:\\n    18|    parser = argparse.ArgumentParser(description=\\\"Generate mechanism shot plans.\\\")\\n    19|    parser.add_argument(\\\"--explainers\\\", default=\\\"data/retention_studio/mechanism_explainers.json\\\")\\n    20|    parser.add_argument(\\\"--out\\\", default=\\\"data/mechanism_shot_plans\\\")\\n    21|    parser.add_argument(\\\"--title-contains\\\", default=None)\\n    22|    args = parser.parse_args()\\n    23|\\n    24|    explainers = json.loads(Path(args.explainers).read_text(encoding=\\\"utf-8\\\"))\\n    25|    if args.title_contains:\\n    26|        needle = args.title_contains.lower()\\n    27|        explainers = [item for item in explainers if needle in item[\\\"title\\\"].lower()]\\n    28|\\n    29|    paths = write_mechanism_shot_plans(explainers, Path(args.out))\\n    30|    print(f\\\"Generated {len(paths)} mechanism shot plans in {args.out}\\\")\\n    31|    for path in paths:\\n    32|        print(path)\\n    33|\\n    34|\\n    35|if __name__ == \\\"__main__\\\":\\n    36|    main()\\n    37|\", \"total_lines\": 36, \"file_size\": 1207, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_qZB5knRcTxL4uJ1IcokOacfo", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_ix00TTIfDaNTLtqNJGs2AzQf", "call_id": "call_ix00TTIfDaNTLtqNJGs2AzQf", "response_item_id": "fc_0d1a4d10654d56b4016a11fb35c2288191ba0b67172ce57d78", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\",\"old_string\":\"import argparse\\nimport json\\nfrom pathlib import Path\\n\\nfrom autoshorts.keyframe_prompt import write_i2v_prompt_plans\\n\",\"new_string\":\"import argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n    sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.keyframe_prompt import write_i2v_prompt_plans\\n\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\\n@@ -5,7 +5,12 @@\\n \\n import argparse\\n import json\\n+import sys\\n from pathlib import Path\\n+\\n+PROJECT_ROOT = Path(__file__).resolve().parents[1]\\n+if str(PROJECT_ROOT) not in sys.path:\\n+    sys.path.insert(0, str(PROJECT_ROOT))\\n \\n from autoshorts.keyframe_prompt import write_i2v_prompt_plans\\n from autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_prompt_plans.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_ix00TTIfDaNTLtqNJGs2AzQf", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_TVHUStjIrX0PPfqSfe6jH6Qf", "call_id": "call_TVHUStjIrX0PPfqSfe6jH6Qf", "response_item_id": "fc_0d1a4d10654d56b4016a11fb3ccf0481918aabd975edc11a12", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python scripts/generate_i2v_prompt_plans.py\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_TVHUStjIrX0PPfqSfe6jH6Qf", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_eAkyVJFNeZJhs6D8jbwHLyTW", "call_id": "call_eAkyVJFNeZJhs6D8jbwHLyTW", "response_item_id": "fc_0d1a4d10654d56b4016a11fb421cdc81918453d382f364be4e", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"CLI + Artefakte für Pilot-Prompts erzeugen\",\"id\":\"cli\",\"status\":\"completed\"},{\"content\":\"Tests laufen lassen und Git-Status sauber berichten\",\"id\":\"verify\",\"status\":\"in_progress\"}]}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Repo-Struktur und bestehende Retention-/Mechanism-Module prüfen\", \"status\": \"completed\"}, {\"id\": \"style\", \"content\": \"Style Bible als Projektartefakt erstellen\", \"status\": \"completed\"}, {\"id\": \"keyprompt\", \"content\": \"Keyframe/I2V Prompt Generator per TDD implementieren\", \"status\": \"completed\"}, {\"id\": \"cli\", \"content\": \"CLI + Artefakte für Pilot-Prompts erzeugen\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen und Git-Status sauber berichten\", \"status\": \"in_progress\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 1, \"completed\": 4, \"cancelled\": 0}}", "tool_call_id": "call_eAkyVJFNeZJhs6D8jbwHLyTW", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_uQhABzoELgsAYTk129jV4W3r", "call_id": "call_uQhABzoELgsAYTk129jV4W3r", "response_item_id": "fc_0d1a4d10654d56b4016a11fb463ab481919a56ab570911c282", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\",\"limit\":240}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|{\\n     2|  \\\"schema_version\\\": \\\"i2v_prompt_plan.v1\\\",\\n     3|  \\\"style_profile\\\": \\\"photorealistic_macro_investigation\\\",\\n     4|  \\\"video\\\": {\\n     5|    \\\"id\\\": \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\",\\n     6|    \\\"title\\\": \\\"Warum ein billiges Auto teuer sein kann\\\",\\n     7|    \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n     8|    \\\"pillar\\\": \\\"hidden_money_systems\\\"\\n     9|  },\\n    10|  \\\"strategy\\\": {\\n    11|    \\\"production_mode\\\": \\\"keyframe_to_wan22_i2v_to_deterministic_renderer\\\",\\n    12|    \\\"ai_video_role\\\": \\\"photorealistic texture and motion only\\\",\\n    13|    \\\"renderer_role\\\": \\\"captions, labels, numbers, arrows, mechanism layers and audit-safe truth\\\",\\n    14|    \\\"static_card_limit\\\": 0\\n    15|  },\\n    16|  \\\"scenes\\\": [\\n    17|    {\\n    18|      \\\"scene_id\\\": \\\"scene_01\\\",\\n    19|      \\\"source_scene\\\": 1,\\n    20|      \\\"purpose\\\": \\\"pattern_interrupt\\\",\\n    21|      \\\"asset_type\\\": \\\"cinematic_ai_video\\\",\\n    22|      \\\"style_profile\\\": \\\"photorealistic_macro_investigation\\\",\\n    23|      \\\"wan_preset\\\": \\\"wan22_i2v_quality_416x736\\\",\\n    24|      \\\"keyframe_prompt\\\": \\\"Photorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.\\\",\\n    25|      \\\"i2v_motion_prompt\\\": \\\"Photorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure, high quality automotive inspection footage. Preserve the same car and garage. No text, no logo, no watermark.\\\",\\n    26|      \\\"negative_prompt\\\": \\\"cartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text, unreadable license plate, fake license plate text, brand logos, distorted wheels, melted car body, broken windshield geometry, fake numbers, fake labels\\\",\\n    27|      \\\"motion_directive\\\": \\\"slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis\\\",\\n    28|      \\\"source_visual_direction\\\": \\\"3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\\\",\\n    29|      \\\"retention_goal\\\": \\\"Open a visual loop immediately; make the viewer need the hidden explanation.\\\",\\n    30|      \\\"clip_requirements\\\": {\\n    31|        \\\"subject_identifiable_first_half_second\\\": true,\\n    32|        \\\"no_baked_text\\\": true,\\n    33|        \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n    34|        \\\"preferred_duration_seconds\\\": 2,\\n    35|        \\\"review_required\\\": true\\n    36|      },\\n    37|      \\\"comfyui\\\": {\\n    38|        \\\"workflow_family\\\": \\\"native_wan22_ti2v_i2v\\\",\\n    39|        \\\"model\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n    40|        \\\"text_encoder\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n    41|        \\\"vae\\\": \\\"wan2.2_vae.safetensors\\\",\\n    42|        \\\"resolution\\\": {\\n    43|          \\\"width\\\": 416,\\n    44|          \\\"height\\\": 736\\n    45|        },\\n    46|        \\\"frames\\\": 49,\\n    47|        \\\"fps\\\": 10,\\n    48|        \\\"steps\\\": 16,\\n    49|        \\\"cfg\\\": 5.0\\n    50|      }\\n    51|    },\\n    52|    {\\n    53|      \\\"scene_id\\\": \\\"scene_02\\\",\\n    54|      \\\"source_scene\\\": 2,\\n    55|      \\\"purpose\\\": \\\"problem\\\",\\n    56|      \\\"asset_type\\\": \\\"cinematic_ai_video\\\",\\n    57|      \\\"style_profile\\\": \\\"photorealistic_macro_investigation\\\",\\n    58|      \\\"wan_preset\\\": \\\"wan22_i2v_quality_416x736\\\",\\n    59|      \\\"keyframe_prompt\\\": \\\"Photorealistic vertical macro-investigation image, one clear subject in a realistic environment. Visual task: surface layer peels away to reveal the hidden system underneath.. Bright balanced exposure, clean composition, high contrast, enough negative space for renderer captions, no readable text, no logos, no watermark.\\\",\\n    60|      \\\"i2v_motion_prompt\\\": \\\"Photorealistic vertical video preserving the same subject and environment from the start image. Camera slowly pushes in from a front three-quarter angle with gentle parallax. Motion supports this retention goal: Make the surface-level interpretation feel incomplete.. Balanced exposure, stable composition, realistic texture. No text, no logo, no watermark.\\\",\\n    61|      \\\"negative_prompt\\\": \\\"cartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text\\\",\\n    62|      \\\"motion_directive\\\": \\\"slowly pushes in from a front three-quarter angle with gentle parallax\\\",\\n    63|      \\\"source_visual_direction\\\": \\\"surface layer peels away to reveal the hidden system underneath.\\\",\\n    64|      \\\"retention_goal\\\": \\\"Make the surface-level interpretation feel incomplete.\\\",\\n    65|      \\\"clip_requirements\\\": {\\n    66|        \\\"subject_identifiable_first_half_second\\\": true,\\n    67|        \\\"no_baked_text\\\": true,\\n    68|        \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n    69|        \\\"preferred_duration_seconds\\\": 3.6,\\n    70|        \\\"review_required\\\": true\\n    71|      },\\n    72|      \\\"comfyui\\\": {\\n    73|        \\\"workflow_family\\\": \\\"native_wan22_ti2v_i2v\\\",\\n    74|        \\\"model\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n    75|        \\\"text_encoder\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n    76|        \\\"vae\\\": \\\"wan2.2_vae.safetensors\\\",\\n    77|        \\\"resolution\\\": {\\n    78|          \\\"width\\\": 416,\\n    79|          \\\"height\\\": 736\\n    80|        },\\n    81|        \\\"frames\\\": 49,\\n    82|        \\\"fps\\\": 10,\\n    83|        \\\"steps\\\": 16,\\n    84|        \\\"cfg\\\": 5.0\\n    85|      }\\n    86|    }\\n    87|  ],\\n    88|  \\\"renderer_only_scenes\\\": [\\n    89|    {\\n    90|      \\\"scene_id\\\": \\\"scene_03\\\",\\n    91|      \\\"source_scene\\\": 3,\\n    92|      \\\"purpose\\\": \\\"mechanism_reveal\\\",\\n    93|      \\\"asset_type\\\": \\\"mechanism_animation\\\",\\n    94|      \\\"visual_direction\\\": \\\"3D car x-ray with price tag layers and risk meters\\\",\\n    95|      \\\"visual_motion\\\": \\\"sequential layer reveal every 1-2 seconds\\\",\\n    96|      \\\"note\\\": \\\"Renderer-generated motion layer; keep labels, arrows, numbers and captions deterministic.\\\"\\n    97|    },\\n    98|    {\\n    99|      \\\"scene_id\\\": \\\"scene_04\\\",\\n   100|      \\\"source_scene\\\": 4,\\n   101|      \\\"purpose\\\": \\\"twist\\\",\\n   102|      \\\"asset_type\\\": \\\"mechanism_animation\\\",\\n   103|      \\\"visual_direction\\\": \\\"camera snaps from obvious cause to real cause; wrong layer fades out.\\\",\\n   104|      \\\"visual_motion\\\": \\\"snap zoom + wrong answer collapses\\\",\\n   105|      \\\"note\\\": \\\"Renderer-generated motion layer; keep labels, arrows, numbers and captions deterministic.\\\"\\n   106|    },\\n   107|    {\\n   108|      \\\"scene_id\\\": \\\"scene_05\\\",\\n   109|      \\\"source_scene\\\": 5,\\n   110|      \\\"purpose\\\": \\\"follow_trigger\\\",\\n   111|      \\\"asset_type\\\": \\\"kinetic_text_overlay\\\",\\n   112|      \\\"visual_direction\\\": \\\"series tag appears over a clean animated system map.\\\",\\n   113|      \\\"visual_motion\\\": \\\"map draws itself, final node glows\\\",\\n   114|      \\\"note\\\": \\\"Renderer-generated motion layer; keep labels, arrows, numbers and captions deterministic.\\\"\\n   115|    }\\n   116|  ]\\n   117|}\\n   118|\", \"total_lines\": 117, \"file_size\": 6816, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_uQhABzoELgsAYTk129jV4W3r", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": "**Improving scene prompts**\n\nI’m noticing that Scene 2 isn’t working out as planned. It seems like the tests didn’t catch issues with the car in the same position, perhaps by a pillar. I need to consider adjusting car detection based on the title and pillar context. I think building the prompt from the shot plan might help, especially since the visual shows no car but the video does. I’m realizing I need to update the _scene_prompt_spec to better accept the shot plan and add tests for Scene 02 too.", "finish_reason": "tool_calls", "reasoning_content": "**Improving scene prompts**\n\nI’m noticing that Scene 2 isn’t working out as planned. It seems like the tests didn’t catch issues with the car in the same position, perhaps by a pillar. I need to consider adjusting car detection based on the title and pillar context. I think building the prompt from the shot plan might help, especially since the visual shows no car but the video does. I’m realizing I need to update the _scene_prompt_spec to better accept the shot plan and add tests for Scene 02 too.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEftOpYLoF4IHbCAh1MMQtoGgqWXY91uE58SQir7S-h7jJ3daZTKNVOn39ZIREVz7okrC_yS48jrAAKbz8VMgbw5T3gbbAJpZ7L9QgD6Gr0XbHK_u7Zlcv4QEQj9_8bGAzYgG3z6Vn6HqdQPNL1fM-A3euwu1AlVPEqzbmdd5KXvla7mNvi0GYLTTNtOSdwvtu8cneBCeQOFEilGavX1iKywUv5drwZpMZZuaUPgisxxnER14R9-APs86okGKtlt32Lx-LZ8fPYPZZ5Ab8outp031agB8AjX6RMBwvQOBratOi8g7tDo-6x0GvsiTm9PyKkcH-WykQInGBkM4xzdmVFWFMNcz-qfvNlbSSFX6hweHvPZMX7Fh8WEOn_Q3KM07tuBKpxxvl9RBy1lGrR1lMZFIiPbvoTzcNEDzs-VaZCCGQpalXyE0hnJ9piAmlWRp1keBpJd8tLm4fKS-ht-oE130jox0IkGRi9apdu0272UxWpJwQ-99qnDgbtNnDPGrz-iGov1VHkbg2BFmijwZZV6X9fXuea88qWqpug7iOtV2zIHMpBoi9kmbXm7bpXUkhdMdIYSe_cp-HxfL5Ncdd2xtB3DeqHVYpgr2JhRoNXCg7OSrR1bG4b5AuzcQrf18CmyZmlaS51QofLS1i0zrdt4vX9xWVynyV_vKMfUFsO_cyqRYKP9bE9LzQsxtVSpIncNsZznrrj0T1ptGMi0lGoHn8AaOzMQSUN9OeUKWn5a0eC-_QwKecp-5QN-VbuYSIWnHiy_7lLd4aGSJyOTxYR78ROsHrUBJPcPTvoOxPuIG3NpTcyF5Ks8xq1dNl152KehqQIKqw-UYJ4ViQMdTrXmPSCFMJnuGECB0yRfu05DcMXd2036MXDWvPoA7aE-XDRTZtxZ3rEnx0Fy3OL-dbLMwqlCVkswMnOaDDg0zvMoMaTqnCrcy2cU7pob7D67TCpH4ZAarYkSsbmDkEOklxpjIPlxDLsvjKpmDWZhMG81JbK5xspZ14FkHKiJkjFgACGgPo3-tqBtaDJLg4QLhJrpQfOTru7EGlwzk5LGVEXtSdQl8dGu8xY_4DU5ymbQToYyTp9leWYkQ0eWDaGqzPMmkWDtGk847hqF66U6JLjcvGs3XW6XIqc44I-XT_Zz2zb6yxc-FhHgU8iQ78vMG-IRZUlti6CAp6Z7b57Z0ww8FVu6FFoe4Ws50Tj3Ab1qzEWsxcKHgP_OiaWJFYIx8kfzKv3N0Dq4u9aar0iuz8HEtTWgn2DzT5RbQ70HvzrOF8TBxLlNNVEGrxlC2zgkILRlJp8UQgRua-sh_8SuOrYeQM2H_fJBN_m0S03Jc3qPoWLNKEXnH05dUI65KcaCp96AqU--0-PXDJiLxjX7C96-BiL4a9RN9_pXefuOC7sO_xsqCF2sjCDdOI6sYu2OOWGbhbu1bzT4JCuB76SW3sPWcpTpbQ3oCsL98tLALbuxHfThMfhBaguXL7gkpctl-UHaroceT9-Gj4waMwTjXYLMFuBA=", "id": "rs_0d1a4d10654d56b4016a11fb4add20819180df107bcf6c7fa1", "summary": [{"type": "summary_text", "text": "**Improving scene prompts**\n\nI’m noticing that Scene 2 isn’t working out as planned. It seems like the tests didn’t catch issues with the car in the same position, perhaps by a pillar. I need to consider adjusting car detection based on the title and pillar context. I think building the prompt from the shot plan might help, especially since the visual shows no car but the video does. I’m realizing I need to update the _scene_prompt_spec to better accept the shot plan and add tests for Scene 02 too."}]}], "tool_calls": [{"id": "call_jlNA8Mzbg6GM0t6rAW1YOtoP", "call_id": "call_jlNA8Mzbg6GM0t6rAW1YOtoP", "response_item_id": "fc_0d1a4d10654d56b4016a11fb4e32388191b327af2a6e10b740", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\",\"old_string\":\"def test_i2v_motion_prompt_has_one_camera_move_and_preserves_identity():\\n    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][0]\\n\\n    motion_prompt = scene[\\\"i2v_motion_prompt\\\"]\\n\\n    assert \\\"Camera slowly pushes in\\\" in motion_prompt\\n    assert \\\"Preserve the same car and garage\\\" in motion_prompt\\n    assert \\\"No text, no logo, no watermark\\\" in motion_prompt\\n    assert \\\"slowly pushes in\\\" in scene[\\\"motion_directive\\\"]\\n    assert scene[\\\"clip_requirements\\\"][\\\"subject_identifiable_first_half_second\\\"] is True\\n    assert scene[\\\"clip_requirements\\\"][\\\"caption_safe_space\\\"] == \\\"upper third and lower third\\\"\\n\",\"new_string\":\"def test_i2v_motion_prompt_has_one_camera_move_and_preserves_identity():\\n    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][0]\\n\\n    motion_prompt = scene[\\\"i2v_motion_prompt\\\"]\\n\\n    assert \\\"Camera slowly pushes in\\\" in motion_prompt\\n    assert \\\"Preserve the same car and garage\\\" in motion_prompt\\n    assert \\\"No text, no logo, no watermark\\\" in motion_prompt\\n    assert \\\"slowly pushes in\\\" in scene[\\\"motion_directive\\\"]\\n    assert scene[\\\"clip_requirements\\\"][\\\"subject_identifiable_first_half_second\\\"] is True\\n    assert scene[\\\"clip_requirements\\\"][\\\"caption_safe_space\\\"] == \\\"upper third and lower third\\\"\\n\\n\\ndef test_problem_scene_keeps_same_car_context_not_generic_macro_prompt():\\n    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][1]\\n\\n    assert \\\"red used compact car\\\" in scene[\\\"keyframe_prompt\\\"]\\n    assert \\\"inspection garage\\\" in scene[\\\"i2v_motion_prompt\\\"]\\n    assert \\\"surface layer\\\" in scene[\\\"i2v_motion_prompt\\\"]\\n\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\\n@@ -46,6 +46,14 @@\\n     assert scene[\\\"clip_requirements\\\"][\\\"caption_safe_space\\\"] == \\\"upper third and lower third\\\"\\n \\n \\n+def test_problem_scene_keeps_same_car_context_not_generic_macro_prompt():\\n+    scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][1]\\n+\\n+    assert \\\"red used compact car\\\" in scene[\\\"keyframe_prompt\\\"]\\n+    assert \\\"inspection garage\\\" in scene[\\\"i2v_motion_prompt\\\"]\\n+    assert \\\"surface layer\\\" in scene[\\\"i2v_motion_prompt\\\"]\\n+\\n+\\n def test_non_ai_mechanism_scenes_become_renderer_notes_not_wan_jobs():\\n     prompt_plan = build_i2v_prompt_plan(_pilot_shot_plan())\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_keyframe_prompt.