{"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": "2026-05-23T15:59:27.410671"}
{"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": "2026-05-23T15:59:27.410671"}
{"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": "2026-05-23T15:59:27.410671"}
{"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. 77 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 ---\n\n", "tool_calls": [{"id": "call_33WaXUX9J1QyJKj0NyCqN4fy", "call_id": "call_33WaXUX9J1QyJKj0NyCqN4fy", "response_item_id": "fc_0fc4be2aa452dede016a118705d5348191b060516611045231", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_local_motion_renderer.py -q\",\"timeout\":180,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_Il4P6jg3BXtTHz3wwFFM9r4w", "call_id": "call_Il4P6jg3BXtTHz3wwFFM9r4w", "response_item_id": "fc_0979d5e0850e1994016a118732ff748191a74924caccdb1bcd", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":300,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Inspecting display issues**\n\nI think there's an issue with the mechanism_pipeline where the on-screen text might be clipped. I’ll need to figure out how to fix it. Maybe using voiceover could be a good alternative? It's important to address this so that everything displays properly. I'll start by inspecting the details and see what adjustments I can make to resolve the problem. Let's get this sorted out!", "reasoning_content": "**Inspecting display issues**\n\nI think there's an issue with the mechanism_pipeline where the on-screen text might be clipped. I’ll need to figure out how to fix it. Maybe using voiceover could be a good alternative? It's important to address this so that everything displays properly. I'll start by inspecting the details and see what adjustments I can make to resolve the problem. Let's get this sorted out!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYczlptApOffzeG7x8ZsILRXLKL6pyzfYOeW-hc5gRwM0-JixEkeTwED04ybQ6yeqbJY16D5ElDY0KYQKYI4sYKbLA7ZJ4YmPRbEwnk8AZEKWvvBXnvP9XaZ5MsdnKxcCuqfxjNFJg_Z-hqLB9-eXRNy5IlW0MDvIhgBj4CwCYryFqABEFqfx4RA_QOr6pZw1NIY6LDm8i0Uje40BcUh8WauRdYYADBO5rmS9rppi5TAGqiTCSw0_t0eqy_NJstwFj49kVwFiQhgj1WfbjgNyXurTq7uhQTCUjRdEVCXAjTx_v8zNqnjYy3zRbpVOVsPMlklqFXQh_7ukJvf_9zzA5dRHuR9PPihIOe-b14V98mlW8UwEu0v6vXb5JFZS4QgVHAKLLUlYlfPGXfq7lhD--b4SUDDhjg9FqcHPmqSz9TCvHels3V7SbI6QBy3lZLtFNKOS0fQ75Se7T6B4r5VpPpGLFMQUiW9oZ8F0q81oItMQdOtKtRVb7R4PmxUTLgEsQBott5H_byixalNcFcRWIHEaIJthNm4_4t5viARLXQortHBW-0VjI1bNtjBx-9P4T-lr3znNSSA-24bNofZIhWMe5lqdzqRFuTBEO7AGtkDo05l1ovy0yxRjNcLopfGhPnAGtB8K-AfyVcOarz5OMicIGb-3zvl1Nx-j_fwQBK6rl7lkhNZImxWymsMgTj8Q2tUUJpXjJzsT_X3c_tpPcH4c3p3HoMep8C_J9Fpsa_4N7fzEtsmHqg3YTsTU-7HYNooT9QlG0fexUkGlmgx0O4XyvlEegZjacniMRU020ZOQ3WEnYQlN8RNEd-AGeS37IKpCPz_CNlaCAcgYbs1_06D2XXe7oaE78FjHBP0y0o5lUe1oYLgt7J724_Pg7kzRTHlz3AxbCn1OaRhA7YGF7xouhewhLP3l6Ceio0VKC3X2gn4pt7Xue1K9XDxvE4LYZWHvLwrWX8w_i7lUqsvjOii05ankQof1SsvblLnmnIBeT1y4rsBdJ5zEoT8WBPd9qpoyrRIxPv0BeLgwc5Nqg4PKEZIKuLTaonOvp5jhP4W-8s=", "id": "rs_0979d5e0850e1994016a11873197a481918d5bb9e5da5e9105", "summary": [{"type": "summary_text", "text": "**Inspecting display issues**\n\nI think there's an issue with the mechanism_pipeline where the on-screen text might be clipped. I’ll need to figure out how to fix it. Maybe using voiceover could be a good alternative? It's important to address this so that everything displays properly. I'll start by inspecting the details and see what adjustments I can make to resolve the problem. Let's get this sorted out!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py from line 1 (3,226 chars)", "tool_call_id": "call_86qZuMd3nFXCG9wOfAzAyQOm", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_GhFAkX57sVEHOfhZJaYvWiIB", "call_id": "call_GhFAkX57sVEHOfhZJaYvWiIB", "response_item_id": "fc_00ecf5534a2032ac016a11896c99888191b70a39129fa81fab", "type": "function", "function": {"name": "patch", "arguments": "{\"new_string\": \"def test_mechanism_scene_overlay_truncation_marks_open_sentence():\\n    plan = build_mechanism_shot_plan(_auto_explainer())\\n    overlay = plan[\\\"scenes\\\"][2][\\\"on_screen_text\\\"]\\n\\n    assert overlay.endswit...[truncated]\", \"old_string\": \"def test_write_mechanism_shot_plans_outputs_markdown(tmp_path):\\n\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py (810 chars result)", "tool_call_id": "call_GhFAkX57sVEHOfhZJaYvWiIB", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[terminal] ran `pytest tests/test_mechanism_pipeline.py::test_mechanism_scene_overlay_truncat...` -> exit 1, 1 lines output", "tool_call_id": "call_I265trRh0GnliXZaFI23foLZ", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_udM2mxAp2KW0WWlUMZP3Sj2A", "call_id": "call_udM2mxAp2KW0WWlUMZP3Sj2A", "response_item_id": "fc_0fb308f2a387872e016a118978b5f481918841d7a77aed8a4a", "type": "function", "function": {"name": "patch", "arguments": "{\"new_string\": \"def _overlay_text(voiceover: str, limit: int = 72) -> str:\\n    cleaned = \\\" \\\".join(voiceover.split())\\n    if len(cleaned) <= limit:\\n        return cleaned\\n    truncated = cleaned[: limit - 1].rsplit(\\\" ...[truncated]\", \"old_string\": \"def _overlay_text(voiceover: str, limit: int = 72) -> str:\\n    cleaned = \\\" \\\".join(voiceover.split())\\n    if len(cleaned) <= limit:\\n        return cleaned\\n    return cleaned[:limit].rsplit(\\\" \\\", 1)[0].r...[truncated]\", \"path\": \"/home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYl45MLnMvEH5x4GewV0sOkaLSVtLaeUhD1_4r4C65am7j0sxEhcm6yySRf6tr512WlrYISZlycZK89y714QhLuKIjYmNJgzvsAy7h7viWu2EBb7HrJUJ46NQ0-_9PzrLr1vociA1_7gV5grTX8Q91itXIJ0uxcE8psgV_1fUALNZLDKCDpwwH-utDGti9Om4Xk_veJrGcaiupee23ynZ7OTKfCynvzs9ew0XWRJTVnm1jHWqZ8K-rjUZNIxvDVRXJThxsSyldMZykLdfdj91lVqBF76ZPxER_bAOWURTyGYJwAYmI1Cj6mqGyfAD5uudqZmmEVNybQ3lXzX77bt4apRNM5uGJ9WT4q_4PMMfvR8BrxNBm-6S_jOdM1MqIyorXVZwTsSrSrDHwc5p7iLXAYoDT6q9k7eBmdpWrfa6oC0NkcmhdcsFQKcJB9rRl2iyFD-3pC78gFyAjuupNRVaxbXoFKzHCPqE5ocPL2FpkfbOHq_DtyGiWMXis2srauOqkolM5j0z4FeiDbe0CcHulg-yPlV4SshKMmkWlMC9-1LB_lUojAmJdOk-PwWsMY_diPw1UZbe5zoOz64pR47YARo0HZcPnpLQiYkIO2OmxpNShLgFQZTK7MBOTo3X8k5h_sSoKu5eNM9de7g2-haGfhqgrEWYYsRyERsB7OHqYJba8J3HRRam3veGSAcSziGWSFQJdc0tqwDi4EHim3Ly_fyg8q1_CHJrzlN0z9TERR9v9jh8a8dlsS1uawzAqeCieP-2_x6Sm3ZVHAQ8p0ShRWTxFaO_Ap6qUBXV66n_1GPdOesTvcCMQGFzLBAqxzJPcUkem0_VZGc10d7dIGPoYEolmpEltWKABrteJVIKWTWqz1kmx_h8HNfjfFanDZh8LTMQj4DFZTQGSfJzPw6oKdV9vwR9y4MOBXLlFA62VJ1w14=", "id": "rs_0fb308f2a387872e016a11897853b88191972e40d0aa319ec5", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py (924 chars result)", "tool_call_id": "call_udM2mxAp2KW0WWlUMZP3Sj2A", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_fudqgMtSV3vovyHag2f1DuCm", "call_id": "call_fudqgMtSV3vovyHag2f1DuCm", "response_item_id": "fc_06e352364b1a45b0016a1189826aec81918229be18b2dc75fc", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"pytest tests/test_mechanism_pipeline.py::test_mechanism_scene_overlay_truncation_marks_open_sentence -q\",\"timeout\":120,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \".                                                                        [100%]\\n1 passed in 0.03s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_fudqgMtSV3vovyHag2f1DuCm", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_NEOJXuBg1R29ksnxZNCAZ5yr", "call_id": "call_NEOJXuBg1R29ksnxZNCAZ5yr", "response_item_id": "fc_0a92726500efb3ce016a118988e6a88191b14b913f00ce3a68", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":180,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_render_manifests.py\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Assessing artifact regeneration**\n\nI’m considering whether I need to regenerate all downstream artifacts from the shot plan. There’s a mention of scripts generating mechanisms that render from retention. I think I have to run a specific sequence to generate the mechanism shot plans, possibly just in markdown format. Then, I need to render manifests from the script and inspect the arguments involved. It feels like a bit of a process, but it sounds necessary!", "reasoning_content": "**Assessing artifact regeneration**\n\nI’m considering whether I need to regenerate all downstream artifacts from the shot plan. There’s a mention of scripts generating mechanisms that render from retention. I think I have to run a specific sequence to generate the mechanism shot plans, possibly just in markdown format. Then, I need to render manifests from the script and inspect the arguments involved. It feels like a bit of a process, but it sounds necessary!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYmIkhG8ICrvo7A1Fj4I671TkfUX6oJ4ZOaB2Xsfr-yU5vHFX-72-IebKfwPoJEFyawXdiY_EiSIVDQSzb02l4iAD51pF8JkON2_m-rogQFzpkJjrR5rhI_zWQrfwpMZ4QTy3loeOcFFZ3i4afEueIQsgE-raxZNAJASV-xJWAft2jke6xBddhtJ6rWlX7v3ceSZq6Uqrnexf1p0J4j0uRTG7TQdscYFcK9K56zWrgo72j9FMY2Ki9keDsXMTfGZwsleeSBpKyhewCp3HfeP1ncEyRyypc9zAxRUR9vYlDR7hRtEYevDorVszS5aTigMpHjaNOLIEYq8WEiU7XRlLq5NDxTBQLtEkt14GB1BO2TJ-N0ez-ObwMbwWY7s2kTOkP6_ksgku7mywm7M_wtfKv8Nwyf_9L7MNzISFDBsT1jCpUAoKTZHrGoH8stgoGzQR_v-hO_1DPKizG-oBlJJkKS3tNim6p0eyjBRThICNfBfMaVLL3M7XIsz2LNtCvhMU629VK12rf5Rh7YISDrU4AwZEg0aT87kGbHIL18fHSfLFAZ9kYbX4MZfKqoY6MG8OTcDwR0qA24400xxbIededsGnNcYYe_loDemkrht55nppoxBTsdU6HV_Hhj2BZ1ZnEck61TqwS96O254shy_mDCzepkq2iYw_68bIYEamjZmmnmsMwN8Yy-m0HT3MVDo4p_o6j1y3GIOqAHuoeIbS05HkydqFy-iCy7sTJusJ2fG-lPinB1-4OrPAt3EbbQEpZfi9KWlyBGAk35rqwGn9Bktlxu4-7RYAua7RQM4FwRKvTQHUZJEbeRpWcxk8P9Cp8astiEmgSejVgbKFnynVFR4iPuSrKSin99OXnMg9YqMrRSrwWHeMkiyNmM1ESmv_H6ycEQkF-Teb18CIoRI8zQOSKFyney6lhm6AGsC7ZdhLdynXu-rUQFgUf9YSRPcg7H4EKC5awST5pejP06xYsQfCW9U4L9G2iwU3OD_uAYOqhtECtYvbzBK4_SZgvUibBnewQ1Ank2dFmkwJv6Dd7h8pfZ6ywGbJx-dIQQAX-EqI95UP9USDt7_N5VWe_1WeaT2vYoRoLgadh-xMPQuAHA5j4AIDmMloV3dwSMRtzWqmZDtBi4yr32yYMY8b8jhSaQPPDWj5EbFUeFBkBtKY49bfO7iJUY0Luk2sooANEeah7Un-QffPhpKYx1COcH3cEbQ", "id": "rs_0a92726500efb3ce016a1189861a3881918f00e5142e6b2fd2", "summary": [{"type": "summary_text", "text": "**Assessing artifact regeneration**\n\nI’m considering whether I need to regenerate all downstream artifacts from the shot plan. There’s a mention of scripts generating mechanisms that render from retention. I think I have to run a specific sequence to generate the mechanism shot plans, possibly just in markdown format. Then, I need to render manifests from the script and inspect the arguments involved. It feels like a bit of a process, but it sounds necessary!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_render_manifests.py from line 1 (1,811 chars)", "tool_call_id": "call_NEOJXuBg1R29ksnxZNCAZ5yr", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_N4zESNO8kj7U4S5EkRFtEI1Q", "call_id": "call_N4zESNO8kj7U4S5EkRFtEI1Q", "response_item_id": "fc_01b57cbf55ca1cd7016a11898d6b9c819199afba6d61bccae0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"python scripts/generate_mechanism_shot_plans.py --out data/mechanism_shot_plans && python scripts/generate_mechanism_render_manifests.py --out data/render_manifests/mechanism_pilots && python scripts/...[truncated]\", \"timeout\": 600, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `python scripts/generate_mechanism_shot_plans.py --out data/mechanism_shot_pla...` -> exit 0, 1 lines output", "tool_call_id": "call_N4zESNO8kj7U4S5EkRFtEI1Q", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_p2E7SkY2vzIU9mEAuOq56IgK", "call_id": "call_p2E7SkY2vzIU9mEAuOq56IgK", "response_item_id": "fc_04c906f9eb5f0c89016a1189a89b2c8191904d0259f3a18ebf", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ffmpeg -y -ss 00:00:08 -i data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4 -frames:v 1 /tmp/autoshorts_preview_check/frame_8s_final.jpg >/dev...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Inspecting generated plans**\n\nI accidentally generated 10 plans, which seems excessive since it used to just be one. I need to clarify if multiple files were intended. The summary mentioned only a pilot, but there's also a chance we need retention explainers for all 10. Should I include everything or is that overkill? I'll check the git status and see if the file sizes are manageable. Plus, those German slugs replacing umlauts with hyphens look odd to me. I really should verify the screenshot again at the 8-second mark.", "reasoning_content": "**Inspecting generated plans**\n\nI accidentally generated 10 plans, which seems excessive since it used to just be one. I need to clarify if multiple files were intended. The summary mentioned only a pilot, but there's also a chance we need retention explainers for all 10. Should I include everything or is that overkill? I'll check the git status and see if the file sizes are manageable. Plus, those German slugs replacing umlauts with hyphens look odd to me. I really should verify the screenshot again at the 8-second mark.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYmoeU-gu1Y3XmZ1uYVVF3_8-BsDnzHTKGK2825BlTIp8Rf-mqrQ21upSI0QFUYDMU8YCLhSa6usgS9uI3h3lDqsT3LlFaUqjH8JIbaGtawq3F3UN2WMUCRSYHDYiMyekp9V_OxDB6nRJ8FK1BmOhoqIJ4gZ-jTqYmyd0vB7IkrAhefLevDt669je-x_YyF3DoAzX8frWm1ZZKc2kfTWl_V7Q7SZUpCew_9vW8fLGqIvKmpZbbZq07mXAaKOwFW8VB2SdQnuYJppZhoEZuCw8pkvWSSIU9uqjZRlBsCzjnd2mmIDyQoFvuRE53gkh5gAk8xwDQLccuh42ayZCVC7sdtZItEmT2abHDz5o3anb3Cl_UvEXskpuBfgQKEBn2wC6XQhMBV5yKd3WsdDRlsbcJdw4ssNCSRQtWB_U5XFjhCjWj5YArGY26crcP5m2vHmVBGSfNE3U8W9UkBtaCft2j0GednhhI_ImoZ7VDjjyQ0KjG9uRbqHv91DFdMj_6dv5BIovbvgxRS8iR1PwhZPoco_wkA-Hb-WISo8Vyj5s2AGaw9_8jhfxL52cD1RgOCOR6AXHHuBAXt6lBeSBLXW0IeReGVqQedQZcQkjM7nQTucP6eY1cDfsDxk8oaESzaPTEOovebobVNodDyKAUweQl6aFNfRDPCaRZDJ-5_MH9h9vEo-6q1wO0XMAVRIuEy-9zLb_UE4lzlgtGqYfsdq1gjJv9tt2-bHX3FSb7_Q8q0T02AZre2F-RKU116q5JpfRaE3BsaF-jV3tPnD3XTlLw9OhWNqo7PN6MUuuCG6Lw9Q-Dop0PIzEuamBtUGeiUqzoSrB7rnrrNVnpEpJeY-VjonTjBu6NNMvX3ITnIHIJo3l2j_iW8RW78VKYkeD9VAcgv5v6pA9c_biumD5j_uNofgQ6mNgP8P75Bmkj2jnhYhzbaiBlFRatjC3DS4WgRI-RhQVORfb0QJadRoIOtWDqMbshc4lHuj7jN6iO8KUYr87oP6QUmlrO74jYUIzlDxl9ZG3ggqjtkvxz8nkpDmS9zhLpL9AnUSbWus3Sn1xfqD48TONn0gT0y5W7uEMkmnDFwtMnsVTz-nAeGn6Xga1qsi1kx3HW3OUL8mcX8M76hPA8SMwhLBPAMLlU1tu7kX4d6S0i0Vnrs7R3la9T3pTYn2vZd32MjF1udeTefLnOaRjIi5paYEglryn1PSSJm9ab-g4rTImRK0kraKF3xUG55mliRmh0jSNMdNyQIg85Cefvmb-tbsfG1eH79xXwlWZUaG6o8M9Z3gN1ALbd0GUDQJTjRXyCGFUJ_-XIqpQdJefnvQvlCZPGpaJtJdXtOpmNRf4o8PPBnT1Ip0GufWhXBxG3siSTtQncZL6jQKu0ZhlztaKVxWOpYVBCUXvd-32-xeNvXt5t1iBu2JoOVhyNU7XoBiNSmaFVHTOQqeboOJj26v8LMPgHeY61KDsL_2u_-9gj_pJP0NsU9sO81_K1kyzwUI-lCxx_2HDKPl3FUGI-oQQi4Eqpf9KXHe8VF8YyL9CQmAeVnyG9w7IrsjUz0uIudHqm0Y2sjHTq4rpyD20-CazkOjAmAU5QfXdM3SNUdsuE8hrybI7JRloXuhF-PoKHiYPZlJD6kMKkkO3xh-fktOiUifbHfy2jhGOj9zVVuNcGfAv07XPdYMNwfyv3pjZw==", "id": "rs_04c906f9eb5f0c89016a1189a42c948191b2f4b1fee90b46bc", "summary": [{"type": "summary_text", "text": "**Inspecting generated plans**\n\nI accidentally generated 10 plans, which seems excessive since it used to just be one. I need to clarify if multiple files were intended. The summary mentioned only a pilot, but there's also a chance we need retention explainers for all 10. Should I include everything or is that overkill? I'll check the git status and see if the file sizes are manageable. Plus, those German slugs replacing umlauts with hyphens look odd to me. I really should verify the screenshot again at the 8-second mark."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `ffmpeg -y -ss 00:00:08 -i data/local_motion_renders/mechanism_pilots/hidden-m...` -> exit 0, 1 lines output", "tool_call_id": "call_p2E7SkY2vzIU9mEAuOq56IgK", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_HUCkD1gYjYuwTLDIowfAYqe5", "call_id": "call_HUCkD1gYjYuwTLDIowfAYqe5", "response_item_id": "fc_00498405f1f60d86016a1189b1c5c881919a6acb0eaac6a6d0", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\":200,\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/.gitignore\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering artifact files**\n\nI'm trying to decide whether to include 10 new artifact files. They were generated by accident, but maybe they're fine to include since the previous activity was only for the pilot. I think it's better to remove untracked files to keep things clear for other pilots. \n\nI'm considering using a Python script to safely delete those untracked files, making sure not to touch any tracked or modified files. The .gitignore could help with managing these generated files.", "reasoning_content": "**Considering artifact files**\n\nI'm trying to decide whether to include 10 new artifact files. They were generated by accident, but maybe they're fine to include since the previous activity was only for the pilot. I think it's better to remove untracked files to keep things clear for other pilots. \n\nI'm considering using a Python script to safely delete those untracked files, making sure not to touch any tracked or modified files. The .gitignore could help with managing these generated files.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYmx5MqZV4tWjtk6uaPiwdslAES6hhrQ8A0uoUQsRX1R9LBrgH8CpiXtTMr9gQKzLGyXF_i_S2IaBaS0g4ts2mGFzUDmDIHTO84POwRV-BnKAnTrg1E5Idp9DdsObYUfQWaRghLv_lfp3vXEB65IKhLy8NYQgD2OX7cM_im1O-3MvydDDfoh1sl_wKEE4dl7elCzzVTYTpScwjXqERCeexJP3T4E5wuXDiEqjtbPfPaSigmM2gU4fLzsl_0A5FuyDD6uhQDf-Dd3dOOp5OSuolbHwaemKD4ei_ceMJC7r40K_2x83acGt6vcdX3ibW3_lTqRKTiVfIDEpZTGyRIi14ll0xrd3Dj-06QfIwROkNHDiUr_MONbhlc3oReeaTridds6P9wGoD6iHkMa4JUp1FvyX3hC3O06dIN6ETJBPSiSnh5kXm_qdQpFGLTcIpl8enOw9QNMcdmgITGpUMHVA-k_DBYIiQ1xeVNeAFclhciNHYrcQZziIlggNikY4WMvXBzTHAs6Qsad_UF7xa8y7QZQHBZQqARgcmspvnNZ2MUi7Kgm979-N0QY-ox2dWjL11V2nvExf-F1hR7TTVJxPfS2VrFZJfzWrrQRNkuRqQR4dowslDuA39MnMGE_RsUWQhV9vVTo3NGa-4kgj-M_1-3hzLs-PKBWJ98hxh0OSpigh7RcLJZ-A8bytPCq6rp2jA5ihNmB3lR4DbxjhrzlrLcYk-1pO1L30fH5Kq9a5ixy0CMGGlHQU0rMnXZHB9eNKxKZ2mgZsWDNeH1o04DpcSJ6OGnaWOeOM9UAh0Jhx3gaaTgeklH9XNhb-xq6iM_PKeQG93YRcTl7LEPL8dMRPUARHq8DIPZgyu_RdggzsmFklHhBu1GnBAkLS8UhnqO3T5M02_xikZFs4s-EptmaMTZx2o3XenxRocu6eJmGJynYWrjcOtlELxkYAj4rZ9EdeTn8sN4a94gR-1lJgP8HriwGZ2yFL9svGb15SJH4S24OZtEnEFQ2QOrzIUrDJX1Ka3ckJnzGSLqs4FSofD8PpBzGAt3uzOlDmd1Lvw_iyxgflK_fwK_DjXsYINrGpHoncWcWEg17L8MeiAs1KGDy_JL-lSUk8MOOQt_1yzvI0BsSUOd-7jY8mEgaJhotuw2TuQD9wLm7t90doCGIhU31WY4GCVtC1boYf5GiXwii3VXBYgJnvLpPW0TvhvzdbXa9Wiy5j96xgRHs_4hRYrWZzssMBJ7ToXjYE2lD8uMRmIbzgiuNnzJaTolK8hje2-UrIeRkqVYqwZn-c30r4laDSMVa7mXWgEoUq3NHOcFEXTq75VsOBe0sz4ojUcQsJj907H2Ry6jLJzq2sWtmRK9bM3XxB39QGd-Yprr0PH2AoCORCeC4-GBXmh52dPeOqyETnDCf5NI783ldzGsL9HUcRSMK1D9BKsKI3gNunb9oD7AhgIz5vBq9dX0-Upo-jiKpEtjyLXMTG9NhnQ7AcsTvNxsxctCT6A7N5oMKLm5FGWV95duaKtX48A6df2NBms-vwuXC9Ywhccd5s5smkVG_Hu5ms3V9Bvqw6UhgnWEGLY8Xh2R01-RHEIzRbKSy1PdDs_bCqXBOnEdPvLscBhCkximtkd0EZYdX-_UeacDn2bx1hT6_vfED7NbdDReHDZfGgU5QWeZgyRoBf3D6wa0BG8WPAMhZF8flKtzJvTVU_K3H4aFUKhtIIarIiMEo2z5IL1puKSA1Uqta7RNBoT3v5nE4azfX2i_xm46U3Stczj8SFhEux7OhynzvyaoW1Q2nDmQEOqRsjtHhciZ8jqioZxiddI_n1hLwmXdv2EcVC95vfTKog4ES90hNqIAyv4TpyRJjmU0vgUxEVUyAM_iiH8CbQuopXqj2cti7dxcfBRTgEmR3Q09VXijFF-cNkfNSdPps7N_y2X8tlsx6zxbY7MQ2ugsQpIXCumqni10rkt3AekXPiOJK9QmeCFVvEWzMdcXfmH-pDD-NB9toC-sthHMIJTvnLDu0vP6A-vcl2t8wBIjhGZTV0CVRbrGQnwe57eql7TKogVXQJPpX62rUFHkOyxd5Q_9Em6w1WaYUU7vOn3M=", "id": "rs_00498405f1f60d86016a1189acd358819187f52d9868c3c28f", "summary": [{"type": "summary_text", "text": "**Considering artifact files**\n\nI'm trying to decide whether to include 10 new artifact files. They were generated by accident, but maybe they're fine to include since the previous activity was only for the pilot. I think it's better to remove untracked files to keep things clear for other pilots. \n\nI'm considering using a Python script to safely delete those untracked files, making sure not to touch any tracked or modified files. The .gitignore could help with managing these generated files."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/.gitignore from line 1 (740 chars)", "tool_call_id": "call_HUCkD1gYjYuwTLDIowfAYqe5", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_NvYW6BItQSbyWAK0teCToIrF", "call_id": "call_NvYW6BItQSbyWAK0teCToIrF", "response_item_id": "fc_0a274b9bcb9712b3016a118a722c4c8191a993d3d6c67170ef", "type": "function", "function": {"name": "execute_code", "arguments": "{\"code\": \"from pathlib import Path\\nroot=Path('/home/agent/jarvis_runtime/AutoShortsBot')\\npilot='hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann'\\ndirs=[\\n 'data/asset_plans/mechanism_pilots',\\n 'data/...[truncated]\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering code changes**\n\nI’ve noticed that the .mp4 files are being ignored, but the JSON metadata isn't being tracked. I could add `data/local_motion_renders/` to the `.gitignore` file to fix that. This involves some code changes, but I’m wondering if I need test-driven development (TDD) or configuration — which might not be required here. I should also consider deleting extra untracked artifacts since they’re JSON/MD and not ignored. Additionally, I should remove the extra directories. I can use `execute_code` for this.", "reasoning_content": "**Considering code changes**\n\nI’ve noticed that the .mp4 files are being ignored, but the JSON metadata isn't being tracked. I could add `data/local_motion_renders/` to the `.gitignore` file to fix that. This involves some code changes, but I’m wondering if I need test-driven development (TDD) or configuration — which might not be required here. I should also consider deleting extra untracked artifacts since they’re JSON/MD and not ignored. Additionally, I should remove the extra directories. I can use `execute_code` for this.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYpyCJ7TYFJJEud35_RNEkhENSlFvgEK_RkD8mX6ERlj4B0oE_cGNEfmRQYZv5crhhX-9Os4lUtgaDa3a8WfaW76P5qFCgRVHWRYLJsREYEM42Ex6DQ8jbPMT8wtE5OavsbB_wdUMlQ6nk1KEI1F-OVpgswQR6oDYgIGgKhqoImgjOYjlqSeVb_MeQUWq2NVoEmaBHFOzZVQh4omaAE4I619iCEAppLHhFylUptZvLFXa_y4MlUqRuinCvGE9TMOYPdguVxU1D0GuzfHMsbnlqjtCgJwiqmG-89kr52HJyKdTy7ujV6Fw2fbld7O2LqJ5ReMqucHi9nrbyESFX6cBfQjkToPSM07nfI4Dl3-OAn4FU1FyPq2ZJk9ixPHESD1RDdbi_u-NNkbE5E7KQdjpDJfDvqwqFTjYQ3wb8-RGvhDb_qYPW6eBvQt3baJd7lVGuhSM5BF7BCclxfce6YxASZuSqAyNVcMEwJyJX4VrGLfrshVsRANiclrVrxDvD99N_DJFOKvzLRcSxdgSznh4UiNH9zZgMwFL3h9KCHHq9Xvlw3NcffPm7SEjAy3r1m2A2ITp1pyskf1jvVUmQCTePWCKsAbS1_YYdWbG3KBfWcC7PKFRLnKn0IJx_niiBy0ha1JyMxJRKU8o-0TV5iMeP2emq5o2a7PvZ30dnq5nAMgpqo1vynp2FuqYK_XvFtxenQ0Zui9DyeZjKOk2hnElIS68GugNaEz394TwgEDXHqoLxZlXx6qjGXgP3eIhwHe2nSqrdyZkeVp8K7QuW-Eom-xh8TyCToMp8mviFF_XDJR_wclvDWZVUszy2DaZkgAzq8QtSlDE06D6JV8lEjrt7JheIi3dRvO7fZMO5PnPtWfsKeLFBaQh_pxdfAMwzGcQWOlCM1IKez3YpK-R83yYX93XTu-DKaIpSMgwCBM76WFfUZiN64YCqU7GZEKus4ba46cSYeyAJ5uSQf2Vs-Ugf0sGtJ1rN9aU1B87BTPJrfj-OJWUDkQ5QieYb2CsDYWQuZR2Ej63df0aU9pHhJcvl6nwEnNy1uP_v0tUbH0UnP2WrontnZJxJ9okDrFDyi74J6LuwS7nqEUcj-GT1FvbXMLESqtd8ZWx8peFxFyKBR2dWktrlFxxrE8mLWhvyKs8WWx6FCEwJKQ150KHZ84osk38GJ8R0W58y4pMjTknkqIyAwOKzzMQNhK-5ZIPV8Vu5CJA-hTmm4g3aPTqN9aNPCe9bawUq5XpENwc8hEozj9IBcA6lnQ7MQVqDKjxuITYhZjjwL5ccACeP4O-fuqoPnRZA==", "id": "rs_0a274b9bcb9712b3016a118a6eada08191a0bbacd726a2d4b7", "summary": [{"type": "summary_text", "text": "**Considering code changes**\n\nI’ve noticed that the .mp4 files are being ignored, but the JSON metadata isn't being tracked. I could add `data/local_motion_renders/` to the `.gitignore` file to fix that. This involves some code changes, but I’m wondering if I need test-driven development (TDD) or configuration — which might not be required here. I should also consider deleting extra untracked artifacts since they’re JSON/MD and not ignored. Additionally, I should remove the extra directories. I can use `execute_code` for this."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[execute_code] `from pathlib import Path root=Path('/home/agent/jarvis_runti...` (1 lines output)", "tool_call_id": "call_NvYW6BItQSbyWAK0teCToIrF", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_yL7BYfKIscDxotfMNx3N49i1", "call_id": "call_yL7BYfKIscDxotfMNx3N49i1", "response_item_id": "fc_0931243020da8856016a118a7a904c8191b7bef6cb23f1a903", "type": "function", "function": {"name": "patch", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/.gitignore\",\"old_string\":\"data/performance/*.local.json\\ndata/generated/\\n\",\"new_string\":\"data/performance/*.local.json\\ndata/generated/\\ndata/local_motion_renders/\\n\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/.gitignore (409 chars result)", "tool_call_id": "call_yL7BYfKIscDxotfMNx3N49i1", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_YzLeLT4e4ULMRbBE9irXs8hF", "call_id": "call_YzLeLT4e4ULMRbBE9irXs8hF", "response_item_id": "fc_09668b18fba65115016a118a829b608191b101a9155993ddb0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYqCdB7OCc1qpEa9eeG6N9IdFvd4RjLga_EaXtvCli7M_GSLFmdNFvMFIj4Jk-6fSjnaJXpgq6XLy3IXCr0he_r-p1AkhQxTLVLYLTAmVF5UGF5JgEZ1gu7FrwcfBZ-xp6aIsV6So3fXdojJ0zhbC-m3tDZDmfDQFDTaAORg2r46E3yWfimmt-ovo3P936h2IqhOAg1SpfwPnOuin-zbRiSdl3whCXHDHp4ADjy1nRKCyqAQ5ivNFCYcbSnQzo-6pIF-EZ7eHdjQa9_VYxITFY3jNqLNSCq01naWqG2-ZBgqwt85Ph4H8jPVaWx6XvtBlaQa14gejXGE5iFWbAopPDWXnisuDwIPwgctUm5tAfCX4xAlsbq5luckVxjC1BSkoaJCD8mfTr8qbcC0leLA23DvVZQwUPNpe95Eyew3RuCpR_VXLkXMTQf3lVrymkpsoIr9pSJAHP0upJ9z6idNSYWxGU5z--8X_w1fCyfeoK8-8ChwcJY-3_5J4WNUZd_rpsaoFICEkmu6-lSeLsPrghPiw3uUF-1fzO_L79bo7NfdD2fxyDJ5CzVkDRm2vES3uTsjgOsbssVVonY99gSn396GhB8ilNJsYeSe9TYk4jO0rUl-fuZS2oF5mIwiNSyysbWG1lbQV8D3emIosJJeUETCWKcGcRa8-RGyVB0VtLe0fO9x81Ds-jdo0APAQjiEW__bIAG_l9EYaNAfEMdLYxV1sKnkl0dsTHsXjK0NMrg2wDfkXICL21UoH2eT0hCwnvbNealisz9SnoamGH0geN03BdWZaUMt5_dEK1fflna0Vh11Lljon8UZ20PcXuhzS6p2uWdwYXNsm_9Rn9hSS9XjRH4NP0cyp0S3GERJniiW64YrGOr5lRUx30y1se8Z2PswRlb8hk8AW3mtWBppWwdY2oeMGSvf4KTbSaApW44GSUR_0p4fqwPHJKWxwEWYm0H5", "id": "rs_09668b18fba65115016a118a822ab881918bd36f58dc4b1b2a", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `git status --short` -> exit 0, 1 lines output", "tool_call_id": "call_YzLeLT4e4ULMRbBE9irXs8hF", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_UXjlZZR9ozHQAVQD8eYUcX1t", "call_id": "call_UXjlZZR9ozHQAVQD8eYUcX1t", "response_item_id": "fc_0f5c877e4eeae650016a118a8a78f4819189683659c27bd017", "type": "function", "function": {"name": "skill_manage", "arguments": "{\"action\": \"patch\", \"name\": \"social-media-shorts-growth-engine\", \"old_string\": \"Rendering and external API calls can be integration-tested separately.\\n\\n## Pitfalls\", \"new_string\": \"Rendering and external API calls can be integration-tested separately.\\n\\nFor this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_produc...[truncated]\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Updating skills for workflow**\n\nI see there's an untracked review that needs attention, and I might need to update my skills. We recently discovered a free local renderer workflow, which could be complex. I think I should patch the skill related to this. The social-media skill might need an update as well, but it's generally applicable. I wonder if I should note anything specific about implementation quality. However, I notice the user didn't ask for this, so I'll focus on making sure the skill is complete and patching it accordingly.", "reasoning_content": "**Updating skills for workflow**\n\nI see there's an untracked review that needs attention, and I might need to update my skills. We recently discovered a free local renderer workflow, which could be complex. I think I should patch the skill related to this. The social-media skill might need an update as well, but it's generally applicable. I wonder if I should note anything specific about implementation quality. However, I notice the user didn't ask for this, so I'll focus on making sure the skill is complete and patching it accordingly.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYqKg5RDszaOeE-q7tnVP0zY3Z8X1BfmPtbKDW8bNly4BDZ3AxJ8BXcB4wcGYXuGQFkK5MfluMLetM1fwkAhudzjqc1mOPvOqitcJh2CwYZK-z0OAoUDYOh8tRWLBpGrpS6IiBReIRyOoXqBeYfzgPkmkhteyT-kccXGiYwOeMn0U7Q1-ojyqufWpHkguwMfSO2FYJ42ljzolboERp4bONXZduvhEaWJMlwkA2hdDoph1TESSxVfUgx_FSry9UVBRfX1n7cvkQX8rCR4HhqVbdLKrPDlSoGDdUiIjTcKA-r_kbWXoNPq3xufj3pMlOd-VFZvGjqh6G5H4Niw1Mx0YRW2saGQAijEi4Y0pxyoruYqHIHXztZ5tVHRKoJlhOGNXRE-4Xy23DPipNliL5Es1-w_ixEFwxQe89cqC3mHy3R4cjrvLPBYe9TPuM1WyxyGulrktRFrKGJNtOtCLD1ATjGGTc9NXKzbGBR37A8YdDE9g89cQSOmcsVVi5mPrIeRsEXf7FAtva0hV-o6-Q-A_wJEdtGxPN1TfdD8nyp8Z4DniqBLBJTHQevyEo0r2BUp2yCbbmy8A-5Htuo35cp0Ff9n2DthkJOycJu-IsHSgO9BzPcq6v4GpbW6uipZja50tsQxpVm5xoVWcoWzGuCiEKFFnsL83yNv1RBovntQ3Hi3nmk9ouIuWmA1fDFzjimTRjzcy1p3eaDrR6nJvJanCkyPrVzXwVFj5IDHQYC22vkq1E_HggjTr6DuDtfzdn9v_wtDAYxP7CGL_EuxRoUoSGQhxE7WpPRIaobZQFKW9A4sAhhDpOBkiGU9zYACrErggRbxfiPo5pAP1QpKgXneUCnh4A9pnDYv8GdkGpoqxRsFzA8S9nOUTLBF7vTJWMdZDz953u5fHvAIZUutTLSn0s9YfSXbMAmgWrE4Sg2GJKync_sffPbBV5q1r4f_4_jdZTV3SnrusQg0EaSz7eBzwPxKmiCbIhgQEiBaeR6NiF6CuBldbCt5HvTU6mJsgMTwycNFcorVypfKvr6jOH4poDJ7GoyldxUmlLMG3G9LGeepb0ymSUYm9cwsFui9VlJhz1Qh2KxVrYwIVotCzdVxPx6dMznIYeuZSsLXJ7tG1o-e1hxomq83lsRKf0dlwUWnRVeN64ze1XUKxM5GsLhrGI4MUhdFagthlDdLl4g4HMrJhhBb6FVsQ9CGzsIlAP3a6q27uR1HQz3ZOBlcMl03wIfZK4vRfZs2pQkdPC4w8o193dblGLb3R61mZqG5WC-cHCwkxkzOcKPsBSZ1BARo-6_mf35fW5K0JwdOlag42iBeJi2HOBdPfGt6nQzsKcHGosB2zcaOTgFnCmALiNb6rWBnMOkAFuS7wBQo8OGfYodLv49L_yw53-oaJzPfKnnvutgY1Y5W-w9ttr3ymLQk-y1dG-Ly3AmFPyqc12NqNUntYFc-wrOhPCai01GDl8T_cHTmjybBdSh6OKii8qIqQ4dvYqz-HXJvUDNb18BVSF9Sx_cSInR27TTEhI16NkJGKNq7ifgHKncoPDXPR-LEHOlOYvad_eJ5pLkEGQ5h6dVjhsmRhge5YLfDkb9fzMzF3GWTewx7XkhvhdYVLDuC4-o-1FAvtz3Om803brjdQNPGUVhIFklYOTcAQos9bhKd-0NVbQZ9wnfpqUmV9LqeQ6a0ytDEsbZYCSo1Y9KCVLBxmrU=", "id": "rs_0f5c877e4eeae650016a118a870330819193480736f9849282", "summary": [{"type": "summary_text", "text": "**Updating skills for workflow**\n\nI see there's an untracked review that needs attention, and I might need to update my skills. We recently discovered a free local renderer workflow, which could be complex. I think I should patch the skill related to this. The social-media skill might need an update as well, but it's generally applicable. I wonder if I should note anything specific about implementation quality. However, I notice the user didn't ask for this, so I'll focus on making sure the skill is complete and patching it accordingly."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"success\": true, \"message\": \"Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).\"}", "tool_call_id": "call_UXjlZZR9ozHQAVQD8eYUcX1t", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "{\"output\": \"...................................................                      [100%]\\n51 passed in 3.32s\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_58Rhvi7JGrCECM6pCnXwSMWl", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `git diff --stat && git diff -- . ':!data/local_motion_renders' | grep -iE 'ap...` -> exit 0, 1 lines output", "tool_call_id": "call_uluMuRvirwvE9qgGSorTWD5o", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"540\\n960\\n15/1\\n34.000000\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_VuPJgVu3kwP4DsYPUfBLONOS", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_HfZ2Y68F6t80J3u8biGLN3ws", "call_id": "call_HfZ2Y68F6t80J3u8biGLN3ws", "response_item_id": "fc_007475096435aa57016a118aa0b9b88191bfe2b7d3ad300157", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git add .gitignore autoshorts/asset_plan.py autoshorts/mechanism_pipeline.py autoshorts/local_motion_renderer.py scripts/render_free_local_motion.py tests/test_free_production_jobs.py tests/test_mecha...[truncated]\", \"timeout\": 60, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYqgZwl6Ny-i9tEja4-20wH9ToFw1wtMFsP3n60dSizGCvaltwUmJIXXuuIFZbTen9E7AzeZFU1jf3n6tOLWwUhDz4mGabaxkfYfO9NVlwF6vbrCmBqurnT4s4f3alTfhpGnZhLDBZu9NXZyd7Kk_tLxO_G0vq8Wr8wqdmIwIGzjDJbde2V5ODGorw3O9Lm-rJikdKsvOB8yTCeqfy4t7HMLPmeJNm6ZyzLMo4_r1pSmfmwUZudGNZtAhhsvgBCucvbuc92hrq2JUoeGBrlkXbGb9ZHb-KGF7azcPFmMMOQv-vKGtNDdKtPOjmZLqu8s0BXkP6olo6cBJDkMKcrYA92AkAob7uTCaFFbN_llDSdFOb470-u15vg-wPbUlr-EBU4eAJvbsFvRDbAUarq1TOcnVQtL4T2efnTK-At7CCJVKhf9tPn0Cwydvt-P6vwv0Pzhgju_5ZhdpFwaem4Mah4h7fQG4vDeq94oVSSyTtM_wMozonAjxSCgX9DudOrv3DyKXsrRCpTr1asJphEPDN95BzaUSeBHF3ys0uajjmcQ1DIav-tpbX_hhxZka6cus7sWe-wI9KNB2Ek0u4XRQoaiF3uxqycv-vVN6w3DQFUHYEyF5pmzwuqqNRHpg1_5HxIj57Xosa2Vv1uTWin092lHEpqxH7-RX2pp-JVeVEFOoxGQnRl-8n8aUcoGHRujWJjidgPYf5s7y3d8LG4-9qU2gJaKhShH7S5tbwk2PbqDoiaPrlzZp9PCW-JcNlRsBxWyzldkHihudDH6nrCUtSjfCvcObqCYQgyIjmKnB_5StqEkPEVgLzeT1FuMOlhuCrC6lDAcrGTXVuq_UEg8kvw67wFaD77F_xKPpTvurs62jpZoHUBUS8XEN6OXX1B8O5w8LAz3XsXBXlhGOKMOzT98yFa5WDY0qdFHu4bwkd3Xnf3j8CyW0ww9e_vw3cRgwuecFvR-QGTVwAXCKvFuBaNRc0Ddh8oTwQQzVjm1CyJ6_HRc0iWjQjlizvpxbhH76EI-IHIYsgSdoJSTVnuol9KX0VIp9FcTPh2d9DyV3-4dJeeZfRGdsbEKrh4bTCl2rkoLMvVXIadnEoOneqgztpgCtx8LglxFvAnpX-mClRD96P-2sAcDC_2qFBNXV0Kvh5owtB9-Lj9tJvyR58bNCTKoLpUGDo9ZMK-Ii0QDlVSkzn6Ojefy-KGSs4wzNdeZqZqhV80e3Yy3to82DJKZM3uLt38h2XVJ0MuNjbt28Vrg87t6-ijnq7shlbQMLT-sAxRfaa_QNFq7e88RmDBfZyQokCBTxp-iAZWFG9wsLJ7p6sAcSAvYFB-E4wZcYefuEaYvx4qfMoGLa4J3-8q5lgUb5JzKlJDJoaF5W6oPHmqleiIf9bieKmMaxIkFk6uXhFWAN67li3d5lHY7xfoXVl8GO2-_4e7SDfQTP2vUVwQOotqw8XgcnM9ZJlMtVu2Lq2dUMCp0VNCYbeCYSP1QuzQ5rFjGR_weUT5dIOoWiOm8DBdTfVSaxj3W5LWGy2mtztPqjFbmXnAiqldI3coqJLWKkED_BgLp4w4pQkzSvLbuGcUPO7pLOFcVCDWYMHBs_C_G9SOj4SMgCxRw7uOYeZD5X1-T8bpz16aQXwY3pLew2oQ=", "id": "rs_007475096435aa57016a118a9f0e8c819195f8030aa3f289ac", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `git add .gitignore autoshorts/asset_plan.py autoshorts/mechanism_pipeline.py ...` -> exit 0, 1 lines output", "tool_call_id": "call_HfZ2Y68F6t80J3u8biGLN3ws", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[terminal] ran `git diff --cached --check && git diff --cached | grep -iE 'api[_-]?key|token|...` -> exit 128, 1 lines output", "tool_call_id": "call_lKBostwnOkO6kCOwSvIHhQgW", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[skill_view] name=github-auth (11,442 chars)", "tool_call_id": "call_LUzwjyZlAqQFaet2IwxbJDZc", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_IGGjz2rzd4zN9beQEuRCEiJr", "call_id": "call_IGGjz2rzd4zN9beQEuRCEiJr", "response_item_id": "fc_0b933a621a780345016a118ab2d7d48191a60a4b90997b9078", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git remote -v && gh auth status 2>/dev/null || true && git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `git remote -v && gh auth status 2>/dev/null || true && git status --short` -> exit 0, 1 lines output", "tool_call_id": "call_IGGjz2rzd4zN9beQEuRCEiJr", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_LiECv6HhCVVLnOcmiLJHlVYE", "call_id": "call_LiECv6HhCVVLnOcmiLJHlVYE", "response_item_id": "fc_0107e2cad3bb6e1f016a118ab828948191ac93f132f5a543d8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os\\nfor name in ['GITHUB_TOKEN','GH_TOKEN']:\\n    print(f'{name}=' + ('set' if os.environ.get(name) else 'unset'))\\nPY\\nif [ -f ~/.git-credentials ]; then grep -q github.com ~/.git-credentials && echo git_credentials_has_github || true; fi\\nif [ -f ~/.hermes/.env ]; then grep -q '^GITHUB_TOKEN=' ~/.hermes/.env && echo hermes_env_has_github_token || true; fi\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Checking commit status**\n\nAfter I make a commit, it looks like there’s only an untracked review that remains. I know I need to push my changes, but I also need to authenticate that process. I’m wondering if a token is available in my environment variables to facilitate this. It’s all about getting this push done smoothly without any hiccups. I’ll have to verify that token situation so I can proceed!", "reasoning_content": "**Checking commit status**\n\nAfter I make a commit, it looks like there’s only an untracked review that remains. I know I need to push my changes, but I also need to authenticate that process. I’m wondering if a token is available in my environment variables to facilitate this. It’s all about getting this push done smoothly without any hiccups. I’ll have to verify that token situation so I can proceed!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYq4bXIkXQJevpDHk3_zq1E6Xm_SZPazvVSU3riSXBQ2gBtZIlKUajToybrsG1oPMAQxQwU1fb3d8P-mI4cu7PR5a2bp5NbXXv6ntufHY7ZTEavSc_9hs8qNLivK3TqSM-xCs1PirdVv8LkaX6B28glFre0DO_7R2RE9L6YUL1pOoKvWTPlJFizcKABZEz0Unw9AzTF5OSC1qBolkUS2-WT7NYwO2vnrqt7JplBeNpeuP8zWtFdRFMRifed_7f3dedaN9EPQeKoyWTSEiMsNgj5JhyAapS14c8URPLHR_xq_HlzUdA1Z3QJ0s6z5HH3mjTbp40uTwGovNbRideIhot7upXPe4WIcJ5QZxWYUXXQTGwvfHUuiGGR1gasTfHRskOKnwYmGeywMSfzdKlD46_vwdTq7C-HDWrg1IQzsqE2XBo74PvkJYkGloPuH5Ki6QWmM8QHFMNyinlhr0KBW2sTLQpE7yjJcNIzhzB6DMmf8B8QPx0S8lryey31XyD3cS05pfEIP4XKhlSxw3jI7LXXOFYTUbkfSV1QrojUBaOlREkuDyqDPRC2fvsZnEGq7TFXiCkU6jSUZB3FBEHb7F5xEFRde2hdarKUZiqb30UJeq50BGxQoJDdIh7twV59hFm6BADxI6f7KgSbmnx-f1gYphUalm2xEz05VGrsBjhfLyHfUKwLju3F_z5kXbOwoz0QoElDP5IDRuEP5lIV_hfCeOw5d2bFl2uXoIBB3VjKhImYQfCC0xJTS4HkgJIV79vHMDHmU54fy-RVk2K4ua655LX35dSnRdC8Gl65vgP66L2rE8kDeiyjAzl3-m9xRghP2lh-5ZV0L_Ser_6AdpiqQkol7v6J6Ak-sLKsufkb2sCAB2jT14w9MJ1ri8BBxdfudzQugfVrOER286iZsppxBaAl3k-Q-LNLuCqelZSHz0a0OIEsnlkBAD8-J-xnvil5mzV_9lv4oN11AJKrUTb46rnuHPc3OMqN0a9fDFTIs3yujCVscM59NPi3p1tLdnRy5WbDDizJEbZEl4TtdxtIfsQ==", "id": "rs_0107e2cad3bb6e1f016a118ab5e610819180be74d04549dccc", "summary": [{"type": "summary_text", "text": "**Checking commit status**\n\nAfter I make a commit, it looks like there’s only an untracked review that remains. I know I need to push my changes, but I also need to authenticate that process. I’m wondering if a token is available in my environment variables to facilitate this. It’s all about getting this push done smoothly without any hiccups. I’ll have to verify that token situation so I can proceed!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"GITHUB_TOKEN=***\\nGH_TOKEN=***\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_LiECv6HhCVVLnOcmiLJHlVYE", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_dYLXspI9E3Px6A1jVcqOVBst", "call_id": "call_dYLXspI9E3Px6A1jVcqOVBst", "response_item_id": "fc_0a495d8bbb431dc8016a118abca3c08191807d19ae385bd4f1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"set +x\\nBRANCH=$(git branch --show-current)\\nTOKEN=${GITHUB_TOKEN:-${GH_TOKEN:-}}\\nif [ -z \\\"$TOKEN\\\" ]; then echo \\\"NO_TOKEN\\\"; exit 2; fi\\nAUTH_URL=\\\"https://${TOKEN}@github.com/Gamexgit/AutoShortsBot.git\\\"\\ng...[truncated]\", \"timeout\": 300, \"workdir\": \"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYq8L9yfGHHHCoLdQWa9QsnAYINKOBEzynh_7sbrqp_2qEuhOWmsYYmav2h0GCNmfC0Sh4X6m7Meho_RrJPjtryuBwGcBdHfloeC769cJEjZNARZ6PqZrz5GksY89zpDICdlYIkOkBQRhrCg0bGTs8siwv7xKKIu88RKLozH5F032Rpn65VjrOYoNBfavwJVjdwCmgRLD6UFfdkjxjnFX7jS_aXVBAKZ1JzvEyyb6w2EVDGBW1hsB6mKyOfhBLND-74JY3LICyL7S9nYusbza_uH6GnY5_Ag6YeEb90ZLpj-yhxiuXfqAiaAKDGKvhbE1KfXLvXhLXkGCX7eaf5_9rN0gN7dZG6w06HjF2_gi8SvXLommK0jbCsgebJv05orG7tp-8da_ilUY8HQGe0OSWBCxsLtQ_oq5-J5IQx_kA84GXkVJGzUsEwYrG2HmT8qMc4SfYeMpDt-v-OYAFeJUlzqf5-hsG_0UbdyBsoQ4p4lv39Evy1cKJAy0Cn-f9KFrYWxtxLXwC62BVPT10oxU--Cn7UOVDcpV41RXNf2y4SbuikKYjLbCHKMk84p8cLOdls3WgT7HZuYFqoYB97KVfXaMeH2GOd0SeWyr5kx2Ij3zlkh_22iVlWg7TzzpC7IR3XqZdZGJlnOodZwLHg06ztZv3ndGCf4azkPb4BM4ZlDwxeoaVPCTMhP6SXbVqTFA7bgD3-HXsrZnZa67O-Naqwtp37iTRJpSOa4c9ygVUoEI8UijV2jy7un1IsH8qQIfPYqX7zR3w51MvvCLoO0mYurilEOabTQvzrzFAKF4fls7iubyhNNbGfLYVx6G_jf1PNeI2FlB0_g0uY9VuGFMXQH36tycQxsZMAkifnm10qkeFY4ukeebJ-jR95UOl2SBmxNhyxoyyiyTy6SEa2X6pdjr-HeevIxkjaspPIAW5TGw_gcQug4dIXSKIoqsk0sINxU41p8SQsmstv4o9y5C4RgbTOO7UG6UrJLsDHf3LUGe53vXsXhyeD4Da0psRAARRqqi3kj4hZmzqV-i_SVqaUhvw==", "id": "rs_0a495d8bbb431dc8016a118abbdab08191b713309069ac8713", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"NO_TOKEN\", \"exit_code\": 2, \"error\": null}", "tool_call_id": "call_dYLXspI9E3Px6A1jVcqOVBst", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_YKRIUM6fPxX1TTOH7upbODv2", "call_id": "call_YKRIUM6fPxX1TTOH7upbODv2", "response_item_id": "fc_01b8def93e9bc999016a118ac5e1408191b71827808455b349", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport os\\nfor name in ['GITHUB_TOKEN','GH_TOKEN']:\\n    val=os.environ.get(name)\\n    print(name, 'len', len(val) if val else 0, 'prefix', val[:4] if val else '')\\nPY\\nprintf 'bash GITHUB len %s GH len %s\\\\n' \\\"${#GITHUB_TOKEN}\\\" \\\"${#GH_TOKEN}\\\"\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYrF5sY_TG0aVUhAVGT6j_2sGKKIDR03x-4g7pjp4YTxCt0son3G5r1EngCCJNqkm1DOcjTAOlkeepBfm8SAsJW9V0-_2y1zYzEnDC1OofZxiplNC1zbzxw3oTIwEfRuMNlb_jtyYm4fxUNEjwu8bpcD_KMpWXdashfbOx6R6N3AQQUuhl5waw8zGWKOE8suSv48qomFCvnHXVlzNxt-6BLTdgCkBLsQS12EYg9XFcMZkWVL1a5iEqfpGSwoGouA4_2yx7yDLB0EdHXp7ceICdeFeEomVCZ2DwTzzjxMvYR5glHPjqDBZuWdooi3LPot9kh1Kw7Z8Yaw7HyPO2fM7Tj3b0ALeX5GtidHqlvb7tFp3Eq70rnFtCoF2wybNc53ubsnWns-26bLGoHM07vtw2Stb1A3-BEGKTxaULxkpJDV0UGCd7S10ZoY1uFvtxiWl4C2A9qknhTkZmCs0Iim05GUgvMw-bH0sXc3NDbDHMtEw3SAj3ueIQ2qbCOHTFZypA_1IFkcXKsPEl3Z_9-cmCkEK3l7WPiOj4ydVNkf6FlbGneHurp2H__YZn-eC0NX4aLaeklRDHmRPt1tvirX0RYJUxtiwjQFYN6Xn_tuL67eiMtxn7q78W-sV88BUBArVyhdPveBF63Pc0UDwseFEzOnytX2wSvTr9Vc8ePVyaWL63KR6CEskJeiOFxA5fW6kgIa87-C9SGtfuxYNvVMUpTxz8YiTn8gacoad7ZhFwYU-we5en-nMBfbDCQW3L2k1x-kr-koq1iousXA2R0PifRSfPd1TR98x5jy_Ac-Ite2VkXbkcLiXT1ogc66Bf5EVuRyRq-Z73knSgD7kk4JKwLN25r8ci3STlEjLI-SheNOskk7CNw16O2S19NyeRxs3St2adFqQgx8NZnfVp-JBK0DApZd82fPlbsr9gc7f3FyCPpQ2EQAXXZFEiFVbRdc4_lKJ9BArQdovN4z_Lj-fmNvXtN3xC_sb3shU0dvHgBN9xEDfHGTWnmPfLjWDIRi7T4lE4t78wqXdT6830K9P_9QRj3LC4QujupkQ9CpZUKqJ1YiZx4RJrXm0wcVue9vduCdDqBk_PDhXX2cP45AALNM94wMGletqX_qnd8jgq9IkVYPNrQikzqYO3d7XBpsebaqMP85m4uAhqChINl4P3btdvGkE7OiMDTIxqHeoFx8VCYip5KWrGHiP3kyKeIhqqPZjRibm9mFuHOSVZaKkesUl6mdq3W9zrVa2l9GvEW52NWuVAD3LYFvAqmjNP9Huz1xK9ibB4CgiRz6uyLyKAyjfxfVzO7qyD86izexNaL9d4bIfiz8dQoa7GmBxdZrwfI08r3f2nH6UUJh6oq5m2TthRk9TwULhJ8HK2S30HJ06qvC4xBoQ3u29wTsAVOWGLxvQ1wguP3VrSHYlBvtmE5Ht1pWwevARI8A4L-_vI3NmIEb48OpE3lTP4IPCOJOCMkeHfVemOotEZotV7oraAjg7_K5qByzONl0HwxL2-nW32Rk7E8DpkCXk2KTLEAwlBJWbClRJ15TyCVFtnq5emc5cAi741FHxWaG6uLHrmd5Dg8hzEnRQFwoPN1pml8nlx5NEUQcvYXUHSAFd4ZqeN2vIoKfjEOgzfXf3V0-ia_zm3vof91b5DBQY5GsmOcRR_QFmOkVZOY3FNDUbyUW-2z1Yy4t1Zr3kMnZlJdKWBVhVEU=", "id": "rs_01b8def93e9bc999016a118ac30bd08191a0e70b5bae9c66d6", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"GITHUB_TOKEN len 0 prefix \\nGH_TOKEN len 0 prefix \\nbash GITHUB len 0 GH len 0\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_YKRIUM6fPxX1TTOH7upbODv2", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_dEyYkYE5tUWdM7788zXKKEOM", "call_id": "call_dEyYkYE5tUWdM7788zXKKEOM", "response_item_id": "fc_004d0d07f4c4dce5016a118ae56b188191a9dd2d849211a426", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"ssh -T -o BatchMode=yes -o StrictHostKeyChecking=accept-new git@github.com 2>&1 | head -5; true\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering SSH options**\n\nI’m thinking that maybe I don’t need a push if I can just ask. But if that doesn’t work, I realize I can state that the push is blocked. I ought to consider using SSH remotely. I should check if my SSH keys are configured. If they are, I could successfully use SSH to access what I need without any problem. It's good to weigh my options here!", "reasoning_content": "**Considering SSH options**\n\nI’m thinking that maybe I don’t need a push if I can just ask. But if that doesn’t work, I realize I can state that the push is blocked. I ought to consider using SSH remotely. I should check if my SSH keys are configured. If they are, I could successfully use SSH to access what I need without any problem. It's good to weigh my options here!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYrlFbhZEuXOhdnk9RJoIrUO6tLm-AJbxtM3ZkCgCEUGXA9HSgAno2pt6crFwZmHhNLSzn7E0OjMmXWJimPqXKRZRdwprj1wxDVvTe1sOpXtbkMvUPJXpKpGAyEt5QkUqWmmhX_bLWTTtSqWP-MmjxUN-UikB7q5MzfeqsEsuvTF7H4EEAmhOMEjw8Ni4OiR-QAyDOBa2g9N0U70zDBxcFFP6xQ-_EnrNjr0yo3vyozKn5e73YPFSn8DRoEO9_gcdIvXuh7Lj-DKg6kr0PozEx8ylzsnNQckLq4vxUABSYumvbmgezI8P4PmC44N3nzaJ9lHVj8kgVrzXdbOscqyoJtbBdIUbtEPQD79YLYkXrZt_FxapZF3nIAPdr6MAJ2xtNBr78j64hNsZ54HjhPo_8bid814Ul4numUeub-fSMPmpTAB7-ER5-2jb3cZ799hRLCgeGKeIXD3kxmV5NLA380UevkbC3xjM1pCVEAlc3CzTp6kcL5cZFcVEKB_qVmZKI_41g9YOpbqLy8V_EvPiO_QGJTz_0DpxgaLIMSoz9K7kd6JdCKfYfyGMvo92f7-tkEdt_OBYp2cJxm8TF9SVkNgKrrqneB1dS52vw29jwrPi9dZ4x2C59AXg-rjBl4ZOhmhnj7zrZtbMhE6qtqcn_TVaJP1UXjS4MxPd9TXTZuCixBMbOeTpFQ44oSAoHX5omdJ4vpju8lEXlCHB4_9dspKv9GHPMMCbAywil2psuP0maSAzkiP0P6SySUFff1smJnKctuX3mf9soRK9F8e_050IIItgIOXR8oRHTLxcnMdFSBydYmQfnhV8CYiDx6PejYmA3VczIrNvVHIionpzxNv5nHMpIOO9vZqW0FRaqkSJHJTN3tEH5qAtRTdncqTLHxCAamooH5wMLCVMBeVlKCBp7CI88Ys1mBDNbNymV9omGMG4LvOjnIkhEQKYVkjKLGcOj9yrqUkLVbdKDl9g5oTYnnbfpOsRyLfaKnmvOgMpJgDuyM5PZhkSz3wnRlkmW-JiVKjbxUYbNfr2zs_LrjfEu5P2gvmSyCGCvidrLU_UjVnNVKIKZqNEoZD8iq8osfVDUWtJSjrJnLVJahhvltm8A==", "id": "rs_004d0d07f4c4dce5016a118ae34f0c8191b1d547acded89b7d", "summary": [{"type": "summary_text", "text": "**Considering SSH options**\n\nI’m thinking that maybe I don’t need a push if I can just ask. But if that doesn’t work, I realize I can state that the push is blocked. I ought to consider using SSH remotely. I should check if my SSH keys are configured. If they are, I could successfully use SSH to access what I need without any problem. It's good to weigh my options here!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[terminal] ran `git log --oneline -3 && git status --short && git rev-parse --abbrev-ref HEAD...` -> exit 0, 1 lines output", "tool_call_id": "call_suydXT91R89LchRI8vCh6EDl", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_3bWyFmhw9r1ETXZXv6LW3eNA", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Erledigt, Sir.\n\n## Free Local Motion Renderer v1 ist gebaut\n\nDer Workflow ist jetzt wirklich **gratis**:\n\n```text\nFree Production Jobs\n→ Local Motion Renderer\n→ FFmpeg MP4\n```\n\nKeine Hailuo/Kling/Runway/Veo/Pika/Luma Credits. Kein bezahlter Video-Provider. Nur lokal:\n\n- Pillow\n- FFmpeg\n- procedural motion\n- mechanism layers\n- kinetic captions\n- optional NVIDIA LLM nur für Text-Refinement, nicht fürs Video\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/local_motion_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_free_local_motion.py\n```\n\nTests:\n\n```text\ntests/test_local_motion_renderer.py\n```\n\n## Pilot-Video gerendert\n\nFür:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nOutput:\n\n```text\ndata/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4\n```\n\nTechnisch geprüft:\n\n```text\nwidth: 540\nheight: 960\nfps: 15\nduration: 34.000000s\n```\n\nPreview:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4\n\n## Was der Renderer v1 jetzt macht\n\nEr erzeugt lokal bewegte Szenen:\n\n- Scene 1/2: procedural cinematic motion mit Car/X-Ray/Preis-Risiko-Tags\n- Scene 3/4: Mechanism-Layer-Reveal mit Preis, Reparatur, Standtage, Marge etc.\n- Scene 5: kinetic text overlay\n- Caption-Overlay mit sicherem Text-Fitting\n- sichtbare Bewegung über die gesamte Laufzeit\n- Metadaten-JSON neben dem Render\n\nAlso nicht mehr nur statische Preview-Cards. Noch nicht „Netflix“, aber auch nicht „PowerPoint im Witness Protection Program“.\n\n## Timing-Fix\n\nIch habe außerdem einen wichtigen Fehler behoben:\n\nVorher waren die Free-Production-Jobs auf Szenenlängen von 3–6 Sekunden gecappt. Dadurch wäre aus dem 34s-Pilot nur ein 25s-Video geworden.\n\nJetzt gilt:\n\n```text\nRender Manifest duration\n→ Asset Plan duration\n→ Free Job duration\n→ Local Render duration\n```\n\nFür den Pilot jetzt korrekt:\n\n```text\n1.4s + 3.6s + 13s + 10s + 6s = 34s\n```\n\n## Caption-Fix\n\nBeim visuellen Check war die Caption in Scene 3 zuerst zu breit und wurde seitlich abgeschnitten. Behoben:\n\n- adaptive Caption-Layout-Berechnung\n- max. Safe-Area-Breite\n- Font-Fallback\n- Tests gegen abgeschnittene lange deutsche Captions\n\nZusätzlich: gekürzte Overlay-Texte enden jetzt sauber mit `…` und nicht awkward auf „und“. Ein kleiner Sieg gegen deutsche Nebensatzmonster.\n\n## Tests\n\nFull suite:\n\n```text\n51 passed in 3.32s\n```\n\n## Git\n\nLokaler Commit erstellt:\n\n```text\nff5df62 feat: add free local motion renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nWichtig: Push zu GitHub ist aktuell blockiert, weil in dieser Umgebung keine GitHub-Credentials verfügbar sind:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nSSH ist ebenfalls nicht authentifiziert:\n\n```text\nPermission denied (publickey)\n```\n\nDer Commit ist lokal sauber vorhanden, aber noch **nicht remote gepusht**.\n\n## Weiterhin ignoriert\n\nDie versehentlichen Telegram-Review-Dateien sind weiterhin untracked und nicht angefasst:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich den Renderer v2 bauen:\n\n```text\nLocal Motion Renderer v1\n→ bessere Mechanism Visual Grammar v2\n```\n\nKonkret:\n\n- echte Objekt-Layer statt grober Rechtecke\n- bessere Auto-X-Ray-Darstellung\n- per-scene motion presets:\n  - hard zoom\n  - layer peel\n  - risk meter fill\n  - wrong-answer collapse\n  - final map glow\n- einfache lokale SFX-Spur via FFmpeg/Sine/Noise/Clicks\n- danach wieder ein neues MP4 zur Freigabe.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEYr0RIa-vibCvsI5_3VcvR-rML1XR7oiB-Lupu8inqpbH8xYl4xyyONo6xT8bmDh1E33XSPVPHf2e21idZO-fmAnSICdyOVdqS8mS1gR93DC9-OeLO2xY2_6n4-1IuVMzcYZDrAyE1OkO5awj5yGPtxnwkQGRHf3PyRSzVB4InC842Vc4k8GkqKqTNkd6Q-2ApufqMoicd4kH82ZI8mFYyk9MHOYnc6q_YZfCnfjXLtyjG_PfNkfQ94t69ptFN_MuTs2PLht8BJVa44ATQu3ryrlbt93XIKFZyXOQel-YX2V8PQyn6DPa7Q1dnlILyd59znBGSbRduSqWdgkNPy6pHFzLXJfk0h05McycYP2pWKl75-89DQV0Z5bM7-6NYauQa0L4Pz-VYwKQ82FRufQbF_orGYiGp3pjnnnLK9tVyUbP2yySHjVpuHfQZ15T32SHQNoqndW4KppBMUMBiKt_ATs59bZvEGG3os69r2QTizjcOc8BCojlOETu44Mqpjq2Iw_B3ZFDedIlU340o-Q01iAcZqIi8lDGUDbH2C_7kJ1VamNJoB0JDQMzQveGYQSwO-t_1BLYOh-t3mP4uRN6wHhmhXds0q6m5muDr1NaG6Y9Z7mCtPIPy7KiRHan5FievtEI_570IfI_CUcJ93YR9hCcLWrZg4ukKXIU55_zNK9XALwcljv6S8J6c1ANMDVyltn5in3U1_nnTUgtk1AyrLBEl-98BCdc08RLwdkWijhoVHLP2EKqNNkyjwU-1RXmNT5v1bhJCmk-YrBzv5aDNkiD5NnF7YUqB_nVHw46aBy906vAhmBG_2ZiReVSIwJjk1JizolPXINNzrbqOd3F3MZMxR_pqO6XbWYUT6L7eIz6jFYzlQ3AJg1g9tvL5zhXGnKWYIAWja_R7kh2YDIRMxZ-fhle_hYISHECrwJLgkO28VBg0m98tRRnMbXB7yLjOtXZuljJ0R_CmPw9HANaIp5N656MD5RdFU17CzZbbXnHGk1uXbPmcjtWB1_slYOHPyEyzSegNNZDg2GohyVorteVoIjRzuqH8toQvfRw0La43aMfRg2BmbOI9ON8C-wBGBuhfxD03Q0rtaqeZ3syXWd3q006KIk_tWA6zoza9BRfyt_cKWNeCZdby1TtvLygBOTRnyKc4fiqWzoHdlciuyLc4fAxvOEv3Jf0xBUu7TFA-YuNZ2U9YDX53PvujeCQFZt2CukFN7tZ5cYW8HCRwv2kMWaANGNos4Fh3M2dZxjWiBDUQ8umv69wAJmaiU64dD3UaFas6kB0IpWX17XHScLaw==", "id": "rs_0d1a4d10654d56b4016a118af390ec8191a42e816e4c752c28", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Erledigt, Sir.\n\n## Free Local Motion Renderer v1 ist gebaut\n\nDer Workflow ist jetzt wirklich **gratis**:\n\n```text\nFree Production Jobs\n→ Local Motion Renderer\n→ FFmpeg MP4\n```\n\nKeine Hailuo/Kling/Runway/Veo/Pika/Luma Credits. Kein bezahlter Video-Provider. Nur lokal:\n\n- Pillow\n- FFmpeg\n- procedural motion\n- mechanism layers\n- kinetic captions\n- optional NVIDIA LLM nur für Text-Refinement, nicht fürs Video\n\n## Neu implementiert\n\nCode:\n\n```text\nautoshorts/local_motion_renderer.py\n```\n\nCLI:\n\n```text\nscripts/render_free_local_motion.py\n```\n\nTests:\n\n```text\ntests/test_local_motion_renderer.py\n```\n\n## Pilot-Video gerendert\n\nFür:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nOutput:\n\n```text\ndata/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4\n```\n\nTechnisch geprüft:\n\n```text\nwidth: 540\nheight: 960\nfps: 15\nduration: 34.000000s\n```\n\nPreview:\n\nMEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4\n\n## Was der Renderer v1 jetzt macht\n\nEr erzeugt lokal bewegte Szenen:\n\n- Scene 1/2: procedural cinematic motion mit Car/X-Ray/Preis-Risiko-Tags\n- Scene 3/4: Mechanism-Layer-Reveal mit Preis, Reparatur, Standtage, Marge etc.\n- Scene 5: kinetic text overlay\n- Caption-Overlay mit sicherem Text-Fitting\n- sichtbare Bewegung über die gesamte Laufzeit\n- Metadaten-JSON neben dem Render\n\nAlso nicht mehr nur statische Preview-Cards. Noch nicht „Netflix“, aber auch nicht „PowerPoint im Witness Protection Program“.\n\n## Timing-Fix\n\nIch habe außerdem einen wichtigen Fehler behoben:\n\nVorher waren die Free-Production-Jobs auf Szenenlängen von 3–6 Sekunden gecappt. Dadurch wäre aus dem 34s-Pilot nur ein 25s-Video geworden.\n\nJetzt gilt:\n\n```text\nRender Manifest duration\n→ Asset Plan duration\n→ Free Job duration\n→ Local Render duration\n```\n\nFür den Pilot jetzt korrekt:\n\n```text\n1.4s + 3.6s + 13s + 10s + 6s = 34s\n```\n\n## Caption-Fix\n\nBeim visuellen Check war die Caption in Scene 3 zuerst zu breit und wurde seitlich abgeschnitten. Behoben:\n\n- adaptive Caption-Layout-Berechnung\n- max. Safe-Area-Breite\n- Font-Fallback\n- Tests gegen abgeschnittene lange deutsche Captions\n\nZusätzlich: gekürzte Overlay-Texte enden jetzt sauber mit `…` und nicht awkward auf „und“. Ein kleiner Sieg gegen deutsche Nebensatzmonster.\n\n## Tests\n\nFull suite:\n\n```text\n51 passed in 3.32s\n```\n\n## Git\n\nLokaler Commit erstellt:\n\n```text\nff5df62 feat: add free local motion renderer\n```\n\nBranch:\n\n```text\nfeat/initial-growth-engine\n```\n\nWichtig: Push zu GitHub ist aktuell blockiert, weil in dieser Umgebung keine GitHub-Credentials verfügbar sind:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nSSH ist ebenfalls nicht authentifiziert:\n\n```text\nPermission denied (publickey)\n```\n\nDer Commit ist lokal sauber vorhanden, aber noch **nicht remote gepusht**.\n\n## Weiterhin ignoriert\n\nDie versehentlichen Telegram-Review-Dateien sind weiterhin untracked und nicht angefasst:\n\n```text\nautoshorts/review_package.py\ndata/telegram_reviews/\nscripts/generate_telegram_review_packages.py\ntests/test_review_package.py\n```\n\n## Nächster sinnvoller Schritt\n\nJetzt würde ich den Renderer v2 bauen:\n\n```text\nLocal Motion Renderer v1\n→ bessere Mechanism Visual Grammar v2\n```\n\nKonkret:\n\n- echte Objekt-Layer statt grober Rechtecke\n- bessere Auto-X-Ray-Darstellung\n- per-scene motion presets:\n  - hard zoom\n  - layer peel\n  - risk meter fill\n  - wrong-answer collapse\n  - final map glow\n- einfache lokale SFX-Spur via FFmpeg/Sine/Noise/Clicks\n- danach wieder ein neues MP4 zur Freigabe."}], "id": "msg_0d1a4d10654d56b4016a118af4fb808191a62d6cf14f15f054", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_2XUgbhw9kh3rtgQRNxuZpgQt", "call_id": "call_2XUgbhw9kh3rtgQRNxuZpgQt", "response_item_id": "fc_0d1a4d10654d56b4016a11903b7a808191bb9d3711e8c12efc", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Researching video providers**\n\nI need to gather current information, so using the web seems essential. I’m considering relevant social video providers, and while I don't see a specific web search tool, I can use the browser for navigation. Additionally, I might use Python requests or other methods to access the web from terminal. I'll also check if I need to load any relevant skills, especially concerning social media and web scraping. Overall, I'll focus on researching and updating the registry with free tier provider specifications.", "reasoning_content": "**Researching video providers**\n\nI need to gather current information, so using the web seems essential. I’m considering relevant social video providers, and while I don't see a specific web search tool, I can use the browser for navigation. Additionally, I might use Python requests or other methods to access the web from terminal. I'll also check if I need to load any relevant skills, especially concerning social media and web scraping. Overall, I'll focus on researching and updating the registry with free tier provider specifications.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZA7e9G1QtD4hbQaI1rS2Gt_N4ryh3R8gasJAlNBMA2-828kPAGMQQV3c5uvesxJhhYD2pKokR94mJGzc7UC_XvAERlZZa1MpJkEhdzKtYE1DtC_gtpivsHpgO19WzRgLr0UiXEZktdSRcpR0DqnldDy2Wxm844w_ydKCGNGj06QoDutBz3qgvPjVJ3rgMooLV4f9_vduW6UFabvx-__9P4AZhWBVofBwBl0vugRazhEVZZ_ks4o4opd9pjMQC-ngrQ9wBnMgmdh4DOe98h7K-BbYr0ERcg8ETJlKEbSScfUaAgqBrY1FQlYzgDOhOIwSrea-T6-NXw9V5BGHI08a1aXSkM0D5X4Avexm_40oFuJJqsldf_ufxaaHssk8_i-SXV3zEkeUAyNBac39TrR51Lt5L4wO69DVhfaBSe5rY8YXPAgTiZ5DGDYe1RSeeKbdazkJkOU2nJqDk1WZRtQ7ONzK_gd8qeBZcV5NKH6B2i_R9X8p6IyQzYF6LVYhoq7df9z5PZBdoMVSW5vYiBV1QkMiPMo5LkcxR0eZlLvIx-tC1Htg5HnZZfbCGDl8Qc4nWTByVA2b5k36xTKghta0A-2uQzOkvsIIgfAYM811nAlovimfZ4_M2Z1AwlHiko7Imm4_xOzVOKEzy_2vgByh9JgMP1NTgDqVdW3yhd7iyWGPrt9oqbQNrU98ijGVFhwwW0ZYjTutxDLt7bVBTIdTvrYneWQtLw280UirK4gu0hPM28l5Gd3SykgE6pRObOfxzmApE5Bso87HKotOMdFNDx4IpYryNdBlkdRF-2NAtulN30Co_tCSvKIczhrU6b2hIxiQRBNRzpVkMDlu5tTIX7HysMU5sPnzw_jlFMgCBaO6DFqxb1tKk7dolmiQkPT0-7q56Rhmrp9Ws-jsEdRTBspGVfvmUfihTCA3hDJbgxgck5epVSnPfQloy9xZJWWQp6Ewqvw4IZz92dFHQ7SJFcJE3jAtUph2PJbMvjUHfXaH_A6viu-R9n3OtDKrw_twxROmqsGw1Kfj_qEXeii-eePELZDVhiJSGX4m-ZyUqOHejrjLX3VGQSSZaSQ1eOq-MfnFiKD5aPnnfS0HPgukQGDOz0QqtLvLrmxi-v-1F1KYVtftjkZkyRpwzNPmsdu8ty8LVspmC-5xaj-sL_8dI6W2B1TrTl5yV1D8_yZzBc2Ua_9KNHheMffa0H46i23eHNU6kf2tpv5Nov7yzbOAp2gR5BOH35p4eb579sZar-Yk2qgZRR6rHYNUSgpvt4qp2HyfAGpdySnKPkfikN5zmfUZsCOpQQMcFzElMwYfPJsHxdMW8IC-bODPcpbru20gBT6WlX2UcCwDweQlVCR7vIU80gEsmV-CiBrE7GERD63kjNf5z2oybU54IxspW0WtyHiw-PWHnceQ4D8UqLufzstG0krk4iJ-YHZ9SwQTTdJ5eyagrYu1xY2guD27lqgn9vIKvYiksfbPqZNPkRcnpVpva_NpC-7kxGcTbfAvCwNJ8N3ZTfk3iagFPbw_rFLkwrIqhTGQui3KpLeI_YN0m1DgV1u9WZHJPoca_GTHjy-ieH4qdP84bof_rEehhpJgPLuN4B1QvTpHmtDvaIMe4xVvYOk0L_-I9TuMX7QvqqTjplRso1EwX2vKHY7YaqirR1H4aCj-ugBRs3E-sAzJ7NyuA0n-PCUkSa1YV7sp37NIdfUtx3Sm-24wvfIfNOMeSeJGdYdFsvLAdeFteAGYOG39nOwIikgsJraSANciuiEC8zQCdGgs3gLBZ6VQTNeH8gDddyLI7xSFisbVglSuhOW0TLfzN0UKtOABct8UlAlw-zv5Stj1PzK8_2tGT0kgSVB-oiRzI0Usjf3GgWUYZISaZnRkJ4mKCJisVa8Ennb1PfdsPMMolNjjvT7j3xVJxOUuro9BieGDCjscDJ0nC0L6D7o8G4cTZDaUsrtVwhDJ18=", "id": "rs_0d1a4d10654d56b4016a119037086c8191995124ce9bd4fb1a", "summary": [{"type": "summary_text", "text": "**Researching video providers**\n\nI need to gather current information, so using the web seems essential. I’m considering relevant social video providers, and while I don't see a specific web search tool, I can use the browser for navigation. Additionally, I might use Python requests or other methods to access the web from terminal. I'll also check if I need to load any relevant skills, especially concerning social media and web scraping. Overall, I'll focus on researching and updating the registry with free tier provider specifications."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[skill_view] name=social-media-shorts-growth-engine (8,591 chars)", "tool_call_id": "call_2XUgbhw9kh3rtgQRNxuZpgQt", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_uu6MMkTuQw4JDJBLP5FzVNKz", "call_id": "call_uu6MMkTuQw4JDJBLP5FzVNKz", "response_item_id": "fc_0d1a4d10654d56b4016a11903f37a88191bc33bf555771b8aa", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"web-scraping\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[skill_view] name=web-scraping (5,754 chars)", "tool_call_id": "call_uu6MMkTuQw4JDJBLP5FzVNKz", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_EJDz0ZsP1y8hxElQKGrthAVk", "call_id": "call_EJDz0ZsP1y8hxElQKGrthAVk", "response_item_id": "fc_0d1a4d10654d56b4016a11904365b0819182a5048114ab0955", "type": "function", "function": {"name": "todo", "arguments": "{\"todos\":[{\"id\":\"research\",\"content\":\"Free/freemium KI-Video-Tools für realistischere Shorts recherchieren\",\"status\":\"in_progress\"},{\"id\":\"decide\",\"content\":\"Gratis-Workflow-Entscheidung für AutoShortsBot ableiten\",\"status\":\"pending\"},{\"id\":\"implement\",\"content\":\"Provider-/Free-Tier-Registry und Job-Specs im Repo ergänzen\",\"status\":\"pending\"},{\"id\":\"verify\",\"content\":\"Tests, Artefakte und Git-Status verifizieren\",\"status\":\"pending\"}]}"}}], "finish_reason": "tool_calls", "reasoning": "**Searching for information**\n\nI think I need to do some research, which probably means I should utilize the browser search. It seems like the best way to gather the information I'm looking for. I'll start by searching the web for relevant details. Let's see what comes up! It's all about finding the right sources to help me get the answers I need.", "reasoning_content": "**Searching for information**\n\nI think I need to do some research, which probably means I should utilize the browser search. It seems like the best way to gather the information I'm looking for. I'll start by searching the web for relevant details. Let's see what comes up! It's all about finding the right sources to help me get the answers I need.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZBDW362V26kh0tgD5Q2s2N7gb8hXisi5s4PWsCVLKe_qxgRtRQJ2qX1RotcsdAEZFSduM9iYKylgHjQ1ebue9mjTdBjXQa7YByg53RRxA2GQkqkhK4GSulkhrsUfmhzT26kR3A1HZ_WePzIptzD6aPz8hQ4V_-XwhgyPAq8_DvZHL0kvd-V0lWK7OmjjaE4onm8Fth0np3xJg9OvFQFM9iiO4FAOXJyareIxykDOiVezVejjUhJYFP_LSUdAmY1H3WL5J-FruiFK6Ez4q1TtFWVwMjmO9m-CTGvuWJ152ZGFxuk4_4p1FunagGmM4aMG6OBOAYQxpdC42_AY1i6ztgX9J_aoOS6R25kq6csdXxfuDCkBYCTdr-rZxT8U2utL46zp6ykyglM1i7w3DqE8yhcRyxfmi4STsxzn9XKS5bOfwK-F8uIvDk_hzRDW-7uTUGGQckVIZ-0Ezt261i21-Gua0VqooxlxlCpEu26YRLhCnPoYdhRcnSzNzVW9_aMXH6zkKN4q6losn5SoVQf8eEbBuoIg3Oys54koblvTCKaYuj8eYoWro2-AIDVfMpKAHQSnay5NqyhVLH8XiSY-ha8QTy9HjPpLFAePtmYqfB4v1sPP-dO3TG2zUfz3Y2dSUHmuu8Tb5cGD-q99gEBjLMEu1TfbVKHWRvmNvXp9lef9p-Wl_WzV27Lt58gQlrbY-pCPzDnIv1IFPShe2CjeVv_Szz2o1DFJMQDPsvyc8__VlY3eq0kUTXkYPat4cnFhVzjPTIlyLZ8dl3fN59LSOjnuxEYZL2tAmONoO8ptrchTAOrrNpCyMQpSbrxw0eocQ-mUDbRuH4M8Cs1ZW382FJm2gm84RamKFmtVljYzGi2G2kiTDkmyLbGtVQK9MCZSEUGlBSXK9mDqkz5S-MMUTqHRo-RpPv1h3U_Gx8-yS19aDILfxLWeEjhNLjV05aAQJ-Pp6GjmIeeORp-KpHDMmyPou72T2tbOFyBRiIXKys75R8=", "id": "rs_0d1a4d10654d56b4016a119042194481918fc9af8136f91f3b", "summary": [{"type": "summary_text", "text": "**Searching for information**\n\nI think I need to do some research, which probably means I should utilize the browser search. It seems like the best way to gather the information I'm looking for. I'll start by searching the web for relevant details. Let's see what comes up! It's all about finding the right sources to help me get the answers I need."