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_jlNA8Mzbg6GM0t6rAW1YOtoP", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_ZFzckgXaZBb7drGf75g6yueo", "call_id": "call_ZFzckgXaZBb7drGf75g6yueo", "response_item_id": "fc_0d1a4d10654d56b4016a11fb597430819188005459de2a5b1d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_keyframe_prompt.py::test_problem_scene_keeps_same_car_context_not_generic_macro_prompt -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"F                                                                        [100%]\\n=================================== FAILURES ===================================\\n______ test_problem_scene_keeps_same_car_context_not_generic_macro_prompt ______\\n\\n    def test_problem_scene_keeps_same_car_context_not_generic_macro_prompt():\\n        scene = build_i2v_prompt_plan(_pilot_shot_plan())[\\\"scenes\\\"][1]\\n    \\n>       assert \\\"red used compact car\\\" in scene[\\\"keyframe_prompt\\\"]\\nE       AssertionError: assert 'red used compact car' in 'Photorealistic vertical macro-investigation image, one clear subject in a realistic environment. Visual task: surface...ean composition, high contrast, enough negative space for renderer captions, no readable text, no logos, no watermark.'\\n\\ntests/test_keyframe_prompt.py:52: AssertionError\\n=========================== short test summary info ============================\\nFAILED tests/test_keyframe_prompt.py::test_problem_scene_keeps_same_car_context_not_generic_macro_prompt\\n1 failed in 0.06s\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_ZFzckgXaZBb7drGf75g6yueo", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEftenS8bSOhgBQkxGbf5-PK3xEs2qr_7GBP03Y7ICIyUOu-MM00UcVPgXz21DBEQIIBSyHyG6AW7rgUw1XbBDB_TE2sBzJ7jHt8t4SnXNReallq4RTwYZL2aCshaNTt-OU2BDnETISmryiWHtmH-yim9og3wCa-dP8JGF02sI0d01ietGabCaMZ4EU0smV7AQr9Uk49QMiTDlgy3KYhhYvY3ROBlMahdb3nrnao2LsjKFeJQTDVVXIE0-VVDY6AL67gtwVrKc28q42SM1c4Y3pjO2qWpnIgLV5M4piAWTzGL_3j2AhRxW7GlqYYBYH6Dx12u3HyOtpvp4eqSjmYK-qdTzsgVR1UIqILNonEa7AZam6FqYF26KAbKJ9y-kLCAbAIuKQfyxIwZZjxBP7UhS4KynwIbLXRhbsLPgLNwt0b4SHOiMUL02MG93RRKJM0I4A0BMdk09YS98QnVyujkkJLUuAB70hXYS21DAauKL3ljvJNpCntPpIAqvYIuv3SsnOB7IjODehoiuPg4XyrtW5a7--PzP7pxr_unkuyyVrvHYGq-m049ZLhvmVcck5L4QXuhjz0RpQ2nIdgQ3VJR9xS5kErxIFNr1WfvkewsBAVq8JGCusbvbZYNf6RWEWf9ByrlBE9pRFeerS6LApU1954tsEu2Keue1EVFocb6gme7PVhSqTZ6Kz13ktmHMineSjBYoqYdalTfWKfkmrHhR4MumRXXMi-DTC046Tsfcpx9KaR_vESnQ5R7FNbkYaiw8RX_cuOwVD8oZco8wJIwkZ8L4mlN-nknGwaAz_OOBg1vIw3FhU7bOq-KpOfgyY-F3CffdjGJUAl27bvveFKQ0RyP2MXD6rZGoXvNeJ_nfxdMCByKAXrQsVs0IMZplAPcuAvObMUybqwl52gYW47FPDLNco0EfNzT4JFvX23oLL3UYMbIDYYB8HV9lWI8gWRbarAZ", "id": "rs_0d1a4d10654d56b4016a11fb5d8e2c8191b01aa17e56b37996", "summary": []}], "tool_calls": [{"id": "call_Plmmr8OvXXgOpLrAv6oLCOyG", "call_id": "call_Plmmr8OvXXgOpLrAv6oLCOyG", "response_item_id": "fc_0d1a4d10654d56b4016a11fb5e3444819196a64ae7be282ab3", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _car_motion_prompt(scene: dict[str, Any]) -> str:\\n    motion = _motion_directive(scene)\\n    mechanism_clause = \\\"\\\"\\n    if scene.get(\\\"purpose\\\") == \\\"problem\\\":\\n        mechanism_clause = \\\" A subtle surface layer starts to peel away visually, hinting that the visible price is not the full system.\\\"\\n    return (\\n        \\\"Photorealistic vertical video of the same red used compact car in a bright inspection \\\"\\n        f\\\"garage. Camera {motion}.{mechanism_clause} Subtle light sweep over the hood and windshield, realistic \\\"\\n        \\\"reflections, stable composition, balanced exposure, high quality automotive inspection \\\"\\n        \\\"footage. Preserve the same car and garage. No text, no logo, no watermark.\\\"\\n    )\\n\",\"old_string\":\"def _car_motion_prompt(scene: dict[str, Any]) -> str:\\n    motion = _motion_directive(scene)\\n    return (\\n        \\\"Photorealistic vertical video of the same red used compact car in a bright inspection \\\"\\n        f\\\"garage. Camera {motion}. Subtle light sweep over the hood and windshield, realistic \\\"\\n        \\\"reflections, stable composition, balanced exposure, high quality automotive inspection \\\"\\n        \\\"footage. Preserve the same car and garage. No text, no logo, no watermark.\\\"\\n    )\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n@@ -47,9 +47,12 @@\\n \\n def _car_motion_prompt(scene: dict[str, Any]) -> str:\\n     motion = _motion_directive(scene)\\n+    mechanism_clause = \\\"\\\"\\n+    if scene.get(\\\"purpose\\\") == \\\"problem\\\":\\n+        mechanism_clause = \\\" A subtle surface layer starts to peel away visually, hinting that the visible price is not the full system.\\\"\\n     return (\\n         \\\"Photorealistic vertical video of the same red used compact car in a bright inspection \\\"\\n-        f\\\"garage. Camera {motion}. Subtle light sweep over the hood and windshield, realistic \\\"\\n+        f\\\"garage. Camera {motion}.{mechanism_clause} Subtle light sweep over the hood and windshield, realistic \\\"\\n         \\\"reflections, stable composition, balanced exposure, high quality automotive inspection \\\"\\n         \\\"footage. Preserve the same car and garage. No text, no logo, no watermark.\\\"\\n     )\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_Plmmr8OvXXgOpLrAv6oLCOyG", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_UaSa7h5FiRl1z38ubsiLJfjH", "call_id": "call_UaSa7h5FiRl1z38ubsiLJfjH", "response_item_id": "fc_0d1a4d10654d56b4016a11fb6896848191ba8fe3f3d56fd227", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"def _scene_prompt_spec(scene: dict[str, Any], shot_plan: dict[str, Any]) -> dict[str, Any]:\\n    visual = \\\" \\\".join(\\n        [\\n            shot_plan.get(\\\"title\\\", \\\"\\\"),\\n            shot_plan.get(\\\"pillar\\\", \\\"\\\"),\\n            scene.get(\\\"visual_direction\\\", \\\"\\\"),\\n            scene.get(\\\"voiceover\\\", \\\"\\\"),\\n        ]\\n    ).lower()\\n    is_car_scene = \\\"auto\\\" in visual or \\\"car\\\" in visual or \\\"occasion\\\" in visual or \\\"garage\\\" in visual\\n\",\"old_string\":\"def _scene_prompt_spec(scene: dict[str, Any]) -> dict[str, Any]:\\n    visual = \\\" \\\".join([scene.get(\\\"visual_direction\\\", \\\"\\\"), scene.get(\\\"voiceover\\\", \\\"\\\")]).lower()\\n    is_car_scene = \\\"auto\\\" in visual or \\\"car\\\" in visual or \\\"occasion\\\" in visual or \\\"garage\\\" in visual\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n@@ -88,8 +88,15 @@\\n     )\\n \\n \\n-def _scene_prompt_spec(scene: dict[str, Any]) -> dict[str, Any]:\\n-    visual = \\\" \\\".join([scene.get(\\\"visual_direction\\\", \\\"\\\"), scene.get(\\\"voiceover\\\", \\\"\\\")]).lower()\\n+def _scene_prompt_spec(scene: dict[str, Any], shot_plan: dict[str, Any]) -> dict[str, Any]:\\n+    visual = \\\" \\\".join(\\n+        [\\n+            shot_plan.get(\\\"title\\\", \\\"\\\"),\\n+            shot_plan.get(\\\"pillar\\\", \\\"\\\"),\\n+            scene.get(\\\"visual_direction\\\", \\\"\\\"),\\n+            scene.get(\\\"voiceover\\\", \\\"\\\"),\\n+        ]\\n+    ).lower()\\n     is_car_scene = \\\"auto\\\" in visual or \\\"car\\\" in visual or \\\"occasion\\\" in visual or \\\"garage\\\" in visual\\n     keyframe_prompt = _car_keyframe_prompt(scene) if is_car_scene else _fallback_keyframe_prompt(scene)\\n     motion_prompt = _car_motion_prompt(scene) if is_car_scene else _fallback_motion_prompt(scene)\\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\\">\\nERROR [169:20] Argument missing for parameter \\\"shot_plan\\\" [reportCallIssue] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_UaSa7h5FiRl1z38ubsiLJfjH", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_gYsw8B47kH61vx4JDT1f1xS9", "call_id": "call_gYsw8B47kH61vx4JDT1f1xS9", "response_item_id": "fc_0d1a4d10654d56b4016a11fb7111048191bec975ca6c7a69ed", "type": "function", "function": {"name": "patch", "arguments": "{\"mode\":\"replace\",\"new_string\":\"        \\\"scenes\\\": [_scene_prompt_spec(scene, shot_plan) for scene in ai_scenes],\",\"old_string\":\"        \\\"scenes\\\": [_scene_prompt_spec(scene) for scene in ai_scenes],\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "patch", "content": "{\"success\": true, \"diff\": \"--- a//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n+++ b//home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\\n@@ -166,7 +166,7 @@\\n             \\\"renderer_role\\\": \\\"captions, labels, numbers, arrows, mechanism layers and audit-safe truth\\\",\\n             \\\"static_card_limit\\\": shot_plan.get(\\\"final_render_intent\\\", {}).get(\\\"static_card_limit\\\", 0),\\n         },\\n-        \\\"scenes\\\": [_scene_prompt_spec(scene) for scene in ai_scenes],\\n+        \\\"scenes\\\": [_scene_prompt_spec(scene, shot_plan) for scene in ai_scenes],\\n         \\\"renderer_only_scenes\\\": [_renderer_note(scene) for scene in renderer_scenes],\\n     }\\n \\n\", \"files_modified\": [\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/keyframe_prompt.py\"], \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_gYsw8B47kH61vx4JDT1f1xS9", "timestamp": "2026-05-23T21:26:39.748126"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"[feat/initial-growth-engine b3ef793] feat: add wan i2v prompt planning\\n 6 files changed, 802 insertions(+)\\n create mode 100644 autoshorts/keyframe_prompt.py\\n create mode 100644 data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\n create mode 100644 docs/style_bible.md\\n create mode 100644 docs/wan22_prompting_playbook.md\\n create mode 100644 scripts/generate_i2v_prompt_plans.py\\n create mode 100644 tests/test_keyframe_prompt.py\\nfatal: could not read Username for 'https://github.com': Kein passendes Gerät bzw. keine passende Adresse gefunden\", \"exit_code\": 128, \"error\": null}", "tool_call_id": "call_hBv5rVPRGfu3hbTnf81noTcp", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": "**Considering GitHub workflow**\n\nIt seems like I’m facing an authentication issue with GitHub, possibly needing to use a token from a CSV file. This might be related to the GitHub workflow skill. The user hasn't specified this, but using the GitHub pull request workflow skill seems crucial here. So, I need to figure out how to properly load GitHub and use the token correctly. Let's explore the necessary steps to address this!", "finish_reason": "tool_calls", "reasoning_content": "**Considering GitHub workflow**\n\nIt seems like I’m facing an authentication issue with GitHub, possibly needing to use a token from a CSV file. This might be related to the GitHub workflow skill. The user hasn't specified this, but using the GitHub pull request workflow skill seems crucial here. So, I need to figure out how to properly load GitHub and use the token correctly. Let's explore the necessary steps to address this!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEfunHHeWfvjVnCIMrktLP2CdgSoMNuXwwzEQCnYniCDUQN581MsHIZNh2UndWYAoAzL988s49OtvwYIZaO9h4g7LgNbzRZRBFoqtccCBC_wyD0vHxZOX8prr3j3dxRnFNAlseI5DHpzzno9yk35YwFbX9W1lG2pwSwaKQi9vWVjQ6MuDZTv4G0nLnusyxSVGKb4xF7IHxV7vhBJM1O7fTkGyQa_Tqn4sBPIV6WL4OTG0Hzz3QEYv1-k1bYk1a8PLKnNzHayykDZ0sVdUCGWw8d90k594NmDSATXGpSvP6CnkgYps38OBSQgjKvd0s5bJdGvl-ZQfrUQyeH0VKfWp7m57gxVGCOy7K72Fu5l42H6QHzU9ee595h1hHq7u8iNdp_31uMFGjT_cyElE8mFnNE6j440AuIUihrTZQcp5gh1xblu-XkPcd5MykNCnJli5e5fsKAWQMgnx0AKlvDQ5binxvjQILiOww-zewvQJaQXDMr2pJoP3YsaoKXLFw2Qj-kFpKyyXSfl8sstZKda96QqN3FAcOJRcKaMSWsgrA18ipzg570VfLFrmzG_9uhqKOeX1GT7NX6_OnwqgVWhD8QP-tmEpbo2g4tJIOM2jE11Cfqc4-h3yCf19ofVJExR5Koz3zV1Yj3ewtEwDsC872TbUeMQ1XS18kD-IZzQWX0HHQv1MtGLVLBdmxxq0qhQVBpoA8XCcQZSfhC7IUl592-juuH5puTKbUMd7aymFKtU4-h5ehOzoP-EMKot_AhDG_1V8eCzXMfdagRdJsipLcQboMsuYLnCV2dmKtATFTlUNAtPsdct9f8SKSmzI_x1YFiow9BZGSX3OwAbu9U4ziBPQnOpx0Tccl8XoV789sldtjY6127NRQ3wSpTDaUmt-9YSl9yuyZ4Jg36ix3vgEPZ5g8haVVd_njsaIt2VRLOkAoc635dPuBEkMLAZCcsFMx0t6lM_dNKF1snVrRu4TXe04LYN2HPOZe2oo9JAOCJt0AvnTIapDvEAjGdtzG63StWnwbOVyhYWOG0y2miYWuI4ITeN0t9DnnINwDuCTmI2y0rBZgUfrI63-0NDS53JlZ5v1_1cWTJl8hsS-u0McFQZXpkNkqP7RkjG1RVAn6eQmuj7bWEEm7SL8V4peF-NqmgT2r-VuZVZX-GVZVSmlnJaD5TqTYwfQXWiuiKcOMxOb-Bk=", "id": "rs_0d1a4d10654d56b4016a11fba52dd48191a5876e23ab638d3c", "summary": [{"type": "summary_text", "text": "**Considering GitHub workflow**\n\nIt seems like I’m facing an authentication issue with GitHub, possibly needing to use a token from a CSV file. This might be related to the GitHub workflow skill. The user hasn't specified this, but using the GitHub pull request workflow skill seems crucial here. So, I need to figure out how to properly load GitHub and use the token correctly. Let's explore the necessary steps to address this!"}]}], "tool_calls": [{"id": "call_keIBOBkIxgb8zkS6eq84osNj", "call_id": "call_keIBOBkIxgb8zkS6eq84osNj", "response_item_id": "fc_0d1a4d10654d56b4016a11fba755d88191a036fafccbd5204b", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"github-pr-workflow\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"github-pr-workflow\", \"description\": \"GitHub PR lifecycle: branch, commit, open, CI, merge.\", \"tags\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"], \"content\": \"---\\nname: github-pr-workflow\\ndescription: \\\"GitHub PR lifecycle: branch, commit, open, CI, merge.\\\"\\nversion: 1.1.0\\nauthor: Hermes Agent\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n  hermes:\\n    tags: [GitHub, Pull-Requests, CI/CD, Git, Automation, Merge]\\n    related_skills: [github-auth, github-code-review]\\n---\\n\\n# GitHub Pull Request Workflow\\n\\nComplete guide for managing the PR lifecycle. Each section shows the `gh` way first, then the `git` + `curl` fallback for machines without `gh`.\\n\\nFor private HTTPS remotes where an agent must push with a token but PR creation may be blocked by token scope, see `references/private-repo-token-push-pr.md`.\\n\\n## Prerequisites\\n\\n- Authenticated with GitHub (see `github-auth` skill)\\n- Inside a git repository with a GitHub remote\\n\\n### Quick Auth Detection\\n\\n```bash\\n# Determine which method to use throughout this workflow\\nif command -v gh &>/dev/null && gh auth status &>/dev/null; then\\n  AUTH=\\\"gh\\\"\\nelse\\n  AUTH=\\\"git\\\"\\n  # Ensure we have a token for API calls\\n  if [ -z \\\"$GITHUB_TOKEN\\\" ]; then\\n    if [ -f ~/.hermes/.env ] && grep -q \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env; then\\n      GITHUB_TOKEN=$(grep \\\"^GITHUB_TOKEN=\\\" ~/.hermes/.env | head -1 | cut -d= -f2 | tr -d '\\\\n\\\\r')\\n    elif grep -q \\\"github.com\\\" ~/.git-credentials 2>/dev/null; then\\n      GITHUB_TOKEN=$(grep \\\"github.com\\\" ~/.git-credentials 2>/dev/null | head -1 | sed 's|https://[^:]*:\\\\([^@]*\\\\)@.*|\\\\1|')\\n    fi\\n  fi\\nfi\\necho \\\"Using: $AUTH\\\"\\n```\\n\\n### Extracting Owner/Repo from the Git Remote\\n\\nMany `curl` commands need `owner/repo`. Extract it from the git remote:\\n\\n```bash\\n# Works for both HTTPS and SSH remote URLs\\nREMOTE_URL=$(git remote get-url origin)\\nOWNER_REPO=$(echo \\\"$REMOTE_URL\\\" | sed -E 's|.*github\\\\.com[:/]||; s|\\\\.git$||')\\nOWNER=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f1)\\nREPO=$(echo \\\"$OWNER_REPO\\\" | cut -d/ -f2)\\necho \\\"Owner: $OWNER, Repo: $REPO\\\"\\n```\\n\\n---\\n\\n## 1. Branch Creation\\n\\nThis part is pure `git` — identical either way:\\n\\n```bash\\n# Make sure you're up to date\\ngit fetch origin\\ngit checkout main && git pull origin main\\n\\n# Create and switch to a new branch\\ngit checkout -b feat/add-user-authentication\\n```\\n\\nBranch naming conventions:\\n- `feat/description` — new features\\n- `fix/description` — bug fixes\\n- `refactor/description` — code restructuring\\n- `docs/description` — documentation\\n- `ci/description` — CI/CD changes\\n\\n## 2. Making Commits\\n\\nUse the agent's file tools (`write_file`, `patch`) to make changes, then commit:\\n\\n```bash\\n# Stage specific files\\ngit add src/auth.py src/models/user.py tests/test_auth.py\\n\\n# Commit with a conventional commit message\\ngit commit -m \\\"feat: add JWT-based user authentication\\n\\n- Add login/register endpoints\\n- Add User model with password hashing\\n- Add auth middleware for protected routes\\n- Add unit tests for auth flow\\\"\\n```\\n\\nCommit message format (Conventional Commits):\\n```\\ntype(scope): short description\\n\\nLonger explanation if needed. Wrap at 72 characters.\\n```\\n\\nTypes: `feat`, `fix`, `refactor`, `docs`, `test`, `ci`, `chore`, `perf`\\n\\n## 3. Pushing and Creating a PR\\n\\n### Push the Branch (same either way)\\n\\n```bash\\ngit push -u origin HEAD\\n```\\n\\nIf the remote is HTTPS and interactive username/password prompts fail in an agent/non-TTY environment, use a temporary `GIT_ASKPASS` helper instead of embedding tokens in the remote URL. Keep the token in an environment variable, never echo it, and remove the helper after push/verification:\\n\\n```bash\\n# Ensure the askpass child process can see the token.\\nexport GITHUB_TOKEN\\nASKPASS=$(mktemp)\\ncat > \\\"$ASKPASS\\\" <<'SH'\\n#!/bin/sh\\ncase \\\"$1\\\" in\\n  *Username*) printf '%s\\\\n' x-access-token ;;\\n  *Password*) printf '%s\\\\n' \\\"$GITHUB_TOKEN\\\" ;;\\n  *) printf '\\\\n' ;;\\nesac\\nSH\\nchmod 700 \\\"$ASKPASS\\\"\\ntrap 'rm -f \\\"$ASKPASS\\\"' EXIT\\nGIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git push -u origin HEAD\\n\\n# Optional authenticated remote-hash verification for private repos.\\nBRANCH=$(git branch --show-current)\\nLOCAL=$(git rev-parse HEAD)\\nREMOTE=$(GIT_ASKPASS=\\\"$ASKPASS\\\" GIT_TERMINAL_PROMPT=0 git ls-remote origin \\\"refs/heads/$BRANCH\\\" | awk '{print $1}')\\ntest \\\"$LOCAL\\\" = \\\"$REMOTE\\\"\\n\\nrm -f \\\"$ASKPASS\\\"\\n```\\n\\nThe `export GITHUB_TOKEN` line is not cosmetic: `GIT_ASKPASS` executes as a child process, so a shell variable that is not exported will look empty and GitHub will reject the push as an invalid username/token even when token extraction succeeded.\\n\\nIf push succeeds through a temporary authenticated URL or helper but local `git status` still shows `ahead`, the tracking ref may simply be stale because later `ls-remote`/`fetch origin` was unauthenticated. Verify with an authenticated `git ls-remote` against the same branch, then update `refs/remotes/origin/<branch>` via an authenticated fetch. Treat push success and remote-hash equality as the source of truth; do not print token-bearing URLs or persist them as remotes.\\n\\nDo not set token-bearing remotes, print token values, or leave askpass scripts behind. Keep the temporary askpass file alive through every authenticated step, including `git ls-remote` remote-hash verification; deleting it immediately after `git push` makes the later verification fail even though the push succeeded. Prefer `trap 'rm -f \\\"$ASKPASS\\\"' EXIT` over early manual removal.\\n\\nIf `git push` succeeds but the subsequent `git ls-remote` hash check fails with a transient network error (`Couldn't connect to server`, DNS/443 hiccup), do not blindly push again or claim failure. First check basic GitHub reachability (`curl -I --connect-timeout 5 https://github.com` or equivalent), then rerun only the authenticated `ls-remote` hash verification with `GIT_ASKPASS`/`GIT_TERMINAL_PROMPT=0`. Treat remote-hash equality as the completion signal.\\n\\nWhen extracting a token from a local CSV/secret file for `GIT_ASKPASS`, select by token shape/prefix (`github_pat_` or `ghp_`) rather than by excluding strings that start with `github`. Fine-grained GitHub tokens intentionally start with `github_pat_`; skipping all `github*` values can silently pass an empty/wrong password and produce `Invalid username or token`. Inspect only row/column counts or token lengths/prefix classes, never the token value.\\n\\n**Important permission pitfall:** a token that can push via HTTPS is not guaranteed to have REST/GraphQL permission to create PRs. After push, verify local/remote hash equality first. If PR creation returns `403 Forbidden`, do not retry by printing or broadening token handling in chat; report the pushed branch and GitHub's `/pull/new/<branch>` URL so the user can create the PR manually or provide a token with the required repo/pull-request scope.\\n\\n### Create the PR\\n\\n**With gh:**\\n\\n```bash\\ngh pr create \\\\\\n  --title \\\"feat: add JWT-based user authentication\\\" \\\\\\n  --body \\\"## Summary\\n- Adds login and register API endpoints\\n- JWT token generation and validation\\n\\n## Test Plan\\n- [ ] Unit tests pass\\n\\nCloses #42\\\"\\n```\\n\\nOptions: `--draft`, `--reviewer user1,user2`, `--label \\\"enhancement\\\"`, `--base develop`\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\ncurl -s -X POST \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  -H \\\"Accept: application/vnd.github.v3+json\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/pulls \\\\\\n  -d \\\"{\\n    \\\\\\\"title\\\\\\\": \\\\\\\"feat: add JWT-based user authentication\\\\\\\",\\n    \\\\\\\"body\\\\\\\": \\\\\\\"## Summary\\\\nAdds login and register API endpoints.\\\\n\\\\nCloses #42\\\\\\\",\\n    \\\\\\\"head\\\\\\\": \\\\\\\"$BRANCH\\\\\\\",\\n    \\\\\\\"base\\\\\\\": \\\\\\\"main\\\\\\\"\\n  }\\\"\\n```\\n\\nThe response JSON includes the PR `number` — save it for later commands.\\n\\nTo create as a draft, add `\\\"draft\\\": true` to the JSON body.\\n\\n## 4. Monitoring CI Status\\n\\n### Check CI Status\\n\\n**With gh:**\\n\\n```bash\\n# One-shot check\\ngh pr checks\\n\\n# Watch until all checks finish (polls every 10s)\\ngh pr checks --watch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\n# Get the latest commit SHA on the current branch\\nSHA=$(git rev-parse HEAD)\\n\\n# Query the combined status\\ncurl -s \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n  | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nprint(f\\\\\\\"Overall: {data['state']}\\\\\\\")\\nfor s in data.get('statuses', []):\\n    print(f\\\\\\\"  {s['context']}: {s['state']} - {s.get('description', '')}\\\\\\\")\\\"\\n\\n# Also check GitHub Actions check runs (separate endpoint)\\ncurl -s \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/check-runs \\\\\\n  | python3 -c \\\"\\nimport sys, json\\ndata = json.load(sys.stdin)\\nfor cr in data.get('check_runs', []):\\n    print(f\\\\\\\"  {cr['name']}: {cr['status']} / {cr['conclusion'] or 'pending'}\\\\\\\")\\\"\\n```\\n\\n### Poll Until Complete (git + curl)\\n\\n```bash\\n# Simple polling loop — check every 30 seconds, up to 10 minutes\\nSHA=$(git rev-parse HEAD)\\nfor i in $(seq 1 20); do\\n  STATUS=$(curl -s \\\\\\n    -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n    https://api.github.com/repos/$OWNER/$REPO/commits/$SHA/status \\\\\\n    | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['state'])\\\")\\n  echo \\\"Check $i: $STATUS\\\"\\n  if [ \\\"$STATUS\\\" = \\\"success\\\" ] || [ \\\"$STATUS\\\" = \\\"failure\\\" ] || [ \\\"$STATUS\\\" = \\\"error\\\" ]; then\\n    break\\n  fi\\n  sleep 30\\ndone\\n```\\n\\n## 5. Auto-Fixing CI Failures\\n\\nWhen CI fails, diagnose and fix. This loop works with either auth method.\\n\\n### Step 1: Get Failure Details\\n\\n**With gh:**\\n\\n```bash\\n# List recent workflow runs on this branch\\ngh run list --branch $(git branch --show-current) --limit 5\\n\\n# View failed logs\\ngh run view <RUN_ID> --log-failed\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nBRANCH=$(git branch --show-current)\\n\\n# List workflow runs on this branch\\ncurl -s \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  \\\"https://api.github.com/repos/$OWNER/$REPO/actions/runs?branch=$BRANCH&per_page=5\\\" \\\\\\n  | python3 -c \\\"\\nimport sys, json\\nruns = json.load(sys.stdin)['workflow_runs']\\nfor r in runs:\\n    print(f\\\\\\\"Run {r['id']}: {r['name']} - {r['conclusion'] or r['status']}\\\\\\\")\\\"\\n\\n# Get failed job logs (download as zip, extract, read)\\nRUN_ID=<run_id>\\ncurl -s -L \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/actions/runs/$RUN_ID/logs \\\\\\n  -o /tmp/ci-logs.zip\\ncd /tmp && unzip -o ci-logs.zip -d ci-logs && cat ci-logs/*.txt\\n```\\n\\n### Step 2: Fix and Push\\n\\nAfter identifying the issue, use file tools (`patch`, `write_file`) to fix it:\\n\\n```bash\\ngit add <fixed_files>\\ngit commit -m \\\"fix: resolve CI failure in <check_name>\\\"\\ngit push\\n```\\n\\n### Step 3: Verify\\n\\nRe-check CI status using the commands from Section 4 above.\\n\\n### Auto-Fix Loop Pattern\\n\\nWhen asked to auto-fix CI, follow this loop:\\n\\n1. Check CI status → identify failures\\n2. Read failure logs → understand the error\\n3. Use `read_file` + `patch`/`write_file` → fix the code\\n4. `git add . && git commit -m \\\"fix: ...\\\" && git push`\\n5. Wait for CI → re-check status\\n6. Repeat if still failing (up to 3 attempts, then ask the user)\\n\\n## 6. Merging\\n\\n**With gh:**\\n\\n```bash\\n# Squash merge + delete branch (cleanest for feature branches)\\ngh pr merge --squash --delete-branch\\n\\n# Enable auto-merge (merges when all checks pass)\\ngh pr merge --auto --squash --delete-branch\\n```\\n\\n**With git + curl:**\\n\\n```bash\\nPR_NUMBER=<number>\\n\\n# Merge the PR via API (squash)\\ncurl -s -X PUT \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER/merge \\\\\\n  -d \\\"{\\n    \\\\\\\"merge_method\\\\\\\": \\\\\\\"squash\\\\\\\",\\n    \\\\\\\"commit_title\\\\\\\": \\\\\\\"feat: add user authentication (#$PR_NUMBER)\\\\\\\"\\n  }\\\"\\n\\n# Delete the remote branch after merge\\nBRANCH=$(git branch --show-current)\\ngit push origin --delete $BRANCH\\n\\n# Switch back to main locally\\ngit checkout main && git pull origin main\\ngit branch -d $BRANCH\\n```\\n\\nMerge methods: `\\\"merge\\\"` (merge commit), `\\\"squash\\\"`, `\\\"rebase\\\"`\\n\\n### Enable Auto-Merge (curl)\\n\\n```bash\\n# Auto-merge requires the repo to have it enabled in settings.\\n# This uses the GraphQL API since REST doesn't support auto-merge.\\nPR_NODE_ID=$(curl -s \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/repos/$OWNER/$REPO/pulls/$PR_NUMBER \\\\\\n  | python3 -c \\\"import sys,json; print(json.load(sys.stdin)['node_id'])\\\")\\n\\ncurl -s -X POST \\\\\\n  -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\\\\n  https://api.github.com/graphql \\\\\\n  -d \\\"{\\\\\\\"query\\\\\\\": \\\\\\\"mutation { enablePullRequestAutoMerge(input: {pullRequestId: \\\\\\\\\\\\\\\"$PR_NODE_ID\\\\\\\\\\\\\\\", mergeMethod: SQUASH}) { clientMutationId } }\\\\\\\"}\\\"\\n```\\n\\n## 7. Complete Workflow Example\\n\\n```bash\\n# 1. Start from clean main\\ngit checkout main && git pull origin main\\n\\n# 2. Branch\\ngit checkout -b fix/login-redirect-bug\\n\\n# 3. (Agent makes code changes with file tools)\\n\\n# 4. Commit\\ngit add src/auth/login.py tests/test_login.py\\ngit commit -m \\\"fix: correct redirect URL after login\\n\\nPreserves the ?next= parameter instead of always redirecting to /dashboard.\\\"\\n\\n# 5. Push\\ngit push -u origin HEAD\\n\\n# 6. Create PR (picks gh or curl based on what's available)\\n# ... (see Section 3)\\n\\n# 7. Monitor CI (see Section 4)\\n\\n# 8. Merge when green (see Section 6)\\n```\\n\\n## Useful PR Commands Reference\\n\\n| Action | gh | git + curl |\\n|--------|-----|-----------|\\n| List my PRs | `gh pr list --author @me` | `curl -s -H \\\"Authorization: token $GITHUB_TOKEN\\\" \\\"https://api.github.com/repos/$OWNER/$REPO/pulls?state=open\\\"` |\\n| View PR diff | `gh pr diff` | `git diff main...HEAD` (local) or `curl -H \\\"Accept: application/vnd.github.diff\\\" ...` |\\n| Add comment | `gh pr comment N --body \\\"...\\\"` | `curl -X POST .../issues/N/comments -d '{\\\"body\\\":\\\"...\\\"}'` |\\n| Request review | `gh pr edit N --add-reviewer user` | `curl -X POST .../pulls/N/requested_reviewers -d '{\\\"reviewers\\\":[\\\"user\\\"]}'` |\\n| Close PR | `gh pr close N` | `curl -X PATCH .../pulls/N -d '{\\\"state\\\":\\\"closed\\\"}'` |\\n| Check out someone's PR | `gh pr checkout N` | `git fetch origin pull/N/head:pr-N && git checkout pr-N` |\\n\", \"path\": \"github/github-pr-workflow/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/github/github-pr-workflow\", \"linked_files\": {\"references\": [\"references/private-repo-token-push-pr.md\", \"references/conventional-commits.md\", \"references/ci-troubleshooting.md\"], \"templates\": [\"templates/pr-body-bugfix.md\", \"templates/pr-body-feature.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\": [\"GitHub\", \"Pull-Requests\", \"CI/CD\", \"Git\", \"Automation\", \"Merge\"], \"related_skills\": [\"github-auth\", \"github-code-review\"]}}}", "tool_call_id": "call_keIBOBkIxgb8zkS6eq84osNj", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details.", "finish_reason": "tool_calls", "reasoning_content": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEf82RcKWymfMvJa-sNW_gE08oMbdP9psABaL_jUhQRC8H_lJ7LJQl4OUppQW1YnVCA3fT__4yG1cjtFyUTIRccVuJLbZHN1b4Sy_70BmHsLBSwHkoyT0dEf51vNpjOoyiwc6MeP-zlU__7B4IPQyk053Qyf9pJRd4Z8Slm9MhO7UhbfYveP6m21LISZL1BnvDJfnpVF5mR_ZuZr7Jz0nTQVpgiGuSQNulUzvEFgmV3cvcnO39kUEozzAS_zV39P-WnHBojYiikZy8sybPEpkxvVAs8MjTtDBSHeMFfFFDvo3iFcdRon24eQEvmGsZG9Qg475-c5BqxjrP2HGk-1GWkTc5nRa0BjNRUq1IkGWOF5Wd8vBbmzBSEJN44l5_-dXqAococjMQT-l-lPHIVdRYo4d9MIcy2TEPUSphlxaXDII5YPF54loilPzMtyxW1oP33A_GYixPNPRa1IutWnK7M0tBexvUleqjJ8tfmwtBQs25tEduFKsYqhUF8C8gK7TFwh83t1L4xQrqWq3rmdOMTbcE2JRMlnNewVrmwXVNxzqu2aqhM29pU7bod-Kwe9-AYLL9AvYePq8FxYY3PH9q0cY5XZVqJ8xcg3IwegPE-Jp8UjxYgYc1VdAqf1quYadxQe11pW6jbtk__EV9r1rmeRajdSw_gC5l_963nFKioxqVdMd4rYRwiA3LLwT7OW0r5w2DmiY6k_8gLK8Nvs57jKvs6NnmZoLgBM5LzkxJuhk17uVBA9s7PEUQfXqUo36LLlKYUbCxcYFxnsd9DYqUCeo9WIJVgtsFcvZcpf_oFduLMa8eR871DoGAWm6kxs1JOYHRZaNUo7m1kQ3gkE_fJzHHS9AMo3s0Oua8_ox5Bg40wYwVYvEMZFsYQ_UKzVptCiCgAdq92UNCteoqBl6IwQWawuvP6RzEUsAP-D6EMP1knU7MDUyZF113cjXPbg6KPmva-jvDTV7o_dMTEK7EmYms8i6250GC0DqkMx7zfkJb_Y9_EveH6HKXFNqHbzfIZVX_gMOVHE-Vera-zSpJ0G8i4eHplZkKI3NjGHE5dsL7ag=", "id": "rs_0d1a4d10654d56b4016a11ff33e2c88191837e36be9de08170", "summary": [{"type": "summary_text", "text": "**Figuring out token retrieval**\n\nI’m trying to work with a token that I need to push from memory. The token shape should be extracted from a CSV file, but I need to do this without printing anything to the console. I think using Python with the askpass module could help with this. Let’s see if I can structure the code to accomplish this effectively! I want to make sure I’m careful with the details."}]}], "tool_calls": [{"id": "call_ORYOWtURvZbrVtSdDq9j1joC", "call_id": "call_ORYOWtURvZbrVtSdDq9j1joC", "response_item_id": "fc_0d1a4d10654d56b4016a11ff365cd081918f1ae668676075d5", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport csv, pathlib, re\\np=pathlib.Path('/home/agent/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv')\\nprint('exists', p.exists(), 'size', p.stat().st_size if p.exists() else None)\\nif p.exists():\\n    text=p.read_text(errors='ignore')\\n    tokens=re.findall(r'(github_pat_[A-Za-z0-9_]+|ghp_[A-Za-z0-9_]+)', text)\\n    print('token_count', len(tokens), 'prefixes', [t[:10] for t in tokens[:3]], 'lengths', [len(t) for t in tokens[:3]])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"exists True size 141\\ntoken_count 1 prefixes ['github_pat'] lengths [93]\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_ORYOWtURvZbrVtSdDq9j1joC", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_IsTKOhTH3Us1oGjI3gnZ0j0b", "call_id": "call_IsTKOhTH3Us1oGjI3gnZ0j0b", "response_item_id": "fc_0d1a4d10654d56b4016a11ff3dba288191be8f5749c69dcc12", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os, pathlib, re, subprocess, tempfile\\nrepo=pathlib.Path('/home/agent/jarvis_runtime/AutoShortsBot')\\nsecret=pathlib.Path('/home/agent/jarvis_runtime/finance-system/secrets/Github_token_FinanceManager.csv').read_text(errors='ignore')\\ntoken=re.findall(r'(github_pat_[A-Za-z0-9_]+|ghp_[A-Za-z0-9_]+)', secret)[0]\\nask=tempfile.NamedTemporaryFile('w', delete=False)\\nask.write('#!/bin/sh\\\\ncase \\\"$1\\\" in\\\\n  *Username*) printf \\\"%s\\\\\\\\n\\\" x-access-token ;;\\\\n  *Password*) printf \\\"%s\\\\\\\\n\\\" \\\"$GITHUB_TOKEN\\\" ;;\\\\n  *) printf \\\"\\\\\\\\n\\\" ;;\\\\nesac\\\\n')\\nask.close()\\nos.chmod(ask.name,0o700)\\nenv=os.environ.copy(); env['GITHUB_TOKEN']=token; env['GIT_ASKPASS']=ask.name; env['GIT_TERMINAL_PROMPT']='0'\\ntry:\\n    r=subprocess.run(['git','push','-u','origin','HEAD'], cwd=repo, env=env, text=True, capture_output=True, timeout=180)\\n    print(r.stdout)\\n    print(r.stderr)\\n    print('exit', r.returncode)\\n    if r.returncode==0:\\n        branch=subprocess.check_output(['git','branch','--show-current'], cwd=repo, text=True).strip()\\n        local=subprocess.check_output(['git','rev-parse','HEAD'], cwd=repo, text=True).strip()\\n        r2=subprocess.run(['git','ls-remote','origin',f'refs/heads/{branch}'], cwd=repo, env=env, text=True, capture_output=True, timeout=60)\\n        remote=r2.stdout.split()[0] if r2.stdout.split() else ''\\n        print('remote_hash_matches', local==remote)\\nfinally:\\n    pathlib.Path(ask.name).unlink(missing_ok=True)\\nPY\",\"timeout\":240,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"remote: Write access to repository not granted.\\nfatal: konnte nicht auf 'https://github.com/Gamexgit/AutoShortsBot.git/' zugreifen: The requested URL returned error: 403\\n\\nexit 128\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_IsTKOhTH3Us1oGjI3gnZ0j0b", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEf9Lwf65U1YbLU0pnuBJWDPNleLHg3vwZAsL9vheq2Tg3kpUJM6Nmi2tQI9tHvkI4bswHhib39KPNZztvO5bkZt5tRO8cBNW8o5hJs1VtdmoW17KNk7n8SbxKxbstOm7sX19AcS-g1NSO0zC5VcJilzgQjBnAjQuiRH7o1ue0HGB2yiqwGMVvCKPkMFRgLPSV80LMVpp-p26pQr_EQv19vhWArk8zv2lk5Nk19zk86p_Vt2wDrG3_m3lEiRB4PDMcqklto8BcL0JiwEZTWS-BxSCZtEtLHAubYgp9GrK44wobGTmijxydD-uX5ILHiq4GLpq0H6_g2tXNjfCkSYc91dpqXsWqwnqJtKQcWROnEiqPq2OJu_xxt2xOkn4kuqbSHoIcbpe3gAVlXlGPMRh-zu8jdTA90XnX96ejbMeHH4cGm5ysHDxF6_R1SBAXnh5NDH46lhuur2HLl9N-5mnhwAM510XWzvXoe29bAyw22uVGLCeQarkqLK-g6z2dWen4vKb14Z4ed--zldyGjCWq_8xPWkuIouE--MrikiZypXWIClzKiRwlr4ZFyZ0U6_2bEpJB37XWcGLHgs5hySW0ch0SddqBggpiH1vM5AAF45-76_fQDu831PVg0ydXKNftaZJM6NeVo0ZcTXaDoX0urjSrSBgzIAv6tca5Tv3vFALxi-xwa1MRI3fa2sy-t5L_PzNjKhJtOTDWaC9C9IuC4duuOX1OhySNDDFsAUyNgusMJczehXCH55ECSnCzbZEOIgGe8GXpX4fXEBVAVGgEtyskTYwGdp380gq5sK8TbILXdPaMB7iEr--rONVfToN18cSncW-Vit5OeH8a7mwaL28pKzAq5iJYa2EggyP2_fc7iFjfmRMbxiQmul5ygA3DkhixWf3iY6YImpSw5bx8SXEdpaX7M7GCQIXa8SiSdxkUzK1aZ2A0Jg-bCbCgFB5tlv67HHneGcrSuDMz2slzNkSLzamrmvr5CEGgFSRhhCBJU9pb-r385QTtVpluA1Xm27q21Uv9M5iSuvDYui7DO7xO3Tgo-9yckegah7jkks9CqKBRb0ff4IEDqcnpDa-WEnm", "id": "rs_0d1a4d10654d56b4016a11ff4a8ca481919374548cc57de22b", "summary": []}], "tool_calls": [{"id": "call_xovMGShuEdVNafYtXZiVVSh1", "call_id": "call_xovMGShuEdVNafYtXZiVVSh1", "response_item_id": "fc_0d1a4d10654d56b4016a11ff4ba75881918401a63296403552", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git log --oneline -1 && git branch --show-current\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"?? autoshorts/review_package.py\\n?? data/comfyui_native_wan22_house_jump_5s_workflow.json\\n?? data/comfyui_native_wan22_rabbit_i2v_5s_workflow.json\\n?? data/comfyui_native_wan22_rabbit_i2v_crop_5s_workflow.json\\n?? data/comfyui_scene01_gpu_480x832_workflow.json\\n?? data/comfyui_scene01_gpu_preview_workflow.json\\n?? data/comfyui_scene01_probe_workflow.json\\n?? data/comfyui_wan22_scene01_416x736_t5legacy_workflow.json\\n?? data/comfyui_wan22_scene01_416x736_tiled_workflow.json\\n?? data/comfyui_wan22_scene01_416x736_workflow.json\\n?? data/comfyui_wan22_scene01_bright_416x736_workflow.json\\n?? data/comfyui_wan_micro_smoke_workflow.json\\n?? data/comfyui_wan_smoke_workflow.json\\n?? data/telegram_reviews/\\n?? scripts/generate_telegram_review_packages.py\\n?? tests/test_review_package.py\\nb3ef793 feat: add wan i2v prompt planning\\nfeat/initial-growth-engine\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_xovMGShuEdVNafYtXZiVVSh1", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_ij3iAKkO8dH5b3JUvHzm6GXe", "call_id": "call_ij3iAKkO8dH5b3JUvHzm6GXe", "response_item_id": "fc_0d1a4d10654d56b4016a11ff5165fc819195610f56d0ddc6cc", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests laufen lassen und Git-Status sauber berichten\",\"id\":\"verify\",\"status\":\"completed\"}]}"}}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"inspect\", \"content\": \"Repo-Struktur und bestehende Retention-/Mechanism-Module prüfen\", \"status\": \"completed\"}, {\"id\": \"style\", \"content\": \"Style Bible als Projektartefakt erstellen\", \"status\": \"completed\"}, {\"id\": \"keyprompt\", \"content\": \"Keyframe/I2V Prompt Generator per TDD implementieren\", \"status\": \"completed\"}, {\"id\": \"cli\", \"content\": \"CLI + Artefakte für Pilot-Prompts erzeugen\", \"status\": \"completed\"}, {\"id\": \"verify\", \"content\": \"Tests laufen lassen und Git-Status sauber berichten\", \"status\": \"completed\"}], \"summary\": {\"total\": 5, \"pending\": 0, \"in_progress\": 0, \"completed\": 5, \"cancelled\": 0}}", "tool_call_id": "call_ij3iAKkO8dH5b3JUvHzm6GXe", "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Weitergebaut: Wan2.2 I2V-Produktionsschicht\n\nIch habe den nächsten Projektblock umgesetzt:\n\n```text\nRetention/Mechanism Shot Plan\n→ Style Bible\n→ Wan2.2 Keyframe Prompt\n→ Wan2.2 I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI Preset\n```\n\nDamit haben wir jetzt nicht mehr nur lose Prompt-Ideen, sondern eine erste reproduzierbare **I2V-Prompt-Planungsschicht** für unser Retention-Studio.