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_EJDz0ZsP1y8hxElQKGrthAVk", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_5xnEAlvR5BPXMGOOmX6TzNcq", "call_id": "call_5xnEAlvR5BPXMGOOmX6TzNcq", "response_item_id": "fc_0d1a4d10654d56b4016a1190484b7c8191b58a083f0167feae", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://www.google.com/search?q=AI+video+generator+free+tier+Pika+PixVerse+CapCut+2026\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://www.google.com/search?q=AI+video+generator+free+tier+Pika+PixVerse+CapCut+2026 (920 chars)", "tool_call_id": "call_5xnEAlvR5BPXMGOOmX6TzNcq", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_3cT1j4EhetqJhjCdFb9PVYZy", "call_id": "call_3cT1j4EhetqJhjCdFb9PVYZy", "response_item_id": "fc_0d1a4d10654d56b4016a11905270008191a09ad3b6a0864349", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://www.perplexity.ai/search?q=AI%20video%20generator%20free%20tier%20Pika%20PixVerse%20CapCut\"}"}}], "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZBSswFt1P3ojkDlPkDkeJBTOF7SVLkPv9Ppff697vTOIMRnhKg12EzsFB5Jv3zxa_zYrFxcMl_N2P5F47k5UEuZtN9B_Am68dGvk-g-Q_dQICBKpPuUVi_HBa_LSM2SdxbUpBG1WGDJaxdaC1SHQsrdF6hisRNPwcbsM3XyHoosNnGd_Rpjqc65Ny1HhcDdrkYJAspvKJN8aj_T9J-JeRFWTIbkbKm8sQg_f-QLYL-0EC0LUQnUjf4qYPtGVO1BztEs5kT-V1cCoofMdrmWhzj0LvaTKh1gCTQ-2oH5UBL_dt5TWcKQ9w510ZwnMpcFP0-uBseEZVL_5VEWZ9SeuguBfVbBVjvgmW1pZh5zHR6IvW_QmGt7nhwe10lYbDXQRmeME2qugColG9215wGT4pUWC5wv_jxh6BGp5Z1-uTU742hqZAXCfNdN81-o6ffGGXoWs4263xPEUEcRIc_648w_xUwYG_5r4BmmAg0ClULMqLS54Zoa2lltXSZSQFhjZNJhR-43gnbPXi3iTZmh2mm8JoZfjrJ46jtDIt3Fl3mGjCaZh3ommFTli617O4McwLJtFkMFnzaIKf0jGWS6HeLOYHg9vvHljy3OjE-uOzAqWBGYv8N7nttzJVKp9cXxJzhvaHyKdRwGJeXlR0Db0NuoQlZdbem042akLFuItu2PM3CIgOs3Z99VKp2qiccQGvvVRCeqyC4F3m_0BefEVrwypcObAT_-Rqyme7gzznWakLGpV6bm42wKEGKw4J9vZy5_B0ZbtzjzUe15as7TNPDEsi6kqOhbSw-eUUPdGdLaU-n2kaaA7dGQ4JhNHh1qckJr7LPrL4Z0KWLCpz9ErZk1xxQTszWo-eDgTZo8oqhZuA4MyZZ6fw9jCU-q4jt2-2ow293Qn4ix6iEA6AdP98N-fwwYuG9GPHm44Ez_TPJDaMF-EaNMofIwXMdPa-lNVRsRjQ6M5zeZugqlj4o5CQyZLA==", "id": "rs_0d1a4d10654d56b4016a119051ca988191aeb6764b835189d5", "summary": []}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://www.perplexity.ai/search?q=AI%20video%20generator%20free%20tier%20Pika%20PixVerse%20CapCut (922 chars)", "tool_call_id": "call_3cT1j4EhetqJhjCdFb9PVYZy", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_GZqN5iJUZRjYV7WOgw8eWUrm", "call_id": "call_GZqN5iJUZRjYV7WOgw8eWUrm", "response_item_id": "fc_0d1a4d10654d56b4016a11905b765c8191ba368c92814d36cb", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://pika.art/pricing\"}"}}, {"id": "call_8AxwgSUcPXB5tkb68DlRrPfi", "call_id": "call_8AxwgSUcPXB5tkb68DlRrPfi", "response_item_id": "fc_0d1a4d10654d56b4016a11905b76888191a413be6a5e801c60", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://pixverse.ai/pricing\"}"}}, {"id": "call_a31wLqT0Ct8mGVwXxJm3CHdU", "call_id": "call_a31wLqT0Ct8mGVwXxJm3CHdU", "response_item_id": "fc_0d1a4d10654d56b4016a11905b769481919ec78b135958c28a", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://www.capcut.com/tools/ai-video-generator\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Exploring alternatives for information**\n\nI need to find an alternative approach, using specific web pages directly for accurate information. I'll consider using a browser or a terminal curl command to access these pages. I'm particularly interested in finding current facts about CapCut AI video generator, Pika pricing, PixVerse pricing, and possibly Hailuo, along with runway. I think I should focus on official pages that list credits and other relevant details. Let's open those official sources!", "reasoning_content": "**Exploring alternatives for information**\n\nI need to find an alternative approach, using specific web pages directly for accurate information. I'll consider using a browser or a terminal curl command to access these pages. I'm particularly interested in finding current facts about CapCut AI video generator, Pika pricing, PixVerse pricing, and possibly Hailuo, along with runway. I think I should focus on official pages that list credits and other relevant details. Let's open those official sources!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZBboAYDo3R19sN8APkcaLK30a49o8FDnCte2PY58eKckiKDq-h4UaTNF6T57COwd7lzuRn3gB0SDXsJWrKny4YuUB_rcK885K0aRVZNfPR4cORez-qm4HuHfVI4rXA52jQxAxdH_9oBhXQ9Xq5ofnV6s5Y8Nn_K17oK9HokuQ5VhaTz8RI0IORlHMUYBrFB7FVRQ1-cbAt8zrOMNJKdg3KMSIn8TrdtI4BKRAuBcN8jKD7lxb2HkXr3LpNiqEcTFKsnxVsBlv6cougazicFpqCRdHqIXlhu4Lai11aXVoBKkUpL7ame7p5TNjDKhaMkJ7KfjhSUK9dsFE43qouhWqzrvvpUXeFwGQU3RJkDxIK_s41NmclZ3JBFHk5plhpSrGWcDHCstYjr-1pMV8eG40YpbFAcGDAWoUOYyDNQWUuCvCICIE4M51NJteAnvGoCu4w6kpZbf0qMdUAayht2Ye2sr4sI6OSY1XnxxXtP8IigXR_arp2sMt2AsktqHWL8S3BLoN4uZj73hzZSytB88PGYGlws56EsOwICWsRcEwkj7KCK20tfmTWfCV3HrUwfnjArZoTrDrjgTqE9YbA75_72Ap4vRLZ0q1oW_uKXXeIdEoZkxvAr-tD58_A3g2PCgT-XD8ibJ3i-kBSpRmfiXqD8C-mTRokabKLZ5KWv3ECavKl693hsrC2wD_8ui0yCeDloG2vjxHI_KAzLgXiutv7D_4Ns-gjccDAMf4YSw_ewmawCp7o9h5eyCTkAHr2few2vLLUi8caG0s4DBOPibyojglyz2xOdMM7g0RwH2rkPNPaG4s_V1CAq4ZljMcpbsqW8muhgpMfjjmrumVJ5U1EqQtx_Zn7RQnWLw7JeNuxM-NwxXjajhgXhhmA_TczSjoosQYKeFdTgjGpiPDK86S5vybE0VO0Wi6fzBCwzmfwdw5Zo5WVznuRGWskDdWw_Ksp5J_qGEApDQSJI9qLa8zZRbA0UmHmC7KVeAWhwBZQCWwyclfoNBtHOJ5LjkwIc1PuOFYawmgad83ePIBzuQ_BSV3if9b4ne4jaqT5Dw0HC6UifC7vVM4yh7x23-BSQy_RRZlE7OXkZzt_esJizHg1DrE1hktgW1ICfep918RxavQlZxzInDmN0Kb6jVCoY9AbYarXGuL6MvB6zN5JQ6QAhPwjCzLeMREAWbBmcwMzhlGRw5cxd3HVtllE7-PgvflJ3DcOHnQNnlXQQHWNBkKlew5EXFGjb3Ag-IWggVh5lSzc1PUEDF-bvRZN9s2-iinGxvwKLWkf7aErIKdbyqAmYO6SbnYxyAbH1NqzR2buOJxQ=", "id": "rs_0d1a4d10654d56b4016a119058334c81918e2e6739f7199693", "summary": [{"type": "summary_text", "text": "**Exploring alternatives for information**\n\nI need to find an alternative approach, using specific web pages directly for accurate information. I'll consider using a browser or a terminal curl command to access these pages. I'm particularly interested in finding current facts about CapCut AI video generator, Pika pricing, PixVerse pricing, and possibly Hailuo, along with runway. I think I should focus on official pages that list credits and other relevant details. Let's open those official sources!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[Duplicate tool output — same content as a more recent call]", "tool_call_id": "call_GZqN5iJUZRjYV7WOgw8eWUrm", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://pixverse.ai/pricing (1,085 chars)", "tool_call_id": "call_8AxwgSUcPXB5tkb68DlRrPfi", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://www.capcut.com/tools/ai-video-generator (8,498 chars)", "tool_call_id": "call_a31wLqT0Ct8mGVwXxJm3CHdU", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_2WSHT6ZpBrx4WRjbfnIQWGVW", "call_id": "call_2WSHT6ZpBrx4WRjbfnIQWGVW", "response_item_id": "fc_0d1a4d10654d56b4016a11906b3dcc81918d93fdb59db80b12", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://app.pixverse.ai/pricing\"}"}}, {"id": "call_Wn5V1FrDzZ2l2gfar7Wgvwfs", "call_id": "call_Wn5V1FrDzZ2l2gfar7Wgvwfs", "response_item_id": "fc_0d1a4d10654d56b4016a11906b3df8819184b071aaf42bafea", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://pixverse.ai/\"}"}}, {"id": "call_AOLghzHwF8OhRUD1kSqD6Tj1", "call_id": "call_AOLghzHwF8OhRUD1kSqD6Tj1", "response_item_id": "fc_0d1a4d10654d56b4016a11906b3e188191b05aae1a5d8c011e", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://runwayml.com/pricing/\"}"}}], "finish_reason": "tool_calls", "reasoning": "**Considering multi-browser navigation**\n\nI'm wondering about using multiple browser tabs in parallel. It seems like the last action might overwrite previous ones, but it might still provide enough output. I should try to focus on locating PixVerse and perhaps explore the pricing root there. I need to be careful with how I navigate and confirm that each step is yielding the information I want without losing anything essential. Let's see how this unfolds!", "reasoning_content": "**Considering multi-browser navigation**\n\nI'm wondering about using multiple browser tabs in parallel. It seems like the last action might overwrite previous ones, but it might still provide enough output. I should try to focus on locating PixVerse and perhaps explore the pricing root there. I need to be careful with how I navigate and confirm that each step is yielding the information I want without losing anything essential. Let's see how this unfolds!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZBrNkVMPkNX4OiFXEe7PrFMrgpaDU8Vpyv7GKvs85LnHIRdxo_Oq16C5sHHO4ogq9RdNgijhKrQUNi1AGgfIENEovyyRBMkwDC4LdBlNWzcLA-bqzUgeRPiztE2ceWfH8GJVm7oDLNrCcXg73ysDfeSPhKSLg_TmurY2j_v6oCZHP_qKVK42WdYhM3oBrhcrRVGLHEBe5GYk_L5RsIbq0N-SS96OPhmavU7U7xSuudEJlZN21ofWjQAvd5GRYUzYxKO0tmX4HpwrW2290xbwEh1RRY7XUhEjvp-l79JL-jrPO0MRKaDvWwhI6Z4f2IQlsRIRXaZj9NBfe6M2fZXsk1gqdKYhKxgw9-5Y6DPKOqsUIflhZhEmogQd-x5Zlo8OK8xfa1l9JzPONXUxRKgjcrjDG0WUL3Gm7j1VYiTEgYFJfE8R9GRNUkli37iobHQAI6j-DGRpd-CZPOvTuq5IHKyQCXp3os0BHbzC66-C37wK2JU8vauM1rJMM5Qe_t8aYEY4iXAza7meatbm_gsL2EBWvOWHIJ_CswQteMdDiCEmzfKxMJnj318_V2fxrHQej4MVIZ3qTEt4UNlIGDRhnYr9BGEmiWgqf1Dx0BnWfCB32sNaaRCAvMcMV65nTpSC57oC-zJ3smXpgeM339aY1CQs234KZyrQxnHoavhFXNSmJj7eV6Lv4mhb4817SrA9mhekY5F7PrBaIqtQ5Wh2-mc7EHrhar9UhlPI_I6tAFrXVzhXR6uHWJF6H_ZXzBTFx2Z8zRb5r54jXl-mUi_GBfAfG0oW8LdTuiDDoq-nqrEe83nHJOGHqWM0qZK-xMruxI0cvlpzPkUyX0vUJO-bfSL4UfeQGnIxah5eQILSNk0AGFe3ZvlFfXmZm3Mk5nxIyh0AIoPbGTrQemdpp1o0-EmyA-IJxLbEphu3WtKyAz_9Fq11PzdieB-S7OWtgMa0Z8Oia1lFLZv5cuDqGRS9TZp6KtBBSMmLdpmE7hUeJMuf7kKbwG-vHAOhdZqiuAit2yDgYf22ZgpbPTEzCMWWnp6g83ORp2wuhbgRKCMtsXvhQtIRSvTWnJc0VmY2vkAyHr1S16KpIPhkKvFUFh4oAi4gQ==", "id": "rs_0d1a4d10654d56b4016a119068c66081919fe16868de3f00c7", "summary": [{"type": "summary_text", "text": "**Considering multi-browser navigation**\n\nI'm wondering about using multiple browser tabs in parallel. It seems like the last action might overwrite previous ones, but it might still provide enough output. I should try to focus on locating PixVerse and perhaps explore the pricing root there. I need to be careful with how I navigate and confirm that each step is yielding the information I want without losing anything essential. Let's see how this unfolds!"}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://app.pixverse.ai/pricing (1,326 chars)", "tool_call_id": "call_2WSHT6ZpBrx4WRjbfnIQWGVW", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://pixverse.ai/ (8,594 chars)", "tool_call_id": "call_Wn5V1FrDzZ2l2gfar7Wgvwfs", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[browser_navigate] https://runwayml.com/pricing/ (6,054 chars)", "tool_call_id": "call_AOLghzHwF8OhRUD1kSqD6Tj1", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[skill_view] name=test-driven-development (11,198 chars)", "tool_call_id": "call_Y69A14B8rEVESv8sXl2VsZgW", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[terminal] ran `git status --short && git log --oneline -3` -> exit 0, 1 lines output", "tool_call_id": "call_ZsqByl8LFs10sLmlevMKLdFj", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[terminal] ran `git status --short && git log --oneline -4` -> exit 0, 1 lines output", "tool_call_id": "call_NvpKGhBchBzKkfoVXyLaDW6L", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"fatal: 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_6NMVOe38VzA0TmnAqXNeZYGv", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_snPJADNtDsJXAjfg5o5N7YDc", "call_id": "call_snPJADNtDsJXAjfg5o5N7YDc", "response_item_id": "fc_0d1a4d10654d56b4016a1191a888b48191997b63284edcd214", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Provider-/Free-Tier-Registry und Job-Specs im Repo ergänzen\",\"id\":\"implement\",\"status\":\"completed\"},{\"content\":\"Tests, Artefakte und Git-Status verifizieren\",\"id\":\"verify\",\"status\":\"completed\"}]}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_snPJADNtDsJXAjfg5o5N7YDc", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Ja, Sir — exakt: **das aktuelle MP4 ist noch Konzept/Animatic**, kein fertiges realistisches Video.\n\nEs zeigt Timing, Layer, Captions und Mechanik. Für ein „einigermaßen realistisches“ Endprodukt brauchen wir tatsächlich **KI-generierte Videoclips** als B-Roll/Scene-Clips — aber eben nur über **Free Tiers**, nicht über bezahlte Credits.\n\nIch habe recherchiert und den Workflow entsprechend weitergebaut.\n\n## Recherche: gratis / free-tier KI-Video-Tools\n\n## 1. PixVerse\n\nGefunden auf:\n\n```text\nhttps://app.pixverse.ai/\n```\n\nDie App zeigt aktuell:\n\n```text\n90\nTäglich aktualisieren\nJetzt registrieren und kostenlose Credits erhalten\n```\n\nEinschätzung:\n\n- sehr interessant als **Primary Free-Tier Candidate**\n- offenbar tägliche Free-Credits\n- geeignet für realistische 9:16 Short-Clips\n- manuelle Browser-Nutzung/Login nötig\n- keine paid Top-ups erlaubt\n\nPriorität:\n\n```text\n#1 PixVerse\n```\n\n## 2. Pika\n\nGefunden auf offizieller Pricing-Seite:\n\n```text\nhttps://pika.art/pricing\n```\n\nFree Basic:\n\n```text\n$0\n80 monthly video credits\nAccess to Pika 2.5, 480p only\nDownload videos with no watermark\nCommercial use\n```\n\nFür Text/Image-to-Video laut Pricing:\n\n```text\n480p / 5s = 12 credits Free\n```\n\nEinschätzung:\n\n- sehr brauchbarer Fallback\n- Free-Output nur 480p, aber für Shorts mit lokalem Upscale/Compositing akzeptabel\n- no-watermark + commercial-use Claim ist stark\n- monatlich begrenzter als PixVerse\n\nPriorität:\n\n```text\n#2 Pika\n```\n\n## 3. CapCut AI Video Maker\n\nGefunden auf:\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nCapCut sagt u.a.:\n\n```text\nFree AI Video Generator\nExport the finished video in HD quality for free\n```\n\nEinschätzung:\n\n- gut für manuelle Politur, Templates, Captions, Schnitt\n- Free-Quota öffentlich weniger klar\n- CapCut kann schnell in Pro-Assets/Upsell laufen\n- trotzdem nützlich als kostenloser Editor-/Assembler-Kandidat\n\nPriorität:\n\n```text\n#3 CapCut\n```\n\n## 4. Hailuo\n\nGefunden auf:\n\n```text\nhttps://hailuoai.video/\n```\n\nDie Seite zeigt einen Generator mit:\n\n```text\n768p\n6s\n25 credits\n```\n\nEinschätzung:\n\n- nur verwenden, falls kostenlose vorhandene Credits da sind\n- keine Käufe, keine Top-ups\n- weil du Hailuo explizit als „kein Geld für Credits“ genannt hast: nur Free-Quota-Fallback, nicht Hauptpfad\n\nPriorität:\n\n```text\n#4 Hailuo, nur falls Free-Credits verfügbar\n```\n\n## Nicht als Free-First geeignet\n\nAktuell nicht als Primary Free Workflow:\n\n```text\nRunway\nLuma\nKling\nVeo\nSora API\n```\n\nGrund:\n\n- entweder paid-first\n- Free-Kontingent unklar/klein\n- API/Automation meist bezahlt\n- oder nicht stabil genug für unseren kostenlosen Produktionspfad\n\n## Neue Workflow-Entscheidung\n\nDer Zielworkflow ist jetzt:\n\n```text\n1. Hermes/ChatGPT/NVIDIA LLM:\n   Shot plan, prompts, captions, scene specs\n\n2. Free AI Video Provider:\n   PixVerse / Pika / CapCut / Hailuo-Free\n   erzeugen realistische kurze Clips manuell im Browser\n\n3. Import:\n   Clips lokal speichern unter data/imported_ai_clips/...\n\n4. Local Renderer:\n   Mechanism-Layer, Captions, SFX, Timing, Assembly\n\n5. FFmpeg:\n   finales 9:16 MP4\n```\n\nDas heißt:  \n**Die realistischen Teile kommen aus kostenlosen KI-Video-Free-Tiers.**  \n**Die System-Erklärung, Captions und Montage bleiben lokal kontrolliert.**\n\nDas ist der richtige Hybrid. Nur lokal wird zu sehr Konzeptgrafik. Nur KI-Video wird generischer Slop. Zusammen wird es brauchbar. Welch seltene Gelegenheit, dass zwei halbe Lösungen zusammen eine ganze ergeben.\n\n## Im Repo neu gebaut\n\nNeue Komponente:\n\n```text\nautoshorts/free_ai_video_providers.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_free_ai_video_handoffs.py\n```\n\nNeue Tests:\n\n```text\ntests/test_free_ai_video_providers.py\n```\n\nNeues Artefakt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Was dieses Handoff enthält\n\nFür den Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nwerden jetzt **2 realistische AI-Video-Jobs** erzeugt:\n\n```text\nscene_01 → realistische KI-Video-Szene\nscene_02 → realistische KI-Video-Szene\n```\n\nProvider-Reihenfolge:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur Free-Credits\n```\n\nGuardrails:\n\n```text\npaid_topups_allowed: false\npaid_subscriptions_allowed: false\ndo_not_buy_credits: true\navoid_pro_templates_or_paid_assets: true\nexport_without_baked_captions: true\nfinal_captions_added_locally: true\n```\n\nDie Mechanismus-Szenen bleiben vorerst lokal:\n\n```text\nscene_03 → local mechanism animation\nscene_04 → local mechanism animation\nscene_05 → local kinetic text\n```\n\nWarum? Weil KI-Video-Tools bei präzisen Layer-Erklärungen oft halluzinieren. Realistische Clips: ja. Exakte Erklärgrafik: lieber lokal kontrolliert.\n\n## Beispiel Prompt für Scene 1\n\nDas Handoff erzeugt provider-ready Prompts wie:\n\n```text\nVertical 9:16 realistic cinematic short video.\nA realistic creator at a desk late evening, laptop open to an AI chat,\nthoughtful but in control, hands moving between keyboard and notebook.\nMood: focused, modern, high-retention social video.\nScene intent: 3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\nCamera: slow handheld push-in, subtle parallax, natural micro-movements.\nNo captions, no readable text, no logos, no watermarks.\nLeave clean negative space in the lower third for captions.\nThe clip should feel like real b-roll for a mechanism explainer, not a slideshow.\n```\n\nWichtig: Captions kommen später lokal drauf. Kein KI-Gibberish im Bild.\n\n## Tests\n\nFull suite:\n\n```text\n55 passed in 3.55s\n```\n\n## Git\n\nNeuer lokaler Commit:\n\n```text\n9f41a9f feat: add free ai video handoff specs\n```\n\nDavor liegt noch:\n\n```text\nff5df62 feat: add free local motion renderer\n```\n\nPush ist weiterhin blockiert, weil GitHub-Credentials in dieser Umgebung fehlen:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\n## Antwort auf deine Kernfrage\n\nJa: **Das Endprodukt soll realistischer werden.**\n\nWas du aktuell gesehen hast, ist:\n\n```text\nAnimatic / Motion Concept / Local Placeholder\n```\n\nNicht das finale Video.\n\nDer nächste Produktionsschritt ist jetzt konkret:\n\n```text\nPixVerse oder Pika öffnen\n→ scene_01 + scene_02 mit Free Credits generieren\n→ Clips herunterladen\n→ lokal importieren\n→ Local Renderer ersetzt Placeholder durch echte KI-Clips\n→ Mechanism Layers + Captions + SFX darüber\n→ finales MP4\n```\n\n## Nächster sinnvoller Schritt\n\nIch würde als Nächstes bauen:\n\n```text\nImported AI Clip Assembler\n```\n\nDamit das System automatisch prüft:\n\n```text\ndata/imported_ai_clips/<video_id>/scene_01.mp4\ndata/imported_ai_clips/<video_id>/scene_02.mp4\n```\n\nWenn Clips vorhanden sind:\n\n```text\nechte AI-Clips verwenden\n```\n\nWenn nicht:\n\n```text\nlocal placeholder verwenden\n```\n\nDann können wir Free-Tier-Clips manuell aus PixVerse/Pika holen und sofort in denselben Renderprozess einbauen.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZGxfmrYptRmaZ_IHaerF6myAeuW7LZYlWiFY_u0VdRaGSWGR1G86CPJssYaNmmi6fB6HmiJHvE46h_mq_4OOuey_MNt46Ss65bbAF4qCoUVZDykMplP7_QZfNX_G9nYM0vJOyO9UBRzIh-Mwk1QSoZkC2ESPmFk954Lqyp1YfyN7SI7GKoTXiDrxLe-4xlA-xkwk5LxtKO4-rjE1skDMbYqiqE9nr9-qDrk_XR5C0DAGyOp301RBSOwG_SPoanJt8ZkZUICi9A8L_fU469YOzd75lp-xygmCvUH8v254VUA1Nfg-oDAH5hxr1GgKvhLnNTBvPAlfCtdy5VZyjI8cWzWcRCP4OLHOCJvOM_kx5eDPTiQCREpgc_Rs5El6lVRrOle8r4QXe9uKAGuXjIcU1r8jBTvz8ZZHryWkRxVZzbCYyObqJgT-Btuvyh0lEAM35HdC0dspUEIkeS6BJKGORZi3zKHH9mAPHPEAqHRyFrKtGX9WfQQuvZfo6d8RPTzX77FU6qD4HR97ld49uyIKy3d5PVJ0LTPcuNXr28RBUSAo4bln6g60lui_20pkrSopVlrcU9xZ-ms8jerJLW7XPb0tiIAl6revVyu7eEzXUL0fAbaWPpWd3hs5saV2T3kPeJUvjSeSIOkj2s4m7SkbPbDpAnK97j6v3Vp99jdLbb4d5mdSiTp_0XaF3zLIK-h_dzjgsDugTYsUQMODx3jFRum208q5orr_lbMpkd6rNQYJqozY7i_GSJxqNJXCqZPelevy0gmFAwkRDpZBHWSyA5Bn85DP_esIt7zBImMbXA81ENwQYndMnAjHoxoxFEBOr4SNevYTlYbDIpzLuXcnEpjQeucjGptKbX3_0KbfHGF8yvdeHCHfrj4pRmkfPV37yK76CyBBp9iBg3PTlac-7MolcdxEmBhqUbio6LGfCfdBhfplWrzEaavcBrn0NKNRsPho1PVjTbrIpCZp1O7kX1J_fmn2VOLvvNAmz2hxBF6eynprf81ZVb4ROQKFjWH3nWLPv9a4qQRzn0c1VkRh1OpVNA4oqB6oq0qN63Jhbc4lF8DfV08xgUYYmn9owvMA_Fq-VmPOWeWu3801xLacyHOjSAuxfnZwf8DoRPRaKw2JIR_S_9H9zCbEB8SBULTJlqjKRApS2ZEnvPEMhs5bSCZlnOkbWNf0KPo7ky9AMdxcZr-RFUYmEkAOogEXSzUWVZT9X8rxftjjXH1a5fNTM6dERDdrUOPpZOw91W_NDeNCqhBP5Tz7ChXoxHTVgQdXfwSYvCRXqZLetDCFPplk89cry2AXB5bQfjHklMSAoPZAFwBR0jGQJgMeTJADMh2DGIPQ9YAIfJ5Rd3tTniFgKl7aPqahRXA6OSalE2STfQ5DvHOtEPSHud91VCGdkk5Opx0D2yOhVTcDI-AuGtTS9QqVajD_dH2nVP2p4_wvYkgTdYqbebQ48R7eCfGkn8d8h2jkDC6qaCi_obyYnhL9__nbzKDeVHv7lk0AVtBrMpH1OynPWBA-tAAqArznBKzqZOBg2cCSAv4WTpInAfcbBYXfJy8YH0DsLjXr6jUTqFO8v43W1TEmD1lvXXJkPWfS-Svyv83b4pDgOM-KEZugPN8pg8fUncYewO7H-HLYD8X6s-Che6P3HtW00vyNiPjrSSjk_UGYmsZYbcwLMx9M8ZI0kkT1L_nm31zviODY1uCiuwppFfCLv5nNps0QqUP5X1nL2n1xjYfsZdUEjTud26D-ClVtcLvLKEiAHcv5dPtFybXvk5I4P4JOE9R68_Rg_6pTFZBITif8smub_AwVSlwQJDxciMxZ_H_0tJ2AkrrbOBkphNer0dLQin_QX5zt-mkFPOcsga9gDMTuYRhepSK3RKwXs_3xWjUKNw8nz6ZY4lNIYFXCCt4kSiTQn3XB2BBlVIf07yJhmYe4U6CKhdTM4s1WLCc49e6NMZ33MFf5t5y905uoYC3lLK2y1IEDfT0FJWHbChp5UC2z_Zbp1xhug==", "id": "rs_0d1a4d10654d56b4016a1191ae1b408191a1683d577486d8d5", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — exakt: **das aktuelle MP4 ist noch Konzept/Animatic**, kein fertiges realistisches Video.\n\nEs zeigt Timing, Layer, Captions und Mechanik. Für ein „einigermaßen realistisches“ Endprodukt brauchen wir tatsächlich **KI-generierte Videoclips** als B-Roll/Scene-Clips — aber eben nur über **Free Tiers**, nicht über bezahlte Credits.\n\nIch habe recherchiert und den Workflow entsprechend weitergebaut.\n\n## Recherche: gratis / free-tier KI-Video-Tools\n\n## 1. PixVerse\n\nGefunden auf:\n\n```text\nhttps://app.pixverse.ai/\n```\n\nDie App zeigt aktuell:\n\n```text\n90\nTäglich aktualisieren\nJetzt registrieren und kostenlose Credits erhalten\n```\n\nEinschätzung:\n\n- sehr interessant als **Primary Free-Tier Candidate**\n- offenbar tägliche Free-Credits\n- geeignet für realistische 9:16 Short-Clips\n- manuelle Browser-Nutzung/Login nötig\n- keine paid Top-ups erlaubt\n\nPriorität:\n\n```text\n#1 PixVerse\n```\n\n## 2. Pika\n\nGefunden auf offizieller Pricing-Seite:\n\n```text\nhttps://pika.art/pricing\n```\n\nFree Basic:\n\n```text\n$0\n80 monthly video credits\nAccess to Pika 2.5, 480p only\nDownload videos with no watermark\nCommercial use\n```\n\nFür Text/Image-to-Video laut Pricing:\n\n```text\n480p / 5s = 12 credits Free\n```\n\nEinschätzung:\n\n- sehr brauchbarer Fallback\n- Free-Output nur 480p, aber für Shorts mit lokalem Upscale/Compositing akzeptabel\n- no-watermark + commercial-use Claim ist stark\n- monatlich begrenzter als PixVerse\n\nPriorität:\n\n```text\n#2 Pika\n```\n\n## 3. CapCut AI Video Maker\n\nGefunden auf:\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nCapCut sagt u.a.:\n\n```text\nFree AI Video Generator\nExport the finished video in HD quality for free\n```\n\nEinschätzung:\n\n- gut für manuelle Politur, Templates, Captions, Schnitt\n- Free-Quota öffentlich weniger klar\n- CapCut kann schnell in Pro-Assets/Upsell laufen\n- trotzdem nützlich als kostenloser Editor-/Assembler-Kandidat\n\nPriorität:\n\n```text\n#3 CapCut\n```\n\n## 4. Hailuo\n\nGefunden auf:\n\n```text\nhttps://hailuoai.video/\n```\n\nDie Seite zeigt einen Generator mit:\n\n```text\n768p\n6s\n25 credits\n```\n\nEinschätzung:\n\n- nur verwenden, falls kostenlose vorhandene Credits da sind\n- keine Käufe, keine Top-ups\n- weil du Hailuo explizit als „kein Geld für Credits“ genannt hast: nur Free-Quota-Fallback, nicht Hauptpfad\n\nPriorität:\n\n```text\n#4 Hailuo, nur falls Free-Credits verfügbar\n```\n\n## Nicht als Free-First geeignet\n\nAktuell nicht als Primary Free Workflow:\n\n```text\nRunway\nLuma\nKling\nVeo\nSora API\n```\n\nGrund:\n\n- entweder paid-first\n- Free-Kontingent unklar/klein\n- API/Automation meist bezahlt\n- oder nicht stabil genug für unseren kostenlosen Produktionspfad\n\n## Neue Workflow-Entscheidung\n\nDer Zielworkflow ist jetzt:\n\n```text\n1. Hermes/ChatGPT/NVIDIA LLM:\n   Shot plan, prompts, captions, scene specs\n\n2. Free AI Video Provider:\n   PixVerse / Pika / CapCut / Hailuo-Free\n   erzeugen realistische kurze Clips manuell im Browser\n\n3. Import:\n   Clips lokal speichern unter data/imported_ai_clips/...\n\n4. Local Renderer:\n   Mechanism-Layer, Captions, SFX, Timing, Assembly\n\n5. FFmpeg:\n   finales 9:16 MP4\n```\n\nDas heißt:  \n**Die realistischen Teile kommen aus kostenlosen KI-Video-Free-Tiers.**  \n**Die System-Erklärung, Captions und Montage bleiben lokal kontrolliert.**\n\nDas ist der richtige Hybrid. Nur lokal wird zu sehr Konzeptgrafik. Nur KI-Video wird generischer Slop. Zusammen wird es brauchbar. Welch seltene Gelegenheit, dass zwei halbe Lösungen zusammen eine ganze ergeben.\n\n## Im Repo neu gebaut\n\nNeue Komponente:\n\n```text\nautoshorts/free_ai_video_providers.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_free_ai_video_handoffs.py\n```\n\nNeue Tests:\n\n```text\ntests/test_free_ai_video_providers.py\n```\n\nNeues Artefakt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Was dieses Handoff enthält\n\nFür den Pilot:\n\n```text\nWarum ein billiges Auto teuer sein kann\n```\n\nwerden jetzt **2 realistische AI-Video-Jobs** erzeugt:\n\n```text\nscene_01 → realistische KI-Video-Szene\nscene_02 → realistische KI-Video-Szene\n```\n\nProvider-Reihenfolge:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur Free-Credits\n```\n\nGuardrails:\n\n```text\npaid_topups_allowed: false\npaid_subscriptions_allowed: false\ndo_not_buy_credits: true\navoid_pro_templates_or_paid_assets: true\nexport_without_baked_captions: true\nfinal_captions_added_locally: true\n```\n\nDie Mechanismus-Szenen bleiben vorerst lokal:\n\n```text\nscene_03 → local mechanism animation\nscene_04 → local mechanism animation\nscene_05 → local kinetic text\n```\n\nWarum? Weil KI-Video-Tools bei präzisen Layer-Erklärungen oft halluzinieren. Realistische Clips: ja. Exakte Erklärgrafik: lieber lokal kontrolliert.\n\n## Beispiel Prompt für Scene 1\n\nDas Handoff erzeugt provider-ready Prompts wie:\n\n```text\nVertical 9:16 realistic cinematic short video.\nA realistic creator at a desk late evening, laptop open to an AI chat,\nthoughtful but in control, hands moving between keyboard and notebook.\nMood: focused, modern, high-retention social video.\nScene intent: 3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal.\nCamera: slow handheld push-in, subtle parallax, natural micro-movements.\nNo captions, no readable text, no logos, no watermarks.\nLeave clean negative space in the lower third for captions.\nThe clip should feel like real b-roll for a mechanism explainer, not a slideshow.\n```\n\nWichtig: Captions kommen später lokal drauf. Kein KI-Gibberish im Bild.\n\n## Tests\n\nFull suite:\n\n```text\n55 passed in 3.55s\n```\n\n## Git\n\nNeuer lokaler Commit:\n\n```text\n9f41a9f feat: add free ai video handoff specs\n```\n\nDavor liegt noch:\n\n```text\nff5df62 feat: add free local motion renderer\n```\n\nPush ist weiterhin blockiert, weil GitHub-Credentials in dieser Umgebung fehlen:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\n## Antwort auf deine Kernfrage\n\nJa: **Das Endprodukt soll realistischer werden.**\n\nWas du aktuell gesehen hast, ist:\n\n```text\nAnimatic / Motion Concept / Local Placeholder\n```\n\nNicht das finale Video.\n\nDer nächste Produktionsschritt ist jetzt konkret:\n\n```text\nPixVerse oder Pika öffnen\n→ scene_01 + scene_02 mit Free Credits generieren\n→ Clips herunterladen\n→ lokal importieren\n→ Local Renderer ersetzt Placeholder durch echte KI-Clips\n→ Mechanism Layers + Captions + SFX darüber\n→ finales MP4\n```\n\n## Nächster sinnvoller Schritt\n\nIch würde als Nächstes bauen:\n\n```text\nImported AI Clip Assembler\n```\n\nDamit das System automatisch prüft:\n\n```text\ndata/imported_ai_clips/<video_id>/scene_01.mp4\ndata/imported_ai_clips/<video_id>/scene_02.mp4\n```\n\nWenn Clips vorhanden sind:\n\n```text\nechte AI-Clips verwenden\n```\n\nWenn nicht:\n\n```text\nlocal placeholder verwenden\n```\n\nDann können wir Free-Tier-Clips manuell aus PixVerse/Pika holen und sofort in denselben Renderprozess einbauen."}], "id": "msg_0d1a4d10654d56b4016a1191b18b388191be3fead6ca5498a6", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_Rl4MhYkKziCrxsazStJhgJRE", "call_id": "call_Rl4MhYkKziCrxsazStJhgJRE", "response_item_id": "fc_0d1a4d10654d56b4016a11934f2c4481918c6fbe8e9f423911", "type": "function", "function": {"name": "todo", "arguments": "{\"todos\":[{\"id\":\"chatgpt-pro\",\"content\":\"Aktuelle ChatGPT Pro Video-/Sora-Leistungen prüfen\",\"status\":\"in_progress\"},{\"id\":\"tdd-import\",\"content\":\"TDD für Imported AI Clip Assembler schreiben und RED verifizieren\",\"status\":\"pending\"},{\"id\":\"build-import\",\"content\":\"Imported AI Clip Assembler implementieren\",\"status\":\"pending\"},{\"id\":\"verify\",\"content\":\"Tests/Artefakte/Git-Status verifizieren\",\"status\":\"pending\"}]}"}}], "finish_reason": "tool_calls", "reasoning": "**Assessing ChatGPT Pro features**\n\nI need to gather current facts about ChatGPT Pro features, so I'll check for the latest info. The task indicates I should continue building the imported assembler, which requires relevant skills in social and TDD that I might already have. I might also want to search for video generation related to ChatGPT Pro. Using a web search for OpenAI pricing and help could be beneficial as well, along with coding what I find.", "reasoning_content": "**Assessing ChatGPT Pro features**\n\nI need to gather current facts about ChatGPT Pro features, so I'll check for the latest info. The task indicates I should continue building the imported assembler, which requires relevant skills in social and TDD that I might already have. I might also want to search for video generation related to ChatGPT Pro. Using a web search for OpenAI pricing and help could be beneficial as well, along with coding what I find.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZNPqik-bRW0OtmcEys1zs9M7alC-PS9Ods9BOEMM1GeRrOVgxhIKNmjslDvUPRSKjlZp4pLhwK8GT6-Bl22eMwJ_p1cgUB5fvO7TxPdJNXmu299afZa7-R5IyEsmoeWQi7GYL9O_2BJJklc6MjWCAltsyhqhqnXxVbb1J1ZoHFgRmXRMcRFxNmsVXsaSvWPc6wLyxlUPJlz_wGm7PLL58ZyOEaIK7ab8Zep2oHXCwKwqDzs8zg7JbQK4q2c9Z0rQcxcQ3zAdR9TqdweZ3UP-KUIPse8DsBv0ZUQhAP4HyiZejumLfVM9jpuP2rlx6ORw2wK3ujpHH27gtfCwTAbYUJQjaWicIorPJ8OFb6phSi18fXfz0WVvfaQ4LnsC9P4KAh3UOZ0Vrj3KaS8QpcFvnzJakRIMGlNbkylghOOpsqNvGmiy5EHeOrKbb8fmou2Y0lNSp_r2ar8eUnd2YAhOPkOY_dEPSKtwBDCmKFZq1emAMROhcf1DIxK3B5VeBNoXfrumKMoFMUYXd_J9rCn4n0FUfkx4ykmvP28CvFu4V_x8YrBkS1G-wOmvhTY180jcpFzpm-A8QOWX88HPQF5OomAXWLKqRSv7PjG2bviyoAqAt1oAlc5oz6Nr_Ubz84dAtqMry4OebQ6aqeVFZsJH7i44fYCcOCBOuFLOsrxVbcYIIxJXAuUvwEudOBWcdd54gfuY74OYs7qGwHCNiXSJ0b_Kotw-9dBw8-iqVafXf_6pOWAhMsS4zEupXUJJDQZB9A1U0m-F2DdTsvh0xsxpUZchpc8alZ5qGFd964IGcTSyZz7n3voQelpFI77PBP_NI-2fu9vBEMsSyVv3D5baNScU718v7uYPoXo3e7ZpZQ4f1vxw5b6hIYPEMNXQY6iFiO0GxXPHpIh9ArKtIy8PQA89Pg8JhSUd6fwTqXJrEEq2XAlQfNuVdFXJWQSlgDCf4Wiv4fdzJgzZw3KSa1HXq8OJdcAUlWnYYcd8vkuezx1SN1n065TaGT0jeC9wa0YTGKp7fM3tIa1AArzKq5mluUnrDWLze-scCrAZnmk7RT9OaIVqh-69eQt8W5GSl3KgnLjxsRxBWHGNASKVIvTpn2WgHVFuN8JmU3pXjLkwCyZk92Dn_9Bmhffl8Wo9VEVimJ3cEPsQxzTdbM6EXhUVv82e3UovMnfRvfADb-m1Ik2Cs1vEuDkLfWkea477uEqqSze-VELhXY5DbceI0HKJcidofrBnHs4nDshqPDwXvu8mE4MoXmbfakowFPetRozLYk2fNe1lYSZoXeH92_SnQiEAi6CtpmU4xDIRFqBcEwQ2stOhiyYXqvvhV_IgjtKUsmUfZgwhV1xZtPQGR6cwYdbRQ==", "id": "rs_0d1a4d10654d56b4016a11934c5c308191860ac90b482c95d7", "summary": [{"type": "summary_text", "text": "**Assessing ChatGPT Pro features**\n\nI need to gather current facts about ChatGPT Pro features, so I'll check for the latest info. The task indicates I should continue building the imported assembler, which requires relevant skills in social and TDD that I might already have. I might also want to search for video generation related to ChatGPT Pro. Using a web search for OpenAI pricing and help could be beneficial as well, along with coding what I find."