\n\n---\n\n## Neu erstellt\n\n### 1. Style Bible\n\n```text\ndocs/style_bible.md\n```\n\nDefiniert jetzt verbindlich:\n\n- Serienpositionierung\n- Look & Feel\n- Farbpalette\n- Caption-Stil\n- AI-Video-Regeln\n- No-Go-Liste\n- Shot Families\n- Pilot-Style für „billiges Auto“\n- Quality Gate\n\nKernlook:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nAlso: reale Textur durch Wan/AI-Clips, Erklärung/Zahlen/Labels durch unseren Renderer.\n\n---\n\n### 2. Wan2.2 Prompting Playbook\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nEnthält recherchierte und getestete Regeln für Wan2.2:\n\n- I2V bevorzugen statt reinem T2V\n- ein Motiv\n- eine Hauptbewegung\n- ein Kameramove\n- balanced exposure\n- keine Texte/Logos/Watermarks im AI-Clip\n- negative prompts gegen Flicker, Text, Deformationen\n- Keyframe-Anforderungen\n- A5000-Settings-Ladder\n\nWichtigste Regel:\n\n```text\nT2V = Exploration\nI2V = Produktion\n```\n\n---\n\n### 3. Neues Modul\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nFunktion:\n\n```python\nbuild_i2v_prompt_plan(shot_plan)\n```\n\nErzeugt aus einem Mechanism Shot Plan:\n\n- Keyframe Prompt\n- I2V Motion Prompt\n- Negative Prompt\n- Wan2.2 Preset\n- Clip Requirements\n- Renderer-only Scene Notes\n\n---\n\n### 4. Neue CLI\n\n```text\nscripts/generate_i2v_prompt_plans.py\n```\n\nAusführen:\n\n```bash\npython scripts/generate_i2v_prompt_plans.py\n```\n\nOutput:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n### 5. Tests\n\n```text\ntests/test_keyframe_prompt.py\n```\n\nTestet u.a.:\n\n- nur `cinematic_ai_video`-Szenen werden Wan2.2-I2V-Jobs\n- Mechanism-/Kinetic-Szenen bleiben Renderer-only\n- Auto-Pilot bekommt konkrete Car/Inspection-Bay Prompts\n- kein generischer Macro-Müll für Szene 2\n- Negative Prompts enthalten Anti-Text/Anti-License-Plate/Anti-Fake-UI-Regeln\n\n---\n\n## Generierter Prompt-Plan für Pilot\n\nDatei:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nEnthält jetzt für Szene 1:\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area...\n```\n\nUnd I2V Motion:\n\n```text\nCamera slowly pushes in from a front three-quarter angle...\nPreserve the same car and garage.\nNo text, no logo, no watermark.\n```\n\nSzene 3–5 sind korrekt als Renderer-only markiert:\n\n```text\nscene_03 mechanism_animation\nscene_04 mechanism_animation\nscene_05 kinetic_text_overlay\n```\n\nDas ist wichtig: Wan soll keine Zahlen, Labels oder Erklärgrafiken halluzinieren. Das macht unser Renderer.\n\n---\n\n## Validierung\n\nFull test suite:\n\n```text\n64 passed in 3.66s\n```\n\nCommit lokal erstellt:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\n---\n\n## GitHub Push\n\nPush ist fehlgeschlagen wegen fehlender Schreibrechte des vorhandenen Tokens:\n\n```text\nremote: Write access to repository not granted.\nfatal: ... 403\n```\n\nHeisst:\n\n```text\nCommit ist lokal vorhanden.\nRemote ist noch nicht aktualisiert.\n```\n\nIch habe keine Tokens ausgegeben oder gespeichert. Sehr unglamourös, aber sicher.\n\nDu brauchst entweder:\n\n- Token mit Schreibrecht für `Gamexgit/AutoShortsBot`, oder\n- lokal selbst pushen, oder\n- mir später einen korrekt berechtigten GitHub-Zugang bereitstellen.\n\n---\n\n## Untracked bewusst nicht angefasst\n\nWeiterhin untracked und ignoriert:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nAußerdem liegen noch ComfyUI-Testworkflow-JSONs untracked herum. Ich habe sie nicht committed, weil sie eher Experimente/Arbeitsartefakte sind, nicht saubere Projektlogik.\n\n---\n\n# Was ist jetzt der nächste sinnvolle Schritt?\n\nJetzt kommt die eigentliche Integration:\n\n```text\nI2V Prompt Plan\n→ Keyframe Image Input\n→ ComfyUI native Wan2.2 Client\n→ mehrere Seeds/Varianten\n→ Clip Review JSON\n```\n\nKonkret als nächstes würde ich bauen:\n\n```text\nautoshorts/comfyui_client.py\nautoshorts/i2v_clip_jobs.py\nscripts/run_i2v_clip_jobs.py\ntests/test_i2v_clip_jobs.py\n```\n\nZiel:\n\n```text\nFür Szene 1 automatisch 3 Wan2.2-I2V Varianten erzeugen\nund speichern unter:\ndata/generated_clips/<slug>/scene_01/seed_001.mp4\n```\n\nDann bewerten wir nicht mehr manuell einzelne Dateien, sondern bekommen ein kleines Review-Paket:\n\n```text\nscene_01_seed_001.mp4\nscene_01_seed_002.mp4\nscene_01_seed_003.mp4\nclip_review.json\n```\n\nKurz: **Style und Prompt-Plan stehen. Als Nächstes automatisieren wir die lokale Wan2.2-I2V-Ausführung und Clip-Auswahl.**", "reasoning": null, "finish_reason": "stop", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEf9Wk_-SXtVBOqP1Jp1ZUvWhHU1tJugyfdmWqrUGxUf2ZvY0ElERBzHzE4vu5t1VupWGyLfd7xGopwRZBP4FY-dNCdFa8L3P9sfyELaH8ypn5NWu86KzDjg3vxEAJjA64GG3jsRhrUxc-UmOY0xg835CjaB2skHlJA_7uNcBXp1aVbnQ2__Ohzqg_0YE5v48ESyZ56Or-5wAE62kAtDnALUWH5ebfwvcfvQaBKdGVEsqXw6FypdNGI9PkVENPpHGsWTQpbtY-Hbj37T-2zpxggcaeP2ytV19JBlOCqYpWZOd8hQd5xcsQk44WJjH9b-UkPLDpWpqS1Bnv2lDHBnNSeE44CJ5NlvsTXi7pf7IMdsRF9_inUARh_c8BTYMTVrUXV0qHwYZMAOMq4q5XYntTyId6IQIZK8xWhjHUycRBaFJyKtXJJ8lg59OMUskxgRRcGSBgHWbZfSiY774UkbMZ0IiWEd293X5q_thyIPctNlDyxe1eBfAFMAIQW_fWLo2Wm__oFOnHZS5MXuLUQt6fPQBMyLaUhrjmFtojv9uTdd55Et9phUXrHfUHAYXT3RjGTRu9v0SotGGDjFgS8TAy-A62aYVSwrnbvrR6LbP9SqeKfl1KIYXGccdx-1lk60suBkso_UFaArCST3EI3kfr__xSXzW_Ce0gXIoCc4WUafAaxkGrohUfp-LJ1CosE4hY2FYCBhTigFd8M2bZrte-r33qJ9rBxdXGFd16S1wppu3QbwQnpfW_gnKWYIEwKzuwudlrDkGxdmc4eopm561p-OX1XRX5ndg1-oAdenJUf4zfSUqg88RM0ccENGedV4-AvqICEWSeNYiIc7dYWeL_M5pSrZ_5Rpeko39G85uYWjMUXCMkAx-l9Rd0mWlQW3Bbapoxw5a0s4LyRhanPrwKFUhVPpJ0j8F0kCVeOKMLN92BayPHsR6IXUiAuLL1tOTnMJNvHoxFHkUgiHo941w2sLhruuJuPPpveT28Rf7BrL0enz7xUitvFjaMDq5Lt7q4NuNq6wRG4cUOaTw_M7kVlZo4TPe5S7Glc6_fJQWfivDtxvA9OTxPft9ckOx1WzQB4EfaUkJhvCad7Kvr7CY5mKoQk-3kiZiidbSFfTV591RrPbAvxp7WA-NUiphHujfSdYY5qhNoMUkSjNbFt63zMmukLG3ptY0T4axQcYxhwa2PUQNOyBDZZvOZM76ksZ0yHp49i2O0A5kB8TKz77q9kcRlul3jBj1xDbZiHWIxafPmbjHGwiaT_ne_2KIgfVR7GyXDRUx87TOgrk1fJyq4JKrjhDkHzUvM-A_NRAqOAiIlc4SFHqhnrtJMCKYBKcr5uiJsdpefDdyR4W4KeILUOQNXQ==", "id": "rs_0d1a4d10654d56b4016a11ff54b40081919b8a872bc057e461", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Weitergebaut: Wan2.2 I2V-Produktionsschicht\n\nIch habe den nächsten Projektblock umgesetzt:\n\n```text\nRetention/Mechanism Shot Plan\n→ Style Bible\n→ Wan2.2 Keyframe Prompt\n→ Wan2.2 I2V Motion Prompt\n→ Negative Prompt\n→ ComfyUI Preset\n```\n\nDamit haben wir jetzt nicht mehr nur lose Prompt-Ideen, sondern eine erste reproduzierbare **I2V-Prompt-Planungsschicht** für unser Retention-Studio.\n\n---\n\n## Neu erstellt\n\n### 1. Style Bible\n\n```text\ndocs/style_bible.md\n```\n\nDefiniert jetzt verbindlich:\n\n- Serienpositionierung\n- Look & Feel\n- Farbpalette\n- Caption-Stil\n- AI-Video-Regeln\n- No-Go-Liste\n- Shot Families\n- Pilot-Style für „billiges Auto“\n- Quality Gate\n\nKernlook:\n\n```text\nphotorealistic macro investigation + clean mechanism overlays\n```\n\nAlso: reale Textur durch Wan/AI-Clips, Erklärung/Zahlen/Labels durch unseren Renderer.\n\n---\n\n### 2. Wan2.2 Prompting Playbook\n\n```text\ndocs/wan22_prompting_playbook.md\n```\n\nEnthält recherchierte und getestete Regeln für Wan2.2:\n\n- I2V bevorzugen statt reinem T2V\n- ein Motiv\n- eine Hauptbewegung\n- ein Kameramove\n- balanced exposure\n- keine Texte/Logos/Watermarks im AI-Clip\n- negative prompts gegen Flicker, Text, Deformationen\n- Keyframe-Anforderungen\n- A5000-Settings-Ladder\n\nWichtigste Regel:\n\n```text\nT2V = Exploration\nI2V = Produktion\n```\n\n---\n\n### 3. Neues Modul\n\n```text\nautoshorts/keyframe_prompt.py\n```\n\nFunktion:\n\n```python\nbuild_i2v_prompt_plan(shot_plan)\n```\n\nErzeugt aus einem Mechanism Shot Plan:\n\n- Keyframe Prompt\n- I2V Motion Prompt\n- Negative Prompt\n- Wan2.2 Preset\n- Clip Requirements\n- Renderer-only Scene Notes\n\n---\n\n### 4. Neue CLI\n\n```text\nscripts/generate_i2v_prompt_plans.py\n```\n\nAusführen:\n\n```bash\npython scripts/generate_i2v_prompt_plans.py\n```\n\nOutput:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n### 5. Tests\n\n```text\ntests/test_keyframe_prompt.py\n```\n\nTestet u.a.:\n\n- nur `cinematic_ai_video`-Szenen werden Wan2.2-I2V-Jobs\n- Mechanism-/Kinetic-Szenen bleiben Renderer-only\n- Auto-Pilot bekommt konkrete Car/Inspection-Bay Prompts\n- kein generischer Macro-Müll für Szene 2\n- Negative Prompts enthalten Anti-Text/Anti-License-Plate/Anti-Fake-UI-Regeln\n\n---\n\n## Generierter Prompt-Plan für Pilot\n\nDatei:\n\n```text\ndata/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\nEnthält jetzt für Szene 1:\n\n```text\nPhotorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area...\n```\n\nUnd I2V Motion:\n\n```text\nCamera slowly pushes in from a front three-quarter angle...\nPreserve the same car and garage.\nNo text, no logo, no watermark.\n```\n\nSzene 3–5 sind korrekt als Renderer-only markiert:\n\n```text\nscene_03 mechanism_animation\nscene_04 mechanism_animation\nscene_05 kinetic_text_overlay\n```\n\nDas ist wichtig: Wan soll keine Zahlen, Labels oder Erklärgrafiken halluzinieren. Das macht unser Renderer.\n\n---\n\n## Validierung\n\nFull test suite:\n\n```text\n64 passed in 3.66s\n```\n\nCommit lokal erstellt:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\n---\n\n## GitHub Push\n\nPush ist fehlgeschlagen wegen fehlender Schreibrechte des vorhandenen Tokens:\n\n```text\nremote: Write access to repository not granted.\nfatal: ... 403\n```\n\nHeisst:\n\n```text\nCommit ist lokal vorhanden.\nRemote ist noch nicht aktualisiert.\n```\n\nIch habe keine Tokens ausgegeben oder gespeichert. Sehr unglamourös, aber sicher.\n\nDu brauchst entweder:\n\n- Token mit Schreibrecht für `Gamexgit/AutoShortsBot`, oder\n- lokal selbst pushen, oder\n- mir später einen korrekt berechtigten GitHub-Zugang bereitstellen.\n\n---\n\n## Untracked bewusst nicht angefasst\n\nWeiterhin untracked und ignoriert:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nAußerdem liegen noch ComfyUI-Testworkflow-JSONs untracked herum. Ich habe sie nicht committed, weil sie eher Experimente/Arbeitsartefakte sind, nicht saubere Projektlogik.\n\n---\n\n# Was ist jetzt der nächste sinnvolle Schritt?\n\nJetzt kommt die eigentliche Integration:\n\n```text\nI2V Prompt Plan\n→ Keyframe Image Input\n→ ComfyUI native Wan2.2 Client\n→ mehrere Seeds/Varianten\n→ Clip Review JSON\n```\n\nKonkret als nächstes würde ich bauen:\n\n```text\nautoshorts/comfyui_client.py\nautoshorts/i2v_clip_jobs.py\nscripts/run_i2v_clip_jobs.py\ntests/test_i2v_clip_jobs.py\n```\n\nZiel:\n\n```text\nFür Szene 1 automatisch 3 Wan2.2-I2V Varianten erzeugen\nund speichern unter:\ndata/generated_clips/<slug>/scene_01/seed_001.mp4\n```\n\nDann bewerten wir nicht mehr manuell einzelne Dateien, sondern bekommen ein kleines Review-Paket:\n\n```text\nscene_01_seed_001.mp4\nscene_01_seed_002.mp4\nscene_01_seed_003.mp4\nclip_review.json\n```\n\nKurz: **Style und Prompt-Plan stehen. Als Nächstes automatisieren wir die lokale Wan2.2-I2V-Ausführung und Clip-Auswahl.**"}], "id": "msg_0d1a4d10654d56b4016a11ff569a64819184dc47f81750bd59", "phase": "final_answer"}], "timestamp": "2026-05-23T21:26:39.748126"}
{"role": "user", "content": "[M G] Ok, bauen wir weiter.", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": "**Planning project implementation**\n\nI need to continue building and consider using skills related to TDD, and maybe ComfyUI in a social context? I've already loaded some components, but I need to load the new turn if it's relevant. It's time to take action and implement the ComfyUI client or job. I also need to check the git status and be cautious with any untracked files. Should I also plan an offline test with fake HTTP? Let's make that happen!", "finish_reason": "tool_calls", "reasoning_content": "**Planning project implementation**\n\nI need to continue building and consider using skills related to TDD, and maybe ComfyUI in a social context? I've already loaded some components, but I need to load the new turn if it's relevant. It's time to take action and implement the ComfyUI client or job. I also need to check the git status and be cautious with any untracked files. Should I also plan an offline test with fake HTTP? Let's make that happen!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEgEXrFv8C51o-ENR5Vn6teuyQk50EWiZ-ipjwTpyxkP5kXOXzzRwyfYZuev_S7HUGTeMzTg5itCjVnrh_6isoqcIvpg-jAevpyabSdbQGeZs54hSNT4IeNRPE-Z_Mi6IHfCoOVVwKjiPHXG54tyndTPZ_iAAL2bXjOAe3b7VSWxdYiPBpTXSaAVdDacxdnbivUvpX_EhZwFFCZEgg36BmcOmJhGtRYQ4EtX2WBlsY3x1Ry8Y6WciNhh9Pp0ImjVB7_BsdoKKrpXgtfeMGBXQnvR-AtMvr0VtiNaTtrfgvAnAAmfR_41IIqLtN35YA34bAyCtP65GxKN9pBMJQ3TxI9mp5Wi9vdcuoevbhLwTiCveA7MQGZZjK_P0ufSTFh-57tzw-2NfUfCGbgyXgrIU8KzkuwZxcJjjoF9FqWIKhJVuNlKKtc-Xbqt3eOgSMSHypLqPB-yAf9YxSo29H7Ozq3oWWHRjQ21bDHOl-K1E1sPo4k_ftEN7wx6nB_ZlFxxSsNJwrpEMXPccbdp1E-FG1HWxLcKBChYtj2lYfqry2q0Zd2Bi0GO88u0zJJVggrQCiahT7xXbZ02xeZIu0nXzimy_e87MBFmiS5XvfZEW-tfuuqJgVZf1sn3VMPAfO2gtGRrrQhKZelSRXdSO2iX5WdQILurjuHOqIDFe8pZsSmuQS_YI5BiYGzuykg4Dp6AkM6t7voy687eb8fTPbnfGDoDNbEC_cSemEpGZgx226KNpfOAlk3AAhmViBE5LapQB74lrnXoOIi2jXUJRDzehNfDKNe-C8PfJcg8n_j1TIOh63ug0lTyyYWBdoCMCewCVPfjMFxLeO8xF5IO67v_HeODyuL2gNGvnFSbHKQP3clPnnttUdshUxLNBTEXYps9M0estYA7Isn8hz8B_cyXDvtq1yqhCB_sJOjbsA56RtKCNNLGi3Sm52MT09BsCBirrDCKAVCnbwmLSAwZhNKHj5NsBk0g10DUzUuk22h0UnT22O7b_JV4DsMVVc_ztL9exc9nEa4a0ztM1ayT7ejTEFsBOzYeOFZ_TnVbdduS0_J5j80psQZe6yB3xUKqS0JLZeoE5LKmtxay7nLcp4kKVfTyAMRFM1-l-RrXvL-TOzuZ580kEBzrSlLdj76OYhc4l289Xt4PjJJct9PopzAVp9ffKOy-9AkgaCZ43_W6RNCaosN6r94pAjqNip4JAFq1zTvw4J3p445mRj38fvN258_teme9_97UYzeAJjFvi4zQKJePpO-Ul9dbr2t8PIBJcWIUG1FXUueLHRa6TtZGNWxQC4kIuG8IuS9ML4-jWtEHdBhOR2Lk1AAl-cfrIie9d4yA137-Zgsv3X9g79Lg5t-r6Zw==", "id": "rs_0d1a4d10654d56b4016a120113e5a881918eaad5b7ba2f559c", "summary": [{"type": "summary_text", "text": "**Planning project implementation**\n\nI need to continue building and consider using skills related to TDD, and maybe ComfyUI in a social context? I've already loaded some components, but I need to load the new turn if it's relevant. It's time to take action and implement the ComfyUI client or job. I also need to check the git status and be cautious with any untracked files. Should I also plan an offline test with fake HTTP? Let's make that happen!"}]}], "tool_calls": [{"id": "call_jKBspEvJ36KzTkO1u16D59lM", "call_id": "call_jKBspEvJ36KzTkO1u16D59lM", "response_item_id": "fc_0d1a4d10654d56b4016a1201175694819183230b808ab06b5b", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"test-driven-development\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"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### 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## 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\\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\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"testing\", \"tdd\", \"development\", \"quality\", \"red-green-refactor\"], \"related_skills\": [\"systematic-debugging\", \"writing-plans\", \"subagent-driven-development\"]}}}", "tool_call_id": "call_jKBspEvJ36KzTkO1u16D59lM", "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "tool", "name": "skill_view", "content": "{\"success\": true, \"name\": \"comfyui\", \"description\": \"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\", \"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"content\": \"---\\nname: comfyui\\ndescription: \\\"Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.\\\"\\nversion: 5.1.0\\nauthor: [kshitijk4poor, alt-glitch, purzbeats]\\nlicense: MIT\\nplatforms: [macos, linux, windows]\\ncompatibility: \\\"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\\\"\\nprerequisites:\\n  commands: [\\\"python3\\\"]\\nsetup:\\n  help: \\\"Run scripts/hardware_check.py FIRST to decide local vs Comfy Cloud; then scripts/comfyui_setup.sh auto-installs locally (or use Cloud API key for platform.comfy.org).\\\"\\nmetadata:\\n  hermes:\\n    tags:\\n      - comfyui\\n      - image-generation\\n      - stable-diffusion\\n      - flux\\n      - sd3\\n      - wan-video\\n      - hunyuan-video\\n      - creative\\n      - generative-ai\\n      - video-generation\\n    related_skills: [stable-diffusion-image-generation, image_gen]\\n    category: creative\\n---\\n\\n# ComfyUI\\n\\nGenerate images, video, audio, and 3D content through ComfyUI using the\\nofficial `comfy-cli` for setup/lifecycle and direct REST/WebSocket API\\nfor workflow execution.\\n\\n## What's in this skill\\n\\n**Reference docs (`references/`):**\\n\\n- `official-cli.md` — every `comfy ...` command, with flags\\n- `rest-api.md` — REST + WebSocket endpoints (local + cloud), payload schemas\\n- `workflow-format.md` — API-format JSON, common node types, param mapping\\n- `template-integrity.md` — converting `comfyui-workflow-templates` from\\n  editor format to API format: Reroute bypass, dotted dynamic-input keys\\n  (`values.a`, `resize_type.width`), Cloud quirks (302 redirect, 1 concurrent\\n  free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch.\\n  Authored by [@purzbeats](https://github.com/purzbeats). Load this whenever\\n  you're starting from an official template.\\n- `wan-video-tailscale-a5000.md` — session-proven setup and API smoke-test ladder for driving `ComfyUI-WanVideoWrapper` on a remote 16 GB NVIDIA A5000 Laptop GPU via Tailscale, including `hf download`, `--lowvram`, CPU text encoding, and Wan2.1 1.3B FP8 settings.\\n- `wan-video-a5000-tailscale.md` — remote RTX A5000 / 16 GB VRAM Wan2.1 setup over Tailscale, `hf download` model commands, smoke-test validation, and RAM-pressure crash handling.\\n- `wan22-ti2v-a5000.md` — session-proven Wan2.2 TI2V 5B FP8 scaled profile for RTX A5000 16 GB over Tailscale: launch flags, wrapper-compatible T5, VAE tiling, OOM fixes, and prompt exposure lessons.\\n- `wan22-native-i2v-a5000-quality.md` — native ComfyUI Wan2.2 5B quality workflow notes: official model/T5/VAE stack, I2V keyframe lessons, RAM/offload guidance, cleanup candidates, and the critical distinction between technical success and production usability.\\n- `wan-video-a5000-optimization.md` — optimized non-`--lowvram` launch/workflow settings for efficient Wan2.1/Wan2.2 local video generation on a 16GB A5000, including GPU T5 encoding, preview profiles, and Wan2.2 TI2V 5B FP8 scaled test ladder.\\n- `wan22-native-i2v-prompting.md` — native Wan2.2 I2V prompting and keyframe rules for quality-first local Retention Studio clips; covers scaled T5, negative prompts, A5000 ladders, and blunt review criteria.\\n\\n**Scripts (`scripts/`):**\\n\\n| Script | Purpose |\\n|--------|---------|\\n| `_common.py` | Shared HTTP, cloud routing, node catalogs (don't run directly) |\\n| `hardware_check.py` | Probe GPU/VRAM/disk → recommend local vs Comfy Cloud |\\n| `comfyui_setup.sh` | Hardware check + comfy-cli + ComfyUI install + launch + verify |\\n| `extract_schema.py` | Read a workflow → list controllable params + model deps |\\n| `check_deps.py` | Check workflow against running server → list missing nodes/models |\\n| `auto_fix_deps.py` | Run check_deps then `comfy node install` / `comfy model download` |\\n| `run_workflow.py` | Inject params, submit, monitor, download outputs (HTTP or WS) |\\n| `run_batch.py` | Submit a workflow N times with sweeps, parallel up to your tier |\\n| `ws_monitor.py` | Real-time WebSocket viewer for executing jobs (live progress) |\\n| `health_check.py` | Verification checklist runner — comfy-cli + server + models + smoke test |\\n| `fetch_logs.py` | Pull traceback / status messages for a given prompt_id |\\n\\n**Example workflows (`workflows/`):** SD 1.5, SDXL, Flux Dev, SDXL img2img,\\nSDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See\\n`workflows/README.md`.\\n\\n## When to Use\\n\\n- User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.\\n- User wants to run a specific ComfyUI workflow file\\n- User wants to chain generative steps (txt2img → upscale → face restore)\\n- User needs ControlNet, inpainting, img2img, or other advanced pipelines\\n- User asks to manage ComfyUI queue, check models, or install custom nodes\\n- User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.\\n\\n## Architecture: Two Layers\\n\\n```\\n┌─────────────────────────────────────────────────────┐\\n│ Layer 1: comfy-cli (official lifecycle tool)        │\\n│   Setup, server lifecycle, custom nodes, models     │\\n│   → comfy install / launch / stop / node / model    │\\n└─────────────────────────┬───────────────────────────┘\\n                          │\\n┌─────────────────────────▼───────────────────────────┐\\n│ Layer 2: REST/WebSocket API + skill scripts         │\\n│   Workflow execution, param injection, monitoring   │\\n│   POST /api/prompt, GET /api/view, WS /ws           │\\n│   → run_workflow.py, run_batch.py, ws_monitor.py    │\\n└─────────────────────────────────────────────────────┘\\n```\\n\\n**Why two layers?** The official CLI is excellent for installation and server\\nmanagement but has minimal workflow execution support. The REST/WS API fills\\nthat gap — the scripts handle param injection, execution monitoring, and\\noutput download that the CLI doesn't do.\\n\\n## Quick Start\\n\\n### Detect environment\\n\\n```bash\\n# What's available?\\ncommand -v comfy >/dev/null 2>&1 && echo \\\"comfy-cli: installed\\\"\\ncurl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo \\\"server: running\\\"\\n\\n# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)\\npython3 scripts/hardware_check.py\\n```\\n\\nIf nothing is installed, see **Setup & Onboarding** below — but always run the\\nhardware check first.\\n\\n### One-line health check\\n\\n```bash\\npython3 scripts/health_check.py\\n# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?\\n```\\n\\n## Core Workflow\\n\\n### Step 1: Get a workflow JSON in API format\\n\\nWorkflows must be in API format (each node has `class_type`). They come from:\\n\\n- ComfyUI web UI → **Workflow → Export (API)** (newer UI) or\\n  the legacy \\\"Save (API Format)\\\" button (older UI)\\n- This skill's `workflows/` directory (ready-to-run examples)\\n- Community downloads (civitai, Reddit, Discord) — usually editor format,\\n  must be loaded into ComfyUI then re-exported\\n\\nEditor format (top-level `nodes` and `links` arrays) is **not directly\\nexecutable**. The scripts detect this and tell you to re-export.\\n\\n### Step 2: See what's controllable\\n\\n```bash\\npython3 scripts/extract_schema.py workflow_api.json --summary-only\\n# → {\\\"parameter_count\\\": 12, \\\"has_negative_prompt\\\": true, \\\"has_seed\\\": true, ...}\\n\\npython3 scripts/extract_schema.py workflow_api.json\\n# → full schema with parameters, model deps, embedding refs\\n```\\n\\n### Step 3: Run with parameters\\n\\n```bash\\n# Local (defaults to http://127.0.0.1:8188)\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflow_api.json \\\\\\n  --args '{\\\"prompt\\\": \\\"a beautiful sunset over mountains\\\", \\\"seed\\\": -1, \\\"steps\\\": 30}' \\\\\\n  --output-dir ./outputs\\n\\n# Cloud (export API key once; uses correct /api routing automatically)\\nexport COMFY_CLOUD_API_KEY=\\\"comfyui-...\\\"\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflow_api.json \\\\\\n  --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n  --host https://cloud.comfy.org \\\\\\n  --output-dir ./outputs\\n\\n# Real-time progress via WebSocket (requires `pip install websocket-client`)\\npython3 scripts/run_workflow.py \\\\\\n  --workflow flux_dev.json \\\\\\n  --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n  --ws\\n\\n# img2img / inpaint: pass --input-image to upload + reference automatically\\npython3 scripts/run_workflow.py \\\\\\n  --workflow sdxl_img2img.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --args '{\\\"prompt\\\": \\\"make it watercolor\\\", \\\"denoise\\\": 0.6}'\\n\\n# Batch / sweep: 8 random seeds, parallel up to cloud tier limit\\npython3 scripts/run_batch.py \\\\\\n  --workflow sdxl.json \\\\\\n  --args '{\\\"prompt\\\": \\\"abstract\\\"}' \\\\\\n  --count 8 --randomize-seed --parallel 3 \\\\\\n  --output-dir ./outputs/batch\\n```\\n\\n`-1` for `seed` (or omitting it with `--randomize-seed`) generates a fresh\\nrandom seed per run.\\n\\n### Step 4: Present results\\n\\nThe scripts emit JSON to stdout describing every output file:\\n\\n```json\\n{\\n  \\\"status\\\": \\\"success\\\",\\n  \\\"prompt_id\\\": \\\"abc-123\\\",\\n  \\\"outputs\\\": [\\n    {\\\"file\\\": \\\"./outputs/sdxl_00001_.png\\\", \\\"node_id\\\": \\\"9\\\",\\n     \\\"type\\\": \\\"image\\\", \\\"filename\\\": \\\"sdxl_00001_.png\\\"}\\n  ]\\n}\\n```\\n\\n## Decision Tree\\n\\n| User says | Tool | Command |\\n|-----------|------|---------|\\n| **Lifecycle (use comfy-cli)** | | |\\n| \\\"install ComfyUI\\\" | comfy-cli | `bash scripts/comfyui_setup.sh` |\\n| \\\"start ComfyUI\\\" | comfy-cli | `comfy launch --background` |\\n| \\\"stop ComfyUI\\\" | comfy-cli | `comfy stop` |\\n| \\\"install X node\\\" | comfy-cli | `comfy node install <name>` |\\n| \\\"download X model\\\" | comfy-cli | `comfy model download --url <url> --relative-path models/checkpoints` |\\n| \\\"list installed models\\\" | comfy-cli | `comfy model list` |\\n| \\\"list installed nodes\\\" | comfy-cli | `comfy node show installed` |\\n| **Execution (use scripts)** | | |\\n| \\\"is everything ready?\\\" | script | `health_check.py` (optionally with `--workflow X --smoke-test`) |\\n| \\\"what can I change in this workflow?\\\" | script | `extract_schema.py W.json` |\\n| \\\"check if W's deps are met\\\" | script | `check_deps.py W.json` |\\n| \\\"fix missing deps\\\" | script | `auto_fix_deps.py W.json` |\\n| \\\"generate an image\\\" | script | `run_workflow.py --workflow W --args '{...}'` |\\n| \\\"use this image\\\" (img2img) | script | `run_workflow.py --input-image image=./x.png ...` |\\n| \\\"8 variations with random seeds\\\" | script | `run_batch.py --count 8 --randomize-seed ...` |\\n| \\\"show me live progress\\\" | script | `ws_monitor.py --prompt-id <id>` |\\n| \\\"fetch the error from job X\\\" | script | `fetch_logs.py <prompt_id>` |\\n| **Direct REST** | | |\\n| \\\"what's in the queue?\\\" | REST | `curl http://HOST:8188/queue` (local) or `--host https://cloud.comfy.org` |\\n| \\\"cancel that\\\" | REST | `curl -X POST http://HOST:8188/interrupt` |\\n| \\\"free GPU memory\\\" | REST | `curl -X POST http://HOST:8188/free` |\\n\\n## Setup & Onboarding\\n\\nWhen a user asks to set up ComfyUI, **the FIRST thing to do is ask whether\\nthey want Comfy Cloud (hosted, zero install, API key) or Local (install\\nComfyUI on their machine)**. Don't start running install commands or hardware\\nchecks until they've answered.\\n\\n**Official docs:** https://docs.comfy.org/installation\\n**CLI docs:** https://docs.comfy.org/comfy-cli/getting-started\\n**Cloud docs:** https://docs.comfy.org/get_started/cloud\\n**Cloud API:** https://docs.comfy.org/development/cloud/overview\\n\\n### Step 0: Ask Local vs Cloud (ALWAYS FIRST)\\n\\nSuggested script:\\n\\n> \\\"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?\\n>\\n> - **Comfy Cloud** — hosted on RTX 6000 Pro GPUs, all common models pre-installed,\\n>   zero setup. Requires an API key (paid subscription required to actually run\\n>   workflows; free tier is read-only). Best if you don't have a capable GPU.\\n> - **Local** — free, but your machine MUST meet the hardware requirements:\\n>   - NVIDIA GPU with **≥6 GB VRAM** (≥8 GB for SDXL, ≥12 GB for Flux/video), OR\\n>   - AMD GPU with ROCm support (Linux), OR\\n>   - Apple Silicon Mac (M1+) with **≥16 GB unified memory** (≥32 GB recommended).\\n>   - Intel Macs and machines with no GPU will NOT work — use Cloud instead.\\n>\\n> Which would you like?\\\"\\n\\nRouting:\\n\\n- **Cloud** → skip to **Path A**.\\n- **Local** → run hardware check first, then pick a path from Paths B–E based on the verdict.\\n- **Unsure** → run the hardware check and let the verdict decide.\\n\\n### Step 1: Verify Hardware (ONLY if user chose local)\\n\\n```bash\\npython3 scripts/hardware_check.py --json\\n# Optional: also probe `torch` for actual CUDA/MPS:\\npython3 scripts/hardware_check.py --json --check-pytorch\\n```\\n\\n| Verdict    | Meaning                                                       | Action |\\n|------------|---------------------------------------------------------------|--------|\\n| `ok`       | ≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon)       | Local install — use `comfy_cli_flag` from report |\\n| `marginal` | SD1.5 works; SDXL tight; Flux/video unlikely                  | Local OK for light workflows, else **Path A (Cloud)** |\\n| `cloud`    | No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python | **Switch to Cloud** unless user explicitly forces local |\\n\\nThe script also surfaces `wsl: true` (WSL2 with NVIDIA passthrough) and\\n`rosetta: true` (x86_64 Python on Apple Silicon — must reinstall as ARM64).\\n\\nIf verdict is `cloud` but the user wants local, do not proceed silently.\\nShow the `notes` array verbatim and ask whether they want to (a) switch to\\nCloud or (b) force a local install (will OOM or be unusably slow on modern models).\\n\\n### Choosing an Installation Path\\n\\nUse the hardware check first. The table below is the fallback for when the\\nuser has already told you their hardware:\\n\\n| Situation | Recommended Path |\\n|-----------|------------------|\\n| `verdict: cloud` from hardware check | **Path A: Comfy Cloud** |\\n| No GPU / want to try without commitment | **Path A: Comfy Cloud** |\\n| Windows + NVIDIA + non-technical | **Path B: ComfyUI Desktop** |\\n| Windows + NVIDIA + technical | **Path C: Portable** or **Path D: comfy-cli** |\\n| Linux + any GPU | **Path D: comfy-cli** (easiest) |\\n| macOS + Apple Silicon | **Path B: Desktop** or **Path D: comfy-cli** |\\n| Headless / server / CI / agents | **Path D: comfy-cli** |\\n\\nFor the fully automated path (hardware check → install → launch → verify):\\n\\n```bash\\nbash scripts/comfyui_setup.sh\\n# Or with overrides:\\nbash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy\\n```\\n\\nIt runs `hardware_check.py` internally, refuses to install locally when the\\nverdict is `cloud` (unless `--force-cloud-override`), picks the right\\n`comfy-cli` flag, and prefers `pipx`/`uvx` over global `pip` to avoid polluting\\nsystem Python.\\n\\n---\\n\\n### Path A: Comfy Cloud (No Local Install)\\n\\nFor users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.\\n\\n**Docs:** https://docs.comfy.org/get_started/cloud\\n\\n1. Sign up at https://comfy.org/cloud\\n2. Generate an API key at https://platform.comfy.org/login\\n3. Set the key:\\n   ```bash\\n   export COMFY_CLOUD_API_KEY=\\\"comfyui-xxxxxxxxxxxx\\\"\\n   ```\\n4. Run workflows:\\n   ```bash\\n   python3 scripts/run_workflow.py \\\\\\n     --workflow workflows/flux_dev_txt2img.json \\\\\\n     --args '{\\\"prompt\\\": \\\"...\\\"}' \\\\\\n     --host https://cloud.comfy.org \\\\\\n     --output-dir ./outputs\\n   ```\\n\\n**Pricing:** https://www.comfy.org/cloud/pricing\\n**Concurrent jobs:** Free/Standard 1, Creator 3, Pro 5. Free tier\\n**cannot run workflows via API** — only browse models. Paid subscription\\nrequired for `/api/prompt`, `/api/upload/*`, `/api/view`, etc.\\n\\n---\\n\\n### Path B: ComfyUI Desktop (Windows / macOS)\\n\\nOne-click installer for non-technical users. Currently Beta.\\n\\n**Docs:** https://docs.comfy.org/installation/desktop\\n- **Windows (NVIDIA):** https://download.comfy.org/windows/nsis/x64\\n- **macOS (Apple Silicon):** https://comfy.org\\n\\nLinux is **not supported** for Desktop — use Path D.\\n\\n---\\n\\n### Path C: ComfyUI Portable (Windows Only)\\n\\n**Docs:** https://docs.comfy.org/installation/comfyui_portable_windows\\n\\nDownload from https://github.com/comfyanonymous/ComfyUI/releases, extract,\\nrun `run_nvidia_gpu.bat`. Update via `update/update_comfyui_stable.bat`.\\n\\n---\\n\\n### Path D: comfy-cli (All Platforms — Recommended for Agents)\\n\\nThe official CLI is the best path for headless/automated setups.\\n\\n**Docs:** https://docs.comfy.org/comfy-cli/getting-started\\n\\n#### Install comfy-cli\\n\\n```bash\\n# Recommended:\\npipx install comfy-cli\\n# Or use uvx without installing:\\nuvx --from comfy-cli comfy --help\\n# Or (if pipx/uvx unavailable):\\npip install --user comfy-cli\\n```\\n\\nDisable analytics non-interactively:\\n```bash\\ncomfy --skip-prompt tracking disable\\n```\\n\\n#### Install ComfyUI\\n\\n```bash\\ncomfy --skip-prompt install --nvidia              # NVIDIA (CUDA)\\ncomfy --skip-prompt install --amd                 # AMD (ROCm, Linux)\\ncomfy --skip-prompt install --m-series            # Apple Silicon (MPS)\\ncomfy --skip-prompt install --cpu                 # CPU only (slow)\\ncomfy --skip-prompt install --nvidia --fast-deps  # uv-based dep resolution\\n```\\n\\nDefault location: `~/comfy/ComfyUI` (Linux), `~/Documents/comfy/ComfyUI`\\n(macOS/Win). Override with `comfy --workspace /custom/path install`.\\n\\n#### Launch / verify\\n\\n```bash\\ncomfy launch --background                       # background daemon on :8188\\ncomfy launch -- --listen 0.0.0.0 --port 8190    # LAN-accessible custom port\\ncurl -s http://127.0.0.1:8188/system_stats      # health check\\n```\\n\\n---\\n\\n### Path E: Manual Install (Advanced / Unsupported Hardware)\\n\\nFor Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.\\n\\n**Docs:** https://docs.comfy.org/installation/manual_install\\n\\n```bash\\ngit clone https://github.com/comfyanonymous/ComfyUI.git\\ncd ComfyUI\\npip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130\\npip install -r requirements.txt\\npython main.py\\n```\\n\\n---\\n\\n### Post-Install: Download Models\\n\\n```bash\\n# SDXL (general purpose, ~6.5 GB)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# Flux Dev fp8 (smaller variant, ~12 GB)\\ncomfy model download \\\\\\n  --url \\\"https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors\\\" \\\\\\n  --relative-path models/checkpoints\\n\\n# CivitAI (set token first):\\ncomfy model download \\\\\\n  --url \\\"https://civitai.com/api/download/models/128713\\\" \\\\\\n  --relative-path models/checkpoints \\\\\\n  --set-civitai-api-token \\\"YOUR_TOKEN\\\"\\n```\\n\\nList installed: `comfy model list`.\\n\\n### Post-Install: Install Custom Nodes\\n\\n```bash\\ncomfy node install comfyui-impact-pack             # popular utility pack\\ncomfy node install comfyui-animatediff-evolved     # video generation\\ncomfy node install comfyui-controlnet-aux          # ControlNet preprocessors\\ncomfy node install comfyui-essentials              # common helpers\\ncomfy node update all\\ncomfy node install-deps --workflow=workflow.json   # install everything a workflow needs\\n```\\n\\n### Post-Install: Verify\\n\\n```bash\\npython3 scripts/health_check.py\\n# → comfy_cli on PATH? server reachable? checkpoints? smoke test?