}]}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_Rl4MhYkKziCrxsazStJhgJRE", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_WeC2fDi1ybgOcSnuLkrgEcPK", "call_id": "call_WeC2fDi1ybgOcSnuLkrgEcPK", "response_item_id": "fc_0d1a4d10654d56b4016a119354ae4081919f059d0a5e92184a", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://openai.com/chatgpt/pricing/\"}"}}, {"id": "call_PwdXJBXQMOG3uWKLuqM6HOdT", "call_id": "call_PwdXJBXQMOG3uWKLuqM6HOdT", "response_item_id": "fc_0d1a4d10654d56b4016a119354ae548191857188158af5d36a", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://help.openai.com/en/articles/9957612-generating-videos-on-sora\"}"}}, {"id": "call_nUvuznE7Ajp6lF2yRtx4dvFv", "call_id": "call_nUvuznE7Ajp6lF2yRtx4dvFv", "response_item_id": "fc_0d1a4d10654d56b4016a119354ae608191a15663be2eca58ba", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git status --short && git log --oneline -3\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "tool_calls": [{"id": "call_TSqDRlwI9ng1Ig6MjhwnlZw2", "call_id": "call_TSqDRlwI9ng1Ig6MjhwnlZw2", "response_item_id": "fc_0d1a4d10654d56b4016a11941c33d0819184d6ebeac361675d", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Aktuelle ChatGPT Pro Video-/Sora-Leistungen prüfen\",\"id\":\"chatgpt-pro\",\"status\":\"completed\"},{\"content\":\"TDD für Imported AI Clip Assembler schreiben und RED verifizieren\",\"id\":\"tdd-import\",\"status\":\"completed\"},{\"content\":\"Imported AI Clip Assembler implementieren\",\"id\":\"build-import\",\"status\":\"in_progress\"}]}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_TSqDRlwI9ng1Ig6MjhwnlZw2", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_Ul6e0COcbJrdRdQKoSFYuwIS", "call_id": "call_Ul6e0COcbJrdRdQKoSFYuwIS", "response_item_id": "fc_0d1a4d10654d56b4016a1194210788819197e73fe7ed3cf451", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python scripts/generate_imported_clip_assembly_plans.py --jobs data/free_production_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json --out data/imported_clip_assembly_plans/mechanism_pilots && pytest tests/ -q\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `python scripts/generate_imported_clip_assembly_plans.py --jobs data/free_prod...` -> exit 0, 1 lines output", "tool_call_id": "call_Ul6e0COcbJrdRdQKoSFYuwIS", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/data/imported_clip_assembly_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json from line 1 (4,559 chars)", "tool_call_id": "call_WrexHwJfaY6qGUJx8OQmUb3X", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Need maybe maybe need implement actual hybrid render? But time, OK. Need final include answer. Need git commit. Check.", "tool_calls": [{"id": "call_GFp1GRPJgP5yY1v3p0K0hGU0", "call_id": "call_GFp1GRPJgP5yY1v3p0K0hGU0", "response_item_id": "fc_0d1a4d10654d56b4016a11945a7b788191958cc098ff9c765c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git diff --stat && git status --short\",\"timeout\":60,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}, {"id": "call_azgHB4s5JnnkHgHdAuO1KPf7", "call_id": "call_azgHB4s5JnnkHgHdAuO1KPf7", "response_item_id": "fc_0d1a4d10654d56b4016a11945a7fac81918880b0c50be0e07d", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git diff -- . ':!data/local_motion_renders' | grep -iE 'api[_-]?key|token|secret|password|credential' || true\",\"timeout\":60,\"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 maybe maybe need implement actual hybrid render? But time, OK. Need final include answer. Need git commit. Check."}], "id": "msg_0d1a4d10654d56b4016a11945787288191b044807ac7dde3e0", "phase": "commentary"}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[terminal] ran `git diff --stat && git status --short` -> exit 0, 1 lines output", "tool_call_id": "call_GFp1GRPJgP5yY1v3p0K0hGU0", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "{\"output\": \"\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_azgHB4s5JnnkHgHdAuO1KPf7", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_ebt2PSWnqMriGbqAMLmpAHz2", "call_id": "call_ebt2PSWnqMriGbqAMLmpAHz2", "response_item_id": "fc_0d1a4d10654d56b4016a11945ebcd88191a770f867d772cfbf", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"git add autoshorts/imported_clip_assembler.py scripts/generate_imported_clip_assembly_plans.py tests/test_imported_clip_assembler.py data/imported_clip_assembly_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json && git diff --cached --check && git commit -m \\\"feat: add imported clip assembly plans\\\" && git status --short\",\"timeout\":300,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "content": "{\"output\": \"fatal: 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_X4RpeHPK7la9nBqWXBxCeyEr", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "tool_calls": [{"id": "call_3p9sXNV8B78VsV5IeO3KIkOg", "call_id": "call_3p9sXNV8B78VsV5IeO3KIkOg", "response_item_id": "fc_0d1a4d10654d56b4016a119469b6fc8191a5f0d88d8a55bdea", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"Imported AI Clip Assembler implementieren\",\"id\":\"build-import\",\"status\":\"completed\"},{\"content\":\"Tests/Artefakte/Git-Status verifizieren\",\"id\":\"verify\",\"status\":\"completed\"}]}"}}], "finish_reason": "tool_calls", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "content": "[todo] updated task list", "tool_call_id": "call_3p9sXNV8B78VsV5IeO3KIkOg", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Weitergebaut, Sir.\n\n## Kurzantwort zu ChatGPT Pro / Video\n\nNach aktuellem OpenAI-Stand: **Nein, dein ChatGPT Pro Abo ist derzeit kein brauchbarer Video-Generator für unseren Workflow.**\n\nIch habe die aktuellen OpenAI-Seiten geprüft:\n\n## ChatGPT Pro enthält laut Pricing\n\nAuf der ChatGPT-Pricing-Seite steht bei Pro u.a.:\n\n- 5x oder 20x mehr Nutzung\n- GPT-5.5 Pro\n- maximale Codex-Aufgaben\n- unbegrenztes GPT-5.3 und Datei-Uploads\n- unbegrenzte und schnellere Bilderstellung\n- maximale Deep Research und Agentenmodus\n- maximale Erinnerung / maximaler Kontext\n- neue Funktionen als experimentelle Vorschau\n\nAber: **keine aktive neue Videogeneration als ChatGPT-Pro-Feature**.\n\n## Sora-Status\n\nOpenAI Help Center sagt aktuell:\n\n```text\nThe Sora web and app experiences were discontinued on April 26, 2026.\nThe Sora API will be discontinued on September 24, 2026.\n```\n\nZusätzlich steht im Credits-Artikel:\n\n```text\nCredits currently can only be used with Codex and ChatGPT for Excel.\n```\n\nBedeutung für uns:\n\n```text\nChatGPT Pro → sehr gut für Planung, Scripting, Prompts, Code, Bilder\nChatGPT Pro → aktuell nicht als Video-Generator für neue Clips einplanen\n```\n\nAlso: **ChatGPT Pro bleibt unser Gehirn, nicht unsere Kamera.**  \nEine sehr teure Kamera wäre es ohnehin gewesen. Charmant, aber unpraktisch.\n\n## Workflow-Entscheidung aktualisiert\n\nFür realistischere Clips bleibt der Free-Tier-Hybrid:\n\n```text\nChatGPT Pro / Hermes:\n  Script, Shot Plan, Prompts, Captions, Renderlogik\n\nPixVerse / Pika / CapCut / ggf. Hailuo-Free:\n  manuell erzeugte realistische 9:16 Clips\n\nLocal Renderer:\n  Mechanismus-Layer, Captions, SFX, Timing\n\nFFmpeg:\n  finales MP4\n```\n\nOpenAI/Sora wird im System ausdrücklich markiert als:\n\n```text\ndo_not_use_for_new_video_generation\n```\n\n## Weitergebaut: Imported AI Clip Assembler\n\nNeu implementiert:\n\n```text\nautoshorts/imported_clip_assembler.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_imported_clip_assembly_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_imported_clip_assembler.py\n```\n\nNeues Artefakt:\n\n```text\ndata/imported_clip_assembly_plans/mechanism_pilots/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Was der neue Assembler macht\n\nEr prüft künftig diesen Import-Pfad:\n\n```text\ndata/imported_ai_clips/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/\n    scene_01.mp4\n    scene_02.mp4\n```\n\nWenn Clips vorhanden sind:\n\n```text\nscene_01 → imported_ai_clip\nscene_02 → imported_ai_clip\n```\n\nWenn nicht:\n\n```text\nscene_01 → local_renderer_fallback\nscene_02 → local_renderer_fallback\n```\n\nMechanismus-Szenen bleiben lokal:\n\n```text\nscene_03 → local_renderer\nscene_04 → local_renderer\nscene_05 → local_renderer\n```\n\nAktueller Assembly-Plan sagt erwartungsgemäß:\n\n```text\nimported_ai_clip_count: 0\nlocal_fallback_count: 2\nlocal_renderer_count: 3\n```\n\nWeil noch keine PixVerse/Pika-Clips importiert wurden.\n\n## OpenAI/Sora-Hinweis im Artefakt\n\nDer Assembly-Plan enthält jetzt explizit:\n\n```json\n{\n  \"provider\": \"chatgpt_pro_sora\",\n  \"available_for_new_video_generation\": false,\n  \"decision\": \"do_not_use_for_new_video_generation\"\n}\n```\n\nMit Quellenhinweis auf:\n\n```text\nSora web/app discontinued: 2026-04-26\nSora API discontinued: 2026-09-24\nCredits currently only for Codex and ChatGPT for Excel\n```\n\n## Tests\n\nFull suite:\n\n```text\n58 passed in 3.39s\n```\n\n## Git\n\nNeuer lokaler Commit:\n\n```text\n85ec56a feat: add imported clip assembly plans\n```\n\nAktueller lokaler Commit-Verlauf:\n\n```text\n85ec56a feat: add imported clip assembly plans\n9f41a9f feat: add free ai video handoff specs\nff5df62 feat: add free local motion renderer\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDie versehentlichen Telegram-Review-Dateien bleiben untracked und unberührt.\n\n## Nächster praktischer Produktionsschritt\n\nJetzt brauchen wir konkret zwei manuell generierte Clips:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAm besten zuerst in PixVerse, sonst Pika.\n\nAblage danach:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\nDann kann ich den nächsten Baustein bauen:\n\n```text\nHybrid Final Renderer:\nimported AI clips\n+ local mechanism scenes\n+ local kinetic captions\n+ FFmpeg assembly\n→ finales MP4\n```", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Weitergebaut, Sir.\n\n## Kurzantwort zu ChatGPT Pro / Video\n\nNach aktuellem OpenAI-Stand: **Nein, dein ChatGPT Pro Abo ist derzeit kein brauchbarer Video-Generator für unseren Workflow.**\n\nIch habe die aktuellen OpenAI-Seiten geprüft:\n\n## ChatGPT Pro enthält laut Pricing\n\nAuf der ChatGPT-Pricing-Seite steht bei Pro u.a.:\n\n- 5x oder 20x mehr Nutzung\n- GPT-5.5 Pro\n- maximale Codex-Aufgaben\n- unbegrenztes GPT-5.3 und Datei-Uploads\n- unbegrenzte und schnellere Bilderstellung\n- maximale Deep Research und Agentenmodus\n- maximale Erinnerung / maximaler Kontext\n- neue Funktionen als experimentelle Vorschau\n\nAber: **keine aktive neue Videogeneration als ChatGPT-Pro-Feature**.\n\n## Sora-Status\n\nOpenAI Help Center sagt aktuell:\n\n```text\nThe Sora web and app experiences were discontinued on April 26, 2026.\nThe Sora API will be discontinued on September 24, 2026.\n```\n\nZusätzlich steht im Credits-Artikel:\n\n```text\nCredits currently can only be used with Codex and ChatGPT for Excel.\n```\n\nBedeutung für uns:\n\n```text\nChatGPT Pro → sehr gut für Planung, Scripting, Prompts, Code, Bilder\nChatGPT Pro → aktuell nicht als Video-Generator für neue Clips einplanen\n```\n\nAlso: **ChatGPT Pro bleibt unser Gehirn, nicht unsere Kamera.**  \nEine sehr teure Kamera wäre es ohnehin gewesen. Charmant, aber unpraktisch.\n\n## Workflow-Entscheidung aktualisiert\n\nFür realistischere Clips bleibt der Free-Tier-Hybrid:\n\n```text\nChatGPT Pro / Hermes:\n  Script, Shot Plan, Prompts, Captions, Renderlogik\n\nPixVerse / Pika / CapCut / ggf. Hailuo-Free:\n  manuell erzeugte realistische 9:16 Clips\n\nLocal Renderer:\n  Mechanismus-Layer, Captions, SFX, Timing\n\nFFmpeg:\n  finales MP4\n```\n\nOpenAI/Sora wird im System ausdrücklich markiert als:\n\n```text\ndo_not_use_for_new_video_generation\n```\n\n## Weitergebaut: Imported AI Clip Assembler\n\nNeu implementiert:\n\n```text\nautoshorts/imported_clip_assembler.py\n```\n\nNeues CLI:\n\n```text\nscripts/generate_imported_clip_assembly_plans.py\n```\n\nNeue Tests:\n\n```text\ntests/test_imported_clip_assembler.py\n```\n\nNeues Artefakt:\n\n```text\ndata/imported_clip_assembly_plans/mechanism_pilots/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\n```\n\n## Was der neue Assembler macht\n\nEr prüft künftig diesen Import-Pfad:\n\n```text\ndata/imported_ai_clips/\n  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/\n    scene_01.mp4\n    scene_02.mp4\n```\n\nWenn Clips vorhanden sind:\n\n```text\nscene_01 → imported_ai_clip\nscene_02 → imported_ai_clip\n```\n\nWenn nicht:\n\n```text\nscene_01 → local_renderer_fallback\nscene_02 → local_renderer_fallback\n```\n\nMechanismus-Szenen bleiben lokal:\n\n```text\nscene_03 → local_renderer\nscene_04 → local_renderer\nscene_05 → local_renderer\n```\n\nAktueller Assembly-Plan sagt erwartungsgemäß:\n\n```text\nimported_ai_clip_count: 0\nlocal_fallback_count: 2\nlocal_renderer_count: 3\n```\n\nWeil noch keine PixVerse/Pika-Clips importiert wurden.\n\n## OpenAI/Sora-Hinweis im Artefakt\n\nDer Assembly-Plan enthält jetzt explizit:\n\n```json\n{\n  \"provider\": \"chatgpt_pro_sora\",\n  \"available_for_new_video_generation\": false,\n  \"decision\": \"do_not_use_for_new_video_generation\"\n}\n```\n\nMit Quellenhinweis auf:\n\n```text\nSora web/app discontinued: 2026-04-26\nSora API discontinued: 2026-09-24\nCredits currently only for Codex and ChatGPT for Excel\n```\n\n## Tests\n\nFull suite:\n\n```text\n58 passed in 3.39s\n```\n\n## Git\n\nNeuer lokaler Commit:\n\n```text\n85ec56a feat: add imported clip assembly plans\n```\n\nAktueller lokaler Commit-Verlauf:\n\n```text\n85ec56a feat: add imported clip assembly plans\n9f41a9f feat: add free ai video handoff specs\nff5df62 feat: add free local motion renderer\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDie versehentlichen Telegram-Review-Dateien bleiben untracked und unberührt.\n\n## Nächster praktischer Produktionsschritt\n\nJetzt brauchen wir konkret zwei manuell generierte Clips:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAm besten zuerst in PixVerse, sonst Pika.\n\nAblage danach:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\nDann kann ich den nächsten Baustein bauen:\n\n```text\nHybrid Final Renderer:\nimported AI clips\n+ local mechanism scenes\n+ local kinetic captions\n+ FFmpeg assembly\n→ finales MP4\n```"}], "id": "msg_0d1a4d10654d56b4016a11946d2c388191a2137d7b7c4b0c92", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] ok, wie gehen wir also weiter vor? generierst du die Videos?", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Evaluating steps for prompts**\n\nI think we can produce precise prompts and steps from the existing handoff. I should use the tool to read the handoff file and pull out those exact prompts. Is the social media skill relevant and mandatory before I respond? I believe we've already loaded it in this session. Hmm, maybe I need to check the skills before replying. It's important to load the relevant social-media-shorts-growth-engine too!", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating steps for prompts**\n\nI think we can produce precise prompts and steps from the existing handoff. I should use the tool to read the handoff file and pull out those exact prompts. Is the social media skill relevant and mandatory before I respond? I believe we've already loaded it in this session. Hmm, maybe I need to check the skills before replying. It's important to load the relevant social-media-shorts-growth-engine too!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZZVoDAoBcOWNJD688UUKUxhZOFDKhtdwFvfuZy_aaBd8GmhOxR8rtztRnWSFFRwegpGRXvtXNUxrlQgUl8mrxbSQxSOHRPY4GMHXm3jRFufjkVgLX-g1paJgDppo24ODm6jPSY-YLVJ_CJJriRzyvG_st9e6SdwxQwKLoDk2ag5s6ATtcNgov-milYPV3DNDXON6ZPhbTh_uQxhndYVDXWEcWwPacfCq_ccwBaxe5LJbTkbTkmvrzgee0BPaymmAOOa_Yul_BkQKBOxhluRw8hZalNyINNClA4whNKkKTZAT1LeleqYWplLZqq6wy2V-z6Ti7oEs7P2WpK7fo3tRtOEmb6BPxp9ZKWHszAdOKMrx-cjBBNdQoQBrIegnTZJSXTjJMJLAzPQmCc0r5ooPMs_dP8bgpchn-zv0qCP2ssq3B7U__z60M0k8VSrCj0DeTRn_Qm6wtracmyATuFOnic2xc90DByadE3YL6tL0BZgZc1JBKJ_XbFxOh161HiT70odLR6V3FZuNOQPAFAmT1KZ_JBqm0ERv0uFIg6447Uf1JmpR2FBxfAX-6dfuFNsjbks-x9N1BUDR1QireRQJVjUBaMcjWBHyzNg4vx0LBszUme61A7sWoQBntyU2xKjQ5_eYFZbjXv43MhAloQTfpq8QyTZIrHPQmexRcFNHlGRWtwuD_lAux80fIn_N4qRH4eG8zDqZTMc4uHhuNkiMy89NxF8IlC6UCYq9cR3dq33AURRRNFKuQRZ53bwQDdGuS3fkC8gbZEkzLjiQFLdzm7NlJwOYBN-A_5--dqBwnU6uWXmqCukNGRuBalcPPXHwXfO_0Bjdrp7iC6GRiR7LVAY6kPR9BhEQhSY7bXHHGKANvPwgjQIiL3HbULJ2tN6BVhw1JidBX55V0-unUYXEjazs5laFOXtQsNl6rUZaNRx5UnTijZrLEELVYsZhkID-QLhSTcHreB-BEk1vQ8BodPtfKglp6cDBLWNM3XLgWkoG5bF0C_nUvbxrCnBFpQHqyxCogc1pJ0U_tAQLJ7-PGf0O67oyzyIDAT9W0o4ppv6mYaVuE2qgONI0Ee5PiQRMC-lCIr5k0nrgQ2BI_7uFl_z2lDuOsKanjoQOTG9Y7Yaz5eovGUpUs4VsMCa71Lp2k8p5PgQYhifLacaqfuZYUdy12xNK6XyGzEzzDl9ubkfVJNwWehK2NBuz4IV5KHp2PFxeqFQ3HydIj_X8ly_In2liYuSfD4GuM-s50pbhIgbP1aW9130LWB4TOCLlq3BACQYR4Vfpr15pPb_tn8Oz5QkfNRP_Oj08Uz9H52ytZBROcPBvY5KHUIpl7pvEXbp4Uo5lSOJ1rY3gvSILSLyYw3LcvzUk0nWFsQEPyH2XNlkMllPuMwRmdo03npoGE0s1X1MWVKyzZpm5cwfQ4c6CAGDFaen_UsLPaVcJ4C2tXRCiQJGmI1DyrjhTmGvZlHxTWt3yFBlu4tpxeZgusd3Z6XcE6980cApzYdbs8VNacc0PdB7RB_e1WnOYlbV1jP1sRYcP_069tb09V_Ng3jklPhFnfrKOLgRYYpfvPIUVCiJAtZsb5pVZBK-59fWCVQixyxZRz6omrt0PFLCZWpiLuswQKkFfj--prEQ9k7RW8qj6NyKcXeS4E0Y9YY9LyMDQ1eSgc1FUEhui8qeAgNAtSBz5DaudFwjFYgZzCmmNVTuKgUO8y5bIBQVMXYLnHbRIuxEtJV3BSX_plRG1c14J3fTPYc6xUUN1-XM5OBXpYYjh-xDhfC3ZvH-4rG7R87HqJ_-xysAOyXdwbZGpzp0Ijsnlcddaj_rCzgfiOUvyVJ05oaDvdnAuwv6HAwTgq3dCVJclvxP4svo-ZFT3AUIZmq4bSXL1zwjrYEMbWV2XxI1hoEk81py2LeakLHCBHH6hXvG1kpPiIuryhslrA3EofsFwT8CFuz0M6_Unzw3jFlYF9Kk9yaK7KvCQfAwGZcVcziAQnfLTNLQYmmVNkWUEF_gvcA4EJYoix3k7E_bs-0lT974H-y8Ao58jy-2pnstXb26JrwLLWgChARdfLphEElfo5i61FuMczCSUcUs76pepPf92PDmsOTzGBzApR-eJKZVDxEGPwWOX2G9tWKuDJu1B0HqwyrRKMNkWzfBFtrFjOtxKTdcO0-1GebB0NY5lYEwHFg4klv1Oft7bsVgml2xY42oLhLB7g1cv9eqNe3RsesitS0aITqXZcIs4a3ScKqtN9-TPEYa9F3FZ_1vT5xV5ChIjdqgu4U_o3Rc22szTc7aHcUSubW3YtiFfS6oVj7rAc4UDj5pANk7Ps5dYjLfn3S6S68rXF-RE_Arz8im1H2hWhxTCn8eXV365mjbPHLUxBKpzd5kipeGNUWwkXYoUaRkqIotaQsyPaIUAVyPrc8jzL3X-rPjHuSuZe-du06IN4LkJf_Yf2xrWZ3lBj486fiY1LdCDlWcmfwGF1L-vRZxnCHbBq1MpAGN1hgQhGBdqT8T8K78MhVrygrU6ZoCfwAe_O_iuGRcX6O96oXwg76AsJU3Jinm0kinbHKttuD-w5hs42Ij47huGBVeW5GRmPaYkZ3t-38qxh74cf4Cuwk=", "id": "rs_0d1a4d10654d56b4016a11964f84a88191a4a72a19ce90eabb", "summary": [{"type": "summary_text", "text": "**Evaluating steps for prompts**\n\nI think we can produce precise prompts and steps from the existing handoff. I should use the tool to read the handoff file and pull out those exact prompts. Is the social media skill relevant and mandatory before I respond? I believe we've already loaded it in this session. Hmm, maybe I need to check the skills before replying. It's important to load the relevant social-media-shorts-growth-engine too!"}]}], "tool_calls": [{"id": "call_hd4J5aR0VMWAHRlnGpV7GmqO", "call_id": "call_hd4J5aR0VMWAHRlnGpV7GmqO", "response_item_id": "fc_0d1a4d10654d56b4016a119655dfa88191a87c86a574b663dc", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"social-media-shorts-growth-engine\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "skill_view", "content": "[skill_view] name=social-media-shorts-growth-engine (9,161 chars)", "tool_call_id": "call_hd4J5aR0VMWAHRlnGpV7GmqO", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Ja — aber mit einer wichtigen Trennung, Sir:\n\n## Was ich direkt generieren kann\n\nIch kann hier vollständig selbst erzeugen:\n\n- Shot Plans\n- Prompts\n- Captions\n- Voiceover-Text\n- lokale Animatics / Placeholder-Videos\n- Mechanism-Animationen\n- Kinetic Captions\n- finale FFmpeg-Montage\n- Review-Pakete\n- Tests / Code / Render-Pipeline\n\nAlso alles, was **lokal** läuft.\n\n## Was ich nicht sauber allein generieren kann\n\nDie **realistischen KI-Videoclips** aus PixVerse/Pika/CapCut kann ich nicht zuverlässig komplett autonom erzeugen, weil:\n\n- Login im Browser nötig ist\n- evtl. CAPTCHA / E-Mail / Account-Session kommt\n- Free Credits accountgebunden sind\n- ich keine Käufe/Top-ups machen darf\n- externe Webseiten häufig Export/Download manuell absichern\n\nIch kann es theoretisch im Browser versuchen, aber praktisch ist der stabile Workflow:\n\n```text\nIch generiere Prompt + genaue Anleitung\ndu / wir erzeugen Clips in PixVerse oder Pika\ndu lädst sie herunter\nich importiere sie in den Renderer\nich baue daraus das finale Video\n```\n\n## Konkreter Ablauf ab jetzt\n\n## Schritt 1 — Ich liefere dir 2 Clip-Aufträge\n\nFür den Pilot brauchen wir zuerst nur:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nDiese zwei Clips sind realistische B-Roll / Hook-Szenen.\n\n## Schritt 2 — Du erzeugst sie gratis\n\nEmpfohlen:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur falls Free Credits vorhanden\n```\n\nKeine paid Credits. Keine Subscription. Keine Top-ups. Natürlich. Wir sind nicht hier, um Kapitalismus zu füttern, bevor das Video überhaupt Views hat.\n\n## Schritt 3 — Clips ablegen\n\nNach Download müssen die Dateien hier landen:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Schritt 4 — Ich rendere final hybrid\n\nDann mache ich:\n\n```text\nimportierte realistische Clips\n+ lokale Mechanism-Layer\n+ Captions\n+ SFX\n+ Timing\n+ FFmpeg Assembly\n→ finales MP4\n```\n\n## Soll ich die Videos generieren?\n\nPräzise Antwort:\n\n```text\nDie lokalen Teile: ja, vollständig.\nDie Free-Tier KI-Clips: ich bereite sie vor; generieren/Download braucht wahrscheinlich dich im Browser/Login.\n```\n\nFalls du willst, kann ich beim nächsten Schritt **sofort** ein Telegram-taugliches Clip-Generation-Paket liefern:\n\n- PixVerse-Link\n- Pika-Link\n- Prompt für scene_01\n- Prompt für scene_02\n- Negative Prompt\n- Export-Einstellungen\n- Dateinamen\n- Qualitätscheckliste\n\nDann erzeugst du die zwei Clips, lädst sie herunter, und ich baue daraus das finale Video.", "reasoning": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!", "reasoning_content": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZZcm_TDgzXmViiVHLs28JhcioK_TbqSDjPRewly1v7iBg13gDR_gs8qIo0mE2wLQ_ZweZ8Xt76ryxjggl0neGVNHmpsuJOQQhlFtQsJI8HTQwZ4Q4I2dkiwb8wjaW5QR2fLTMpUwZxdwpPBF5ZlGHUY9RdhauH0Adg7RI-Aorg_AgCQAKJmkyIOaO9akN16l0Q0ukhrbzzePctkVl1YfjmDIqN6m31PRCUHo0x2xMJPfDmkYAHGgltczuOCIG899uwTyJUbJBptSMBABrWPHJNBNOaty_UKC6ycvHlJpz5iHNm_fOzZKHVXKG0UL5Jc1IaVvHj3qc54um9bJo9YhxTCOcVUdD6LaDpzVyVVLmSwrYSFIhSx6qzOA_s2Omj9IbeMHchSH7EBbeRnw1EQwdpxTHtBRW5DcFOwmqaH2HxzXFYTx_UmSWzYYT-JEFlmiaTqX1fwHpQPtSmPtbBDLuJdfRp6sGwvTzOKf8YiDdMty5sSFsrUTfv9IPFYqBPOAlnG_-zJITRrrL4ltDq3W8GJIeNb5B6NTtjHKGix-BaIlFDZGv2UnIMeM9WIJuSm0ujUjGm8lovojyZCbqhT6fjsOat657OS5j8aWGTT51P0wIzt9vBWbZ5SohAxcOHcUufwxAR51wy7QMPp0NaVmM42KXZiRQAcZXiiBut2W9RkPxUeL8Qm9II0l9I7eWZY2ssRtVe_G2icxec1MzTNE5gK3AwRFLVdT11q4nrAmo1NOpk4M1FUJ3K2_fqrcSZl8W4LaJ5wZr-U2ZtlbaAb85Vlnduc2062HYYvQwwGV8z1nthoVqltAXl-H3-4C7u-Blh83bTW4vHJJJbs9WIL1kI0QR1anW1maoYZX4d5fyzLr0PHkiD-hDXShjE93ZzwDlDxpE2jxL8GwCIhfjSekB9Ffbr8i879BYXaUuvkWXpxxrV9QrH3akEok68_QlJDYS84eeVkhFh6s37nHKsiJKhbyvHiPGLVIO85yUFPIEj9ka9yqpAq9VHlcvjYSac0itye7XAc3HJ9Zcey7AmKyvElU0epIdglSkKwalkup5VDf0YsnM0lJxbmDFnXtSfcSONMKLnNOckRzo1p7zlpyD665CwXfCPE7Y51kemv6uIe8FTQjQV817vrJjYaDcJ-INTI", "id": "rs_0d1a4d10654d56b4016a119659d42c81919c651d0ca6c59cae", "summary": [{"type": "summary_text", "text": "**Considering browser options**\n\nI need to finalize my response. I think I could mention that I could attempt browser generation if the user logs in or provides exported clips, but I’m not sure how reliable that would be. It might be helpful to include the exact steps they need to follow. I'll consider whether I can accomplish this with no additional tools required. It feels important to be clear and helpful for the user!"}]}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja — aber mit einer wichtigen Trennung, Sir:\n\n## Was ich direkt generieren kann\n\nIch kann hier vollständig selbst erzeugen:\n\n- Shot Plans\n- Prompts\n- Captions\n- Voiceover-Text\n- lokale Animatics / Placeholder-Videos\n- Mechanism-Animationen\n- Kinetic Captions\n- finale FFmpeg-Montage\n- Review-Pakete\n- Tests / Code / Render-Pipeline\n\nAlso alles, was **lokal** läuft.\n\n## Was ich nicht sauber allein generieren kann\n\nDie **realistischen KI-Videoclips** aus PixVerse/Pika/CapCut kann ich nicht zuverlässig komplett autonom erzeugen, weil:\n\n- Login im Browser nötig ist\n- evtl. CAPTCHA / E-Mail / Account-Session kommt\n- Free Credits accountgebunden sind\n- ich keine Käufe/Top-ups machen darf\n- externe Webseiten häufig Export/Download manuell absichern\n\nIch kann es theoretisch im Browser versuchen, aber praktisch ist der stabile Workflow:\n\n```text\nIch generiere Prompt + genaue Anleitung\ndu / wir erzeugen Clips in PixVerse oder Pika\ndu lädst sie herunter\nich importiere sie in den Renderer\nich baue daraus das finale Video\n```\n\n## Konkreter Ablauf ab jetzt\n\n## Schritt 1 — Ich liefere dir 2 Clip-Aufträge\n\nFür den Pilot brauchen wir zuerst nur:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nDiese zwei Clips sind realistische B-Roll / Hook-Szenen.\n\n## Schritt 2 — Du erzeugst sie gratis\n\nEmpfohlen:\n\n```text\n1. PixVerse\n2. Pika\n3. CapCut\n4. Hailuo nur falls Free Credits vorhanden\n```\n\nKeine paid Credits. Keine Subscription. Keine Top-ups. Natürlich. Wir sind nicht hier, um Kapitalismus zu füttern, bevor das Video überhaupt Views hat.\n\n## Schritt 3 — Clips ablegen\n\nNach Download müssen die Dateien hier landen:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Schritt 4 — Ich rendere final hybrid\n\nDann mache ich:\n\n```text\nimportierte realistische Clips\n+ lokale Mechanism-Layer\n+ Captions\n+ SFX\n+ Timing\n+ FFmpeg Assembly\n→ finales MP4\n```\n\n## Soll ich die Videos generieren?\n\nPräzise Antwort:\n\n```text\nDie lokalen Teile: ja, vollständig.\nDie Free-Tier KI-Clips: ich bereite sie vor; generieren/Download braucht wahrscheinlich dich im Browser/Login.\n```\n\nFalls du willst, kann ich beim nächsten Schritt **sofort** ein Telegram-taugliches Clip-Generation-Paket liefern:\n\n- PixVerse-Link\n- Pika-Link\n- Prompt für scene_01\n- Prompt für scene_02\n- Negative Prompt\n- Export-Einstellungen\n- Dateinamen\n- Qualitätscheckliste\n\nDann erzeugst du die zwei Clips, lädst sie herunter, und ich baue daraus das finale Video."}], "id": "msg_0d1a4d10654d56b4016a11965cebb08191bdf84c65944450d7", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] ok mach das. recherchiere zudem wo welche solche tools du per API ansteuern kannst, oder per gratis weboberfläche bedienen? irgendwelche opensource lösungen?", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "name": "todo", "content": "[todo] updated task list", "tool_call_id": "call_rz5F0OOnNr86F8mG0srlBSI0", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "name": "browser_navigate", "content": "{\"success\": false, \"error\": \"Operation timed out. The page may still be loading or the element may not exist.\"}", "tool_call_id": "call_m2OGa83tsMpTHQyZEbZstR4X", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "browser_navigate", "content": "[browser_navigate] https://github.com/genmoai/models (8,692 chars)", "tool_call_id": "call_WHXRTVAZMIYUlOtJMTBGi4aF", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "name": "terminal", "content": "[terminal] ran `python - <<'PY'\nimport urllib.request\nurls={\n'wan':'https://raw.githubusercon...` -> exit 0, 1 lines output", "tool_call_id": "call_wOrbnH8SzTXd0IuKlepwzT1W", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Searching for API options**\n\nI'm considering different APIs for my needs. It seems like a \"free API\" might not be feasible. HuggingFace spaces have some potential via gradio_client, but the queues are unreliable. I can look at Replicate or Fal for paid options. Also, I’m thinking about NVIDIA's API, though I might not get video capabilities. I should explore their models on build.nvidia.com. I wonder if I need to use the web for more information?", "finish_reason": "tool_calls", "reasoning_content": "**Searching for API options**\n\nI'm considering different APIs for my needs. It seems like a \"free API\" might not be feasible. HuggingFace spaces have some potential via gradio_client, but the queues are unreliable. I can look at Replicate or Fal for paid options. Also, I’m thinking about NVIDIA's API, though I might not get video capabilities. I should explore their models on build.nvidia.com. I wonder if I need to use the web for more information?", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZzzBkk8S9Bef7fE4ARfLaWVMKT3RJJLRUizEx-jXyVaDxyPH-v8aP7DsWw8NaFpRt1d6k3ULaL9_wMb4ti1oYkSoSEFHL6DgXjb6YsHZwchugF1Wnd9trjbREF7ys9nhsfQqpguxRE3y9OuXJLdYHJgPiVlgKWzWlceQYqHd1-1AgFSd9kjPKRsgzhzV7MfNBea9fHYPuW5kSooYX7DihlkNEtbv-Mbnt3le1adU_VexGYFQszoit-r6QC9rSKGphkVJbGC242CakBK4w-pmOYgkb5p9x0RsCEbJ5xRKiDIRs-zsgHYiqFKmp1y_7BROfuJ4utWlLea-Xv8ikCj8B6xX6yBk9kpkWf7HBoYqlfQmuUp6GhQg7SrgSdriSn24MqbS5WQugZak4xIEXFofNDh23rER2An_qOp5QhfuA8lk52ZDBtO2MPOSailXD4-5cByApsXimJUGKUyXTl-O6AuTqH3JqLOVb5LdBff6RbGC0EOIVSbKBrrJIs4ofWTR_jDGrMr9cs28b_q22upefs9fFJ7rkxQYcTvjpk5Q4YKCKh646L6UrcCAeq18jj479TV4NXqe45jMK68R-C438C96rWqG_hRBTrCUFnAA2OYsvCNANeHItjovtJTmjXxDBZsl3jXAiQy9-P2bso5dBICQHRn0mq5SPwHyJy3WSdp-9o7d7TOkj3LYVStecogJMLrKTvqlAiOxqfEClzayU1Vdi_WIhlJQpipFS-fQHSCX7o48ySnKC3kZFbW37t8CYSmn6kASEFZC0uF-dSHIIQP5LkRCuUg2FNy3o9N2oO9Tq5BaWKVwd2zYieV9xOUMO1bx5n8xYRIz05Xqm-mYvk3ieK5QHTTtG5PfmyIjRuV05Perzr0S1EkBUmhq5kh025gpA8R0d-KaeecLdtAqLwybj2CZHhBk_CtrYdIaVCGOBb-Z_48a64Zn8erlc9VTgBin0hwuEz5n88OnPDaoAVArXzDpF1Um3blh4IwdaEn2b0ra3-5_OoPLAWysgbds-5HxFiePuG3YYAFZp3fUyk4kPOqVxx3PmdJxOdBmO2gmDemINpVrDItbzFRoVl4snvOHnwD_4rpiNbKkGgDQ_b_qcc3YN6oKURs9PANgvrnC67HSE7EucO_4c4d2vwO0V25Ias93BZnEpEZvU1MXdHeTBjyhbkt3fSJmsqDEHp_tQ4inuQaKyQXkV8FwpwCa_OxfUq3rvLeSi0f8I85VLiQQBIfhVNR5qIuWfmHV64guU8lXuYPPew8IxYbRNg_braj", "id": "rs_0d1a4d10654d56b4016a119cf093748191832edaec79893e31", "summary": [{"type": "summary_text", "text": "**Searching for API options**\n\nI'm considering different APIs for my needs. It seems like a \"free API\" might not be feasible. HuggingFace spaces have some potential via gradio_client, but the queues are unreliable. I can look at Replicate or Fal for paid options. Also, I’m thinking about NVIDIA's API, though I might not get video capabilities. I should explore their models on build.nvidia.com. I wonder if I need to use the web for more information?"}]}], "tool_calls": [{"id": "call_ANqGoYgSUGEpHA7rVAftylWk", "call_id": "call_ANqGoYgSUGEpHA7rVAftylWk", "response_item_id": "fc_0d1a4d10654d56b4016a119cf3d06c819188f2f6672929cadd", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://build.nvidia.com/explore/discover?search=video\"}"}}, {"id": "call_tquRdABLlAdDWZmEiRkRbguU", "call_id": "call_tquRdABLlAdDWZmEiRkRbguU", "response_item_id": "fc_0d1a4d10654d56b4016a119cf3d084819198b51e0aac347336", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://replicate.com/collections/text-to-video\"}"}}, {"id": "call_qdZeN6wqWuVOx9Shw2kvP5bZ", "call_id": "call_qdZeN6wqWuVOx9Shw2kvP5bZ", "response_item_id": "fc_0d1a4d10654d56b4016a119cf3d08c8191bbceb3237e08a23b", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://fal.ai/models?categories=video\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "browser_navigate", "content": "[browser_navigate] https://build.nvidia.com/explore/discover?search=video (8,513 chars)", "tool_call_id": "call_ANqGoYgSUGEpHA7rVAftylWk", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "browser_navigate", "content": "[browser_navigate] https://replicate.com/collections/text-to-video (8,387 chars)", "tool_call_id": "call_tquRdABLlAdDWZmEiRkRbguU", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "browser_navigate", "content": "[browser_navigate] https://fal.ai/models?categories=video (8,082 chars)", "tool_call_id": "call_qdZeN6wqWuVOx9Shw2kvP5bZ", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Planning clip package creation**\n\nI need to create a clip package, maybe starting by using the existing handoff JSON read to craft some markdown. I might also include a research file in this process. I’m thinking about writing a package that incorporates prompts and options, possibly using a research JSON. Should I include tests as well? This is an artifact, so I could probably just write it. If coding is involved, that might change things a bit. I’ll consider using the current handoff.", "finish_reason": "tool_calls", "reasoning_content": "**Planning clip package creation**\n\nI need to create a clip package, maybe starting by using the existing handoff JSON read to craft some markdown. I might also include a research file in this process. I’m thinking about writing a package that incorporates prompts and options, possibly using a research JSON. Should I include tests as well? This is an artifact, so I could probably just write it. If coding is involved, that might change things a bit. I’ll consider using the current handoff.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZ0GU50PLNEacNFzApmnemZegMmYRH8hKW4_8GT7TzN_zca5F7jkTq03KehMOEILSTZ4fY7H3xVRB_8uMw5yGuVd24Yn9VhpQfFEo6aL6eFTw644egHxQhcC52ERlwTuvrR_oybL-g-iTAMcL2YY2fbTs_7UuJv_EANlXKWD_DJHvqBo4I1SkHoCiT4YVh1F7mKFCBqrFN85JlduMbs112jY5iOD0QUV8jeHnC0VHTkpWDkM5REtYHACmACDs3m7nHvA7xvO8pyy3JDt9Hk3agIR4p6SzhmKZbdmSREtQCSks37VfTrczK8b6-N85oip9HNMzzE1buvzL_ep_0cQGAZ0S2WdMCE9yFDJRWw_ra62ixU8MRZYhKn-vMBgxJF-ZeAJOjTwGHLu9r4wPLo6l8YJQL1lx0w7Rtn8SXSLj0ts7hy7f2acNlz-dFZZ0tWIRVXylMke-1AsM3KrM_LYbNVRZNsoLKTQcyzGcuqJCfQzZVPSJtTtRvfg8z0G4AD-5clSE_SF6xWWVupooC9f-pwdtMl8EYfoCCAoefah4FSwjMySVTI7_VdD3uCz7Ff66WBChrbUg38_7VqB1OvJlcxAiVUx_UP-KeDl7-ff7MIVPkEDtLoOptcKEStVyPeJVuUpxUQblB8m5jgGKU3KmkfB4H5SFQawWHPG3OhlNfAiSFPq2N8g9GnF0miYbyFoqc0yYiO3E_5pyUrgwSFapvUYrx_39vHU0EsZTGwcw9sml4-8H_SodU3oX4Ji0q-uA9L1GNdqkKjGtq-X5SkEfz7Eb2tS4YX-pFkIqs6B7UQQVQQbKt5NxfOYeBXzDrPkiahUjVL2M5O1y_H-miSNrSPgzA9liI9HeVUsHnNNKtb-gZXK6ZV9BFN9_ZUdscA8x4n6Arxm321vsw5nTxn2iR9ZNU09CUjwhrmMmU4yeN70yZseAyyWaHdy88Y9i789aqfNbXUjVj7WZEORdsnL1L-vjFlY6g0ZUYuZHKPq4LMdzEvLumjaKAnYKNuMnFOsm_npF4fkoIc4btDHyHMlU49y2hXa4oEPbTC1McfXSCYeVXJd5NGiwprpJTz5a6zpv86rzfEjd0hxBSXLUIonQu_elHGYLqKKQDm3JlfDiN0f7oRcDGINYYw-E_bf81xfXc2H_z8OlJMnxPj8_TZ_6fQ248xP_TzWSFiVR-PyoAQQ5LZrKq_69--djraaEznvuJSmkX7q1b4zbOyO7BFcA_XdBaFAny0rmLpOhsHkitOMccOs3MtyFKbYt6oy78Vl_ncTmQ2c6JChOPkeLWaOjS_zRmdqCva2pkmsJ7Yz9ctpGrs=", "id": "rs_0d1a4d10654d56b4016a119d04486881919391dd490becb005", "summary": [{"type": "summary_text", "text": "**Planning clip package creation**\n\nI need to create a clip package, maybe starting by using the existing handoff JSON read to craft some markdown. I might also include a research file in this process. I’m thinking about writing a package that incorporates prompts and options, possibly using a research JSON. Should I include tests as well? This is an artifact, so I could probably just write it. If coding is involved, that might change things a bit. I’ll consider using the current handoff."}]}], "tool_calls": [{"id": "call_y0uRGVApr0Dz3t0KH2H9uB36", "call_id": "call_y0uRGVApr0Dz3t0KH2H9uB36", "response_item_id": "fc_0d1a4d10654d56b4016a119d06d0708191b2bfa0ca1bf0def9", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/free_ai_video_handoffs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json\",\"limit\":320}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|{\\n     2|  \\\"schema_version\\\": \\\"free_ai_video_handoff.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|    \\\"pillar\\\": \\\"hidden_money_systems\\\",\\n     7|    \\\"duration_ms\\\": 34000,\\n     8|    \\\"aspect_ratio\\\": \\\"9:16\\\",\\n     9|    \\\"resolution\\\": {\\n    10|      \\\"width\\\": 1080,\\n    11|      \\\"height\\\": 1920\\n    12|    },\\n    13|    \\\"fps\\\": 30\\n    14|  },\\n    15|  \\\"budget_policy\\\": {\\n    16|    \\\"paid_topups_allowed\\\": false,\\n    17|    \\\"paid_subscriptions_allowed\\\": false,\\n    18|    \\\"allowed_strategy\\\": \\\"free_tier_manual_generation_plus_local_assembly\\\",\\n    19|    \\\"local_fallback_required\\\": true\\n    20|  },\\n    21|  \\\"researched_sources\\\": [\\n    22|    {\\n    23|      \\\"provider\\\": \\\"pixverse\\\",\\n    24|      \\\"source_url\\\": \\\"https://app.pixverse.ai/\\\",\\n    25|      \\\"evidence\\\": \\\"App page showed 90 credits with daily refresh and signup prompt for free credits.\\\"\\n    26|    },\\n    27|    {\\n    28|      \\\"provider\\\": \\\"pika\\\",\\n    29|      \\\"source_url\\\": \\\"https://pika.art/pricing\\\",\\n    30|      \\\"evidence\\\": \\\"Pricing page showed Basic $0 with 80 monthly video credits, Pika 2.5 at 480p only, no-watermark downloads, and commercial use.\\\"\\n    31|    },\\n    32|    {\\n    33|      \\\"provider\\\": \\\"capcut\\\",\\n    34|      \\\"source_url\\\": \\\"https://www.capcut.com/tools/ai-video-generator\\\",\\n    35|      \\\"evidence\\\": \\\"CapCut tool page describes a free AI video generator and says final videos can be exported in HD quality for free.\\\"\\n    36|    },\\n    37|    {\\n    38|      \\\"provider\\\": \\\"hailuo\\\",\\n    39|      \\\"source_url\\\": \\\"https://hailuoai.video/\\\",\\n    40|      \\\"evidence\\\": \\\"Public generator page exposed 768p/6s generation controls and a visible 25-credit create cost, suitable only if free account credits are available.\\\"\\n    41|    }\\n    42|  ],\\n    43|  \\\"hybrid_strategy\\\": {\\n    44|    \\\"realistic_clip_source\\\": \\\"free_tier_ai_video_tools_manual_handoff\\\",\\n    45|    \\\"mechanism_explainer_source\\\": \\\"local_renderer\\\",\\n    46|    \\\"captions\\\": \\\"local_kinetic_renderer_overlay\\\",\\n    47|    \\\"final_assembly\\\": \\\"local_ffmpeg\\\"\\n    48|  },\\n    49|  \\\"ai_video_jobs\\\": [\\n    50|    {\\n    51|      \\\"job_id\\\": \\\"scene_01_free_ai_video_handoff\\\",\\n    52|      \\\"scene_id\\\": \\\"scene_01\\\",\\n    53|      \\\"asset_mode\\\": \\\"cinematic_ai_video\\\",\\n    54|      \\\"status\\\": \\\"manual_generation_required\\\",\\n    55|      \\\"duration_seconds\\\": 1.4,\\n    56|      \\\"aspect_ratio\\\": \\\"9:16\\\",\\n    57|      \\\"preferred_resolution\\\": \\\"480p_or_best_free_available\\\",\\n    58|      \\\"provider_order\\\": [\\n    59|        \\\"pixverse\\\",\\n    60|        \\\"pika\\\",\\n    61|        \\\"capcut\\\",\\n    62|        \\\"hailuo\\\"\\n    63|      ],\\n    64|      \\\"providers\\\": [\\n    65|        {\\n    66|          \\\"provider\\\": \\\"pixverse\\\",\\n    67|          \\\"display_name\\\": \\\"PixVerse\\\",\\n    68|          \\\"url\\\": \\\"https://app.pixverse.ai/\\\",\\n    69|          \\\"free_tier\\\": {\\n    70|            \\\"daily_refresh_credits\\\": 90,\\n    71|            \\\"signup_required\\\": true,\\n    72|            \\\"best_use\\\": \\\"realistic text/image-to-video short clips\\\"\\n    73|          },\\n    74|          \\\"risks\\\": [\\n    75|            \\\"terms and quotas can change\\\",\\n    76|            \\\"manual account/login required\\\",\\n    77|            \\\"possible watermark depending export path\\\"\\n    78|          ]\\n    79|        },\\n    80|        {\\n    81|          \\\"provider\\\": \\\"pika\\\",\\n    82|          \\\"display_name\\\": \\\"Pika\\\",\\n    83|          \\\"url\\\": \\\"https://pika.art/\\\",\\n    84|          \\\"free_tier\\\": {\\n    85|            \\\"monthly_credits\\\": 80,\\n    86|            \\\"max_resolution\\\": \\\"480p\\\",\\n    87|            \\\"text_to_video_5s_credits\\\": 12,\\n    88|            \\\"watermark_free_claimed\\\": true,\\n    89|            \\\"commercial_use_claimed\\\": true,\\n    90|            \\\"signup_required\\\": true\\n    91|          },\\n    92|          \\\"risks\\\": [\\n    93|            \\\"low 480p free resolution\\\",\\n    94|            \\\"monthly quota is small\\\",\\n    95|            \\\"manual account/login required\\\"\\n    96|          ]\\n    97|        },\\n    98|        {\\n    99|          \\\"provider\\\": \\\"capcut\\\",\\n   100|          \\\"display_name\\\": \\\"CapCut AI Video Maker\\\",\\n   101|          \\\"url\\\": \\\"https://www.capcut.com/tools/ai-video-generator\\\",\\n   102|          \\\"free_tier\\\": {\\n   103|            \\\"hd_export_claimed\\\": true,\\n   104|            \\\"credit_amount_publicly_clear\\\": false,\\n   105|            \\\"best_use\\\": \\\"editorial assembly, templates, captions, quick drafts\\\",\\n   106|            \\\"signup_required\\\": true\\n   107|          },\\n   108|          \\\"risks\\\": [\\n   109|            \\\"free quota not clearly exposed on public page\\\",\\n   110|            \\\"may upsell Pro assets\\\",\\n   111|            \\\"manual export required\\\"\\n   112|          ]\\n   113|        },\\n   114|        {\\n   115|          \\\"provider\\\": \\\"hailuo\\\",\\n   116|          \\\"display_name\\\": \\\"Hailuo AI\\\",\\n   117|          \\\"url\\\": \\\"https://hailuoai.video/\\\",\\n   118|          \\\"free_tier\\\": {\\n   119|            \\\"default_visible_generation_cost_credits\\\": 25,\\n   120|            \\\"signup_required\\\": true,\\n   121|            \\\"use_only_existing_free_credits\\\": true\\n   122|          },\\n   123|          \\\"risks\\\": [\\n   124|            \\\"do not buy credits\\\",\\n   125|            \\\"free quota may vary\\\",\\n   126|            \\\"user explicitly wants no paid Hailuo credits\\\"\\n   127|          ]\\n   128|        }\\n   129|      ],\\n   130|      \\\"prompt\\\": \\\"Vertical 9:16 realistic cinematic short video. a realistic creator at a desk late evening, laptop open to an AI chat, thoughtful but in control, hands moving between keyboard and notebook. Mood: focused, modern, high-retention social video, dramatic lighting simulated with gradients and shadows. Scene intent: 3D car x-ray with price tag layers and risk meters freezes mid-action with warning signal. Camera: slow handheld push-in, subtle parallax, natural micro-movements. No captions, no readable text, no logos, no watermarks. Leave clean negative space in the lower third for captions. The clip should feel like real b-roll for a mechanism explainer, not a slideshow.\\\",\\n   131|      \\\"negative_prompt\\\": \\\"no readable text, no subtitles, no logos, no watermarks, no distorted hands, no fake UI text, no slideshow, no static image, no uncanny faces\\\",\\n   132|      \\\"overlay_text\\\": \\\"Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\\",\\n   133|      \\\"free_tier_guardrails\\\": {\\n   134|        \\\"do_not_buy_credits\\\": true,\\n   135|        \\\"use_only_free_daily_or_monthly_quota\\\": true,\\n   136|        \\\"avoid_pro_templates_or_paid_assets\\\": true,\\n   137|        \\\"export_without_baked_captions\\\": true,\\n   138|        \\\"final_captions_added_locally\\\": true\\n   139|      },\\n   140|      \\\"manual_steps\\\": [\\n   141|        \\\"Open the first available provider in provider_order.\\\",\\n   142|        \\\"Use text-to-video or image-to-video with the prompt below; choose vertical 9:16 if available.\\\",\\n   143|        \\\"Set duration to the requested scene duration or the nearest free option, usually 5s.\\\",\\n   144|        \\\"Do not buy credits, subscribe, or select paid-only models/templates.\\\",\\n   145|        \\\"Download the clip, then place it under data/imported_ai_clips/<video_id>/<scene_id>.mp4 for local assembly.\\\"\\n   146|      ],\\n   147|      \\\"expected_clip_path\\\": \\\"data/imported_ai_clips/{video_id}/scene_01.mp4\\\"\\n   148|    },\\n   149|    {\\n   150|      \\\"job_id\\\": \\\"scene_02_free_ai_video_handoff\\\",\\n   151|      \\\"scene_id\\\": \\\"scene_02\\\",\\n   152|      \\\"asset_mode\\\": \\\"cinematic_ai_video\\\",\\n   153|      \\\"status\\\": \\\"manual_generation_required\\\",\\n   154|      \\\"duration_seconds\\\": 3.6,\\n   155|      \\\"aspect_ratio\\\": \\\"9:16\\\",\\n   156|      \\\"preferred_resolution\\\": \\\"480p_or_best_free_available\\\",\\n   157|      \\\"provider_order\\\": [\\n   158|        \\\"pixverse\\\",\\n   159|        \\\"pika\\\",\\n   160|        \\\"capcut\\\",\\n   161|        \\\"hailuo\\\"\\n   162|      ],\\n   163|      \\\"providers\\\": [\\n   164|        {\\n   165|          \\\"provider\\\": \\\"pixverse\\\",\\n   166|          \\\"display_name\\\": \\\"PixVerse\\\",\\n   167|          \\\"url\\\": \\\"https://app.pixverse.ai/\\\",\\n   168|          \\\"free_tier\\\": {\\n   169|            \\\"daily_refresh_credits\\\": 90,\\n   170|            \\\"signup_required\\\": true,\\n   171|            \\\"best_use\\\": \\\"realistic text/image-to-video short clips\\\"\\n   172|          },\\n   173|          \\\"risks\\\": [\\n   174|            \\\"terms and quotas can change\\\",\\n   175|            \\\"manual account/login required\\\",\\n   176|            \\\"possible watermark depending export path\\\"\\n   177|          ]\\n   178|        },\\n   179|        {\\n   180|          \\\"provider\\\": \\\"pika\\\",\\n   181|          \\\"display_name\\\": \\\"Pika\\\",\\n   182|          \\\"url\\\": \\\"https://pika.art/\\\",\\n   183|          \\\"free_tier\\\": {\\n   184|            \\\"monthly_credits\\\": 80,\\n   185|            \\\"max_resolution\\\": \\\"480p\\\",\\n   186|            \\\"text_to_video_5s_credits\\\": 12,\\n   187|            \\\"watermark_free_claimed\\\": true,\\n   188|            \\\"commercial_use_claimed\\\": true,\\n   189|            \\\"signup_required\\\": true\\n   190|          },\\n   191|          \\\"risks\\\": [\\n   192|            \\\"low 480p free resolution\\\",\\n   193|            \\\"monthly quota is small\\\",\\n   194|            \\\"manual account/login required\\\"\\n   195|          ]\\n   196|        },\\n   197|        {\\n   198|          \\\"provider\\\": \\\"capcut\\\",\\n   199|          \\\"display_name\\\": \\\"CapCut AI Video Maker\\\",\\n   200|          \\\"url\\\": \\\"https://www.capcut.com/tools/ai-video-generator\\\",\\n   201|          \\\"free_tier\\\": {\\n   202|            \\\"hd_export_claimed\\\": true,\\n   203|            \\\"credit_amount_publicly_clear\\\": false,\\n   204|            \\\"best_use\\\": \\\"editorial assembly, templates, captions, quick drafts\\\",\\n   205|            \\\"signup_required\\\": true\\n   206|          },\\n   207|          \\\"risks\\\": [\\n   208|            \\\"free quota not clearly exposed on public page\\\",\\n   209|            \\\"may upsell Pro assets\\\",\\n   210|            \\\"manual export required\\\"\\n   211|          ]\\n   212|        },\\n   213|        {\\n   214|          \\\"provider\\\": \\\"hailuo\\\",\\n   215|          \\\"display_name\\\": \\\"Hailuo AI\\\",\\n   216|          \\\"url\\\": \\\"https://hailuoai.video/\\\",\\n   217|          \\\"free_tier\\\": {\\n   218|            \\\"default_visible_generation_cost_credits\\\": 25,\\n   219|            \\\"signup_required\\\": true,\\n   220|            \\\"use_only_existing_free_credits\\\": true\\n   221|          },\\n   222|          \\\"risks\\\": [\\n   223|            \\\"do not buy credits\\\",\\n   224|            \\\"free quota may vary\\\",\\n   225|            \\\"user explicitly wants no paid Hailuo credits\\\"\\n   226|          ]\\n   227|        }\\n   228|      ],\\n   229|      \\\"prompt\\\": \\\"Vertical 9:16 realistic cinematic short video. a realistic creator at a desk late evening, laptop open to an AI chat, thoughtful but in control, hands moving between keyboard and notebook. Mood: focused, modern, high-retention social video, dramatic lighting simulated with gradients and shadows. Scene intent: surface layer peels away to reveal the hidden system underneath. Camera: slow handheld push-in, subtle parallax, natural micro-movements. No captions, no readable text, no logos, no watermarks. Leave clean negative space in the lower third for captions. The clip should feel like real b-roll for a mechanism explainer, not a slideshow.\\\",\\n   230|      \\\"negative_prompt\\\": \\\"no readable text, no subtitles, no logos, no watermarks, no distorted hands, no fake UI text, no slideshow, no static image, no uncanny faces\\\",\\n   231|      \\\"overlay_text\\\": \\\"Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\\",\\n   232|      \\\"free_tier_guardrails\\\": {\\n   233|        \\\"do_not_buy_credits\\\": true,\\n   234|        \\\"use_only_free_daily_or_monthly_quota\\\": true,\\n   235|        \\\"avoid_pro_templates_or_paid_assets\\\": true,\\n   236|        \\\"export_without_baked_captions\\\": true,\\n   237|        \\\"final_captions_added_locally\\\": true\\n   238|      },\\n   239|      \\\"manual_steps\\\": [\\n   240|        \\\"Open the first available provider in provider_order.\\\",\\n   241|        \\\"Use text-to-video or image-to-video with the prompt below; choose vertical 9:16 if available.\\\",\\n   242|        \\\"Set duration to the requested scene duration or the nearest free option, usually 5s.\\\",\\n   243|        \\\"Do not buy credits, subscribe, or select paid-only models/templates.\\\",\\n   244|        \\\"Download the clip, then place it under data/imported_ai_clips/<video_id>/<scene_id>.mp4 for local assembly.\\\"\\n   245|      ],\\n   246|      \\\"expected_clip_path\\\": \\\"data/imported_ai_clips/{video_id}/scene_02.mp4\\\"\\n   247|    }\\n   248|  ],\\n   249|  \\\"local_jobs\\\": [\\n   250|    {\\n   251|      \\\"scene_id\\\": \\\"scene_03\\\",\\n   252|      \\\"asset_mode\\\": \\\"mechanism_animation\\\",\\n   253|      \\\"provider\\\": \\\"local_renderer\\\",\\n   254|      \\\"execution_mode\\\": \\\"mechanism_reveal_animation\\\",\\n   255|      \\\"duration_seconds\\\": 13,\\n   256|      \\\"role\\\": \\\"local_mechanism_or_caption_layer\\\"\\n   257|    },\\n   258|    {\\n   259|      \\\"scene_id\\\": \\\"scene_04\\\",\\n   260|      \\\"asset_mode\\\": \\\"mechanism_animation\\\",\\n   261|      \\\"provider\\\": \\\"local_renderer\\\",\\n   262|      \\\"execution_mode\\\": \\\"mechanism_reveal_animation\\\",\\n   263|      \\\"duration_seconds\\\": 10,\\n   264|      \\\"role\\\": \\\"local_mechanism_or_caption_layer\\\"\\n   265|    },\\n   266|    {\\n   267|      \\\"scene_id\\\": \\\"scene_05\\\",\\n   268|      \\\"asset_mode\\\": \\\"kinetic_text_overlay\\\",\\n   269|      \\\"provider\\\": \\\"local_ffmpeg\\\",\\n   270|      \\\"execution_mode\\\": \\\"kinetic_typography\\\",\\n   271|      \\\"duration_seconds\\\": 6,\\n   272|      \\\"role\\\": \\\"local_mechanism_or_caption_layer\\\"\\n   273|    }\\n   274|  ]\\n   275|}\\n   276|\", \"total_lines\": 275, \"file_size\": 11269, \"truncated\": false, \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_y0uRGVApr0Dz3t0KH2H9uB36", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Considering prompt improvements**\n\nIt seems like the prompts I have might not be quite right — they feel more suited for an AI chat desk than for a car buyer scenario. I need to tailor the package better. Maybe I could generate Markdown with improved prompts? I'll need to research API options and possibly use terminal commands to fetch pricing pages. I should also check if the Gradio client is compatible for exploring specific Hugging Face Spaces, like \"Wan2.1.\"", "finish_reason": "tool_calls", "reasoning_content": "**Considering prompt improvements**\n\nIt seems like the prompts I have might not be quite right — they feel more suited for an AI chat desk than for a car buyer scenario. I need to tailor the package better. Maybe I could generate Markdown with improved prompts? I'll need to research API options and possibly use terminal commands to fetch pricing pages. I should also check if the Gradio client is compatible for exploring specific Hugging Face Spaces, like \"Wan2.1.\"", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEZ0OB3Du2FziXjo0IX5aqiYnZTpG4lxrfuBZdd2UneGHgvfBHRqfZhMRu87druXXwHVyxaIih1weW8-fsN9x5W1LNTZLWq6cErh3FRPjR-Gfk3Z16c_zUwK-6SytZivBVaYyjXiIi2FcmroWNTgmjBjToO-JtvaHstSYW7UYaT9wyaIxwg9StBc_Sce81nWhsXgfJ-kwY5ZrOFmUrmumYyYaM4YRfYc2kjDVDzAnkm8LJiquZWIEaK7qemUPnaeRN0HS8lGhQziewebBHQ2Qy7EIqTuk3w1-CgI662QWlnKd48BRYrSC3UgmUHaBOqvW12o6QMavYco2jixgb7Z0hMEWHAtRSprZplEq5Ww1Z3oqbgC_EEGJjOcxB7Go_6OF2HZU0hs8Bj0E6MVpnbFp4eHXjgG8A2Q5xN_h0U27ipgxWXZ6VWEEmlFnoJKvYkTp1GKUIKS-nR-vsflOkDjPkY5C72kCUGkutIy6g-CQJbyerDISGLhe1KbkgTO2lGLeRGsMtbcXV12w_5I1eXKdt2MoFD1kn9UxEThAEmGN5Nx4cHqkaS0IBEqPu5VypycB5eVC3kuTIqw9Ua9mbnc4eBuSzCOXfX61Y1RHjz3QO4SjZEi2E9lPo5SaDFFzbgO17dyTrrt3ZTg5FtGtoCZrUshBYmXhYlQXO8rcDl8mY0MwHwDDks3tGpM851bfWu5lIgEcf7t5B9inhp_ESMOCQroaSIVfI-vxUnpQphwEpcFNqDdCM1i-7BzA0arbKOisvcz8AnCXWguvoKErbPqNHodP6BulayWfKYn9R3lI6eaMaAWWXNce4-n-RQ9_lGpczIVUGS9HS-edKCMDjIhdVadLaC8xmmCsqtwfhW_x-OK8RL9kvETwwvO0NivLqiVCkX4oziWfMiL8jsAY9LD9k3PMYnzc420nJWhJlsFDOZkctZsJKox_AiZZHzWx05lGxhnsEJ2VJV-w2Gi4nigE4hJBcMSLOyj0-Xjizd3xIqPO2wDQAG6EaHOhDXANC7viApq_sd-74rC1Et6SJVpsUpev5I2g7toiKe8ZEuDiX6xPAULIizXOeEl3ljdZ2sKmGnVi_lMpLPYwBCi-palGAi5zcyVcMAYq9uo53vgGa5ohIYAwpK34P-sWnImtE3KfnV5gdHSP98ohN8ddR823pNMVc_HNgLZ2KL4HU4o91mdtjAwMLotbruVr3skoMQLUDC-MU3Pr9A8xFG5uRbnoKbh5GIhxAPzVkwIEJNolR4yu8ScLVc9P3hv7Y9bnLBG5xwdj1dl8Zp_D0WXPH7c2tVXBjbWoiyXAOgxiJCiD4k8qAixRTURtmlkQhYuLZxeAi7o11dqjFQsgzZNmO8AuUI19jw==", "id": "rs_0d1a4d10654d56b4016a119d0ad99081919da99c62b6417be3", "summary": [{"type": "summary_text", "text": "**Considering prompt improvements**\n\nIt seems like the prompts I have might not be quite right — they feel more suited for an AI chat desk than for a car buyer scenario. I need to tailor the package better. Maybe I could generate Markdown with improved prompts? I'll need to research API options and possibly use terminal commands to fetch pricing pages. I should also check if the Gradio client is compatible for exploring specific Hugging Face Spaces, like \"Wan2.1.\""}]}], "tool_calls": [{"id": "call_BdrrOVtXzmsCbXXqriqOsRzf", "call_id": "call_BdrrOVtXzmsCbXXqriqOsRzf", "response_item_id": "fc_0d1a4d10654d56b4016a119d0e24f88191a3882f4fb752917d", "type": "function", "function": {"name": "browser_navigate", "arguments": "{\"url\":\"https://huggingface.co/spaces?sort=trending&search=Wan2.1\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
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StaticText \\\"Image Upscaling\\\"\\n      - radio \\\"OCR\\\" [checked=false, ref=e66] clickable [cursor:pointer]\\n        - StaticText \\\"OCR\\\"\\n      - radio \\\"Document Analysis\\\" [checked=false, ref=e67] clickable [cursor:pointer]\\n        - StaticText \\\"Document Analysis\\\"\\n      - radio \\\"Visual QA\\\" [checked=false, ref=e68] clickable [cursor:pointer]\\n        - StaticText \\\"Visual QA\\\"\\n      - radio \\\"Image Captioning\\\" [checked=false, ref=e69] clickable [cursor:pointer]\\n        - StaticText \\\"Image Captioning\\\"\\n      - radio \\\"Chatbots\\\" [checked=false, ref=e70] clickable [cursor:pointer]\\n        - StaticText \\\"Chatbots\\\"\\n      - radio \\\"Sentiment Analysis\\\" [checked=false, ref=e71] clickable [cursor:pointer]\\n        - StaticText \\\"Sentiment Analysis\\\"\\n      - radio \\\"Text Summarization\\\" [checked=false, ref=e72] clickable [cursor:pointer]\\n        - StaticText \\\"Text Summarization\\\"\\n      - radio \\\"Music Generation\\\" [checked=false, ref=e73] clickable [cursor:pointer]\\n        - 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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_WqUqgoxtyctQrKjALhmdiVqk", "call_id": "call_WqUqgoxtyctQrKjALhmdiVqk", "response_item_id": "fc_0d1a4d10654d56b4016a119d15fdf88191a65b916a21f92bd8", "type": "function", "function": {"name": "browser_console", "arguments": "{\"expression\":\"Array.from(document.querySelectorAll('article a')).map(a=>a.innerText.trim()).filter(Boolean).slice(0,40)\" }"}}], "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "reasoning": "**Creating markdown artifact**\n\nI’m thinking I need to create a markdown artifact, possibly using a script and the write_file function. I also want to include some API research in there. Maybe I should commit my progress? I’m not sure about needing tests for markdown, but I could write it to data/free_ai_video_handoffs/...md. After that, I might want to check the git status to see where everything stands.", "finish_reason": "tool_calls", "reasoning_content": "**Creating markdown artifact**\n\nI’m thinking I need to create a markdown artifact, possibly using a script and the write_file function. I also want to include some API research in there. Maybe I should commit my progress? I’m not sure about needing tests for markdown, but I could write it to data/free_ai_video_handoffs/...md. 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I also want to include some API research in there. Maybe I should commit my progress? I’m not sure about needing tests for markdown, but I could write it to data/free_ai_video_handoffs/...md. 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PixVerse — primary free-tier candidate; public app showed 90 daily refresh credits.\\n2. Pika — fallback; pricing page showed Basic $0, 80 monthly credits, 480p, no-watermark downloads, commercial use.\\n3. CapCut — editor/fallback; page claims free AI video generator and HD export, quota less explicit.\\n4. Hailuo — only if existing free credits are available; no purchases.\\n5. Hugging Face Spaces / Wan2.1 — experimental fallback; can be slow or unavailable.\\n\\n---\\n\\n## General generation settings\\n\\nUse these settings where available:\\n\\n- Mode: text-to-video; image-to-video only if the tool requires an image seed.\\n- Aspect: 9:16 vertical.\\n- Duration: nearest free option, usually 5s. We will trim locally to exact timing.\\n- Resolution: best free available; 480p is acceptable for Pika.\\n- Camera: slow push-in, handheld micro-motion, shallow depth of field.\\n- Text: **no text baked into the video**. Captions are added locally.\\n- Export: download MP4 if possible.\\n\\nGlobal negative prompt:\\n\\n```text\\nno readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\\n```\\n\\nQuality check before accepting a clip:\\n\\n- Looks like moving video, not a still image with tiny camera shake.\\n- No readable/gibberish text or fake UI text.\\n- No visible watermark if avoidable.\\n- Safe lower-third / upper-third space for local captions.\\n- No grotesque hands/faces.\\n- Scene is visually relevant to car buying / hidden cost / financial risk.\\n- Does not look like a generic AI ad template.\\n\\n---\\n\\n## Scene 01 — Hook / pattern interrupt\\n\\nTarget duration in final edit: `1.4s`.\\nGenerate a 4–5s clip; local renderer will trim it.\\n\\nFinal overlay added locally:\\n\\n```text\\nDieser Occasion-Käufer denkt, er spart 4'000 Franken.\\n```\\n\\n### Prompt A — realistic buyer + car lot\\n\\n```text\\nVertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\\n```\\n\\n### Prompt B — price tag / hidden danger visual metaphor\\n\\n```text\\nVertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\\n```\\n\\nRecommended first attempt: **Prompt A**.\\nIf the generated face/hands look bad: use **Prompt B**, because it avoids faces.\\n\\nSave as:\\n\\n```text\\nscene_01.mp4\\n```\\n\\n---\\n\\n## Scene 02 — Problem / hidden system reveal\\n\\nTarget duration in final edit: `3.6s`.\\nGenerate a 4–5s clip; local renderer will trim it.\\n\\nFinal overlay added locally:\\n\\n```text\\nDas sieht harmlos aus, bis du den Mechanismus dahinter siehst.\\n```\\n\\n### Prompt A — mechanic inspection reveal\\n\\n```text\\nVertical 9:16 realistic cinematic video inside a clean independent car garage. A used car is lifted slightly or parked under inspection lights while a mechanic points a flashlight under the car. The buyer watches as hidden problems are implied, not shown with text. Camera slowly pushes from the buyer's face to the underside of the car, subtle parallax, realistic motion, documentary explainer style, dramatic but believable lighting. No readable text, no subtitles, no logos, no watermarks, no fake UI, no license plate text. Leave clean space for captions.\\n```\\n\\n### Prompt B — abstract but realistic hidden-cost reveal\\n\\n```text\\nVertical 9:16 realistic cinematic video. A used car sits in a dark garage under a scanning light; the camera moves along the side of the car as faint glowing diagnostic reflections sweep across the body, suggesting hidden repair risks beneath the surface. Realistic car, cinematic lighting, smooth push-in, no fantasy hologram text, no readable labels, no logos, no watermarks. Leave negative space for local captions.\\n```\\n\\nRecommended first attempt: **Prompt B** if the provider struggles with people/mechanics.\\nRecommended first attempt: **Prompt A** if the provider handles humans well.\\n\\nSave as:\\n\\n```text\\nscene_02.mp4\\n```\\n\\n---\\n\\n## Manual workflow\\n\\n### PixVerse\\n\\n1. Open: https://app.pixverse.ai/\\n2. Use free daily credits only.\\n3. Select text-to-video, 9:16 if available.\\n4. Paste Scene 01 prompt.\\n5. Add negative prompt if the UI supports it.\\n6. Generate 1–2 candidates; pick the least AI-slop version.\\n7. Download as `scene_01.mp4`.\\n8. Repeat for Scene 02.\\n\\n### Pika\\n\\n1. Open: https://pika.art/\\n2. Use Basic/free monthly credits only.\\n3. Select 9:16 and 480p if free plan limits apply.\\n4. Generate 5s clips; avoid effects/templates that require paid plan.\\n5. Download no-watermark if available.\\n\\n### CapCut\\n\\nUse only if PixVerse/Pika fail or for manual polish. Avoid Pro assets/templates. Export without baked captions where possible.\\n\\n### Hailuo\\n\\nUse only if the account has existing free credits. Do not buy credits.\\n\\n### Hugging Face / Open Source fallback\\n\\nSearch Hugging Face Spaces for `Wan2.1` or `text to video`. This may be free via public Spaces, but queues are unreliable and outputs can be slow. If it works, use it for Scene 01/02 only; keep mechanism layers local.\\n\\n---\\n\\n## Research notes: API / web / open source\\n\\n### API-controllable options\\n\\n- Replicate: strong video API catalog including Runway, Veo, Kling, Seedance, Hailuo, PixVerse, Wan. Usually paid usage; useful later if budget exists, not current primary path.\\n- fal.ai: broad video model API catalog including Seedance, Kling, PixVerse. Usually paid per generation; not current primary path.\\n- Hugging Face Spaces: some Spaces can be driven via `gradio_client` and may be free. Reliability varies; queues/sleep/runtime errors are common. Good experimental fallback, not production-stable.\\n- NVIDIA build.nvidia.com: currently useful for free serverless LLM/NIM development; research page did not show a clear free text-to-video generation API for this workflow. Use NVIDIA for text/prompt refinement only.\\n- ChatGPT Pro / Sora: not planned for new video generation. OpenAI Help says Sora web/app were discontinued on 2026-04-26 and current credits are for Codex / ChatGPT for Excel.\\n\\n### Free web-surface options\\n\\n- PixVerse: best current free-first candidate due daily credits shown publicly.\\n- Pika: good fallback; official pricing showed Basic $0, 80 monthly credits, 480p only, no-watermark downloads and commercial use claim.\\n- CapCut: free web editor/generator candidate; useful for assembly/polish but quota is less explicit.\\n- Hailuo: only with existing free credits; visible 25-credit generation cost means do not treat as free if credits are exhausted.\\n\\n### Open-source/local options\\n\\n- Wan2.1: strongest realistic open-source candidate. README notes consumer-GPU compatibility and 5-second 480p video on RTX 4090 in about 4 minutes. Good if we have a serious NVIDIA GPU available.