\\n\\npython3 scripts/check_deps.py my_workflow.json\\n# → are this workflow's nodes/models/embeddings installed?\\n\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sd15_txt2img.json \\\\\\n  --args '{\\\"prompt\\\": \\\"test\\\", \\\"steps\\\": 4}' \\\\\\n  --output-dir ./test-outputs\\n```\\n\\n## Image Upload (img2img / Inpainting)\\n\\nThe simplest way is to use `--input-image` with `run_workflow.py`:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sdxl_img2img.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --args '{\\\"prompt\\\": \\\"make it cyberpunk\\\", \\\"denoise\\\": 0.6}'\\n```\\n\\nThe flag uploads `photo.png`, then injects its server-side filename into\\nwhatever schema parameter is named `image`. For inpainting, pass both:\\n\\n```bash\\npython3 scripts/run_workflow.py \\\\\\n  --workflow workflows/sdxl_inpaint.json \\\\\\n  --input-image image=./photo.png \\\\\\n  --input-image mask_image=./mask.png \\\\\\n  --args '{\\\"prompt\\\": \\\"fill with flowers\\\"}'\\n```\\n\\nManual upload via REST:\\n```bash\\ncurl -X POST \\\"http://127.0.0.1:8188/upload/image\\\" \\\\\\n  -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n# Returns: {\\\"name\\\": \\\"photo.png\\\", \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n# Cloud equivalent:\\ncurl -X POST \\\"https://cloud.comfy.org/api/upload/image\\\" \\\\\\n  -H \\\"X-API-Key: $COMFY_CLOUD_API_KEY\\\" \\\\\\n  -F \\\"image=@photo.png\\\" -F \\\"type=input\\\" -F \\\"overwrite=true\\\"\\n```\\n\\n## Cloud Specifics\\n\\n- **Base URL:** `https://cloud.comfy.org`\\n- **Auth:** `X-API-Key` header (or `?token=KEY` for WebSocket)\\n- **API key:** set `$COMFY_CLOUD_API_KEY` once and the scripts pick it up automatically\\n- **Output download:** `/api/view` returns a 302 to a signed URL; the scripts\\n  follow it and strip `X-API-Key` before fetching from the storage backend\\n  (don't leak the API key to S3/CloudFront).\\n- **Endpoint differences from local ComfyUI:**\\n  - `/api/object_info`, `/api/queue`, `/api/userdata` — **403 on free tier**;\\n    paid only.\\n  - `/history` is renamed to `/history_v2` on cloud (the scripts route\\n    automatically).\\n  - `/models/<folder>` is renamed to `/experiment/models/<folder>` on cloud\\n    (the scripts route automatically).\\n  - `clientId` in WebSocket is currently ignored — all connections for a\\n    user receive the same broadcast. Filter by `prompt_id` client-side.\\n  - `subfolder` is accepted on uploads but ignored — cloud has a flat namespace.\\n- **Concurrent jobs:** Free/Standard: 1, Creator: 3, Pro: 5. Extras queue\\n  automatically. Use `run_batch.py --parallel N` to saturate your tier.\\n\\n## Queue & System Management\\n\\n```bash\\n# Local\\ncurl -s http://127.0.0.1:8188/queue | python3 -m json.tool\\ncurl -X POST http://127.0.0.1:8188/queue -d '{\\\"clear\\\": true}'    # cancel pending\\ncurl -X POST http://127.0.0.1:8188/interrupt                      # cancel running\\ncurl -X POST http://127.0.0.1:8188/free \\\\\\n  -H \\\"Content-Type: application/json\\\" \\\\\\n  -d '{\\\"unload_models\\\": true, \\\"free_memory\\\": true}'\\n\\n# Cloud — same paths under /api/, plus:\\npython3 scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org\\n```\\n\\n## Pitfalls\\n\\n1. **API format required** — every script and the `/api/prompt` endpoint expect\\n   API-format workflow JSON. The scripts detect editor format (top-level\\n   `nodes` and `links` arrays) and tell you to re-export via\\n   \\\"Workflow → Export (API)\\\" (newer UI) or \\\"Save (API Format)\\\" (older UI).\\n\\n2. **Server must be running** — all execution requires a live server.\\n   `comfy launch --background` starts one. Verify with\\n   `curl http://127.0.0.1:8188/system_stats`.\\n\\n3. **Model names are exact** — case-sensitive, includes file extension.\\n   `check_deps.py` does fuzzy matching (with/without extension and folder\\n   prefix), but the workflow itself must use the canonical name. Use\\n   `comfy model list` to discover what's installed. For WanVideoWrapper,\\n   `/object_info` is especially useful: inspect loader dropdowns to verify\\n   files are in `models/diffusion_models`, `models/text_encoders`, `models/vae`,\\n   and `models/clip_vision` before submitting a long video job.\\n\\n4. **Hugging Face CLI drift** — older docs may show `huggingface-cli download`.\\n   If it warns that it is deprecated, use `hf download <repo> <file> --local-dir <dir>`\\n   instead. For gated/throttled repos, run `hf auth login` and retry.\\n\\n5. **Remote GPU over Tailscale** — when the user has a GPU outside the Hermes VM,\\n   prefer running ComfyUI on that host and exposing only the Tailscale IP/port.\\n   Verify with `curl http://TAILSCALE_IP:8188/system_stats`; do not assume the\\n   Hermes VM has CUDA just because the user's host does.\\n\\n6. **Wan first-run crashes can be RAM pressure, not VRAM** — if `/prompt` accepts\\n   a tiny Wan job with `node_errors: {}` but the server becomes `Connection refused`,\\n   ask for ComfyUI logs plus `free -h` and `ps aux --sort=-%mem | head -20` on the\\n   GPU host. Stop competing model servers such as llama.cpp/Ollama and retry with\\n   `--lowvram`, lower resolution, fewer frames/steps, and CPU/offload settings.\\n   See `references/wan-video-a5000-tailscale.md`.\\n\\n7. **Missing custom nodes** — \\\"class_type not found\\\" means a required node\\n   isn't installed. `check_deps.py` reports which package to install;\\n   `auto_fix_deps.py` runs the install for you.\\n\\n5. **Working directory** — `comfy-cli` auto-detects the ComfyUI workspace.\\n   If commands fail with \\\"no workspace found\\\", use\\n   `comfy --workspace /path/to/ComfyUI <command>` or\\n   `comfy set-default /path/to/ComfyUI`.\\n\\n6. **Cloud free-tier API limits** — `/api/prompt`, `/api/view`, `/api/upload/*`,\\n   `/api/object_info` all return 403 on free accounts. `health_check.py` and\\n   `check_deps.py` handle this gracefully and surface a clear message.\\n\\n7. **Timeout for video/audio workflows** — auto-detected when an output node\\n   is `VHS_VideoCombine`, `SaveVideo`, etc.; the default jumps from 300 s to\\n   900 s. Override explicitly with `--timeout 1800`.\\n\\n8. **Path traversal in output filenames** — server-supplied filenames are\\n   passed through `safe_path_join` to refuse anything escaping `--output-dir`.\\n   Keep this protection on — workflows with custom save nodes can produce\\n   arbitrary paths.\\n\\n9. **Workflow JSON is arbitrary code** — custom nodes run Python, so\\n   submitting an unknown workflow has the same trust profile as `eval`.\\n   Inspect workflows from untrusted sources before running.\\n\\n10. **Auto-randomized seed** — pass `seed: -1` in `--args` (or use\\n    `--randomize-seed` and omit the seed) to get a fresh seed per run.\\n    The actual seed is logged to stderr.\\n\\n11. **`tracking` prompt** — first run of `comfy` may prompt for analytics.\\n    Use `comfy --skip-prompt tracking disable` to skip non-interactively.\\n    `comfyui_setup.sh` does this for you.\\n\\n13. **WanVideo on 16 GB GPUs needs a conservative ramp, then GPU-first optimization** — for `ComfyUI-WanVideoWrapper`, do not jump straight to 720p/long clips/14B models. Start with Wan2.1 T2V 1.3B FP8 for smoke tests. Do not judge quality from 2–4 step micro runs. Use `--lowvram` + CPU text encoding only as an emergency crash/OOM diagnostic; it can push T5 encoding to CPU/offload and make jobs slow while `nvidia-smi` appears underused. Preferred A5000 production launch is `python main.py --listen \\\"$TS_IP\\\" --port 8188 --reserve-vram 1.0`, with T5/text encoding on GPU if RAM/VRAM permit. For Wan2.1 real preview use ~416x736/25f/8 steps or 480x832/33f/10 steps. For Wan2.2 TI2V 5B quality tests, prefer the official native ComfyUI Wan2.2 template/nodes over `ComfyUI-WanVideoWrapper`: the native workflow uses `wan2.2_ti2v_5B_fp16.safetensors`, `umt5_xxl_fp8_e4m3fn_scaled.safetensors`, and `wan2.2_vae.safetensors`. The Kijai wrapper `LoadWanVideoT5TextEncoder` rejects the scaled T5 with `fp8 scaled is not supported`; use unscaled T5 only for wrapper workflows. For quality-first Retention Studio clips, prefer keyframe → native Wan2.2 Image-to-Video over raw T2V; use one subject, one action, one camera move, balanced exposure, and no baked text/logos/watermarks. Be blunt in clip review: technical success is not content success. See `references/wan22-native-i2v-prompting.md`, `references/wan-video-a5000-optimization.md`, and `references/wan22-ti2v-a5000.md`.\\n\\n13. **Hugging Face CLI changed** — if `huggingface-cli download` says it is deprecated/no longer works, use `hf download ... --local-dir ...` and `hf auth login` if needed. Prefer documenting this as the fix, not as a tool failure.\\n\\n## Verification Checklist\\n\\nUse `python3 scripts/health_check.py` to run the whole list at once. Manual:\\n\\n- [ ] `hardware_check.py` verdict is `ok` OR the user explicitly chose Comfy Cloud\\n- [ ] `comfy --version` works (or `uvx --from comfy-cli comfy --help`)\\n- [ ] `curl http://HOST:PORT/system_stats` returns JSON\\n- [ ] `comfy model list` shows at least one checkpoint (local) OR\\n      `/api/experiment/models/checkpoints` returns models (cloud)\\n- [ ] Workflow JSON is in API format\\n- [ ] `check_deps.py` reports `is_ready: true` (or only `node_check_skipped`\\n      on cloud free tier)\\n- [ ] Test run with a small workflow completes; outputs land in `--output-dir`\\n\", \"path\": \"creative/comfyui/SKILL.md\", \"skill_dir\": \"/home/agent/.hermes/skills/creative/comfyui\", \"linked_files\": {\"references\": [\"references/wan-video-tailscale-a5000.md\", \"references/official-cli.md\", \"references/template-integrity.md\", \"references/wan22-native-i2v-a5000-quality.md\", \"references/rest-api.md\", \"references/wan-video-a5000-optimization.md\", \"references/wan-video-a5000-tailscale.md\", \"references/wan22-native-i2v-prompting.md\", \"references/workflow-format.md\", \"references/wan22-ti2v-a5000.md\"], \"scripts\": [\"scripts/auto_fix_deps.py\", \"scripts/ws_monitor.py\", \"scripts/run_batch.py\", \"scripts/fetch_logs.py\", \"scripts/check_deps.py\", \"scripts/hardware_check.py\", \"scripts/_common.py\", \"scripts/health_check.py\", \"scripts/run_workflow.py\", \"scripts/extract_schema.py\", \"scripts/comfyui_setup.sh\"]}, \"usage_hint\": \"To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'\", \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"compatibility\": \"Requires ComfyUI (local, Comfy Desktop, or Comfy Cloud) and comfy-cli (auto-installed via pipx/uvx by the setup script).\", \"metadata\": {\"hermes\": {\"tags\": [\"comfyui\", \"image-generation\", \"stable-diffusion\", \"flux\", \"sd3\", \"wan-video\", \"hunyuan-video\", \"creative\", \"generative-ai\", \"video-generation\"], \"related_skills\": [\"stable-diffusion-image-generation\", \"image_gen\"], \"category\": \"creative\"}}}", "tool_call_id": "call_Pjek4YrCvqqffrQEkmnoninz", "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "assistant", "content": "Need write tests. Design modules: autoshorts/i2v_clip_jobs.py build_i2v_clip_jobs(prompt_plan, keyframe_dir/output_root,seeds); native workflow build perhaps. autoshorts/comfyui_client.py minimal client test with fake transport? Test offline no network. Let's implement perhaps build workflow API in i2v_clip_jobs. Need maybe client upload/submit/download. Tests.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need write tests. Design modules: autoshorts/i2v_clip_jobs.py build_i2v_clip_jobs(prompt_plan, keyframe_dir/output_root,seeds); native workflow build perhaps. autoshorts/comfyui_client.py minimal client test with fake transport? Test offline no network. Let's implement perhaps build workflow API in i2v_clip_jobs. Need maybe client upload/submit/download. Tests."}], "id": "msg_0d1a4d10654d56b4016a1204b564a88191b4bf415837a45d59", "phase": "commentary"}], "tool_calls": [{"id": "call_j3McSuReM80YgSKGWiPg1cVA", "call_id": "call_j3McSuReM80YgSKGWiPg1cVA", "response_item_id": "fc_0d1a4d10654d56b4016a1204b7038081919f0ce16d9ec11f73", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_i2v_clip_jobs.py\",\"content\":\"from pathlib import Path\\n\\nfrom autoshorts.i2v_clip_jobs import (\\n    build_i2v_clip_jobs,\\n    build_native_wan22_i2v_workflow,\\n    write_i2v_clip_jobs,\\n)\\nfrom autoshorts.keyframe_prompt import build_i2v_prompt_plan\\nfrom autoshorts.mechanism_pipeline import build_mechanism_shot_plan\\nfrom autoshorts.retention_studio import PILOT_IDEAS, build_mechanism_explainer\\n\\n\\ndef _prompt_plan():\\n    explainer = build_mechanism_explainer(PILOT_IDEAS[0])\\n    shot_plan = build_mechanism_shot_plan(explainer)\\n    return build_i2v_prompt_plan(shot_plan)\\n\\n\\ndef test_build_i2v_clip_jobs_creates_seeded_scene_jobs_with_expected_paths():\\n    jobs = build_i2v_clip_jobs(\\n        _prompt_plan(),\\n        keyframe_root=Path(\\\"data/keyframes\\\"),\\n        output_root=Path(\\\"data/generated_clips\\\"),\\n        seeds=[101, 202],\\n    )\\n\\n    assert len(jobs) == 4\\n    first = jobs[0]\\n    assert first[\\\"job_id\\\"] == \\\"scene_01_seed_101\\\"\\n    assert first[\\\"scene_id\\\"] == \\\"scene_01\\\"\\n    assert first[\\\"seed\\\"] == 101\\n    assert first[\\\"keyframe_path\\\"] == \\\"data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\\\"\\n    assert first[\\\"output_path\\\"] == \\\"data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\\\"\\n    assert first[\\\"status\\\"] == \\\"needs_keyframe\\\"\\n    assert first[\\\"review\\\"][\\\"human_review_required\\\"] is True\\n\\n\\ndef test_native_wan22_workflow_uses_scaled_t5_uploaded_image_and_no_baked_text():\\n    job = build_i2v_clip_jobs(_prompt_plan(), Path(\\\"keyframes\\\"), Path(\\\"clips\\\"), seeds=[303])[0]\\n\\n    workflow = build_native_wan22_i2v_workflow(job, uploaded_image_name=\\\"scene_01.png\\\")\\n\\n    assert workflow[\\\"1\\\"][\\\"class_type\\\"] == \\\"UNETLoader\\\"\\n    assert workflow[\\\"1\\\"][\\\"inputs\\\"][\\\"unet_name\\\"] == \\\"wan2.2_ti2v_5B_fp16.safetensors\\\"\\n    assert workflow[\\\"2\\\"][\\\"inputs\\\"][\\\"clip_name\\\"] == \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\"\\n    assert workflow[\\\"5\\\"][\\\"inputs\\\"][\\\"vae_name\\\"] == \\\"wan2.2_vae.safetensors\\\"\\n    assert workflow[\\\"6\\\"][\\\"inputs\\\"][\\\"image\\\"] == \\\"scene_01.png\\\"\\n    assert workflow[\\\"8\\\"][\\\"inputs\\\"][\\\"length\\\"] == 49\\n    assert workflow[\\\"9\\\"][\\\"inputs\\\"][\\\"seed\\\"] == 303\\n    assert \\\"No text, no logo, no watermark\\\" in workflow[\\\"3\\\"][\\\"inputs\\\"][\\\"text\\\"]\\n    assert \\\"fake UI text\\\" in workflow[\\\"4\\\"][\\\"inputs\\\"][\\\"text\\\"]\\n    assert workflow[\\\"12\\\"][\\\"inputs\\\"][\\\"filename_prefix\\\"].endswith(\\\"scene_01_seed_303\\\")\\n\\n\\ndef test_write_i2v_clip_jobs_outputs_json_package(tmp_path):\\n    path = write_i2v_clip_jobs(\\n        _prompt_plan(),\\n        output_file=tmp_path / \\\"jobs.json\\\",\\n        keyframe_root=Path(\\\"data/keyframes\\\"),\\n        generated_clip_root=Path(\\\"data/generated_clips\\\"),\\n        seeds=[1, 2, 3],\\n    )\\n\\n    text = path.read_text(encoding=\\\"utf-8\\\")\\n    assert \\\"i2v_clip_jobs.v1\\\" in text\\n    assert \\\"scene_01_seed_1\\\" in text\\n    assert \\\"scene_02_seed_3\\\" in text\\n    assert \\\"keyframe_to_wan22_i2v_to_deterministic_renderer\\\" in text\\n\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_Qd5SHF63UK7sfWhm9QzkSORA", "call_id": "call_Qd5SHF63UK7sfWhm9QzkSORA", "response_item_id": "fc_0d1a4d10654d56b4016a1204cda16c8191a1162d1556bdd43a", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"from pathlib import Path\\n\\nfrom autoshorts.comfyui_client import ComfyUIClient\\n\\n\\nclass FakeTransport:\\n    def __init__(self):\\n        self.uploads = []\\n        self.posts = []\\n        self.downloads = []\\n        self.prompt_id = \\\"prompt-123\\\"\\n\\n    def upload_image(self, base_url, path, *, remote_name, overwrite=True):\\n        self.uploads.append((base_url, str(path), remote_name, overwrite))\\n        return {\\\"name\\\": remote_name, \\\"subfolder\\\": \\\"\\\", \\\"type\\\": \\\"input\\\"}\\n\\n    def post_json(self, base_url, endpoint, payload):\\n        self.posts.append((base_url, endpoint, payload))\\n        if endpoint == \\\"/prompt\\\":\\n            return {\\\"prompt_id\\\": self.prompt_id, \\\"node_errors\\\": {}}\\n        return {}\\n\\n    def wait_for_completion(self, base_url, prompt_id, *, poll_seconds, timeout_seconds):\\n        assert prompt_id == self.prompt_id\\n        return {\\n            \\\"status\\\": {\\\"status_str\\\": \\\"success\\\", \\\"completed\\\": True},\\n            \\\"outputs\\\": {\\n                \\\"12\\\": {\\n                    \\\"images\\\": [\\n                        {\\n                            \\\"filename\\\": \\\"clip_00001_.mp4\\\",\\n                            \\\"subfolder\\\": \\\"\\\",\\n                            \\\"type\\\": \\\"output\\\",\\n                        }\\n                    ]\\n                }\\n            },\\n        }\\n\\n    def download_view(self, base_url, output, destination):\\n        self.downloads.append((base_url, output, str(destination)))\\n        destination.parent.mkdir(parents=True, exist_ok=True)\\n        destination.write_bytes(b\\\"mp4\\\")\\n        return destination\\n\\n\\ndef test_comfyui_client_runs_workflow_uploads_keyframe_and_downloads_output(tmp_path):\\n    keyframe = tmp_path / \\\"scene_01.png\\\"\\n    keyframe.write_bytes(b\\\"png\\\")\\n    output = tmp_path / \\\"seed_001.mp4\\\"\\n    transport = FakeTransport()\\n    client = ComfyUIClient(\\\"http://comfy.local:8188\\\", transport=transport)\\n\\n    result = client.run_i2v_workflow(\\n        workflow={\\\"1\\\": {\\\"class_type\\\": \\\"Test\\\", \\\"inputs\\\": {}}},\\n        keyframe_path=keyframe,\\n        remote_image_name=\\\"scene_01.png\\\",\\n        output_path=output,\\n        poll_seconds=0,\\n        timeout_seconds=10,\\n    )\\n\\n    assert result[\\\"status\\\"] == \\\"success\\\"\\n    assert result[\\\"prompt_id\\\"] == \\\"prompt-123\\\"\\n    assert result[\\\"output_path\\\"] == str(output)\\n    assert output.read_bytes() == b\\\"mp4\\\"\\n    assert transport.uploads == [(\\\"http://comfy.local:8188\\\", str(keyframe), \\\"scene_01.png\\\", True)]\\n    assert transport.posts[0][1] == \\\"/prompt\\\"\\n    assert transport.posts[0][2][\\\"client_id\\\"] == \\\"autoshorts-i2v\\\"\\n    assert transport.downloads[0][1][\\\"filename\\\"] == \\\"clip_00001_.mp4\\\"\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_comfyui_client.py\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2550, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}, \"lsp_diagnostics\": \"LSP diagnostics introduced by this edit:\\n<diagnostics file=\\\"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_comfyui_client.py\\\">\\nERROR [3:6] Import \\\"autoshorts.comfyui_client\\\" could not be resolved [reportMissingImports] (Pyright)\\n</diagnostics>\"}", "tool_call_id": "call_Qd5SHF63UK7sfWhm9QzkSORA", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_E9pdQcM5DOT0JnQte7cgNIsl", "call_id": "call_E9pdQcM5DOT0JnQte7cgNIsl", "response_item_id": "fc_0d1a4d10654d56b4016a1204dec1348191ae1c08293dee2d57", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_i2v_clip_jobs.py tests/test_comfyui_client.py -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"==================================== ERRORS ====================================\\n_________________ ERROR collecting tests/test_i2v_clip_jobs.