\\n- LTX-Video: good local/open option; supports quantized/distilled models and fast inference, especially on strong GPUs.\\n- CogVideoX / CogVideoX1.5: mature open-source text/image-to-video; useful but still GPU-heavy.\\n- HunyuanVideo: strong open-source model, but heavier; better for rented/local high-VRAM GPU than this Hermes box unless GPU exists.\\n- Mochi: open-source but README recommends at least 1 H100 for this implementation; not practical locally without serious GPU.\\n\\nRecommended current decision:\\n\\n```text\\nManual free-tier PixVerse/Pika for realistic B-roll\\n+ local deterministic mechanism/caption renderer\\n+ optional Hugging Face Spaces experiments\\n+ Open-source local only if a suitable GPU is available\\n```\\n\\n---\\n\\n## After clips are downloaded\\n\\nPlace files here:\\n\\n```text\\nmkdir -p data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\n# copy/download files:\\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\\n```\\n\\nThen run/import through the hybrid renderer pipeline.\\n\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "name": "read_file", "content": "{\"content\": \"     1|# Clip Generation Packet — Warum ein billiges Auto teuer sein kann\\n     2|\\n     3|Goal: generate **two realistic free-tier AI video clips** for the pilot, then assemble them locally with mechanism layers, captions, SFX and FFmpeg.\\n     4|\\n     5|Budget rule: **no paid credits, no paid subscription, no top-up, no Pro templates/assets**.\\n     6|\\n     7|Video ID:\\n     8|\\n     9|```text\\n    10|hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann\\n    11|```\\n    12|\\n    13|Required import paths after download:\\n    14|\\n    15|```text\\n    16|data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\\n    17|data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\\n    18|```\\n    19|\\n    20|Preferred provider order:\\n    21|\\n    22|1. PixVerse — primary free-tier candidate; public app showed 90 daily refresh credits.\\n    23|2. Pika — fallback; pricing page showed Basic $0, 80 monthly credits, 480p, no-watermark downloads, commercial use.\\n    24|3. CapCut — editor/fallback; page claims free AI video generator and HD export, quota less explicit.\\n    25|4. Hailuo — only if existing free credits are available; no purchases.\\n    26|5. Hugging Face Spaces / Wan2.1 — experimental fallback; can be slow or unavailable.\\n    27|\\n    28|---\\n    29|\\n    30|## General generation settings\\n    31|\\n    32|Use these settings where available:\\n    33|\\n    34|- Mode: text-to-video; image-to-video only if the tool requires an image seed.\\n    35|- Aspect: 9:16 vertical.\\n    36|- Duration: nearest free option, usually 5s. We will trim locally to exact timing.\\n    37|- Resolution: best free available; 480p is acceptable for Pika.\\n    38|- Camera: slow push-in, handheld micro-motion, shallow depth of field.\\n    39|- Text: **no text baked into the video**. Captions are added locally.\\n    40|- Export: download MP4 if possible.\\n    41|\\n    42|Global negative prompt:\\n    43|\\n    44|```text\\n    45|no readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\\n    46|```\\n    47|\\n    48|Quality check before accepting a clip:\\n    49|\\n    50|- Looks like moving video, not a still image with tiny camera shake.\\n    51|- No readable/gibberish text or fake UI text.\\n    52|- No visible watermark if avoidable.\\n    53|- Safe lower-third / upper-third space for local captions.\\n    54|- No grotesque hands/faces.\\n    55|- Scene is visually relevant to car buying / hidden cost / financial risk.\\n    56|- Does not look like a generic AI ad template.\\n    57|\\n    58|---\\n    59|\\n    60|## Scene 01 — Hook / pattern interrupt\\n    61|\\n    62|Target duration in final edit: `1.4s`.\\n    63|Generate a 4–5s clip; local renderer will trim it.\\n    64|\\n    65|Final overlay added locally:\\n    66|\\n    67|```text\\n    68|Dieser Occasion-Käufer denkt, er spart 4'000 Franken.\\n    69|```\\n    70|\\n    71|### Prompt A — realistic buyer + car lot\\n    72|\\n    73|```text\\n    74|Vertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\\n    75|```\\n    76|\\n    77|### Prompt B — price tag / hidden danger visual metaphor\\n    78|\\n    79|```text\\n    80|Vertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\\n    81|\", \"total_lines\": 210, \"file_size\": 9336, \"truncated\": true, \"hint\": \"Use offset=81 to continue reading (showing 1-80 of 210 lines)\", \"is_binary\": false, \"is_image\": false}", "tool_call_id": "call_JbHlHTxYk5NoPNoX4FgXV5lw", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "Ja, Sir. Vorgehen ist jetzt klar:\n\n## 1. Generiere ich die Videos?\n\n**Teilweise ja.**\n\nIch kann direkt generieren:\n\n- lokale Animatics\n- Mechanism-Animationen\n- Captions\n- Timing\n- SFX/Audio-Layout\n- finalen Schnitt mit FFmpeg\n- Hybrid-Renderer, der echte Clips einbaut\n\nDie **realistischen KI-B-Roll-Clips** selbst sind der schwierige Teil. Die kann ich nur dann autonom erzeugen, wenn:\n\n- ein API-fähiger Anbieter kostenlos nutzbar ist, oder\n- eine gratis Weboberfläche ohne Login/CAPTCHA stabil bedienbar ist, oder\n- wir lokal/Open-Source genug GPU haben.\n\nAktuell ist der robusteste Gratis-Weg:\n\n```text\nIch liefere Clip-Prompts + genaue Anleitung\ndu erzeugst 2 Clips in PixVerse/Pika/CapCut/HF Space\nich importiere sie\nich baue finalen Hybrid-Render\n```\n\nNicht glamourös. Aber kostenlos und kontrollierbar. Zwei Dinge, die selten im selben Raum gesehen werden.\n\n---\n\n## 2. Clip-Generation-Paket ist erstellt\n\nIch habe das konkrete Paket für den Pilot abgelegt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\nhidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann_CLIP_GENERATION_PACKET.md\n```\n\nCommit:\n\n```text\n494898b docs: add free clip generation packet\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDas Paket enthält:\n\n- PixVerse/Pika/CapCut/Hailuo Workflow\n- Prompt für `scene_01`\n- Prompt für `scene_02`\n- Negative Prompt\n- Qualitätscheckliste\n- Download-Dateinamen\n- Importpfade\n- API-/Open-Source-Recherche\n\n---\n\n## 3. Was du jetzt konkret tun sollst\n\nWir brauchen für den ersten Hybrid-Test nur zwei Dateien:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAblage später:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\n\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Scene 01 Prompt\n\nZiel: Hook / Occasion-Käufer denkt, er spart Geld.\n\nNutze zuerst diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Gesichter/Hände hässlich werden:\n\n```text\nVertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\n```\n\nSpeichern als:\n\n```text\nscene_01.mp4\n```\n\n---\n\n## Scene 02 Prompt\n\nZiel: harmloses Auto, aber versteckter Mechanismus/Risiko.\n\nNutze diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video inside a clean independent car garage. A used car is lifted slightly or parked under inspection lights while a mechanic points a flashlight under the car. The buyer watches as hidden problems are implied, not shown with text. Camera slowly pushes from the buyer's face to the underside of the car, subtle parallax, realistic motion, documentary explainer style, dramatic but believable lighting. No readable text, no subtitles, no logos, no watermarks, no fake UI, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Menschen schlecht aussehen:\n\n```text\nVertical 9:16 realistic cinematic video. A used car sits in a dark garage under a scanning light; the camera moves along the side of the car as faint glowing diagnostic reflections sweep across the body, suggesting hidden repair risks beneath the surface. Realistic car, cinematic lighting, smooth push-in, no fantasy hologram text, no readable labels, no logos, no watermarks. Leave negative space for local captions.\n```\n\nSpeichern als:\n\n```text\nscene_02.mp4\n```\n\n---\n\n## Global Negative Prompt\n\nFalls Tool ein Negative-Prompt-Feld hat:\n\n```text\nno readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\n```\n\n---\n\n## Empfohlene Reihenfolge der Tools\n\n## 1. PixVerse\n\n```text\nhttps://app.pixverse.ai/\n```\n\nWarum zuerst:\n\n- App zeigte öffentlich `90` Credits mit täglicher Aktualisierung\n- am besten für realistische kurze Clips geeignet\n- vermutlich höchster Gratis-Nutzen pro Tag\n\nRegel:\n\n```text\nNur Free Credits. Keine Top-ups.\n```\n\n## 2. Pika\n\n```text\nhttps://pika.art/\n```\n\nOffizielles Pricing zeigte:\n\n```text\nBasic $0\n80 monthly video credits\nPika 2.5\n480p only\nno-watermark downloads\ncommercial use\n```\n\nGut als Fallback, aber nur 480p. Für Shorts akzeptabel, weil wir lokal schneiden/captionen.\n\n## 3. CapCut\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nNützlich für:\n\n- schnelle Drafts\n- Editor/Export\n- vielleicht HD-Free-Export\n\nAber: Free-Quota weniger transparent, Pro-Assets vermeiden.\n\n## 4. Hailuo\n\n```text\nhttps://hailuoai.video/\n```\n\nNur falls bestehende Free-Credits da sind. Seite zeigte sichtbare `25 credits` pro 6s/768p Generation. Also:\n\n```text\nnicht kaufen\nnicht aufladen\nnicht abonnieren\n```\n\n---\n\n# Recherche: API / Web / Open Source\n\n## API-fähige Anbieter\n\n## Replicate\n\n```text\nhttps://replicate.com/collections/text-to-video\n```\n\nBietet API für viele starke Modelle:\n\n- Runway\n- Veo\n- Kling\n- Seedance\n- Hailuo\n- PixVerse\n- Wan\n\nAber: praktisch **paid API**. Gut für später, nicht für Gratis-Workflow.\n\n## fal.ai\n\n```text\nhttps://fal.ai/explore?categories=video\n```\n\nBietet API für:\n\n- Seedance\n- Kling\n- PixVerse\n- weitere Video-Modelle\n\nAber ebenfalls: normalerweise **paid per generation**. Nicht unser aktueller Pfad.\n\n## Hugging Face Spaces\n\n```text\nhttps://huggingface.co/spaces\n```\n\nEs gibt viele `Wan2.1` / `text-to-video` Spaces. Vorteil:\n\n- teils gratis im Browser nutzbar\n- manchmal via `gradio_client` automatisierbar\n- kein eigener GPU-Server nötig\n\nNachteil:\n\n- Warteschlangen\n- Spaces schlafen ein\n- Runtime Errors\n- Qualität/Verfügbarkeit stark schwankend\n\nFür uns: **experimenteller kostenloser API/Web-Fallback**.\n\n## NVIDIA build.nvidia.com\n\n```text\nhttps://build.nvidia.com/\n```\n\nDort gibt es free serverless APIs für Entwicklung, aber ich habe keinen klaren kostenlosen Text-to-Video-Generator gefunden, der unseren Clip-Bedarf deckt.\n\nFür uns:\n\n```text\nNVIDIA_API_KEY → Text-/Prompt-Refinement\nnicht Video-Generation\n```\n\n---\n\n# Open-Source / lokal\n\n## Wan2.1\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nSehr interessant. README sagt sinngemäß:\n\n```text\n5-second 480P video on RTX 4090 in about 4 minutes\n```\n\nDas ist der beste lokale Kandidat, falls wir Zugriff auf eine starke NVIDIA-GPU haben.\n\n## LTX-Video\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nGuter Open-Source-Kandidat, quantisierte/distilled Modelle, schneller als viele andere.\n\n## CogVideoX / CogVideoX1.5\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nSolide Open-Source-Text/Image-to-Video, aber GPU-heavy.\n\n## HunyuanVideo\n\n```text\nhttps://github.com/Tencent-Hunyuan/HunyuanVideo\n```\n\nStark, aber eher schwergewichtig. Ohne dicke GPU nicht ideal.\n\n## Mochi\n\n```text\nhttps://github.com/genmoai/mochi\n```\n\nOpen Source, aber README empfiehlt für deren Implementation mindestens 1 H100. Also eher nein, sofern wir nicht zufällig einen kleinen Supercomputer unter dem Sofa finden.\n\n---\n\n# Meine Empfehlung\n\n## Jetzt sofort\n\n```text\nPixVerse manuell testen\nwenn gut: scene_01 + scene_02 herunterladen\nwenn schlecht: Pika testen\nwenn beide schlecht: Hugging Face Wan2.1 Space testen\n```\n\nDanach:\n\n```text\nIch baue Hybrid Final Renderer:\nimportierte Clips\n+ lokale Mechanism-Animation\n+ Captions\n+ SFX\n+ FFmpeg\n```\n\n## Parallel technisch sinnvoll\n\nAls nächstes kann ich prüfen, ob diese Maschine eine brauchbare GPU hat:\n\n```text\nnvidia-smi\n```\n\nWenn ja:\n\n```text\nWan2.1 / LTX-Video lokale Installation evaluieren\n```\n\nWenn nein:\n\n```text\nOpen-Source lokal vergessen\nHF Spaces / Free Web bleiben der Gratisweg\n```\n\nKurz: **Ja, ich generiere alles, was lokal geht. Die realistischen KI-Clips holen wir über Free-Web/HF. Sobald du die zwei MP4s hast, mache ich daraus das finale Video.**", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir. Vorgehen ist jetzt klar:\n\n## 1. Generiere ich die Videos?\n\n**Teilweise ja.**\n\nIch kann direkt generieren:\n\n- lokale Animatics\n- Mechanism-Animationen\n- Captions\n- Timing\n- SFX/Audio-Layout\n- finalen Schnitt mit FFmpeg\n- Hybrid-Renderer, der echte Clips einbaut\n\nDie **realistischen KI-B-Roll-Clips** selbst sind der schwierige Teil. Die kann ich nur dann autonom erzeugen, wenn:\n\n- ein API-fähiger Anbieter kostenlos nutzbar ist, oder\n- eine gratis Weboberfläche ohne Login/CAPTCHA stabil bedienbar ist, oder\n- wir lokal/Open-Source genug GPU haben.\n\nAktuell ist der robusteste Gratis-Weg:\n\n```text\nIch liefere Clip-Prompts + genaue Anleitung\ndu erzeugst 2 Clips in PixVerse/Pika/CapCut/HF Space\nich importiere sie\nich baue finalen Hybrid-Render\n```\n\nNicht glamourös. Aber kostenlos und kontrollierbar. Zwei Dinge, die selten im selben Raum gesehen werden.\n\n---\n\n## 2. Clip-Generation-Paket ist erstellt\n\nIch habe das konkrete Paket für den Pilot abgelegt:\n\n```text\ndata/free_ai_video_handoffs/mechanism_pilots/\nhidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann_CLIP_GENERATION_PACKET.md\n```\n\nCommit:\n\n```text\n494898b docs: add free clip generation packet\n```\n\nPush weiterhin blockiert wegen fehlender GitHub-Credentials:\n\n```text\nfatal: could not read Username for 'https://github.com'\n```\n\nDas Paket enthält:\n\n- PixVerse/Pika/CapCut/Hailuo Workflow\n- Prompt für `scene_01`\n- Prompt für `scene_02`\n- Negative Prompt\n- Qualitätscheckliste\n- Download-Dateinamen\n- Importpfade\n- API-/Open-Source-Recherche\n\n---\n\n## 3. Was du jetzt konkret tun sollst\n\nWir brauchen für den ersten Hybrid-Test nur zwei Dateien:\n\n```text\nscene_01.mp4\nscene_02.mp4\n```\n\nAblage später:\n\n```text\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4\n\ndata/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4\n```\n\n## Scene 01 Prompt\n\nZiel: Hook / Occasion-Käufer denkt, er spart Geld.\n\nNutze zuerst diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video, evening at a used car dealership in Switzerland or central Europe. A normal adult car buyer stands beside a cheap used compact car, holding a phone with a price listing, looking relieved for half a second, then suddenly worried as the camera snap-zooms toward the car. Dramatic documentary style, realistic lighting, slight handheld motion, shallow depth of field, cinematic contrast, high-retention social video hook. No readable text, no subtitles, no logos, no watermarks, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Gesichter/Hände hässlich werden:\n\n```text\nVertical 9:16 realistic cinematic video. Close-up of a cheap used car in a dim garage, a buyer's hand reaches toward the door handle, the camera quickly pushes in as the mood changes from bargain to warning. Subtle red warning light reflection, realistic car paint, cinematic shadows, handheld micro movement, documentary realism. No readable text, no subtitles, no logos, no watermarks, no fake numbers, no license plate text. Leave clean lower-third space for captions.\n```\n\nSpeichern als:\n\n```text\nscene_01.mp4\n```\n\n---\n\n## Scene 02 Prompt\n\nZiel: harmloses Auto, aber versteckter Mechanismus/Risiko.\n\nNutze diesen Prompt:\n\n```text\nVertical 9:16 realistic cinematic video inside a clean independent car garage. A used car is lifted slightly or parked under inspection lights while a mechanic points a flashlight under the car. The buyer watches as hidden problems are implied, not shown with text. Camera slowly pushes from the buyer's face to the underside of the car, subtle parallax, realistic motion, documentary explainer style, dramatic but believable lighting. No readable text, no subtitles, no logos, no watermarks, no fake UI, no license plate text. Leave clean space for captions.\n```\n\nFallback, falls Menschen schlecht aussehen:\n\n```text\nVertical 9:16 realistic cinematic video. A used car sits in a dark garage under a scanning light; the camera moves along the side of the car as faint glowing diagnostic reflections sweep across the body, suggesting hidden repair risks beneath the surface. Realistic car, cinematic lighting, smooth push-in, no fantasy hologram text, no readable labels, no logos, no watermarks. Leave negative space for local captions.\n```\n\nSpeichern als:\n\n```text\nscene_02.mp4\n```\n\n---\n\n## Global Negative Prompt\n\nFalls Tool ein Negative-Prompt-Feld hat:\n\n```text\nno readable text, no subtitles, no logos, no watermarks, no fake UI text, no slideshow, no static image, no uncanny faces, no distorted hands, no extra fingers, no deformed car, no random numbers, no brand names\n```\n\n---\n\n## Empfohlene Reihenfolge der Tools\n\n## 1. PixVerse\n\n```text\nhttps://app.pixverse.ai/\n```\n\nWarum zuerst:\n\n- App zeigte öffentlich `90` Credits mit täglicher Aktualisierung\n- am besten für realistische kurze Clips geeignet\n- vermutlich höchster Gratis-Nutzen pro Tag\n\nRegel:\n\n```text\nNur Free Credits. Keine Top-ups.\n```\n\n## 2. Pika\n\n```text\nhttps://pika.art/\n```\n\nOffizielles Pricing zeigte:\n\n```text\nBasic $0\n80 monthly video credits\nPika 2.5\n480p only\nno-watermark downloads\ncommercial use\n```\n\nGut als Fallback, aber nur 480p. Für Shorts akzeptabel, weil wir lokal schneiden/captionen.\n\n## 3. CapCut\n\n```text\nhttps://www.capcut.com/tools/ai-video-generator\n```\n\nNützlich für:\n\n- schnelle Drafts\n- Editor/Export\n- vielleicht HD-Free-Export\n\nAber: Free-Quota weniger transparent, Pro-Assets vermeiden.\n\n## 4. Hailuo\n\n```text\nhttps://hailuoai.video/\n```\n\nNur falls bestehende Free-Credits da sind. Seite zeigte sichtbare `25 credits` pro 6s/768p Generation. Also:\n\n```text\nnicht kaufen\nnicht aufladen\nnicht abonnieren\n```\n\n---\n\n# Recherche: API / Web / Open Source\n\n## API-fähige Anbieter\n\n## Replicate\n\n```text\nhttps://replicate.com/collections/text-to-video\n```\n\nBietet API für viele starke Modelle:\n\n- Runway\n- Veo\n- Kling\n- Seedance\n- Hailuo\n- PixVerse\n- Wan\n\nAber: praktisch **paid API**. Gut für später, nicht für Gratis-Workflow.\n\n## fal.ai\n\n```text\nhttps://fal.ai/explore?categories=video\n```\n\nBietet API für:\n\n- Seedance\n- Kling\n- PixVerse\n- weitere Video-Modelle\n\nAber ebenfalls: normalerweise **paid per generation**. Nicht unser aktueller Pfad.\n\n## Hugging Face Spaces\n\n```text\nhttps://huggingface.co/spaces\n```\n\nEs gibt viele `Wan2.1` / `text-to-video` Spaces. Vorteil:\n\n- teils gratis im Browser nutzbar\n- manchmal via `gradio_client` automatisierbar\n- kein eigener GPU-Server nötig\n\nNachteil:\n\n- Warteschlangen\n- Spaces schlafen ein\n- Runtime Errors\n- Qualität/Verfügbarkeit stark schwankend\n\nFür uns: **experimenteller kostenloser API/Web-Fallback**.\n\n## NVIDIA build.nvidia.com\n\n```text\nhttps://build.nvidia.com/\n```\n\nDort gibt es free serverless APIs für Entwicklung, aber ich habe keinen klaren kostenlosen Text-to-Video-Generator gefunden, der unseren Clip-Bedarf deckt.\n\nFür uns:\n\n```text\nNVIDIA_API_KEY → Text-/Prompt-Refinement\nnicht Video-Generation\n```\n\n---\n\n# Open-Source / lokal\n\n## Wan2.1\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nSehr interessant. README sagt sinngemäß:\n\n```text\n5-second 480P video on RTX 4090 in about 4 minutes\n```\n\nDas ist der beste lokale Kandidat, falls wir Zugriff auf eine starke NVIDIA-GPU haben.\n\n## LTX-Video\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nGuter Open-Source-Kandidat, quantisierte/distilled Modelle, schneller als viele andere.\n\n## CogVideoX / CogVideoX1.5\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nSolide Open-Source-Text/Image-to-Video, aber GPU-heavy.\n\n## HunyuanVideo\n\n```text\nhttps://github.com/Tencent-Hunyuan/HunyuanVideo\n```\n\nStark, aber eher schwergewichtig. Ohne dicke GPU nicht ideal.\n\n## Mochi\n\n```text\nhttps://github.com/genmoai/mochi\n```\n\nOpen Source, aber README empfiehlt für deren Implementation mindestens 1 H100. Also eher nein, sofern wir nicht zufällig einen kleinen Supercomputer unter dem Sofa finden.\n\n---\n\n# Meine Empfehlung\n\n## Jetzt sofort\n\n```text\nPixVerse manuell testen\nwenn gut: scene_01 + scene_02 herunterladen\nwenn schlecht: Pika testen\nwenn beide schlecht: Hugging Face Wan2.1 Space testen\n```\n\nDanach:\n\n```text\nIch baue Hybrid Final Renderer:\nimportierte Clips\n+ lokale Mechanism-Animation\n+ Captions\n+ SFX\n+ FFmpeg\n```\n\n## Parallel technisch sinnvoll\n\nAls nächstes kann ich prüfen, ob diese Maschine eine brauchbare GPU hat:\n\n```text\nnvidia-smi\n```\n\nWenn ja:\n\n```text\nWan2.1 / LTX-Video lokale Installation evaluieren\n```\n\nWenn nein:\n\n```text\nOpen-Source lokal vergessen\nHF Spaces / Free Web bleiben der Gratisweg\n```\n\nKurz: **Ja, ich generiere alles, was lokal geht. Die realistischen KI-Clips holen wir über Free-Web/HF. Sobald du die zwei MP4s hast, mache ich daraus das finale Video.**"}], "id": "msg_0d1a4d10654d56b4016a119d6abbd48191b788e56720d624de", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] wir haben eine Nvidia RTX A5000 Laptop GPU auf dem host mit 16gb VRAM. wollen wir diese nutzen?", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Checking system requirements**\n\nI need to respond using a tool to check the current environment, maybe by hosting it somewhere. The user is asking if a GPU is necessary, which might involve skills related to MLOps inference or something like ComfyUI for video generation. It's also essential to verify the system state using the terminal, potentially checking something like \"nvidia-smi\" to get the GPU status. Let’s ensure we have everything needed for the task!", "finish_reason": "tool_calls", "reasoning_content": "**Checking system requirements**\n\nI need to respond using a tool to check the current environment, maybe by hosting it somewhere. The user is asking if a GPU is necessary, which might involve skills related to MLOps inference or something like ComfyUI for video generation. It's also essential to verify the system state using the terminal, potentially checking something like \"nvidia-smi\" to get the GPU status. Let’s ensure we have everything needed for the task!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEaAYz2VWrKt5oIylV6ATX8hjSFRkGqM8tQ8tPW22BGP1XlOV844jp_rKxkZe5-QYmCgjBKi8DyepUz3ZnGmYUlZ-tacOFeVrjrbyURRkgNqbQkDCzNro0mdn48H8Ar0yefF_hoPNJ9XKKA5m5MNux8dlTdwpLxAsmvp-aiiN_3MgciAW30axcViqTcz19AyGfaDCCoCAwIoviI03zbvrvCJDuTNEL3VjBCvtuDtKVFM1XB1vrOA1pe5Fnp_uAcaNi1m4tFU19hIZCWUgw0avmm2GslJaoyx7kprP4qeEG5fppk2--z7-P7DaVVOwy5w27Nyy5OV2md_bzjWw0iDTJkre49xdrtwefuzcCCCYCTH2DetXZlj4OWN_hCLx99k-rPUYadG8zNUAQR-shGG9y6UwP-6TNOeQuhls-pmBV4K_MQcwnhNjqwTkJCJ-qFw6Pp2cNDveoTyxBm1rrFPt7hUjej9h8Dp2-bYNauS8UUbP2Q5XJsXU1ZESLXaY5w75TC276Dplh7_-TtZRtyupQ9aUeFqTQPqb7IrQM0RmijsogvB8bZ5tKMeJmdkaPbG1qX4QTOXmsOshk-E6Tm2sE8an04o7hhTC1Uk--uF9z8zTfYj96FIIA5W8fiKwh8F9NCmDrQ4JpAd44o8ga7ZqSW5oEyxjKiXMflkR3Adnm9JaPC1cQtMhZ1ZW8oMSO4nD_bDbfKlW2R9HQumo1DreDNSgo9AFGLJvlW67MJg226IaRg9sPheAA9AkfWScSoi1Suzs1dbUN-InpKVYgwkZLT6iyLO7KFf0ybGbsv_75tptzzRI_4UYwa_4NuG9h1gKjw6q8dOK7-rGUR4vaS16vnf2gaCyBQjHzHAqUipBJTsQrEBNBZVSPfchm4pfxAP7wW9auSMZEmzw44k4cMtzmVINAau3j8Wd4s0D4atp5Xl-Doqqe-gGJUNow-P7U5YmlQsB4nesAnxnEmQx0V3678sB9t2Dexh2pXj56SJ2yGvKtjCv9l7Lf8BvKQ-HQPbAGwcmpwR9c39cOmcNlC4parni37rW86KF2IfXFlMFQ-VlHDbOLwWe8vmdRJY5RNKHhAFNLLCyCgHnV5TPhz9sffCC4uhFD0W-ya6VUOk35LIRHNjYqgjDu-9OhsOJ3P1O02NtuDrja3E2CDWMdkKFcBWsLBXgmupXFe7DbJtLtsS3UjyIOEKyI9H7h0mmK7SmyRE6", "id": "rs_0d1a4d10654d56b4016a11a015ba9481919fffc10a95905380", "summary": [{"type": "summary_text", "text": "**Checking system requirements**\n\nI need to respond using a tool to check the current environment, maybe by hosting it somewhere. The user is asking if a GPU is necessary, which might involve skills related to MLOps inference or something like ComfyUI for video generation. It's also essential to verify the system state using the terminal, potentially checking something like \"nvidia-smi\" to get the GPU status. Let’s ensure we have everything needed for the task!"}]}], "tool_calls": [{"id": "call_fxBpIKcQOGPOA0Va66KpOAd5", "call_id": "call_fxBpIKcQOGPOA0Va66KpOAd5", "response_item_id": "fc_0d1a4d10654d56b4016a11a0180de481919bcf19e6e2a0f19c", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"comfyui\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"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\\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.\\n\\n4. **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\\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/official-cli.md\", \"references/template-integrity.md\", \"references/rest-api.md\", \"references/workflow-format.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_fxBpIKcQOGPOA0Va66KpOAd5", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "reasoning": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there.", "finish_reason": "tool_calls", "reasoning_content": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEaA2aZu6aqVxuGo29IOEns8SUGRTT19p2q8XJqI-yhqITLKKpFpUNg2GZXArOjxYNEHiaKGRP8ZSmNYAPZjseT_hiY5CNYPr-Lo7wotMkHjkB8eXsD0AHeBDRb9cw2uL1a7xm8WuDHq3hDrPPXx9LGeR72NetUGyBQyFGpkstcDMYqLEEZLhNQniyHtVTXhGXYKIJRu2WCeJGvhsl6KfpOIzKxlP5gcgLtmGtkCsYwn7JT4DZr5WnJlJ2Gbxw9AOAavkB1RWu78FBPhMnxk9HLFlPj0OA00cxt_iJpOUftcGsr__gVgArWnYf4SZLgEpsE4RT2r1evcI_eww3RxcEM6KV2DM0tu0dfHT3IFfRl7dKWy-XW_PVlXOq4DXeiCp6Sw96D0KCaTpRZhd7WjDoAQ-oU0i-0G7_Rq7FC2Y8DOHg6xxIN0fh1LPE0jKhyTvKD-lpTyomddUb_o5Dx8U8qyQ8mxtHiVFmSy_aNQzZ4nrwGZIAbTmVe1q9xtnK2HvcP9bHhCfNk8R7eUvcs8wpRTw_3QCByViggUR_HhI8f33NSBJO6ZQ0xDBx8_X_iE3QxBymocN2OzfUuKNFfhAy_5X7qrdG888ufQ4mALSmAdZhNn3rywL8a-Kt5pTK2PLMGoN88VEF2-8xtJJJaE4qQYbGUwLpNCS2OwKVKGPVCUdF4eoL8Nm_tvTbcIs1K4s2QmEZNDTTlajyOb5QWXBfGryE44YAV5d-e6bTW1vDXI35Q_E0cq8tRnqU0Jgn4ZjM9dFoHOBb4CZVlFWTLpynmZRLUNvh0isFP9qbYHqcganfAehrtWPRiTSyqVzn9ZHJsgjYYeCKeuVVTYl2MYYlzNptRL4kLN56ByyzZlw58hTpce3E5fJcx1nU60aHPo9AKj-5Hf5Gc1oGkHQJgYEven7GXuab6anjORpykaYLDyUYwOKbo7JpBAZ3fxzqoe-pumB3N-x1S_PBRlm_VZFNZnTSs_TTWUTG_WnJEVeDR_Nvsit29iXK5GWBf9V5nUnvHlkllPL4OZMKs1bIqHuLML9pVO8lrCKbvhUNAZBRRYzGVf1cAyAe6x1_ZzJjI8vj5E_RMb6zPiOMMjwAvSYjTSIySEvsx1xsL2Dy__2LcVAZLLpKxE0lv88YKGQ6VKdSVUkexFzsVVTBH5JE4mJeOu8UiClQc-QbVfq3Sy9VLU1k7g=", "id": "rs_0d1a4d10654d56b4016a11a03276888191b0cc38d7ef9a47d6", "summary": [{"type": "summary_text", "text": "**Evaluating GPU access**\n\nIt looks like there's no GPU available in my environment, and I’m wondering if that's because the host GPU hasn’t been passed through. I need to find answers about whether I can access the host's GPU via localhost or SSH. I guess I should ask about that, but if it's exposed, it might work. Also, I should search for whether Comfy is installed to see if there's a possibility there."}]}], "tool_calls": [{"id": "call_UFOSgap227zr2jGcvQ5X9TwG", "call_id": "call_UFOSgap227zr2jGcvQ5X9TwG", "response_item_id": "fc_0d1a4d10654d56b4016a11a036709c81919ce463fcfb4ab96f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"command -v comfy || true; command -v nvcc || true; ls -l /dev/nvidia* 2>/dev/null || true\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}, {"id": "call_PcMIExdcjKrgee8h7rbBsYEY", "call_id": "call_PcMIExdcjKrgee8h7rbBsYEY", "response_item_id": "fc_0d1a4d10654d56b4016a11a03670ac8191a4d77d9c4fdb964f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"du -sh /home/agent/jarvis_runtime/AutoShortsBot 2>/dev/null; git status --short\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "Ja, Sir — **die RTX A5000 Laptop GPU mit 16 GB VRAM sollten wir nutzen.** Das ist für unseren Gratis-Workflow der beste Hebel.\n\nAber: In der aktuellen Hermes-Umgebung sehe ich die GPU **noch nicht**.\n\n## Live-Check hier\n\nIch habe geprüft:\n\n```text\nnvidia-smi → nicht gefunden\ntorch cuda_available → False\n/dev/nvidia* → nicht vorhanden\n```\n\nAußerdem ist der lokale Speicher hier knapp:\n\n```text\n/ → 78G total, 68G used, nur 5.8G frei\n```\n\nBedeutung:\n\n```text\nHost hat vermutlich GPU,\naber diese Hermes-Session / Container / VM hat keinen CUDA-Zugriff darauf.\n```\n\nUnd selbst wenn CUDA sichtbar wäre: Für Video-Modelle brauchen wir deutlich mehr freien Speicherplatz als 5.8 GB. Eher:\n\n```text\n50–150 GB frei\n```\n\nje nach Modell/Cache.\n\n---\n\n# Empfehlung\n\n## Ja, wir nutzen die A5000 — aber über lokalen Host/ComfyUI\n\nDer sinnvollste Setup wäre:\n\n```text\nHost mit RTX A5000\n→ ComfyUI lokal installieren/starten\n→ Wan2.1 / LTX-Video / CogVideoX Workflow\n→ Hermes steuert ComfyUI per API\n→ generierte Clips landen in AutoShortsBot Import-Ordner\n→ Hermes baut finalen Hybrid-Render\n```\n\nAlso:\n\n```text\nGPU auf Host = Videogenerator\nHermes = Orchestrator / Renderer / Qualitätskontrolle\n```\n\nDas ist sauberer als Browser-Free-Tier-Gefrickel.\n\n---\n\n# Welche Modelle passen zu 16 GB VRAM?\n\n## 1. Wan2.1 — bevorzugt\n\nRepo:\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nWarum gut:\n\n- Open Source\n- stark für realistische kurze Clips\n- README erwähnt 5s/480p auf RTX 4090 in ca. 4 Minuten\n- gute ComfyUI-Unterstützung in der Community\n- besserer Kandidat als viele ältere Open-Source-Video-Modelle\n\nFür 16 GB VRAM würde ich starten mit:\n\n```text\nWan2.1 T2V 1.3B\n```\n\nNicht direkt mit den riesigen 14B/27B Varianten. Die wären eher Schmerz mit Fortschrittsbalken.\n\n## 2. LTX-Video — sehr interessant\n\nRepo:\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nWarum gut:\n\n- stark auf Geschwindigkeit optimiert\n- quantisierte/distilled Modelle verfügbar\n- vermutlich praktischer auf 16 GB als schwerere Modelle\n- gut für schnelle Iteration\n\n## 3. CogVideoX / CogVideoX1.5 — möglich, aber vorsichtig\n\nRepo:\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nKann funktionieren, aber:\n\n- speicherhungriger\n- Offloading/Quantisierung nötig\n- eventuell langsamer/instabiler\n\n## Eher nicht für 16 GB\n\n```text\nHunyuanVideo\nMochi\ngroße Wan 14B/27B Varianten ohne Spezialsetup\n```\n\nDie sind eher für stärkere GPUs / Multi-GPU / Cloud.\n\n---\n\n# Neuer Ziel-Workflow\n\nIch würde den Workflow jetzt so ändern:\n\n```text\n1. Hermes erzeugt Scene Prompts\n2. Host-ComfyUI generiert scene_01.mp4 und scene_02.mp4 lokal mit RTX A5000\n3. Hermes importiert Clips\n4. Hermes rendert Mechanism-Layer, Captions, SFX\n5. FFmpeg baut finales Short\n```\n\nDas ersetzt PixVerse/Pika als Hauptpfad.\n\nPixVerse/Pika bleiben nur noch Fallback:\n\n```text\nwenn lokales Wan/LTX zu langsam oder qualitativ schlecht ist\n```\n\n---\n\n# Was wir technisch brauchen\n\n## Auf dem Host\n\nPrüfen:\n\n```bash\nnvidia-smi\n```\n\nDann:\n\n```bash\npython -c \"import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n```\n\nWenn das auf dem Host funktioniert, können wir loslegen.\n\n## ComfyUI lokal\n\nEmpfohlener Pfad:\n\n```bash\npipx install comfy-cli\ncomfy --skip-prompt tracking disable\ncomfy --skip-prompt install --nvidia --fast-deps\ncomfy launch -- --listen 0.0.0.0 --port 8188\n```\n\nDann kann Hermes per API sprechen mit:\n\n```text\nhttp://HOST_IP:8188\n```\n\nOder falls Hermes auf demselben Host läuft:\n\n```text\nhttp://127.0.0.1:8188\n```\n\n---\n\n# Wichtige Frage\n\nAktuell sieht Hermes die GPU nicht. Daher brauchen wir eine von zwei Varianten:\n\n## Variante A — GPU in Hermes verfügbar machen\n\nFalls Hermes in Docker/Container läuft:\n\n```bash\ndocker run --gpus all ...\n```\n\noder Compose:\n\n```yaml\ndeploy:\n  resources:\n    reservations:\n      devices:\n        - driver: nvidia\n          count: all\n          capabilities: [gpu]\n```\n\nDann müsste hier funktionieren:\n\n```bash\nnvidia-smi\n```\n\n## Variante B — ComfyUI direkt auf dem Host laufen lassen\n\nDas ist wahrscheinlich einfacher.\n\nDann muss Hermes nur die API erreichen:\n\n```text\nhttp://host-ip:8188\n```\n\nDas würde ich bevorzugen.\n\n---\n\n# Mein Vorschlag für den nächsten Schritt\n\nNicht sofort riesige Modelle installieren. Erst sauberer Spike:\n\n## GPU Spike v1\n\nZiel:\n\n```text\nKann Host-ComfyUI mit RTX A5000 einen 3–5s 9:16 Clip lokal generieren?\n```\n\nMinimaltest:\n\n1. Host-GPU prüfen\n2. ComfyUI installieren/starten\n3. kleines Wan2.1/LTX-Workflow-Setup\n4. Scene 01 Prompt testen\n5. Output nach AutoShortsBot importieren\n6. Hermes baut Hybrid-Render\n\nWenn der Test gut ist:\n\n```text\nlokaler GPU-Workflow wird Hauptpfad\n```\n\nWenn schlecht:\n\n```text\nPixVerse/Pika Free-Tier bleibt B-Roll-Quelle\n```\n\n---\n\n# Antwort auf deine Frage\n\nJa:\n\n```text\nWir wollen die RTX A5000 nutzen.\n```\n\nAber nicht in dieser aktuell sichtbaren Hermes-Umgebung, solange CUDA hier nicht verfügbar ist.\n\nDer beste nächste Schritt ist:\n\n```text\nComfyUI + Wan2.1/LTX-Video auf dem Host mit RTX A5000 starten\nHermes steuert es per API\n```\n\nDann kann ich die Clip-Generierung weitgehend automatisieren — ohne paid Credits, ohne Browser-Gefrickel, und mit lokal kontrollierbarem Output.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEaBD9SHlTEBYATbu5uwObcaWna6t-spubxn15gsR43PLhiNgFia3Utviq6ThmSWSG1j2H053OygrqqlctpP1WB5ZhlPywavIZCJPfYKREcngDVYgknKBhTUuZy-C4hJyIEUlH8OAmbRLTgvGKyVzCCVTR_3UA-6c_eWduvJb3_hmBRhpRiqtu1Wr53XgrwoHSzykC-axc1yxVhg0PAcjTKE35TymYWAD7fVHc99Wb71rlLxTYxxruQtyuoYO0uD4GjlbmlJ0sr7JTL8ZCqx8oVH1WxcMwMHMAPXt4-3s0OczrVU_C95WhOoFacevDxcJYCWQ995sXDcWe3lTinVwxO7sPD55ljpfVKyoou-yZL7GmGT2xQhVxqOQ-gO7GnkWAm5KB8737b_qoxYlYDrnoDwNYtvdi26zoMQsZjWCptrzc8XdOM9Xp332IsOF7NelioXPqfhUwS08YF1QBWWpa1W1lshGWmeCeaWJs-NdUebwW4eYiAsqejKi0LkA6Pdj3RnLn341uTCOCXoMbX-hK9TsdJk31UsmrLRONnqZ9RqkgRRbSyiHdLGxog_1kQECqa4PfiWKW9o2704RJ5C3dBapCqD5OSZfRMoGjlADL59E9WxyH4QUFp0NGYLyNOD1jU9gAgLph0Jf8-G0SRep-M_5SD63GmFtz9EipeZ6-VqsEImhPs0qc4mLz5s4cReKhmdr4mp9Ys5PQBGWP_vW9Y6Vl-QRZmwYcvCsKuyk7hFseFOLGw4-4m46A3nNiZX7LdZJ6AA8FeKpTByIDAhTQu2hu2xL4nfBDxdDYjVFjJTiGPlCtzwy0ajIRd0iCPqQC3USQ7v3wTsmfA7scTr7EXqwMCG4C-crKQfAGLvfiXl29W_6jGs34ZmkvH4alwFp9u7jpEMuZcMWSQV1H0qBHqkQVyDyozqg0w4aHqC0yL3VZlhrmkTD80mhr4LWkmCcRA-Xy7Ewv2_1b0M_oYyHa9OsmAVFVRqPZUAUA84s9DXxIUUsf-b-BAV5exSBQmuZ8af6SaRxS_jdAJkNTNHeHABU9LxxXH8SoxRX02opkuUYahjx5bJkkW_D691BI_Y01ROQD_PSGra7KDHyaFZodN7_fjWLAtzSh4bAIdtHXV0fp_DVgwcKzuvTc8cGpcpmlLAUvN5PsxHmOzoURlVmEOwDhNmB6iICCZKVGWUH6arcXyN7TNpWCE-yIfYSwDFab6eIShC3iAUFSezlS4r68L_XLXXZ9fulmgCxJZFZfoTmFVdC5-1i6w1gzWPt9S4ZPvo6hu1vmoO8Jae-lmJKTtS5OMD2-URMxOcqWSDDtJm-ZsY_UfwJANOiCOjxUAmM5ILPDiXftIX8Z2-rJmq6a_KTAXYj-evGqPfiCy1tLZf5-9mY5XHOWHrKTez6x2sE_9hkaEX2t_1sBpGPmRNSrXSfI0NT_O2z9oRA8J2f1p1FDlkBHZanPgrxasEtRGTJxJSPINuG2vkIlmd_-J1EAUku-Liuf1zobKLn5uJfAavmF7GI9WXuf7xUaQY1Ink-nfAJqnOvCipSMCU-G_bR15Tp1nfdjo_ycEOSc4HSPexBAZGEgXHNlO51jwK2usNZYLGPofj4arRcS0jUoZjATwEHAdrFqDeZSnMdaTmSHU0QNoY9-LzT_o-pk0QUGuqRChO_ziZe7ykqQKR7tWu7KINFvOSV3XLaJfS-doS-E-RCSHwL0l4ESoSQkn-llwJhxerrO6qOeTq8WsypUUucNfxLQkgVijQDcopbbkE9Zt9v4wE1WggeAxif_r_3eKmwP_gjawKqyG19DMxESUvREGRxW3zdzBjxXzoQW7FUwwcekLjmRx3lm78g_16TMlAwEotq_lHvkij22-Ju5piN42NMHYqorRsyoQcDXaxO6AzP67vpnr4QDK9iUq86rlCG2xi26JHk7chbgor47CQYvJq3qHo_fbmATUnGUQot28om8wREIh_WjSZaQhg9fNTJYsqVlXiles0hbf1SvbQW0CHid1baDN6n7gnHKIH6410LiXAYut6D4aj6uwQMjowXfb11ajQiyOHs1nFE7po1jKgcCn5x6jblB2Q6P62a0SNJCC9CLhkHcqwQ39Ku1KunmKi0RO3vLOzoBEg6JiIi49vBogn1eWdKUFK24qUs899KINsDiYIxoOq0odoFnp-uwql9htCSl779vFn15k9x9GZLc79MUyonTx43Gy6XAes7PXy7DwkplySGEuiwlS0rlpmhMQmJXceJNHLMmUpkkE9CGPW5PWA7dtegp6YrSzAkackl1hTX8bHW2ercwobtVQAAlkCSG8hHZXEYmjVVgzWG2GrBtkG_Dq2wJUbtou3XAx8BpL1fyGpI05W3sIH5lO2yxgL7g1mfC-FkZW9iArkbYoGLmRYRHph84zYcCs29shZeeCKVyQO4bptvKfXIHGWOJlc8IlEjZLcS4MCUQwReGjW1DsBvi5JtuXPStS_9dQSAzNVM7IMdrXllmbttW5dXBpWpovvgysyCAS6zQyE4cc6MDGCCKaNsQ7j_m2w0RUR3c6dfksmLNhUOB3cSSpVuZ7ODqduKMWROcCMpVy5if5K2AKD3F2WQPVEx7LaMK5NOdbfASsR4CvrrtSDXonbrRo1nA6yWz7XFBS9L_CL8Bjj1Za05sx6q38aANQCU3zCeFNQi-jY_82Q0S_E8DpgXnD60EoJjvjxlqTujAid8UR0zejELXWffkcdmELiq5CWVb6yoNve8lTSOjeMmiaVziY0GBl1oPfCh_EeiBL-WAQsUnGiPVclTySNXzRXwIWU9m3hCJw5fvajsHLX9ebj4ix-iJwKhqXqnczJJfdCmd00lnNIkBAf1cuEuUPBaNAnvEs6b03LtFxNUG-MIQBSZGGCwJwSWRZEe9sQ0t6ybnPYj6g1AwMc57QqBX_WaAJBAheFvKpuisvyF_jQr84X5hvbsAQD41xeh8ajlaNmMLTm_ARRH0NOHjwSCJncfIR6dOhQAo6pMndD-LTbAi544HIZQbH1a18yeRmu7Z1RCF2wDSjjVNn_V7hqNTVipiMUbr69WdfKhvYP5Qiof_ngxbiuMAGImjFeicrGLUDA0k2UDsE3ogkidQg3GLvDnT226t-JXhymD0sKseGo8Ik1qT01RypK7l9G6-8c-5tvcO4fRaU6DBcyQ1duDdUc_NvdE-0TiUBEkWMMtVo2qVFsn", "id": "rs_0d1a4d10654d56b4016a11a03b961481919b0acef1f613cd3c", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir — **die RTX A5000 Laptop GPU mit 16 GB VRAM sollten wir nutzen.** Das ist für unseren Gratis-Workflow der beste Hebel.\n\nAber: In der aktuellen Hermes-Umgebung sehe ich die GPU **noch nicht**.\n\n## Live-Check hier\n\nIch habe geprüft:\n\n```text\nnvidia-smi → nicht gefunden\ntorch cuda_available → False\n/dev/nvidia* → nicht vorhanden\n```\n\nAußerdem ist der lokale Speicher hier knapp:\n\n```text\n/ → 78G total, 68G used, nur 5.8G frei\n```\n\nBedeutung:\n\n```text\nHost hat vermutlich GPU,\naber diese Hermes-Session / Container / VM hat keinen CUDA-Zugriff darauf.\n```\n\nUnd selbst wenn CUDA sichtbar wäre: Für Video-Modelle brauchen wir deutlich mehr freien Speicherplatz als 5.8 GB. Eher:\n\n```text\n50–150 GB frei\n```\n\nje nach Modell/Cache.\n\n---\n\n# Empfehlung\n\n## Ja, wir nutzen die A5000 — aber über lokalen Host/ComfyUI\n\nDer sinnvollste Setup wäre:\n\n```text\nHost mit RTX A5000\n→ ComfyUI lokal installieren/starten\n→ Wan2.1 / LTX-Video / CogVideoX Workflow\n→ Hermes steuert ComfyUI per API\n→ generierte Clips landen in AutoShortsBot Import-Ordner\n→ Hermes baut finalen Hybrid-Render\n```\n\nAlso:\n\n```text\nGPU auf Host = Videogenerator\nHermes = Orchestrator / Renderer / Qualitätskontrolle\n```\n\nDas ist sauberer als Browser-Free-Tier-Gefrickel.\n\n---\n\n# Welche Modelle passen zu 16 GB VRAM?\n\n## 1. Wan2.1 — bevorzugt\n\nRepo:\n\n```text\nhttps://github.com/Wan-Video/Wan2.1\n```\n\nWarum gut:\n\n- Open Source\n- stark für realistische kurze Clips\n- README erwähnt 5s/480p auf RTX 4090 in ca. 4 Minuten\n- gute ComfyUI-Unterstützung in der Community\n- besserer Kandidat als viele ältere Open-Source-Video-Modelle\n\nFür 16 GB VRAM würde ich starten mit:\n\n```text\nWan2.1 T2V 1.3B\n```\n\nNicht direkt mit den riesigen 14B/27B Varianten. Die wären eher Schmerz mit Fortschrittsbalken.\n\n## 2. LTX-Video — sehr interessant\n\nRepo:\n\n```text\nhttps://github.com/Lightricks/LTX-Video\n```\n\nWarum gut:\n\n- stark auf Geschwindigkeit optimiert\n- quantisierte/distilled Modelle verfügbar\n- vermutlich praktischer auf 16 GB als schwerere Modelle\n- gut für schnelle Iteration\n\n## 3. CogVideoX / CogVideoX1.5 — möglich, aber vorsichtig\n\nRepo:\n\n```text\nhttps://github.com/THUDM/CogVideo\n```\n\nKann funktionieren, aber:\n\n- speicherhungriger\n- Offloading/Quantisierung nötig\n- eventuell langsamer/instabiler\n\n## Eher nicht für 16 GB\n\n```text\nHunyuanVideo\nMochi\ngroße Wan 14B/27B Varianten ohne Spezialsetup\n```\n\nDie sind eher für stärkere GPUs / Multi-GPU / Cloud.\n\n---\n\n# Neuer Ziel-Workflow\n\nIch würde den Workflow jetzt so ändern:\n\n```text\n1. Hermes erzeugt Scene Prompts\n2. Host-ComfyUI generiert scene_01.mp4 und scene_02.mp4 lokal mit RTX A5000\n3. Hermes importiert Clips\n4. Hermes rendert Mechanism-Layer, Captions, SFX\n5. FFmpeg baut finales Short\n```\n\nDas ersetzt PixVerse/Pika als Hauptpfad.\n\nPixVerse/Pika bleiben nur noch Fallback:\n\n```text\nwenn lokales Wan/LTX zu langsam oder qualitativ schlecht ist\n```\n\n---\n\n# Was wir technisch brauchen\n\n## Auf dem Host\n\nPrüfen:\n\n```bash\nnvidia-smi\n```\n\nDann:\n\n```bash\npython -c \"import torch; print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))\"\n```\n\nWenn das auf dem Host funktioniert, können wir loslegen.\n\n## ComfyUI lokal\n\nEmpfohlener Pfad:\n\n```bash\npipx install comfy-cli\ncomfy --skip-prompt tracking disable\ncomfy --skip-prompt install --nvidia --fast-deps\ncomfy launch -- --listen 0.0.0.0 --port 8188\n```\n\nDann kann Hermes per API sprechen mit:\n\n```text\nhttp://HOST_IP:8188\n```\n\nOder falls Hermes auf demselben Host läuft:\n\n```text\nhttp://127.0.0.1:8188\n```\n\n---\n\n# Wichtige Frage\n\nAktuell sieht Hermes die GPU nicht. Daher brauchen wir eine von zwei Varianten:\n\n## Variante A — GPU in Hermes verfügbar machen\n\nFalls Hermes in Docker/Container läuft:\n\n```bash\ndocker run --gpus all ...\n```\n\noder Compose:\n\n```yaml\ndeploy:\n  resources:\n    reservations:\n      devices:\n        - driver: nvidia\n          count: all\n          capabilities: [gpu]\n```\n\nDann müsste hier funktionieren:\n\n```bash\nnvidia-smi\n```\n\n## Variante B — ComfyUI direkt auf dem Host laufen lassen\n\nDas ist wahrscheinlich einfacher.\n\nDann muss Hermes nur die API erreichen:\n\n```text\nhttp://host-ip:8188\n```\n\nDas würde ich bevorzugen.\n\n---\n\n# Mein Vorschlag für den nächsten Schritt\n\nNicht sofort riesige Modelle installieren. Erst sauberer Spike:\n\n## GPU Spike v1\n\nZiel:\n\n```text\nKann Host-ComfyUI mit RTX A5000 einen 3–5s 9:16 Clip lokal generieren?\n```\n\nMinimaltest:\n\n1. Host-GPU prüfen\n2. ComfyUI installieren/starten\n3. kleines Wan2.1/LTX-Workflow-Setup\n4. Scene 01 Prompt testen\n5. Output nach AutoShortsBot importieren\n6. Hermes baut Hybrid-Render\n\nWenn der Test gut ist:\n\n```text\nlokaler GPU-Workflow wird Hauptpfad\n```\n\nWenn schlecht:\n\n```text\nPixVerse/Pika Free-Tier bleibt B-Roll-Quelle\n```\n\n---\n\n# Antwort auf deine Frage\n\nJa:\n\n```text\nWir wollen die RTX A5000 nutzen.\n```\n\nAber nicht in dieser aktuell sichtbaren Hermes-Umgebung, solange CUDA hier nicht verfügbar ist.\n\nDer beste nächste Schritt ist:\n\n```text\nComfyUI + Wan2.1/LTX-Video auf dem Host mit RTX A5000 starten\nHermes steuert es per API\n```\n\nDann kann ich die Clip-Generierung weitgehend automatisieren — ohne paid Credits, ohne Browser-Gefrickel, und mit lokal kontrollierbarem Output."}], "id": "msg_0d1a4d10654d56b4016a11a0435fb08191b94a9e8d576ae6a0", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] die GPU ist ausserhalb der VM; über Tailscale erreichbar.  gib mir nun schritt für schritt die Befehle um das Modell zu isntallieren sodass du es dann erreichen und nutzen kannst.", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_sb82NRdFoCeMYnrMdZI3h0lR", "call_id": "call_sb82NRdFoCeMYnrMdZI3h0lR", "response_item_id": "fc_0d1a4d10654d56b4016a11a12c5118819191da697e0f7a61d9", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request\\nurl='https://raw.githubusercontent.com/kijai/ComfyUI-WanVideoWrapper/main/readme.md'\\ntext=urllib.request.urlopen(url,timeout=20).read().decode('utf-8','ignore')\\nfor pat in ['Installation','Models','Download','fp8','1.3B','T2V','vae','text_encoder','umt5','clip']:\\n    idx=text.lower().find(pat.lower())\\n    print('\\\\n---',pat,idx)\\n    if idx!=-1: print(text[idx:idx+1200])\\nPY\",\"timeout\":60}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"--- Installation 3659\\nInstallation\\n1. Clone this repo into `custom_nodes` folder.\\n2. Install dependencies: `pip install -r requirements.txt`\\n   or if you use the portable install, run this in ComfyUI_windows_portable -folder:\\n\\n  `python_embeded\\\\python.exe -m pip install -r ComfyUI\\\\custom_nodes\\\\ComfyUI-WanVideoWrapper\\\\requirements.txt`\\n\\n## Models\\n\\nhttps://huggingface.co/Kijai/WanVideo_comfy/tree/main\\n\\nfp8 scaled models (personal recommendation):\\n\\nhttps://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled\\n\\nText encoders to `ComfyUI/models/text_encoders`\\n\\nClip vision to `ComfyUI/models/clip_vision`\\n\\nTransformer (main video model) to `ComfyUI/models/diffusion_models`\\n\\nVae to `ComfyUI/models/vae`\\n\\nYou can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:\\n\\n![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4)\\n\\nGGUF models can now be loaded in the main model loader as well.\\n\\n---\\nSupported extra models:\\n\\nSkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9\\n\\nWanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17\\n\\nReCamMaster: https://gith\\n\\n--- Models 88\\nmodels.\\n\\n\\n## Memory use update (again)\\n\\nI've made everythign less reliant on torch.compile for VRAM efficiency, so things should work better even without it. Also figured workaround for some issues when using compile that made first run use drastically more VRAM, issue I battled with myself a lot.\\n\\n\\n## Update notification that can affect memory use in old workflows\\n\\nIn a recent update I changed how unmerged LoRA weights are handled:\\n\\nPreviously mostly due to my laziness they were always loaded from RAM when used, this was of course inefficient and also made using torch.compile for LoRA applying difficult, thus forcing a graph break when using unmerged LoRAs.\\n\\nNow the LoRA weights are assigned as buffers to the corresponding modules, so they are part of the blocks and obey the block swapping unifying the offloading and allowing LoRA weights to benefit from the prefetch feature for async offoading. Downside is that this means if you did not use block swap, you will see increased memory use as the LoRAs are part of the model and all on VRAM.\\n\\nIf you use block swap, the LoRAs are swapped along the rest of the block, but the block size is now larger, this means you may have to compensat\\n\\n--- Download -1\\n\\n--- fp8 4041\\nfp8 scaled models (personal recommendation):\\n\\nhttps://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled\\n\\nText encoders to `ComfyUI/models/text_encoders`\\n\\nClip vision to `ComfyUI/models/clip_vision`\\n\\nTransformer (main video model) to `ComfyUI/models/diffusion_models`\\n\\nVae to `ComfyUI/models/vae`\\n\\nYou can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:\\n\\n![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4)\\n\\nGGUF models can now be loaded in the main model loader as well.\\n\\n---\\nSupported extra models:\\n\\nSkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9\\n\\nWanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17\\n\\nReCamMaster: https://github.com/KwaiVGI/ReCamMaster\\n\\nVACE: https://github.com/ali-vilab/VACE\\n\\nPhantom: https://huggingface.co/bytedance-research/Phantom\\n\\nATI: https://huggingface.co/bytedance-research/ATI\\n\\nUni3C: https://github.com/alibaba-damo-academy/Uni3C\\n\\nMiniMaxRemover: https://huggingface.co/zibojia/minimax-remover\\n\\nMAGREF: https://huggingface.co/MAGREF-Video/MAGREF\\n\\nFantasyTalking: https://github.\\n\\n--- 1.3B 6982\\n1.3B T2V model this used under 5GB VRAM and took 10 minutes to gen on a 5090:\\n\\nhttps://github.com/user-attachments/assets/89b393af-cf1b-49ae-aa29-23e57f65911e\\n\\n---\\n\\n\\nThis very first test was 512x512x81\\n\\n~16GB used with 20/40 blocks offloaded\\n\\nhttps://github.com/user-attachments/assets/fa6d0a4f-4a4d-4de5-84a4-877cc37b715f\\n\\nVid2vid example:\\n\\n\\nwith 14B T2V model:\\n\\nhttps://github.com/user-attachments/assets/ef228b8a-a13a-4327-8a1b-1eb343cf00d8\\n\\nwith 1.3B T2V model\\n\\nhttps://github.com/user-attachments/assets/4f35ba84-da7a-4d5b-97ee-9641296f391e\\n\\n\\n\\n\\n\\n--- T2V 6987\\nT2V model this used under 5GB VRAM and took 10 minutes to gen on a 5090:\\n\\nhttps://github.com/user-attachments/assets/89b393af-cf1b-49ae-aa29-23e57f65911e\\n\\n---\\n\\n\\nThis very first test was 512x512x81\\n\\n~16GB used with 20/40 blocks offloaded\\n\\nhttps://github.com/user-attachments/assets/fa6d0a4f-4a4d-4de5-84a4-877cc37b715f\\n\\nVid2vid example:\\n\\n\\nwith 14B T2V model:\\n\\nhttps://github.com/user-attachments/assets/ef228b8a-a13a-4327-8a1b-1eb343cf00d8\\n\\nwith 1.3B T2V model\\n\\nhttps://github.com/user-attachments/assets/4f35ba84-da7a-4d5b-97ee-9641296f391e\\n\\n\\n\\n\\n\\n--- vae 4306\\nVae to `ComfyUI/models/vae`\\n\\nYou can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:\\n\\n![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4)\\n\\nGGUF models can now be loaded in the main model loader as well.\\n\\n---\\nSupported extra models:\\n\\nSkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9\\n\\nWanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17\\n\\nReCamMaster: https://github.com/KwaiVGI/ReCamMaster\\n\\nVACE: https://github.com/ali-vilab/VACE\\n\\nPhantom: https://huggingface.co/bytedance-research/Phantom\\n\\nATI: https://huggingface.co/bytedance-research/ATI\\n\\nUni3C: https://github.com/alibaba-damo-academy/Uni3C\\n\\nMiniMaxRemover: https://huggingface.co/zibojia/minimax-remover\\n\\nMAGREF: https://huggingface.co/MAGREF-Video/MAGREF\\n\\nFantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking\\n\\nFantasyPortrait: https://github.com/Fantasy-AMAP/fantasy-portrait\\n\\nMultiTalk: https://github.com/MeiGen-AI/MultiTalk\\n\\nEchoShot: https://github.com/D2I-ai/EchoShot\\n\\nStand-In: https://github.com/WeChatCV/Stand-In\\n\\nHuMo: https://githu\\n\\n--- text_encoder 4176\\ntext_encoders`\\n\\nClip vision to `ComfyUI/models/clip_vision`\\n\\nTransformer (main video model) to `ComfyUI/models/diffusion_models`\\n\\nVae to `ComfyUI/models/vae`\\n\\nYou can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:\\n\\n![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4)\\n\\nGGUF models can now be loaded in the main model loader as well.\\n\\n---\\nSupported extra models:\\n\\nSkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9\\n\\nWanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17\\n\\nReCamMaster: https://github.com/KwaiVGI/ReCamMaster\\n\\nVACE: https://github.com/ali-vilab/VACE\\n\\nPhantom: https://huggingface.co/bytedance-research/Phantom\\n\\nATI: https://huggingface.co/bytedance-research/ATI\\n\\nUni3C: https://github.com/alibaba-damo-academy/Uni3C\\n\\nMiniMaxRemover: https://huggingface.co/zibojia/minimax-remover\\n\\nMAGREF: https://huggingface.co/MAGREF-Video/MAGREF\\n\\nFantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking\\n\\nFantasyPortrait: https://github.com/Fantasy-AMAP/fantasy-portrait\\n\\nMultiTalk: https://github.com/MeiG\\n\\n--- umt5 -1\\n\\n--- clip 4192\\nClip vision to `ComfyUI/models/clip_vision`\\n\\nTransformer (main video model) to `ComfyUI/models/diffusion_models`\\n\\nVae to `ComfyUI/models/vae`\\n\\nYou can also use the native ComfyUI text encoding and clip vision loader with the wrapper instead of the original models:\\n\\n![image](https://github.com/user-attachments/assets/6a2fd9a5-8163-4c93-b362-92ef34dbd3a4)\\n\\nGGUF models can now be loaded in the main model loader as well.\\n\\n---\\nSupported extra models:\\n\\nSkyReels: https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9\\n\\nWanVideoFun: https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17\\n\\nReCamMaster: https://github.com/KwaiVGI/ReCamMaster\\n\\nVACE: https://github.com/ali-vilab/VACE\\n\\nPhantom: https://huggingface.co/bytedance-research/Phantom\\n\\nATI: https://huggingface.co/bytedance-research/ATI\\n\\nUni3C: https://github.com/alibaba-damo-academy/Uni3C\\n\\nMiniMaxRemover: https://huggingface.co/zibojia/minimax-remover\\n\\nMAGREF: https://huggingface.co/MAGREF-Video/MAGREF\\n\\nFantasyTalking: https://github.com/Fantasy-AMAP/fantasy-talking\\n\\nFantasyPortrait: https://github.com/Fantasy-AMAP/fantasy-portrait\\n\\nMultiTalk: https://github.com/MeiGen-AI/MultiTalk\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_sb82NRdFoCeMYnrMdZI3h0lR", "timestamp": "2026-05-23T15:59:27.410671"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"## Kijai/WanVideo_comfy_fp8_scaled\\nT2V 0\\nT2V/HoloCine 0\\nT2V/HoloCine/Wan2_2-T2V-A14B-HIGH-HoloCine-full_fp8_e4m3fn_scaled_KJ.safetensors 15001361474\\nT2V/HoloCine/Wan2_2-T2V-A14B-LOW-HoloCine-full_fp8_e4m3fn_scaled_KJ.safetensors 15001361474\\nT2V/Wan2_1-T2V-14B-FastWan-480p_fp8_e4m3fn_scaled_KJ.safetensors 14526211608\\nT2V/Wan2_1-T2V-14B-Phantom_fp8_e4m3fn_scaled_KJ.safetensors 15001361458\\nT2V/Wan2_1-T2V-14B-Phantom_fp8_e5m2_scaled_KJ.safetensors 15001361458\\nT2V/Wan2_1-T2V-14B_fp8_e4m3fn_scaled_KJ.safetensors 14526876890\\nT2V/Wan2_1-T2V-14B_fp8_e5m2_scaled_KJ.safetensors 14526876882\\nT2V/Wan2_2-T2V-A14B-HIGH_4_steps-250928-dyno-lightx2v_fp8_e4m3fn_scaled_KJ.safetensors 14526877584\\nT2V/Wan2_2-T2V-A14B-HIGH_fp8_e5m2_scaled_KJ.safetensors 15001361458\\nT2V/Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors 15001361458\\nT2V/Wan2_2-T2V-A14B-LOW_fp8_e5m2_scaled_KJ.safetensors 15001361458\\nT2V/Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors 15001361458\\n\\n## Kijai/WanVideo_comfy\\nEchoShot/Wan2_1-T2V-1-3B-EchoShot_fp16.safetensors 2838276200\\nFastWan/FastWan_T2V_14B_480p_lora_rank_128_bf16.safetensors 1253192432\\nFastWan/FastWan_T2V_14B_480p_lora_rank_16_bf16.safetensors 163327688\\nFastWan/FastWan_T2V_14B_480p_lora_rank_64_bf16.safetensors 630697104\\nFlashVSR/Wan2_1-T2V-1_3B_FlashVSR_fp32.safetensors 5676070392\\nFlowRVS/wan21_1.3b_flow_rvs_bf16.safetensors 2838077376\\nFlowRVS/wan21_flow_rvs_mask_vae_bf16.safetensors 253795874\\nFun/Lumen/Wan2_1_Lumen-T2V-1.3B-V1.0_bf16.safetensors 3129105448\\nHuMo/whisper_large_v3_encoder_fp16.safetensors 1693870272\\nLightx2v/lightx2v_14B_T2V_cfg_step_distill_lora_adaptive_rank_quantile_0.15_bf16.safetensors 2555119088\\nLightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16.safetensors 1253192432\\nLightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank16_bf16.safetensors 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{"role": "assistant", "content": "Ja, Sir. Wir nutzen die A5000. Für 16 GB VRAM würde ich **nicht** mit den riesigen Wan 14B/27B Modellen starten, sondern mit:\n\n```text\nComfyUI\n+ ComfyUI-WanVideoWrapper\n+ Wan2.1 T2V 1.3B FP8\n+ Wan2.1 VAE BF16\n+ UMT5 XXL FP8 Text Encoder\n```\n\nDas ist der pragmatische kostenlose Einstieg. Danach kann ich ComfyUI über Tailscale per API ansprechen.\n\nUnten sind die Befehle für den **GPU-Host**, nicht für die Hermes-VM.\n\nAnnahme: Host läuft Linux/Ubuntu mit NVIDIA-Treiber. Falls Windows: sag Bescheid, dann gebe ich die Windows-Variante.\n\n---\n\n## 0. Auf dem GPU-Host prüfen\n\n```bash\nnvidia-smi\n```\n\nErwartet ungefähr:\n\n```text\nRTX A5000 Laptop GPU\nMemory-Total: ~16384 MiB\n```\n\nDann:\n\n```bash\ndf -h ~\n```\n\nDu solltest für den Start mindestens haben:\n\n```text\n30 GB frei\n```\n\nBesser:\n\n```text\n50+ GB frei\n```\n\n---\n\n## 1. Systempakete installieren\n\n```bash\nsudo apt update\nsudo apt install -y \\\n  git \\\n  git-lfs \\\n  python3 \\\n  python3-venv \\\n  python3-pip \\\n  ffmpeg \\\n  curl \\\n  wget \\\n  build-essential\n```\n\nGit LFS aktivieren:\n\n```bash\ngit lfs install\n```\n\n---\n\n## 2. Arbeitsverzeichnis anlegen\n\nIch würde alles unter `~/ai` installieren:\n\n```bash\nmkdir -p ~/ai\ncd ~/ai\n```\n\n---\n\n## 3. ComfyUI installieren\n\n```bash\ncd ~/ai\ngit clone https://github.com/comfyanonymous/ComfyUI.git\ncd ~/ai/ComfyUI\n```\n\nPython-Venv:\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\n```\n\nPip aktualisieren:\n\n```bash\npython -m pip install --upgrade pip wheel setuptools\n```\n\nPyTorch mit CUDA installieren:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n```\n\nFalls das scheitert, nimm CUDA 12.4:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n```\n\nComfyUI Requirements:\n\n```bash\npip install -r requirements.txt\n```\n\nCUDA-Test:\n\n```bash\npython - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda:\", torch.cuda.is_available())\nif torch.cuda.is_available():\n    print(\"gpu:\", torch.cuda.get_device_name(0))\n    print(\"vram_gb:\", round(torch.cuda.get_device_properties(0).total_memory / 1024**3, 2))\nPY\n```\n\nErwartet:\n\n```text\ncuda: True\ngpu: NVIDIA RTX A5000 Laptop GPU\nvram_gb: ~16\n```\n\n---\n\n## 4. ComfyUI-WanVideoWrapper installieren\n\n```bash\ncd ~/ai/ComfyUI/custom_nodes\ngit clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git\ncd ComfyUI-WanVideoWrapper\n../../.venv/bin/pip install -r requirements.txt\n```\n\nZurück nach ComfyUI:\n\n```bash\ncd ~/ai/ComfyUI\n```\n\n---\n\n## 5. HuggingFace CLI installieren\n\n```bash\nsource ~/ai/ComfyUI/.venv/bin/activate\npip install -U \"huggingface_hub[cli]\"\n```\n\nOptional anmelden, falls HuggingFace Download throttelt:\n\n```bash\nhuggingface-cli login\n```\n\nWenn kein Token vorhanden: erstmal ohne Login versuchen.\n\n---\n\n## 6. Modellordner anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 7. Wan2.1 1.3B FP8 Modell herunterladen\n\nDas ist der Haupt-Transformer:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 1.5 GB.\n\n---\n\n## 8. Wan2.1 VAE herunterladen\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  Wan2_1_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 250 MB.\n\n---\n\n## 9. Text Encoder herunterladen\n\nFür Wan brauchen wir den UMT5 Text Encoder. Nimm zuerst die FP8-Version, weil VRAM/RAM freundlicher:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 6.7 GB.\n\nFalls FP8 Probleme macht, später BF16:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders \\\n  --local-dir-use-symlinks False\n```\n\nAber BF16 ist ca. 11.4 GB — erstmal vermeiden.\n\n---\n\n## 10. Optional: Clip Vision laden\n\nFür reines Text-to-Video brauchen wir es wahrscheinlich nicht. Für Image-to-Video später:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  open-clip-xlm-roberta-large-vit-huge-14_visual_fp16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/clip_vision \\\n  --local-dir-use-symlinks False\n```\n\nDas kannst du vorerst überspringen.\n\n---\n\n## 11. Kontrolle der Modell-Dateien\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f | sort\n```\n\nDu solltest mindestens sehen:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n## 12. Tailscale-IP des Hosts ermitteln\n\n```bash\ntailscale ip -4\n```\n\nMerken. Beispiel:\n\n```text\n100.x.y.z\n```\n\nIch nenne sie unten:\n\n```text\nTAILSCALE_IP\n```\n\n---\n\n## 13. ComfyUI über Tailscale starten\n\nWichtig: Wir binden ComfyUI am besten direkt auf die Tailscale-IP, nicht öffentlich auf alle Interfaces.\n\nErsetze `TAILSCALE_IP` durch die Ausgabe von `tailscale ip -4`:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\npython main.py \\\n  --listen TAILSCALE_IP \\\n  --port 8188\n```\n\nBeispiel:\n\n```bash\npython main.py \\\n  --listen 100.101.102.103 \\\n  --port 8188\n```\n\nWenn das Probleme macht, vorübergehend:\n\n```bash\npython main.py \\\n  --listen 0.0.0.0 \\\n  --port 8188\n```\n\nDann aber bitte Firewall nur für Tailscale offen halten.\n\n---\n\n## 14. Firewall: Port nur über Tailscale erlauben\n\nFalls `ufw` aktiv ist:\n\n```bash\nsudo ufw status\n```\n\nTailscale-Interface heißt meist `tailscale0`.\n\nErlaube Port 8188 nur auf Tailscale:\n\n```bash\nsudo ufw allow in on tailscale0 to any port 8188 proto tcp\n```\n\nStatus prüfen:\n\n```bash\nsudo ufw status verbose\n```\n\n---\n\n## 15. Von einem anderen Gerät testen\n\nAuf einem Gerät im selben Tailscale-Netz:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nErwartet: JSON mit System/GPU-Infos.\n\nOder im Browser öffnen:\n\n```text\nhttp://TAILSCALE_IP:8188\n```\n\nWenn du die Oberfläche siehst: gut.\n\n---\n\n## 16. Dauerhaft als systemd-Service einrichten\n\nWenn der manuelle Start funktioniert, Service anlegen:\n\n```bash\nsudo tee /etc/systemd/system/comfyui.service >/dev/null <<'EOF'\n[Unit]\nDescription=ComfyUI Wan Video Server\nAfter=network-online.target tailscaled.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=REPLACE_WITH_YOUR_USERNAME\nWorkingDirectory=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI\nEnvironment=\"PYTHONUNBUFFERED=1\"\nExecStart=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI/.venv/bin/python main.py --listen REPLACE_WITH_TAILSCALE_IP --port 8188\nRestart=on-failure\nRestartSec=10\n\n[Install]\nWantedBy=multi-user.target\nEOF\n```\n\nDann Platzhalter ersetzen:\n\n```bash\nsudo sed -i \"s/REPLACE_WITH_YOUR_USERNAME/$USER/g\" /etc/systemd/system/comfyui.service\n```\n\nTailscale-IP einsetzen:\n\n```bash\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\nsudo sed -i \"s/REPLACE_WITH_TAILSCALE_IP/$TS_IP/g\" /etc/systemd/system/comfyui.service\n```\n\nService aktivieren:\n\n```bash\nsudo systemctl daemon-reload\nsudo systemctl enable --now comfyui\n```\n\nLogs prüfen:\n\n```bash\njournalctl -u comfyui -f\n```\n\nHealthcheck:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n---\n\n# Danach brauche ich von dir\n\nSobald es läuft, schick mir bitte:\n\n```text\nComfyUI URL:\nhttp://TAILSCALE_IP:8188\n```\n\nUnd idealerweise die Ausgabe von:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nDann kann ich von hier aus prüfen, ob ich den Server erreiche.\n\n---\n\n# Danach mache ich\n\nWenn ComfyUI erreichbar ist, baue ich hier im AutoShortsBot den nächsten Baustein:\n\n```text\nComfyUI Wan Client\n→ Prompt aus Scene Specs\n→ Job an http://TAILSCALE_IP:8188/api/prompt\n→ Output herunterladen\n→ speichern als scene_01.mp4 / scene_02.mp4\n→ Hybrid Renderer baut finales Short\n```\n\n---\n\n# Wichtiger Hinweis zu 16 GB VRAM\n\nFür den ersten Test bitte klein starten:\n\n```text\n480p\n3–5 Sekunden\nWan2.1 T2V 1.3B FP8\nwenige Steps\n```\n\nNicht direkt:\n\n```text\n720p\n10 Sekunden\n14B\n```\n\nDas wäre wie einen Bentley durch einen Gartenschlauch tanken. Technisch faszinierend, aber langsam und leicht absurd.\n\nSobald der erste Clip läuft, optimieren wir Qualität/Speed.