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_i2v_clip_jobs.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_i2v_clip_jobs.py:3: in <module>\\n    from autoshorts.i2v_clip_jobs import (\\nE   ModuleNotFoundError: No module named 'autoshorts.i2v_clip_jobs'\\n________________ ERROR collecting tests/test_comfyui_client.py _________________\\nImportError while importing test module '/home/agent/jarvis_runtime/AutoShortsBot/tests/test_comfyui_client.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_comfyui_client.py:3: in <module>\\n    from autoshorts.comfyui_client import ComfyUIClient\\nE   ModuleNotFoundError: No module named 'autoshorts.comfyui_client'\\n=========================== short test summary info ============================\\nERROR tests/test_i2v_clip_jobs.py\\nERROR tests/test_comfyui_client.py\\n!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!\\n2 errors in 0.12s\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_E9pdQcM5DOT0JnQte7cgNIsl", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_fbxIbnTzGwmp1fUBomILFqZ6", "call_id": "call_fbxIbnTzGwmp1fUBomILFqZ6", "response_item_id": "fc_0d1a4d10654d56b4016a1204e38fcc81919e3c9a0ec13f91b1", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Tests für I2V Clip Job/ComfyUI Client schreiben und RED verifizieren\",\"id\":\"tests-red\",\"status\":\"completed\"},{\"content\":\"ComfyUI Client + I2V Clip Job Builder implementieren\",\"id\":\"impl\",\"status\":\"in_progress\"}]}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Aktuellen Repo-/Commit-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"tests-red\", \"content\": \"Tests für I2V Clip Job/ComfyUI Client schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"impl\", \"content\": \"ComfyUI Client + I2V Clip Job Builder implementieren\", \"status\": \"in_progress\"}, {\"id\": \"cli\", \"content\": \"CLI für I2V Clip Job Packages ergänzen und Artefakt erzeugen\", \"status\": \"pending\"}, {\"id\": \"verify\", \"content\": \"Tests/Status/Commit durchführen und Ergebnis berichten\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 2, \"in_progress\": 1, \"completed\": 2, \"cancelled\": 0}}", "tool_call_id": "call_fbxIbnTzGwmp1fUBomILFqZ6", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_4D7wdOqryiVpqdb0o4wPIfMA", "call_id": "call_4D7wdOqryiVpqdb0o4wPIfMA", "response_item_id": "fc_0d1a4d10654d56b4016a1204e8e7288191b5563d6d6f8e4f7e", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Build executable Wan2.2 I2V clip jobs from prompt plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nfrom pathlib import Path\\nfrom typing import Any\\n\\n\\nNATIVE_WAN22_CLIENT_ID = \\\"autoshorts-i2v\\\"\\n\\n\\ndef _video_slug(prompt_plan: dict[str, Any]) -> str:\\n    return prompt_plan[\\\"video\\\"][\\\"id\\\"]\\n\\n\\ndef _scene_keyframe_path(keyframe_root: Path, video_slug: str, scene_id: str) -> Path:\\n    return keyframe_root / video_slug / f\\\"{scene_id}.png\\\"\\n\\n\\ndef _scene_output_path(output_root: Path, video_slug: str, scene_id: str, seed: int) -> Path:\\n    return output_root / video_slug / scene_id / f\\\"seed_{seed}.mp4\\\"\\n\\n\\ndef build_i2v_clip_jobs(\\n    prompt_plan: dict[str, Any],\\n    keyframe_root: Path,\\n    output_root: Path,\\n    seeds: list[int],\\n) -> list[dict[str, Any]]:\\n    \\\"\\\"\\\"Create seeded clip jobs for each Wan2.2 I2V scene.\\\"\\\"\\\"\\n    video_slug = _video_slug(prompt_plan)\\n    jobs: list[dict[str, Any]] = []\\n    for scene in prompt_plan[\\\"scenes\\\"]:\\n        for seed in seeds:\\n            scene_id = scene[\\\"scene_id\\\"]\\n            keyframe_path = _scene_keyframe_path(keyframe_root, video_slug, scene_id)\\n            output_path = _scene_output_path(output_root, video_slug, scene_id, seed)\\n            jobs.append(\\n                {\\n                    \\\"schema_version\\\": \\\"i2v_clip_job.v1\\\",\\n                    \\\"job_id\\\": f\\\"{scene_id}_seed_{seed}\\\",\\n                    \\\"video_id\\\": video_slug,\\n                    \\\"scene_id\\\": scene_id,\\n                    \\\"purpose\\\": scene[\\\"purpose\\\"],\\n                    \\\"seed\\\": seed,\\n                    \\\"status\\\": \\\"ready\\\" if keyframe_path.exists() else \\\"needs_keyframe\\\",\\n                    \\\"keyframe_path\\\": str(keyframe_path),\\n                    \\\"output_path\\\": str(output_path),\\n                    \\\"remote_image_name\\\": f\\\"{video_slug}_{scene_id}.png\\\",\\n                    \\\"prompt_spec\\\": scene,\\n                    \\\"review\\\": {\\n                        \\\"human_review_required\\\": True,\\n                        \\\"criteria\\\": [\\n                            \\\"subject clear in first 0.5s\\\",\\n                            \\\"motion supports retention beat\\\",\\n                            \\\"no baked text/logos/watermarks\\\",\\n                            \\\"no distracting deformation/flicker\\\",\\n                            \\\"clean space remains for captions\\\",\\n                        ],\\n                    },\\n                }\\n            )\\n    return jobs\\n\\n\\ndef _filename_prefix(job: dict[str, Any]) -> str:\\n    return f\\\"autoshorts_{job['video_id']}_{job['scene_id']}_seed_{job['seed']}\\\"\\n\\n\\ndef build_native_wan22_i2v_workflow(job: dict[str, Any], uploaded_image_name: str) -> dict[str, Any]:\\n    \\\"\\\"\\\"Build an API-format native ComfyUI Wan2.2 I2V workflow for a clip job.\\\"\\\"\\\"\\n    spec = job[\\\"prompt_spec\\\"]\\n    comfy = spec[\\\"comfyui\\\"]\\n    width = int(comfy[\\\"resolution\\\"][\\\"width\\\"])\\n    height = int(comfy[\\\"resolution\\\"][\\\"height\\\"])\\n    return {\\n        \\\"1\\\": {\\n            \\\"class_type\\\": \\\"UNETLoader\\\",\\n            \\\"inputs\\\": {\\\"unet_name\\\": comfy[\\\"model\\\"], \\\"weight_dtype\\\": \\\"default\\\"},\\n        },\\n        \\\"2\\\": {\\n            \\\"class_type\\\": \\\"CLIPLoader\\\",\\n            \\\"inputs\\\": {\\\"clip_name\\\": comfy[\\\"text_encoder\\\"], \\\"type\\\": \\\"wan\\\", \\\"device\\\": \\\"default\\\"},\\n        },\\n        \\\"3\\\": {\\n            \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n            \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"i2v_motion_prompt\\\"]},\\n        },\\n        \\\"4\\\": {\\n            \\\"class_type\\\": \\\"CLIPTextEncode\\\",\\n            \\\"inputs\\\": {\\\"clip\\\": [\\\"2\\\", 0], \\\"text\\\": spec[\\\"negative_prompt\\\"]},\\n        },\\n        \\\"5\\\": {\\\"class_type\\\": \\\"VAELoader\\\", \\\"inputs\\\": {\\\"vae_name\\\": comfy[\\\"vae\\\"]}},\\n        \\\"6\\\": {\\\"class_type\\\": \\\"LoadImage\\\", \\\"inputs\\\": {\\\"image\\\": uploaded_image_name}},\\n        \\\"7\\\": {\\n            \\\"class_type\\\": \\\"ImageScale\\\",\\n            \\\"inputs\\\": {\\n                \\\"image\\\": [\\\"6\\\", 0],\\n                \\\"upscale_method\\\": \\\"lanczos\\\",\\n                \\\"width\\\": width,\\n                \\\"height\\\": height,\\n                \\\"crop\\\": \\\"disabled\\\",\\n            },\\n        },\\n        \\\"8\\\": {\\n            \\\"class_type\\\": \\\"Wan22ImageToVideoLatent\\\",\\n            \\\"inputs\\\": {\\n                \\\"vae\\\": [\\\"5\\\", 0],\\n                \\\"width\\\": width,\\n                \\\"height\\\": height,\\n                \\\"length\\\": int(comfy[\\\"frames\\\"]),\\n                \\\"batch_size\\\": 1,\\n                \\\"start_image\\\": [\\\"7\\\", 0],\\n            },\\n        },\\n        \\\"9\\\": {\\n            \\\"class_type\\\": \\\"KSampler\\\",\\n            \\\"inputs\\\": {\\n                \\\"model\\\": [\\\"1\\\", 0],\\n                \\\"seed\\\": int(job[\\\"seed\\\"]),\\n                \\\"steps\\\": int(comfy[\\\"steps\\\"]),\\n                \\\"cfg\\\": float(comfy[\\\"cfg\\\"]),\\n                \\\"sampler_name\\\": \\\"uni_pc\\\",\\n                \\\"scheduler\\\": \\\"simple\\\",\\n                \\\"positive\\\": [\\\"3\\\", 0],\\n                \\\"negative\\\": [\\\"4\\\", 0],\\n                \\\"latent_image\\\": [\\\"8\\\", 0],\\n                \\\"denoise\\\": 1.0,\\n            },\\n        },\\n        \\\"10\\\": {\\\"class_type\\\": \\\"VAEDecode\\\", \\\"inputs\\\": {\\\"samples\\\": [\\\"9\\\", 0], \\\"vae\\\": [\\\"5\\\", 0]}},\\n        \\\"11\\\": {\\\"class_type\\\": \\\"CreateVideo\\\", \\\"inputs\\\": {\\\"images\\\": [\\\"10\\\", 0], \\\"fps\\\": float(comfy[\\\"fps\\\"])}},\\n        \\\"12\\\": {\\n            \\\"class_type\\\": \\\"SaveVideo\\\",\\n            \\\"inputs\\\": {\\n                \\\"video\\\": [\\\"11\\\", 0],\\n                \\\"filename_prefix\\\": _filename_prefix(job),\\n                \\\"format\\\": \\\"mp4\\\",\\n                \\\"codec\\\": \\\"h264\\\",\\n            },\\n        },\\n    }\\n\\n\\ndef write_i2v_clip_jobs(\\n    prompt_plan: dict[str, Any],\\n    output_file: Path,\\n    keyframe_root: Path,\\n    generated_clip_root: Path,\\n    seeds: list[int],\\n) -> Path:\\n    \\\"\\\"\\\"Write a clip-job package JSON.\\\"\\\"\\\"\\n    output_file.parent.mkdir(parents=True, exist_ok=True)\\n    package = {\\n        \\\"schema_version\\\": \\\"i2v_clip_jobs.v1\\\",\\n        \\\"video\\\": prompt_plan[\\\"video\\\"],\\n        \\\"strategy\\\": prompt_plan[\\\"strategy\\\"],\\n        \\\"keyframe_root\\\": str(keyframe_root),\\n        \\\"generated_clip_root\\\": str(generated_clip_root),\\n        \\\"jobs\\\": build_i2v_clip_jobs(prompt_plan, keyframe_root, generated_clip_root, seeds),\\n    }\\n    output_file.write_text(json.dumps(package, indent=2, ensure_ascii=False) + \\\"\\\\n\\\", encoding=\\\"utf-8\\\")\\n    return output_file\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/i2v_clip_jobs.py\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5945, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_4D7wdOqryiVpqdb0o4wPIfMA", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_rjWayBNIaMtxxwhm2J49DJkE", "call_id": "call_rjWayBNIaMtxxwhm2J49DJkE", "response_item_id": "fc_0d1a4d10654d56b4016a12050cb4d88191934f4a2e4e55d2b1", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"\\\"\\\"\\\"Small ComfyUI REST client used by AutoShortsBot clip jobs.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport json\\nimport time\\nimport urllib.parse\\nimport urllib.request\\nfrom pathlib import Path\\nfrom typing import Any\\n\\n\\nclass UrllibComfyUITransport:\\n    \\\"\\\"\\\"HTTP transport for local/Tailscale ComfyUI instances.\\\"\\\"\\\"\\n\\n    def upload_image(self, base_url: str, path: Path, *, remote_name: str, overwrite: bool = True) -> dict[str, Any]:\\n        boundary = \\\"----AutoShortsBotBoundary\\\"\\n        body: list[bytes] = []\\n\\n        def add(part: str | bytes) -> None:\\n            body.append(part.encode() if isinstance(part, str) else part)\\n\\n        add(\\n            f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"image\\\"; filename=\\\"{remote_name}\\\"\\\\r\\\\n'\\n            \\\"Content-Type: image/png\\\\r\\\\n\\\\r\\\\n\\\"\\n        )\\n        add(path.read_bytes())\\n        add(\\\"\\\\r\\\\n\\\")\\n        add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"type\\\"\\\\r\\\\n\\\\r\\\\ninput\\\\r\\\\n')\\n        add(f'--{boundary}\\\\r\\\\nContent-Disposition: form-data; name=\\\"overwrite\\\"\\\\r\\\\n\\\\r\\\\n{str(overwrite).lower()}\\\\r\\\\n')\\n        add(f\\\"--{boundary}--\\\\r\\\\n\\\")\\n        request = urllib.request.Request(\\n            f\\\"{base_url}/upload/image\\\",\\n            data=b\\\"\\\".join(body),\\n            headers={\\\"Content-Type\\\": f\\\"multipart/form-data; boundary={boundary}\\\"},\\n        )\\n        return json.loads(urllib.request.urlopen(request, timeout=120).read().decode())\\n\\n    def post_json(self, base_url: str, endpoint: str, payload: dict[str, Any]) -> dict[str, Any]:\\n        request = urllib.request.Request(\\n            f\\\"{base_url}{endpoint}\\\",\\n            data=json.dumps(payload).encode(),\\n            headers={\\\"Content-Type\\\": \\\"application/json\\\"},\\n        )\\n        raw = urllib.request.urlopen(request, timeout=120).read().decode()\\n        return json.loads(raw) if raw else {}\\n\\n    def wait_for_completion(\\n        self,\\n        base_url: str,\\n        prompt_id: str,\\n        *,\\n        poll_seconds: float,\\n        timeout_seconds: int,\\n    ) -> dict[str, Any]:\\n        deadline = time.monotonic() + timeout_seconds\\n        while time.monotonic() < deadline:\\n            queue = json.loads(urllib.request.urlopen(f\\\"{base_url}/queue\\\", timeout=30).read().decode())\\n            running = queue.get(\\\"queue_running\\\", [])\\n            pending = queue.get(\\\"queue_pending\\\", [])\\n            if not running and not pending:\\n                history = json.loads(urllib.request.urlopen(f\\\"{base_url}/history/{prompt_id}\\\", timeout=60).read().decode())\\n                return history.get(prompt_id, {})\\n            time.sleep(poll_seconds)\\n        raise TimeoutError(f\\\"ComfyUI prompt did not finish within {timeout_seconds}s: {prompt_id}\\\")\\n\\n    def download_view(self, base_url: str, output: dict[str, Any], destination: Path) -> Path:\\n        params = urllib.parse.urlencode(\\n            {\\n                \\\"filename\\\": output[\\\"filename\\\"],\\n                \\\"subfolder\\\": output.get(\\\"subfolder\\\", \\\"\\\"),\\n                \\\"type\\\": output.get(\\\"type\\\", \\\"output\\\"),\\n            }\\n        )\\n        destination.parent.mkdir(parents=True, exist_ok=True)\\n        destination.write_bytes(urllib.request.urlopen(f\\\"{base_url}/view?{params}\\\", timeout=300).read())\\n        return destination\\n\\n\\nclass ComfyUIClient:\\n    \\\"\\\"\\\"Minimal client for uploading keyframes, running workflows, and downloading MP4 outputs.\\\"\\\"\\\"\\n\\n    def __init__(self, base_url: str, *, transport: Any | None = None):\\n        self.base_url = base_url.rstrip(\\\"/\\\")\\n        self.transport = transport or UrllibComfyUITransport()\\n\\n    def run_i2v_workflow(\\n        self,\\n        *,\\n        workflow: dict[str, Any],\\n        keyframe_path: Path,\\n        remote_image_name: str,\\n        output_path: Path,\\n        poll_seconds: float = 5,\\n        timeout_seconds: int = 900,\\n    ) -> dict[str, Any]:\\n        upload = self.transport.upload_image(self.base_url, keyframe_path, remote_name=remote_image_name, overwrite=True)\\n        prompt_response = self.transport.post_json(\\n            self.base_url,\\n            \\\"/prompt\\\",\\n            {\\\"prompt\\\": workflow, \\\"client_id\\\": \\\"autoshorts-i2v\\\"},\\n        )\\n        prompt_id = prompt_response[\\\"prompt_id\\\"]\\n        history = self.transport.wait_for_completion(\\n            self.base_url,\\n            prompt_id,\\n            poll_seconds=poll_seconds,\\n            timeout_seconds=timeout_seconds,\\n        )\\n        status = history.get(\\\"status\\\", {})\\n        if status.get(\\\"status_str\\\") != \\\"success\\\":\\n            return {\\\"status\\\": \\\"failed\\\", \\\"prompt_id\\\": prompt_id, \\\"history\\\": history, \\\"upload\\\": upload}\\n        output = _first_video_output(history)\\n        self.transport.download_view(self.base_url, output, output_path)\\n        return {\\n            \\\"status\\\": \\\"success\\\",\\n            \\\"prompt_id\\\": prompt_id,\\n            \\\"output_path\\\": str(output_path),\\n            \\\"remote_output\\\": output,\\n            \\\"upload\\\": upload,\\n        }\\n\\n\\ndef _first_video_output(history: dict[str, Any]) -> dict[str, Any]:\\n    for node_output in history.get(\\\"outputs\\\", {}).values():\\n        for item in node_output.get(\\\"images\\\", []):\\n            if str(item.get(\\\"filename\\\", \\\"\\\")).lower().endswith(\\\".mp4\\\"):\\n                return item\\n    raise ValueError(\\\"No MP4 output found in ComfyUI history\\\")\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/comfyui_client.py\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 5168, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_rjWayBNIaMtxxwhm2J49DJkE", "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"....                                                                     [100%]\\n4 passed in 0.06s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_mIeShIN78Jr4nUnnPRqiOAsB", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_o5YS7emd60wzsJEf4lLrgiTe", "call_id": "call_o5YS7emd60wzsJEf4lLrgiTe", "response_item_id": "fc_0d1a4d10654d56b4016a12053072808191a19e5d1cecce44d8", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"ComfyUI Client + I2V Clip Job Builder implementieren\",\"id\":\"impl\",\"status\":\"completed\"},{\"content\":\"CLI für I2V Clip Job Packages ergänzen und Artefakt erzeugen\",\"id\":\"cli\",\"status\":\"in_progress\"}]}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"status\", \"content\": \"Aktuellen Repo-/Commit-Status prüfen\", \"status\": \"completed\"}, {\"id\": \"tests-red\", \"content\": \"Tests für I2V Clip Job/ComfyUI Client schreiben und RED verifizieren\", \"status\": \"completed\"}, {\"id\": \"impl\", \"content\": \"ComfyUI Client + I2V Clip Job Builder implementieren\", \"status\": \"completed\"}, {\"id\": \"cli\", \"content\": \"CLI für I2V Clip Job Packages ergänzen und Artefakt erzeugen\", \"status\": \"in_progress\"}, {\"id\": \"verify\", \"content\": \"Tests/Status/Commit durchführen und Ergebnis berichten\", \"status\": \"pending\"}], \"summary\": {\"total\": 5, \"pending\": 1, \"in_progress\": 1, \"completed\": 3, \"cancelled\": 0}}", "tool_call_id": "call_o5YS7emd60wzsJEf4lLrgiTe", "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_RLRlLiq6hWTIFDNQatMSqcgL", "call_id": "call_RLRlLiq6hWTIFDNQatMSqcgL", "response_item_id": "fc_0d1a4d10654d56b4016a120534c6b88191922fd0170966b1e6", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"#!/usr/bin/env python3\\n\\\"\\\"\\\"Generate executable Wan2.2 I2V clip-job packages from I2V prompt plans.\\\"\\\"\\\"\\n\\nfrom __future__ import annotations\\n\\nimport argparse\\nimport json\\nimport sys\\nfrom pathlib import Path\\n\\nPROJECT_ROOT = Path(__file__).resolve().parents[1]\\nif str(PROJECT_ROOT) not in sys.path:\\n    sys.path.insert(0, str(PROJECT_ROOT))\\n\\nfrom autoshorts.i2v_clip_jobs import write_i2v_clip_jobs\\n\\n\\nDEFAULT_PROMPT_PLAN = Path(\\n    \\\"data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\\"\\n)\\n\\n\\ndef _parse_seeds(value: str) -> list[int]:\\n    seeds = [int(part.strip()) for part in value.split(\\\",\\\") if part.strip()]\\n    if not seeds:\\n        raise argparse.ArgumentTypeError(\\\"Provide at least one seed\\\")\\n    return seeds\\n\\n\\ndef main() -> int:\\n    parser = argparse.ArgumentParser(description=__doc__)\\n    parser.add_argument(\\\"--prompt-plan\\\", type=Path, default=DEFAULT_PROMPT_PLAN)\\n    parser.add_argument(\\\"--output-file\\\", type=Path, default=Path(\\\"data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\\\"))\\n    parser.add_argument(\\\"--keyframe-root\\\", type=Path, default=Path(\\\"data/keyframes\\\"))\\n    parser.add_argument(\\\"--generated-clip-root\\\", type=Path, default=Path(\\\"data/generated_clips\\\"))\\n    parser.add_argument(\\\"--seeds\\\", type=_parse_seeds, default=[101, 202, 303])\\n    args = parser.parse_args()\\n\\n    prompt_plan = json.loads(args.prompt_plan.read_text(encoding=\\\"utf-8\\\"))\\n    path = write_i2v_clip_jobs(\\n        prompt_plan,\\n        output_file=args.output_file,\\n        keyframe_root=args.keyframe_root,\\n        generated_clip_root=args.generated_clip_root,\\n        seeds=args.seeds,\\n    )\\n    print(path)\\n    return 0\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    raise SystemExit(main())\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_i2v_clip_jobs.py\"}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_A3UeiWwlGPJbiuxcfsf6qinl", "call_id": "call_A3UeiWwlGPJbiuxcfsf6qinl", "response_item_id": "fc_0d1a4d10654d56b4016a12054ef2a0819191289bbb6bb27833", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\",\"offset\":1,\"limit\":80}"}}], "timestamp": "2026-05-23T21:52:53.177857"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|{\\n     2|  \\\"schema_version\\\": \\\"i2v_clip_jobs.v1\\\",\\n     3|  \\\"video\\\": {\\n     4|    \\\"id\\\": \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\",\\n     5|    \\\"title\\\": \\\"Warum ein billiges Auto teuer sein kann\\\",\\n     6|    \\\"series\\\": \\\"Unsichtbare Systeme in 30 Sekunden\\\",\\n     7|    \\\"pillar\\\": \\\"hidden_money_systems\\\"\\n     8|  },\\n     9|  \\\"strategy\\\": {\\n    10|    \\\"production_mode\\\": \\\"keyframe_to_wan22_i2v_to_deterministic_renderer\\\",\\n    11|    \\\"ai_video_role\\\": \\\"photorealistic texture and motion only\\\",\\n    12|    \\\"renderer_role\\\": \\\"captions, labels, numbers, arrows, mechanism layers and audit-safe truth\\\",\\n    13|    \\\"static_card_limit\\\": 0\\n    14|  },\\n    15|  \\\"keyframe_root\\\": \\\"data/keyframes\\\",\\n    16|  \\\"generated_clip_root\\\": \\\"data/generated_clips\\\",\\n    17|  \\\"jobs\\\": [\\n    18|    {\\n    19|      \\\"schema_version\\\": \\\"i2v_clip_job.v1\\\",\\n    20|      \\\"job_id\\\": \\\"scene_01_seed_101\\\",\\n    21|      \\\"video_id\\\": \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\",\\n    22|      \\\"scene_id\\\": \\\"scene_01\\\",\\n    23|      \\\"purpose\\\": \\\"pattern_interrupt\\\",\\n    24|      \\\"seed\\\": 101,\\n    25|      \\\"status\\\": \\\"needs_keyframe\\\",\\n    26|      \\\"keyframe_path\\\": \\\"data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\\\",\\n    27|      \\\"output_path\\\": \\\"data/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\\\",\\n    28|      \\\"remote_image_name\\\": \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann_scene_01.png\\\",\\n    29|      \\\"prompt_spec\\\": {\\n    30|        \\\"scene_id\\\": \\\"scene_01\\\",\\n    31|        \\\"source_scene\\\": 1,\\n    32|        \\\"purpose\\\": \\\"pattern_interrupt\\\",\\n    33|        \\\"asset_type\\\": \\\"cinematic_ai_video\\\",\\n    34|        \\\"style_profile\\\": \\\"photorealistic_macro_investigation\\\",\\n    35|        \\\"wan_preset\\\": \\\"wan22_i2v_quality_416x736\\\",\\n    36|        \\\"keyframe_prompt\\\": \\\"Photorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.\\\",\\n    37|        \\\"i2v_motion_prompt\\\": \\\"Photorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure, high quality automotive inspection footage. Preserve the same car and garage. No text, no logo, no watermark.\\\",\\n    38|        \\\"negative_prompt\\\": \\\"cartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text, unreadable license plate, fake license plate text, brand logos, distorted wheels, melted car body, broken windshield geometry, fake numbers, fake labels\\\",\\n    39|        \\\"motion_directive\\\": \\\"slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis\\\",\\n    40|        \\\"source_visual_direction\\\": \\\"3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\\\",\\n    41|        \\\"retention_goal\\\": \\\"Open a visual loop immediately; make the viewer need the hidden explanation.\\\",\\n    42|        \\\"clip_requirements\\\": {\\n    43|          \\\"subject_identifiable_first_half_second\\\": true,\\n    44|          \\\"no_baked_text\\\": true,\\n    45|          \\\"caption_safe_space\\\": \\\"upper third and lower third\\\",\\n    46|          \\\"preferred_duration_seconds\\\": 2,\\n    47|          \\\"review_required\\\": true\\n    48|        },\\n    49|        \\\"comfyui\\\": {\\n    50|          \\\"workflow_family\\\": \\\"native_wan22_ti2v_i2v\\\",\\n    51|          \\\"model\\\": \\\"wan2.2_ti2v_5B_fp16.safetensors\\\",\\n    52|          \\\"text_encoder\\\": \\\"umt5_xxl_fp8_e4m3fn_scaled.safetensors\\\",\\n    53|          \\\"vae\\\": \\\"wan2.2_vae.safetensors\\\",\\n    54|          \\\"resolution\\\": {\\n    55|            \\\"width\\\": 416,\\n    56|            \\\"height\\\": 736\\n    57|          },\\n    58|          \\\"frames\\\": 49,\\n    59|          \\\"fps\\\": 10,\\n    60|          \\\"steps\\\": 16,\\n    61|          \\\"cfg\\\": 5.0\\n    62|        }\\n    63|      },\\n    64|      \\\"review\\\": {\\n    65|        \\\"human_review_required\\\": true,\\n    66|        \\\"criteria\\\": [\\n    67|          \\\"subject clear in first 0.5s\\\",\\n    68|          \\\"motion supports retention beat\\\",\\n    69|          \\\"no baked text/logos/watermarks\\\",\\n    70|          \\\"no distracting deformation/flicker\\\",\\n    71|          \\\"clean space remains for captions\\\"\\n    72|        ]\\n    73|      }\\n    74|    },\\n    75|    {\\n    76|      \\\"schema_version\\\": \\\"i2v_clip_job.v1\\\",\\n    77|      \\\"job_id\\\": \\\"scene_01_seed_202\\\",\\n    78|      \\\"video_id\\\": \\\"hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\\",\\n    79|      \\\"scene_id\\\": \\\"scene_01\\\",\\n    80|      \\\"purpose\\\": \\\"pattern_interrupt\\\",\\n    81|\", \"total_lines\": 361, \"file_size\": 22984, \"truncated\": true, \"hint\": \"Use offset=81 to continue reading (showing 1-80 of 361 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_A3UeiWwlGPJbiuxcfsf6qinl", "timestamp": "2026-05-23T21:52:53.177857"}
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{"role": "assistant", "content": "Weitergebaut, Sir.\n\n## Neu: I2V Clip Job Packaging\n\nIch habe jetzt die nächste Schicht der Produktionspipeline implementiert:\n\n```text\nI2V Prompt Plan\n→ seeded Clip Jobs\n→ native Wan2.2 Workflow JSON\n→ ComfyUI Client\n→ output path contract\n```\n\nDamit ist der Schritt vorbereitet, aus einem genehmigten Keyframe automatisch mehrere Wan2.2-I2V-Varianten pro Szene zu erzeugen.\n\n---\n\n## Neu implementiert\n\n### 1. ComfyUI Client\n\n```text\nautoshorts/comfyui_client.py\n```\n\nKann jetzt:\n\n- Keyframe zu ComfyUI hochladen\n- Workflow an `/prompt` senden\n- Queue/History pollen\n- MP4 via `/view` downloaden\n- Ergebnis strukturiert zurückgeben\n\nWichtig: Der Client ist testbar gebaut — mit austauschbarem Transport. Keine Netzwerkanfragen in Unit Tests. Man glaubt es kaum: Software, die nicht nur auf meinem Sofa funktioniert.\n\n---\n\n### 2. I2V Clip Job Builder\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt aus dem I2V Prompt Plan konkrete Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nJeder Job enthält:\n\n- Szene\n- Seed\n- Keyframe-Pfad\n- Output-Pfad\n- Remote Image Name\n- Prompt Spec\n- Review-Kriterien\n- native Wan2.2 Workflow-Konfiguration\n\n---\n\n### 3. Native Wan2.2 Workflow Builder\n\nEbenfalls in:\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt API-format ComfyUI Workflow mit:\n\n```text\nUNETLoader\nCLIPLoader\nCLIPTextEncode positive/negative\nVAELoader\nLoadImage\nImageScale\nWan22ImageToVideoLatent\nKSampler\nVAEDecode\nCreateVideo\nSaveVideo\n```\n\nVerwendet korrekt:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nsampler: uni_pc\nscheduler: simple\nframes: 49\nfps: 10\nsteps: 16\ncfg: 5.0\n```\n\n---\n\n### 4. CLI\n\n```text\nscripts/generate_i2v_clip_jobs.py\n```\n\nAusgeführt:\n\n```bash\npython scripts/generate_i2v_clip_jobs.py\n```\n\nErzeugt:\n\n```text\ndata/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n## Generiertes Job Package\n\nFür den Auto-Pilot gibt es jetzt 6 Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nBeispiel Output-Pfad:\n\n```text\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\n```\n\nAktueller Status im Job Package:\n\n```text\nneeds_keyframe\n```\n\nDas ist korrekt, weil wir noch kein finales Keyframe hier abgelegt haben:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\n---\n\n## Tests\n\nNeu:\n\n```text\ntests/test_comfyui_client.py\ntests/test_i2v_clip_jobs.py\n```\n\nFull suite:\n\n```text\n68 passed in 3.59s\n```\n\n---\n\n## Commits\n\nNeuer lokaler Commit:\n\n```text\nb57cee8 feat: add i2v clip job packaging\n```\n\nVorheriger lokaler Commit:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nPush habe ich nicht erneut versucht, weil der GitHub-Token zuletzt keine Schreibrechte hatte:\n\n```text\n403 Write access to repository not granted\n```\n\n---\n\n## Weiterhin bewusst nicht angefasst\n\nUntracked bleiben:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nUnd die ComfyUI-Testworkflow-JSONs aus unseren Experimenten:\n\n```text\ndata/comfyui_*.json\n```\n\nDie sind Arbeits-/Testartefakte, nicht Kernpipeline.\n\n---\n\n# Nächster Schritt\n\nJetzt fehlt der produktive **Keyframe-Schritt**.\n\nWir brauchen pro I2V-Szene ein Startbild:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\nFür Szene 1:\n\n```text\nred used compact car in bright European inspection garage\nfront three-quarter view\nclean empty license plate area\nno text/logos\ncaption space\n```\n\nDann kann die Pipeline automatisch:\n\n```text\nKeyframe upload\n→ Wan2.2 I2V Job seed 101/202/303\n→ MP4 download\n→ Review package\n```\n\n## Meine Empfehlung\n\nAls nächstes baue ich:\n\n```text\nautoshorts/keyframe_package.py\nscripts/prepare_keyframe_requests.py\n```\n\nZiel:\n\n- Prompt-Paket für ChatGPT Pro / Image Generator erzeugen\n- klare Anforderungen je Szene\n- Bilddatei-Zielpfad\n- Checkliste für Keyframe-Qualität\n- danach automatische Job-Status-Aktualisierung von `needs_keyframe` auf `ready`\n\nKurz: **Wir haben jetzt Jobs. Als Nächstes brauchen wir saubere Keyframes, dann können wir echte Clip-Varianten erzeugen.**", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Weitergebaut, Sir.\n\n## Neu: I2V Clip Job Packaging\n\nIch habe jetzt die nächste Schicht der Produktionspipeline implementiert:\n\n```text\nI2V Prompt Plan\n→ seeded Clip Jobs\n→ native Wan2.2 Workflow JSON\n→ ComfyUI Client\n→ output path contract\n```\n\nDamit ist der Schritt vorbereitet, aus einem genehmigten Keyframe automatisch mehrere Wan2.2-I2V-Varianten pro Szene zu erzeugen.\n\n---\n\n## Neu implementiert\n\n### 1. ComfyUI Client\n\n```text\nautoshorts/comfyui_client.py\n```\n\nKann jetzt:\n\n- Keyframe zu ComfyUI hochladen\n- Workflow an `/prompt` senden\n- Queue/History pollen\n- MP4 via `/view` downloaden\n- Ergebnis strukturiert zurückgeben\n\nWichtig: Der Client ist testbar gebaut — mit austauschbarem Transport. Keine Netzwerkanfragen in Unit Tests. Man glaubt es kaum: Software, die nicht nur auf meinem Sofa funktioniert.\n\n---\n\n### 2. I2V Clip Job Builder\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt aus dem I2V Prompt Plan konkrete Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nJeder Job enthält:\n\n- Szene\n- Seed\n- Keyframe-Pfad\n- Output-Pfad\n- Remote Image Name\n- Prompt Spec\n- Review-Kriterien\n- native Wan2.2 Workflow-Konfiguration\n\n---\n\n### 3. Native Wan2.2 Workflow Builder\n\nEbenfalls in:\n\n```text\nautoshorts/i2v_clip_jobs.py\n```\n\nErzeugt API-format ComfyUI Workflow mit:\n\n```text\nUNETLoader\nCLIPLoader\nCLIPTextEncode positive/negative\nVAELoader\nLoadImage\nImageScale\nWan22ImageToVideoLatent\nKSampler\nVAEDecode\nCreateVideo\nSaveVideo\n```\n\nVerwendet korrekt:\n\n```text\nmodel: wan2.2_ti2v_5B_fp16.safetensors\nT5: umt5_xxl_fp8_e4m3fn_scaled.safetensors\nVAE: wan2.2_vae.safetensors\nsampler: uni_pc\nscheduler: simple\nframes: 49\nfps: 10\nsteps: 16\ncfg: 5.0\n```\n\n---\n\n### 4. CLI\n\n```text\nscripts/generate_i2v_clip_jobs.py\n```\n\nAusgeführt:\n\n```bash\npython scripts/generate_i2v_clip_jobs.py\n```\n\nErzeugt:\n\n```text\ndata/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n---\n\n## Generiertes Job Package\n\nFür den Auto-Pilot gibt es jetzt 6 Jobs:\n\n```text\nscene_01_seed_101\nscene_01_seed_202\nscene_01_seed_303\nscene_02_seed_101\nscene_02_seed_202\nscene_02_seed_303\n```\n\nBeispiel Output-Pfad:\n\n```text\ndata/generated_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01/seed_101.mp4\n```\n\nAktueller Status im Job Package:\n\n```text\nneeds_keyframe\n```\n\nDas ist korrekt, weil wir noch kein finales Keyframe hier abgelegt haben:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\n---\n\n## Tests\n\nNeu:\n\n```text\ntests/test_comfyui_client.py\ntests/test_i2v_clip_jobs.py\n```\n\nFull suite:\n\n```text\n68 passed in 3.59s\n```\n\n---\n\n## Commits\n\nNeuer lokaler Commit:\n\n```text\nb57cee8 feat: add i2v clip job packaging\n```\n\nVorheriger lokaler Commit:\n\n```text\nb3ef793 feat: add wan i2v prompt planning\n```\n\nPush habe ich nicht erneut versucht, weil der GitHub-Token zuletzt keine Schreibrechte hatte:\n\n```text\n403 Write access to repository not granted\n```\n\n---\n\n## Weiterhin bewusst nicht angefasst\n\nUntracked bleiben:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\nUnd die ComfyUI-Testworkflow-JSONs aus unseren Experimenten:\n\n```text\ndata/comfyui_*.json\n```\n\nDie sind Arbeits-/Testartefakte, nicht Kernpipeline.\n\n---\n\n# Nächster Schritt\n\nJetzt fehlt der produktive **Keyframe-Schritt**.\n\nWir brauchen pro I2V-Szene ein Startbild:\n\n```text\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png\ndata/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png\n```\n\nFür Szene 1:\n\n```text\nred used compact car in bright European inspection garage\nfront three-quarter view\nclean empty license plate area\nno text/logos\ncaption space\n```\n\nDann kann die Pipeline automatisch:\n\n```text\nKeyframe upload\n→ Wan2.2 I2V Job seed 101/202/303\n→ MP4 download\n→ Review package\n```\n\n## Meine Empfehlung\n\nAls nächstes baue ich:\n\n```text\nautoshorts/keyframe_package.py\nscripts/prepare_keyframe_requests.py\n```\n\nZiel:\n\n- Prompt-Paket für ChatGPT Pro / Image Generator erzeugen\n- klare Anforderungen je Szene\n- Bilddatei-Zielpfad\n- Checkliste für Keyframe-Qualität\n- danach automatische Job-Status-Aktualisierung von `needs_keyframe` auf `ready`\n\nKurz: **Wir haben jetzt Jobs. Als Nächstes brauchen wir saubere Keyframes, dann können wir echte Clip-Varianten erzeugen.**"}], "id": "msg_0d1a4d10654d56b4016a12057ad0bc8191b591a0aa7ed8ba51", "phase": "final_answer"}], "timestamp": "2026-05-23T21:52:53.177857"}