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEaFENldgNQqxsTQYBoHCNbIXSeiYGIBIStj8sIB5vwevWoc0C_eBsVvi8-UuEdTRwOktKu1aUvG4gGXCbOexTF4OcQzEgsNRE64dKQQoalTro4aQ3CZv_gGonLULUJ_D0T4UmFNl_utSvaQT95XvUm11jarDD7qlki00T0fCsN67Q4l-GuwN1ScbLJEk4OzSXsYvHAp738f_KM875R3WZlzYxltsy4OXxEObfptWKfSK9WOhGwBvvTDIXQgaWevs6MeC8obh9cmpDtEESYTcJh9C-icWw8G_By7SKKFTult9sBbK5SMY1BW16GzLMUVqH40x3mlUWR4u-tg30akMjcURKfurwuCnVE5E8YPK3O347DDnf1ASSAKn_SWDEBHk0aW4QgvTwCl-JBrVRdWH6tnL6b9NaXdtmHaYFRxJgCzfR3D4Ffv3J7A-jzk3PEKAevSHb0OBqbIMknbtKceZB4d3u3KT8m1xbndW-KaNGIyFjbpUc5LL3nLv7p2cR7uEqF4dNCGZ9h_UfyJpZKO7m9U1d-eixuUSmoUsH_rckjIwv5QnkaiNaTqe-TopVmQMf5Ycxsp8WMX0g0glbnx8wm8UDGRf7fPF0uvkLb1xBR-W7qdxoWcoMDSIcvgq_Tf6J4O2StZZGMbfn1VnnC1weWGWCC7TfqKlMajpwv9taJ8YDXMCPQN5liZmVZlYRd4EzUo23qWL-Ql_mXMnrUk2Q3c9_NyjlShWWD0kfYW14adaCosvYskITeKvXOe6QwPQdVMmQ4CrzOqlPbX_88RDuN_p2bgL-nd1ZRLIELyYpn6t3CHnljGI-PMCK6ea47qJdArF5Uc6dDeuOy1Ek0MbvdRZJ7iBu_arn-7GL5Zi3fLk0GgMMYucSMs8XMcSt_0371SXrZxyGW3fIpGeia9ljpuBcDxi1QiWIdz9vS-gNdgjUk_5Bk9AznwCG513A7u4bhDiAnQUo-mPz_94WI0CuwO7WfDxNgx5AX-FWqLFBwnf5RUss9NZzZV0yhJBwWk0LFnXr1hoF3LSrq7CekI_b1bkDqXHr_weobyBHpRDRT28QfOvcKebpZloxjK06VlkxNYkVcsiu3CU1XV6iCxYZ7Pz48wz1gMhvFcpi-Ks4yZ-CHs0jCQxojrEBpOz98vqzXi94eWvrd3noDXmLRyfoKXWktw1RJjYZL8itGa40oNJhG_DzEoDeF2R-PLHRK_h7JnCYCkk7zFS04bjN4d9wXYXBfn7kuB1AUkD0uyygIYK4Wz0B542Fh3yq9yM2y1Y0rgVnPu8cc0l6_6mKrhrc1G56RbMziotm1I5OMewY5SNaHmgzCJYUVKe9-v_pOty-anYLA90AALHYW1YExv7bNeAEXL9LBFJwihspKki87tC16uznLiW7WjNqP4lO8UOXNNBui-hy1SLSBVcvloU3yF9qNMickTE8PAWjjw83b_uog91VzOZPwkLnQ2yjvLcXzsxljaNGLMvUg1GFQ1w6WBtYizBplxvQisPCyeoEocWfuJNqcTPOrNcxMHINEdfuYgWQwDeZttkFaBxNDGPdF2s99u8mRduR5ovw1OcivsX9_bGdkLi_Qnl3vpHcsbioJrUrogMqC317ujrEwn2C1GiVaMyYRayC62uOPZ9jnbwIy2ZeNVs03I74jywR0EhAtK8xrKePA1NKknlMeKjrmpktOcGrXPcdIHD5-IO5O3WZto-2BOktz4Oy1KMAQ4YXFh6f4_ZF8Yw_5kTaYfm4Vj9gFY_ksPIPttLuealWscrpus4YLjAs9aq8Hz9SaWfnU3INCM6OMT0wahiGxEbLGzALj9WBRGgvPTjeplOHUuxA6MCZ-41QxJRy2zX1Ra_yiistH_2smukny_vH_KgpcjGKDx-Kx4l6rCv2N_r0hQrglZ-ey5nf7n4KUbPJdJrJp0LSosE_aA7ocNpXkFhaaPfVdbELJFHX5FfYGKn-haslffOZLf2aGxhUr9lUVGAG4gwTrzQ8aS7vlkCSq9jBLPEjTMIkEbkgnY9Ogk4zai8MRFDqaVpvwF3ycTfkMdc37iAGbTWwh1rwPTe9Qkh2YaW0M168Z-6Ft98XEfXCMT5AvGDDc8RhFG37BEMMCC_0Kfsqqacvse78Ksayttlw6Jkc4Iw-IUiJXnZVXWY0LxWW_BhBtdf0GhajdzsF-cH-t-kmfUsAo1uMwHG4aRUi4r-C8XEk9Nn2lW6z-wsJ6E02BbD0SRMmTcNQiOuaEwE629TNuc7GkDtEte2Wlipy3PbwNWu4CpzlNmi7ip_9NDiqCWIMFNScut-8vKB5Tjy1sJKdj2gZh9_npuwhAinfzE1z3yv5kzV9CvkYPZ35cyTM79IudGbWU9z6YucUzroy8DfUyOwcyxpAoSzXZr2rpS8PorpJYXwR6PWys724u_HqTEt4aUCwC2HL-TtG9IqAzAKIk_qaSzk-o9iWKEA1gyn78FomT2_bPAoYsjbzBxUrqgRC8YGwvEQADc6lUZxRR9Yk5hM2LyR6CtyJ4OPthAvaOhdqaKHJb0OIiSnvN5pTtljRVpSqmHGUQWCB4OHxQVLGhCDjanmWnEqpsflR8bJ4yDcM5H-HjFcPLntqdbpcaZ-rJRkt6gBT7iXHwVSk5xl8zvjqam4NhBsLaqxByGrfz4rLeHoP_LXKqjES0q8g7ncM1WAswfGxHQhXNFmXjKaoVceW_y4lxUKycyCpYyFtqucccVENydCxmsb-mnmm9lnrfFSO3jkd9HctmAW4yTYpjhlgocuELZI8UwYj7czsf4cYl0AgqDZNAl4ZAsrZmulBvFDZ8MNDbcB5lNwxYRpffs7ZxIBNNJEhkmHzOEjubAUMzKvu6xHVTnwTSR1Iv7mYL1whVU8UoPPBPoVifVhyjrHhwwVllpD5fXRgdIznkrKDgEZCRi-54IQsbi7ijHMsCo2UbA-bPBK0xz5MmVhWs0WooJ76dZeG7HCSIPEGfSoQF_cnTEwjJFLPdhz2glX9-tjtQl9NvC490xDw52Tf3ujeluqKow8ztkn4c74lcC2AsOqD17a1lwhJ3HuHNPE-99NDP06-ScT51osZmnoVuEL7bC3roxSVl51ktPGJVsiZNpLskXtPuTaZ9r4uczNgl_mHJ4hy3Lem3JWeNkoJpJScZpu8wIoSPacfWT1_U-1d-y3AfANeu6gS3t576PiZ_1cIctMc_LwjGcDuLcEL_aHNjeypR036ooP6eJgYt1NgyVStrKgqvZBtUI6xxEnlvCK-lNapQvhBz4JfED7Aq7IgEMbvI8adSI7e0QJS0NmJxY5zmjphW6sYhkXdIMFtPGxBfHKelF6yWnlWXL5P51k0C_lcEaAf_u7YUrjzFrOV86StbFqZ4A7pM_bbS1RDVZESa4xxr7wB3bwsCjRvImGCjdZGCWwyysanUb-IWn_GkZN0gLihC9zeQ6tcT8vp2eomgYvqwA9pIOky9Fn8APRDbSastTrxYvHgCJO8yt7-mCk2EBQJLFlogDf4Po=", "id": "rs_0d1a4d10654d56b4016a11a13a2e2481919c16a15f90957937", "summary": []}], "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Ja, Sir. Wir nutzen die A5000. Für 16 GB VRAM würde ich **nicht** mit den riesigen Wan 14B/27B Modellen starten, sondern mit:\n\n```text\nComfyUI\n+ ComfyUI-WanVideoWrapper\n+ Wan2.1 T2V 1.3B FP8\n+ Wan2.1 VAE BF16\n+ UMT5 XXL FP8 Text Encoder\n```\n\nDas ist der pragmatische kostenlose Einstieg. Danach kann ich ComfyUI über Tailscale per API ansprechen.\n\nUnten sind die Befehle für den **GPU-Host**, nicht für die Hermes-VM.\n\nAnnahme: Host läuft Linux/Ubuntu mit NVIDIA-Treiber. Falls Windows: sag Bescheid, dann gebe ich die Windows-Variante.\n\n---\n\n## 0. Auf dem GPU-Host prüfen\n\n```bash\nnvidia-smi\n```\n\nErwartet ungefähr:\n\n```text\nRTX A5000 Laptop GPU\nMemory-Total: ~16384 MiB\n```\n\nDann:\n\n```bash\ndf -h ~\n```\n\nDu solltest für den Start mindestens haben:\n\n```text\n30 GB frei\n```\n\nBesser:\n\n```text\n50+ GB frei\n```\n\n---\n\n## 1. Systempakete installieren\n\n```bash\nsudo apt update\nsudo apt install -y \\\n  git \\\n  git-lfs \\\n  python3 \\\n  python3-venv \\\n  python3-pip \\\n  ffmpeg \\\n  curl \\\n  wget \\\n  build-essential\n```\n\nGit LFS aktivieren:\n\n```bash\ngit lfs install\n```\n\n---\n\n## 2. Arbeitsverzeichnis anlegen\n\nIch würde alles unter `~/ai` installieren:\n\n```bash\nmkdir -p ~/ai\ncd ~/ai\n```\n\n---\n\n## 3. ComfyUI installieren\n\n```bash\ncd ~/ai\ngit clone https://github.com/comfyanonymous/ComfyUI.git\ncd ~/ai/ComfyUI\n```\n\nPython-Venv:\n\n```bash\npython3 -m venv .venv\nsource .venv/bin/activate\n```\n\nPip aktualisieren:\n\n```bash\npython -m pip install --upgrade pip wheel setuptools\n```\n\nPyTorch mit CUDA installieren:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n```\n\nFalls das scheitert, nimm CUDA 12.4:\n\n```bash\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124\n```\n\nComfyUI Requirements:\n\n```bash\npip install -r requirements.txt\n```\n\nCUDA-Test:\n\n```bash\npython - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda:\", torch.cuda.is_available())\nif torch.cuda.is_available():\n    print(\"gpu:\", torch.cuda.get_device_name(0))\n    print(\"vram_gb:\", round(torch.cuda.get_device_properties(0).total_memory / 1024**3, 2))\nPY\n```\n\nErwartet:\n\n```text\ncuda: True\ngpu: NVIDIA RTX A5000 Laptop GPU\nvram_gb: ~16\n```\n\n---\n\n## 4. ComfyUI-WanVideoWrapper installieren\n\n```bash\ncd ~/ai/ComfyUI/custom_nodes\ngit clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git\ncd ComfyUI-WanVideoWrapper\n../../.venv/bin/pip install -r requirements.txt\n```\n\nZurück nach ComfyUI:\n\n```bash\ncd ~/ai/ComfyUI\n```\n\n---\n\n## 5. HuggingFace CLI installieren\n\n```bash\nsource ~/ai/ComfyUI/.venv/bin/activate\npip install -U \"huggingface_hub[cli]\"\n```\n\nOptional anmelden, falls HuggingFace Download throttelt:\n\n```bash\nhuggingface-cli login\n```\n\nWenn kein Token vorhanden: erstmal ohne Login versuchen.\n\n---\n\n## 6. Modellordner anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 7. Wan2.1 1.3B FP8 Modell herunterladen\n\nDas ist der Haupt-Transformer:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 1.5 GB.\n\n---\n\n## 8. Wan2.1 VAE herunterladen\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  Wan2_1_VAE_bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/vae \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 250 MB.\n\n---\n\n## 9. Text Encoder herunterladen\n\nFür Wan brauchen wir den UMT5 Text Encoder. Nimm zuerst die FP8-Version, weil VRAM/RAM freundlicher:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders \\\n  --local-dir-use-symlinks False\n```\n\nGröße: ca. 6.7 GB.\n\nFalls FP8 Probleme macht, später BF16:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-bf16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders \\\n  --local-dir-use-symlinks False\n```\n\nAber BF16 ist ca. 11.4 GB — erstmal vermeiden.\n\n---\n\n## 10. Optional: Clip Vision laden\n\nFür reines Text-to-Video brauchen wir es wahrscheinlich nicht. Für Image-to-Video später:\n\n```bash\nhuggingface-cli download Kijai/WanVideo_comfy \\\n  open-clip-xlm-roberta-large-vit-huge-14_visual_fp16.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/clip_vision \\\n  --local-dir-use-symlinks False\n```\n\nDas kannst du vorerst überspringen.\n\n---\n\n## 11. Kontrolle der Modell-Dateien\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f | sort\n```\n\nDu solltest mindestens sehen:\n\n```text\n~/ai/ComfyUI/models/diffusion_models/Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors\n~/ai/ComfyUI/models/vae/Wan2_1_VAE_bf16.safetensors\n```\n\n---\n\n## 12. Tailscale-IP des Hosts ermitteln\n\n```bash\ntailscale ip -4\n```\n\nMerken. Beispiel:\n\n```text\n100.x.y.z\n```\n\nIch nenne sie unten:\n\n```text\nTAILSCALE_IP\n```\n\n---\n\n## 13. ComfyUI über Tailscale starten\n\nWichtig: Wir binden ComfyUI am besten direkt auf die Tailscale-IP, nicht öffentlich auf alle Interfaces.\n\nErsetze `TAILSCALE_IP` durch die Ausgabe von `tailscale ip -4`:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\npython main.py \\\n  --listen TAILSCALE_IP \\\n  --port 8188\n```\n\nBeispiel:\n\n```bash\npython main.py \\\n  --listen 100.101.102.103 \\\n  --port 8188\n```\n\nWenn das Probleme macht, vorübergehend:\n\n```bash\npython main.py \\\n  --listen 0.0.0.0 \\\n  --port 8188\n```\n\nDann aber bitte Firewall nur für Tailscale offen halten.\n\n---\n\n## 14. Firewall: Port nur über Tailscale erlauben\n\nFalls `ufw` aktiv ist:\n\n```bash\nsudo ufw status\n```\n\nTailscale-Interface heißt meist `tailscale0`.\n\nErlaube Port 8188 nur auf Tailscale:\n\n```bash\nsudo ufw allow in on tailscale0 to any port 8188 proto tcp\n```\n\nStatus prüfen:\n\n```bash\nsudo ufw status verbose\n```\n\n---\n\n## 15. Von einem anderen Gerät testen\n\nAuf einem Gerät im selben Tailscale-Netz:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nErwartet: JSON mit System/GPU-Infos.\n\nOder im Browser öffnen:\n\n```text\nhttp://TAILSCALE_IP:8188\n```\n\nWenn du die Oberfläche siehst: gut.\n\n---\n\n## 16. Dauerhaft als systemd-Service einrichten\n\nWenn der manuelle Start funktioniert, Service anlegen:\n\n```bash\nsudo tee /etc/systemd/system/comfyui.service >/dev/null <<'EOF'\n[Unit]\nDescription=ComfyUI Wan Video Server\nAfter=network-online.target tailscaled.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=REPLACE_WITH_YOUR_USERNAME\nWorkingDirectory=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI\nEnvironment=\"PYTHONUNBUFFERED=1\"\nExecStart=/home/REPLACE_WITH_YOUR_USERNAME/ai/ComfyUI/.venv/bin/python main.py --listen REPLACE_WITH_TAILSCALE_IP --port 8188\nRestart=on-failure\nRestartSec=10\n\n[Install]\nWantedBy=multi-user.target\nEOF\n```\n\nDann Platzhalter ersetzen:\n\n```bash\nsudo sed -i \"s/REPLACE_WITH_YOUR_USERNAME/$USER/g\" /etc/systemd/system/comfyui.service\n```\n\nTailscale-IP einsetzen:\n\n```bash\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\nsudo sed -i \"s/REPLACE_WITH_TAILSCALE_IP/$TS_IP/g\" /etc/systemd/system/comfyui.service\n```\n\nService aktivieren:\n\n```bash\nsudo systemctl daemon-reload\nsudo systemctl enable --now comfyui\n```\n\nLogs prüfen:\n\n```bash\njournalctl -u comfyui -f\n```\n\nHealthcheck:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\n---\n\n# Danach brauche ich von dir\n\nSobald es läuft, schick mir bitte:\n\n```text\nComfyUI URL:\nhttp://TAILSCALE_IP:8188\n```\n\nUnd idealerweise die Ausgabe von:\n\n```bash\ncurl http://TAILSCALE_IP:8188/system_stats\n```\n\nDann kann ich von hier aus prüfen, ob ich den Server erreiche.\n\n---\n\n# Danach mache ich\n\nWenn ComfyUI erreichbar ist, baue ich hier im AutoShortsBot den nächsten Baustein:\n\n```text\nComfyUI Wan Client\n→ Prompt aus Scene Specs\n→ Job an http://TAILSCALE_IP:8188/api/prompt\n→ Output herunterladen\n→ speichern als scene_01.mp4 / scene_02.mp4\n→ Hybrid Renderer baut finales Short\n```\n\n---\n\n# Wichtiger Hinweis zu 16 GB VRAM\n\nFür den ersten Test bitte klein starten:\n\n```text\n480p\n3–5 Sekunden\nWan2.1 T2V 1.3B FP8\nwenige Steps\n```\n\nNicht direkt:\n\n```text\n720p\n10 Sekunden\n14B\n```\n\nDas wäre wie einen Bentley durch einen Gartenschlauch tanken. Technisch faszinierend, aber langsam und leicht absurd.\n\nSobald der erste Clip läuft, optimieren wir Qualität/Speed."}], "id": "msg_0d1a4d10654d56b4016a11a14412e0819185326679ca4f5574", "phase": "final_answer"}], "finish_reason": "stop", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] bin bei punkt 8 hängengeblieben: (.venv) gam@PC-1:~/ai/ComfyUI$ huggingface-cli download Kijai/WanVideo_comfy \\\n  Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models \\\n  --local-dir-use-symlinks False\nWarning: huggingface-cli is deprecated and no longer works. Use hf instead.\n\nHint: hf is already installed! Use it directly.\n\nHint: Examples:\n  hf auth login\n  hf download unsloth/gemma-4-31B-it-GGUF\n  hf upload my-cool-model . .\n  hf models ls --search \"gemma\"\n  hf repos ls --format json\n  hf jobs run python:3.12 python -c 'print(\"Hello!\")'\n  hf --help\n\n(.venv) gam@PC-1:~/ai/ComfyUI$\n\n[Your active task list was preserved across context compression]\n- [>] gpu-check. GPU/VRAM und CUDA-Zugriff auf dem Host prüfen (in_progress)\n- [ ] options. Lokale Open-Source-Video-Optionen für 16GB VRAM bewerten (pending)\n- [ ] plan. Empfohlenen nächsten Implementierungsschritt für AutoShortsBot festlegen (pending)", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "", "reasoning": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status.", "finish_reason": "tool_calls", "reasoning_content": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbKjWNEUzJ6SRpSgH2RdsclvUv6RtCMk-AJaiUOm16TiBYd-cAmUdzKB7sRhBw1QyXP3G3AxUGt9VPoz1fk4kzJMX8exmCnzT9yeGoUit41uZKjp4H1uEzNFGwoPAIyeLlxE5rhQHGx6zCeWD6oxnOPdFQZ2hlWGlIkipviC8q4A94FvoH80mGw3JlZ3ZCMR8zIVH4rk_fieti94zpLe8K9VQCJ9ehM03G-l1WZk4DiMvGSaRCi5VNauOwadbocLEWvnCXFwJIT22Zm2DrUNHUL1-CSNuNrId2zw3m00ZRV_eAGhZsJcJZMlP2qplo38cDoA3meI8t_l6uOVk4J-2sW41LDIXAMUlB1LnYRpz6QeRty6fyl2aOHRad3fqd61oeOD0ZjkT7h1m8Apjs3tzmpQgzH9Lnjn4kLsAAn5mkNulF5U-Yiof0hZrv7so54Puav8qriYZ4QSc2AcoWIV4eCSZVoifodLykEnxUEsyZ4MnjkBvI1rqAR8MuEYEjPqMDJx6sBoomWwu2DIM-Hxv2l-9OWPZ-iKwaWvpeq0gaQkwOMl-XksbKPWjE04s3Ny1yjs0j2bbmNy1FbIph8wePBq6exYnB5BcEDXJmfOC3Avp4HX3BpX_OaX8h4Xa8W9sz3QpKmQfTEqXTinx5lHfj4-lK1FMv6g4u6LF1NhdEOQgf0YTOpeTyiEYr4wHnvCmU5m_31g9_LXQtd-5qJlQPatgdoxMl6ZQGd_x4OceGatVfL7AUsGSl05LG701LnP-DJ_rBDa-ZYwy8-03hs3bsEaS8I_eHkZSaHwvQoT3yaDFqz2CJ6678JROCMizwgJSeJc5xpt_iwSeVNK3sMNPokAmceiE74gf5_Vg4PMz9Peo2LdFAFeRsJ0os2gjaYHaKBrCu5cAfVoxwr9sPA-kMZDBe7pCmO-vBXZCidFkkK3QuLaI8qT64pYTLxyLQw7wJNSD7YmV1MzG0SuBvqg1iZMXmq2f8DIoQurQPXsa14ZxzCrlPCSKL_sox5qilK0fN0pxV2JBy2eVgwzub68Un8kZsU2yPNto92tgOt0WqqdCYIhnMTG3W7iXpZDjO5itmZRwmw83GGV9VXpU-J-1Y_Hsat316KQQSrkWbXh7HjayXrho4xoYtnqUKmBvSsiQF_SVbKZQclY-PpXjcS3vcyWKqSNGJB2FJ9pVDeVnICVnObU3_vckA6hbOHymE39sITsRaydrQLMBBoIKgvNbna0rVMBDi56Vaw6ENxbmZ5C50UWoHUrn6fqUiWsUApLuuRspSsvi6nSnpBStV5WXkxsiromJ_W0szoJF7vIz-L2R56CeIQCL3ttxpWpz6IiyuWjQfcEVw50Awpm0Ow6JbuoOYSexj5YKTMJXMfBg88Iu0ltkYFE3nWm0Y3SQzwZhFop1iz3Owqk6gVWmCF2AO5LmPnrLywjlGQf7J0Xpf5OL43eN5XXs8cVFEJHdQ0OX89vLg2NydCH8x600z4MhhpiPTNxzilbA_e0uCvdX1w3cTaREU76mc9EPyAhLSeNIY4B", "id": "rs_0d1a4d10654d56b4016a11b29dcad88191a2cc7a1af117cc25", "summary": [{"type": "summary_text", "text": "**Considering command replacements**\n\nI need to answer using command replacements with HF. It seems like I should also consider pointing out that point 8 relates to transformers. It could be necessary to load the Comfy skill, which I think is already loaded, but Hermes-agent isn’t. I need to provide clear steps. Should I use tools for this? I have some current facts but am wondering if the answer is adequate without them. The user is asking about commands, so I need to confirm the Comfy skill's status."}]}], "tool_calls": [{"id": "call_TeSdpjUyDWMDBWHd7Ctjdz74", "call_id": "call_TeSdpjUyDWMDBWHd7Ctjdz74", "response_item_id": "fc_0d1a4d10654d56b4016a11b2a3bf388191ac08b0c8699d4555", "type": "function", "function": {"name": "skill_view", "arguments": "{\"name\":\"comfyui\"}"}}], "timestamp": "2026-05-23T15:59:27.410671"}
{"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\\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.\\n\\n4. **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\\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/official-cli.md\", \"references/template-integrity.md\", \"references/rest-api.md\", \"references/workflow-format.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_TeSdpjUyDWMDBWHd7Ctjdz74", "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "assistant", "content": "Kein Problem, Sir. Hugging Face hat die CLI geändert. Statt:\n\n```bash\nhuggingface-cli download ...\n```\n\nverwenden wir jetzt:\n\n```bash\nhf download ...\n```\n\nDu bist im richtigen Venv. Führe einfach diese Befehle aus.\n\n## 1. Prüfen, ob `hf` funktioniert\n\n```bash\nhf --help | head\n```\n\nWenn das Output zeigt: gut.\n\nOptional Version:\n\n```bash\nhf version\n```\n\n---\n\n## 2. Modellordner sicher anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 3. Wan2.1 T2V 1.3B FP8 herunterladen\n\nDas ersetzt deinen hängengebliebenen Befehl:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nFalls `hf` wegen Symlinks meckert: ignorieren, solange die Datei im Zielordner liegt.\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/\n```\n\nDu solltest sehen:\n\n```text\nWan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.4–1.5 GB\n```\n\n---\n\n## 4. Wan2.1 VAE herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_1_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:\n\n```text\nWan2_1_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n250 MB\n```\n\n---\n\n## 5. UMT5 Text Encoder FP8 herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nErwartet:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n6.3–6.8 GB\n```\n\nDas ist der dicke Brocken. Nicht erschrecken, er hat nur einen gesunden Appetit. Wie jedes moderne KI-Modell mit Standesdünkel.\n\n---\n\n## 6. Alles zusammen prüfen\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f -name \"*.safetensors\" -exec ls -lh {} \\;\n```\n\nErwartet mindestens:\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\n---\n\n## 7. Falls Download Zugriff/Auth verlangt\n\nFalls `hf download` nach Login oder Token fragt:\n\n```bash\nhf auth login\n```\n\nDann HuggingFace Token einfügen.\n\nDanach den fehlgeschlagenen Download erneut ausführen.\n\n---\n\n## 8. Danach ComfyUI starten\n\nWenn die drei Dateien da sind:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\necho \"$TS_IP\"\n\npython main.py --listen \"$TS_IP\" --port 8188\n```\n\nWenn `--listen \"$TS_IP\"` Probleme macht:\n\n```bash\npython main.py --listen 0.0.0.0 --port 8188\n```\n\nDann bitte nur via Tailscale/Firewall offen lassen.\n\n---\n\n## 9. Healthcheck lokal auf dem Host\n\nIn einem zweiten Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: ComfyUI läuft.\n\n---\n\n## 10. Schick mir danach\n\nBitte poste dann:\n\n```text\n1. Ausgabe von:\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n\n2. Deine Tailscale-ComfyUI-URL:\nhttp://<TAILSCALE_IP>:8188\n```\n\nDann prüfe ich von hier aus die Verbindung und baue den Hermes/AutoShortsBot-Client dafür.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Kein Problem, Sir. Hugging Face hat die CLI geändert. Statt:\n\n```bash\nhuggingface-cli download ...\n```\n\nverwenden wir jetzt:\n\n```bash\nhf download ...\n```\n\nDu bist im richtigen Venv. Führe einfach diese Befehle aus.\n\n## 1. Prüfen, ob `hf` funktioniert\n\n```bash\nhf --help | head\n```\n\nWenn das Output zeigt: gut.\n\nOptional Version:\n\n```bash\nhf version\n```\n\n---\n\n## 2. Modellordner sicher anlegen\n\n```bash\nmkdir -p ~/ai/ComfyUI/models/diffusion_models\nmkdir -p ~/ai/ComfyUI/models/text_encoders\nmkdir -p ~/ai/ComfyUI/models/vae\nmkdir -p ~/ai/ComfyUI/models/clip_vision\n```\n\n---\n\n## 3. Wan2.1 T2V 1.3B FP8 herunterladen\n\nDas ersetzt deinen hängengebliebenen Befehl:\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/diffusion_models\n```\n\nFalls `hf` wegen Symlinks meckert: ignorieren, solange die Datei im Zielordner liegt.\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/diffusion_models/\n```\n\nDu solltest sehen:\n\n```text\nWan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n1.4–1.5 GB\n```\n\n---\n\n## 4. Wan2.1 VAE herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  Wan2_1_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:\n\n```text\nWan2_1_VAE_bf16.safetensors\n```\n\nGröße ungefähr:\n\n```text\n250 MB\n```\n\n---\n\n## 5. UMT5 Text Encoder FP8 herunterladen\n\n```bash\nhf download Kijai/WanVideo_comfy \\\n  umt5-xxl-enc-fp8_e4m3fn.safetensors \\\n  --local-dir ~/ai/ComfyUI/models/text_encoders\n```\n\nPrüfen:\n\n```bash\nls -lh ~/ai/ComfyUI/models/text_encoders/\n```\n\nErwartet:\n\n```text\numt5-xxl-enc-fp8_e4m3fn.safetensors\n```\n\nGröße ungefähr:\n\n```text\n6.3–6.8 GB\n```\n\nDas ist der dicke Brocken. Nicht erschrecken, er hat nur einen gesunden Appetit. Wie jedes moderne KI-Modell mit Standesdünkel.\n\n---\n\n## 6. Alles zusammen prüfen\n\n```bash\nfind ~/ai/ComfyUI/models -maxdepth 2 -type f -name \"*.safetensors\" -exec ls -lh {} \\;\n```\n\nErwartet mindestens:\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\n---\n\n## 7. Falls Download Zugriff/Auth verlangt\n\nFalls `hf download` nach Login oder Token fragt:\n\n```bash\nhf auth login\n```\n\nDann HuggingFace Token einfügen.\n\nDanach den fehlgeschlagenen Download erneut ausführen.\n\n---\n\n## 8. Danach ComfyUI starten\n\nWenn die drei Dateien da sind:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\necho \"$TS_IP\"\n\npython main.py --listen \"$TS_IP\" --port 8188\n```\n\nWenn `--listen \"$TS_IP\"` Probleme macht:\n\n```bash\npython main.py --listen 0.0.0.0 --port 8188\n```\n\nDann bitte nur via Tailscale/Firewall offen lassen.\n\n---\n\n## 9. Healthcheck lokal auf dem Host\n\nIn einem zweiten Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: ComfyUI läuft.\n\n---\n\n## 10. Schick mir danach\n\nBitte poste dann:\n\n```text\n1. Ausgabe von:\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n\n2. Deine Tailscale-ComfyUI-URL:\nhttp://<TAILSCALE_IP>:8188\n```\n\nDann prüfe ich von hier aus die Verbindung und baue den Hermes/AutoShortsBot-Client dafür."}], "id": "msg_0d1a4d10654d56b4016a11b2a957008191a9cb8783bdab507a", "phase": "final_answer"}], "timestamp": "2026-05-23T15:59:27.410671"}
{"role": "user", "content": "[M G] gam@PC-1:~/llama.cpp$ curl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n{\"system\": {\"os\": \"linux\", \"ram_total\": 33339789312, \"ram_free\": 5236817920, \"comfyui_version\": \"0.22.0\", \"required_frontend_version\": \"1.43.18\", \"installed_templates_version\": \"0.9.82\", \"required_templates_version\": \"0.9.82\", \"comfy_package_versions\": [{\"name\": \"comfyui-frontend-package\", \"installed\": \"1.43.18\", \"required\": \"1.43.18\"}, {\"name\": \"comfyui-workflow-templates\", \"installed\": \"0.9.82\", \"required\": \"0.9.82\"}, {\"name\": \"comfyui-embedded-docs\", \"installed\": \"0.5.0\", \"required\": \"0.5.0\"}, {\"name\": \"comfy-kitchen\", \"installed\": \"0.2.8\", \"required\": \"0.2.8\"}, {\"name\": \"comfy-aimdo\", \"installed\": \"0.4.3\", \"required\": \"0.4.3\"}], \"python_version\": \"3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]\", \"pytorch_version\": \"2.12.0+cu126\", \"embedded_python\": false, \"argv\": [\"main.py\", \"--listen\", \"100.101.173.25\", \"--port\", \"8188\"]}, \"devices\": [{\"name\": \"cuda:0 NVIDIA RTX A5000 Laptop GPU : cudaMallocAsync\", \"type\": \"cuda\", \"index\": 0, \"vram_total\": 16758472704, \"vram_free\": 16491151360, \"torch_vram_total\": 0, \"torch_vram_free\": 0}]}gam@PC-1:~/llama.cpp$ To see the GUI go to: http://100.101.173.25:8188", "timestamp": "2026-05-23T16:19:57.145500"}
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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\\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.\\n\\n4. **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\\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/official-cli.md\", \"references/template-integrity.md\", \"references/rest-api.md\", \"references/workflow-format.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_TmXGCZ3pZyijhb7dbrqkl1BF", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "Need use tool now.", "reasoning": null, "finish_reason": "tool_calls", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Need use tool now."}], "id": "msg_0d1a4d10654d56b4016a11b63190608191a7fbd24f8cb2d87e", "phase": "commentary"}], "tool_calls": [{"id": "call_ZNZIau8MgxOaosDkwe6WzAUp", "call_id": "call_ZNZIau8MgxOaosDkwe6WzAUp", "response_item_id": "fc_0d1a4d10654d56b4016a11b631fa388191b999aabe9e746b27", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"curl -s --max-time 10 http://100.101.173.25:8188/system_stats | python -m json.tool | head -80\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\n    \\\"system\\\": {\\n        \\\"os\\\": \\\"linux\\\",\\n        \\\"ram_total\\\": 33339789312,\\n        \\\"ram_free\\\": 5179420672,\\n        \\\"comfyui_version\\\": \\\"0.22.0\\\",\\n        \\\"required_frontend_version\\\": \\\"1.43.18\\\",\\n        \\\"installed_templates_version\\\": \\\"0.9.82\\\",\\n        \\\"required_templates_version\\\": \\\"0.9.82\\\",\\n        \\\"comfy_package_versions\\\": [\\n            {\\n                \\\"name\\\": \\\"comfyui-frontend-package\\\",\\n                \\\"installed\\\": \\\"1.43.18\\\",\\n                \\\"required\\\": \\\"1.43.18\\\"\\n            },\\n            {\\n                \\\"name\\\": \\\"comfyui-workflow-templates\\\",\\n                \\\"installed\\\": \\\"0.9.82\\\",\\n                \\\"required\\\": \\\"0.9.82\\\"\\n            },\\n            {\\n                \\\"name\\\": \\\"comfyui-embedded-docs\\\",\\n                \\\"installed\\\": \\\"0.5.0\\\",\\n                \\\"required\\\": \\\"0.5.0\\\"\\n            },\\n            {\\n                \\\"name\\\": \\\"comfy-kitchen\\\",\\n                \\\"installed\\\": \\\"0.2.8\\\",\\n                \\\"required\\\": \\\"0.2.8\\\"\\n            },\\n            {\\n                \\\"name\\\": \\\"comfy-aimdo\\\",\\n                \\\"installed\\\": \\\"0.4.3\\\",\\n                \\\"required\\\": \\\"0.4.3\\\"\\n            }\\n        ],\\n        \\\"python_version\\\": \\\"3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]\\\",\\n        \\\"pytorch_version\\\": \\\"2.12.0+cu126\\\",\\n        \\\"embedded_python\\\": false,\\n        \\\"argv\\\": [\\n            \\\"main.py\\\",\\n            \\\"--listen\\\",\\n            \\\"100.101.173.25\\\",\\n            \\\"--port\\\",\\n            \\\"8188\\\"\\n        ]\\n    },\\n    \\\"devices\\\": [\\n        {\\n            \\\"name\\\": \\\"cuda:0 NVIDIA RTX A5000 Laptop GPU : cudaMallocAsync\\\",\\n            \\\"type\\\": \\\"cuda\\\",\\n            \\\"index\\\": 0,\\n            \\\"vram_total\\\": 16758472704,\\n            \\\"vram_free\\\": 16491151360,\\n            \\\"torch_vram_total\\\": 0,\\n            \\\"torch_vram_free\\\": 0\\n        }\\n    ]\\n}\", \"exit_code\": 0, \"error\": null, \"approval\": \"Command required approval (Security scan — [MEDIUM] URL uses raw IP address: URL points to IP address 100.101.173.25 instead of a domain name; [HIGH] Plain HTTP URL in execution context: URL 'http://100.101.173.25:8188/system_stats' uses unencrypted HTTP and is being passed to a command that downloads or executes content. An attacker on the network could modify the content.; [MEDIUM] Schemeless URL in sink context: URL without explicit scheme passed to a command that downloads/executes content; [HIGH] Pipe to interpreter: curl | python: Command pipes output from 'curl' directly to interpreter 'python'. Downloaded content will be executed without inspection.\\n  Safer: tirith run http://100.101.173.25:8188/system_stats  — or: vet http://100.101.173.25:8188/system_stats  (https://getvet.sh)) and was approved by the user.\"}", "tool_call_id": "call_ZNZIau8MgxOaosDkwe6WzAUp", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Considering node queries**\n\nI need to determine if I should query object_info for nodes. There’s a thought about using curl to save JSON instead of piping it to Python. Actually, using the terminal with Python's urllib is a possibility too. I must also check if Wan nodes are installed. Alright, there’s a few steps to follow, and I need to make sure to inspect everything properly. Let’s proceed with that!", "finish_reason": "tool_calls", "reasoning_content": "**Considering node queries**\n\nI need to determine if I should query object_info for nodes. There’s a thought about using curl to save JSON instead of piping it to Python. Actually, using the terminal with Python's urllib is a possibility too. I must also check if Wan nodes are installed. Alright, there’s a few steps to follow, and I need to make sure to inspect everything properly. Let’s proceed with that!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbbvn8GaGhdNhb47kTkpyglRWzsJeWzJ3W2srCgqJp_vUXMze3Fzfq-qliSpd33DOMUSt-p5VpgvQZzSDodfVz7D6R2jn1ilhxic1V3JgTOqsuIfjxkSvSLDX4T5wcVh2SNXkXVXQSXk1B1XTbDkmBUwu3DA7dAWw4n-3jYEGWfjlpbS59mDBYas6ieb79lFr9oQdqCw545nc9avoVvwOEZCIc5lmQuOp8d9f0Fdw_nwyEfIV2kefd5Ll_blib_ByYCEKRUB_RCAiUz6WsdMnvqvZPoeBuvDiMRHakwwY5F-uDOQETUom9OZfgGw3a--X7TQp7uVmbRC3X0m3xK-f9hYbeVr7cI0milgYedzmDGoMrZRl4G8t6Q4v0jT6YY08_D8wNojndWUOGj8L91PVgZNIkEzxjMY67QChaH690DftA4QB-CG2VD2JgVwD_URuQkH91DCyO3NJVGtnrr6F28AAb4AiDmAK9dZL9FwO0gKzLB4bXsp4ROMeZgX1pXhcAr_kRk6LBEyq-W9cAjIi7-gGV3OgyOQdwRvb2StloeRK5l45FYOGKTKJ55Zd69ZH6JXV8ODy0EFOqQviUBT9R83CmnDomAT3cE-3SCic0EFWQ-kx4p1tvWY0vEbRxQ4lzL2gkzrpdEAhIKGHOMa8MhLDYEBAR8fsD6IbajddRk4xiCJv_Usof3mbliC6Vy_bxW088fXs0nnxmf0_E-JAyPptFIZuEKy8xmSLviAEl86JU0ysYOluRaBtn1mWICk8QqVw2-0kw0DPBeEJf7KI3sraQHhaZ5BtDAEb6cCNlkClL4DAmoWfujt-PtXAyrDnv3gCFchodfEIG80OPRoxu_2-Ehy9UbyeOHBsO2E7LmmRExVv3mm5C_tVEHVEPFKCJuw-Gu1mOYIcN68uJeQytwh8zLhF76ZQLtfIX88jYPOJhZnkOJ2pzu1wze9ITnJ8jII2Jns6GFCbCn6LG1QvUCH32AHHWTb_0mR5fQruAxO5Its51c74V4tVyYY8PvcOMK47twWP4a77Gtn_mqIZ3JO64J0y_RtM9zZjZgPy3BljX_P2uNChwQkCLZD1Y88NKjZ1qR_tw9FTDsDt40EIMN1VKsxJ7mNnvwasG97-owYFE4Z3s037Cog8XCIUqC4RI32BtBxYBaNIwbklJp-7cy7lA==", "id": "rs_0d1a4d10654d56b4016a11b6eceb4c8191a233fbac67a3f140", "summary": [{"type": "summary_text", "text": "**Considering node queries**\n\nI need to determine if I should query object_info for nodes. There’s a thought about using curl to save JSON instead of piping it to Python. Actually, using the terminal with Python's urllib is a possibility too. I must also check if Wan nodes are installed. Alright, there’s a few steps to follow, and I need to make sure to inspect everything properly. Let’s proceed with that!"}]}], "tool_calls": [{"id": "call_h8MEYaKDNjCYBR3tVjs5BU44", "call_id": "call_h8MEYaKDNjCYBR3tVjs5BU44", "response_item_id": "fc_0d1a4d10654d56b4016a11b6efaf908191986ebd832c13f7bd", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\nwith urllib.request.urlopen(base+'/object_info', timeout=20) as r:\\n    data=json.load(r)\\nkeys=sorted(data)\\nwan=[k for k in keys if 'wan' in k.lower()]\\nvideo=[k for k in keys if 'video' in k.lower() or 'vhs' in k.lower() or 'save' in k.lower()]\\nprint('object_count', len(keys))\\nprint('wan_nodes', len(wan))\\nfor k in wan[:80]: print(k)\\nprint('video_related_sample', video[:80])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"object_count 875\\nwan_nodes 151\\nDummyComfyWanModelObject\\nLoadWanVideoClipTextEncoder\\nLoadWanVideoT5TextEncoder\\nModelMergeWAN2_1\\nPreviewAny\\nWan22FunControlToVideo\\nWan22ImageToVideoLatent\\nWan2ImageToVideoApi\\nWan2ReferenceVideoApi\\nWan2TextToVideoApi\\nWan2VideoContinuationApi\\nWan2VideoEditApi\\nWanAnimateToVideo\\nWanCameraEmbedding\\nWanCameraImageToVideo\\nWanContextWindowsManual\\nWanDancerEncodeAudio\\nWanDancerPadKeyframes\\nWanDancerPadKeyframesList\\nWanDancerVideo\\nWanFirstLastFrameToVideo\\nWanFunControlToVideo\\nWanFunInpaintToVideo\\nWanHuMoImageToVideo\\nWanImageToImageApi\\nWanImageToVideo\\nWanImageToVideoApi\\nWanInfiniteTalkToVideo\\nWanMoveConcatTrack\\nWanMoveTrackToVideo\\nWanMoveTracksFromCoords\\nWanMoveVisualizeTracks\\nWanMove_native\\nWanPhantomSubjectToVideo\\nWanReferenceVideoApi\\nWanSCAILToVideo\\nWanSoundImageToVideo\\nWanSoundImageToVideoExtend\\nWanTextToImageApi\\nWanTextToVideoApi\\nWanTrackToVideo\\nWanVaceToVideo\\nWanVideoATITracks\\nWanVideoATITracksVisualize\\nWanVideoATI_comfy\\nWanVideoAddBindweaveEmbeds\\nWanVideoAddControlEmbeds\\nWanVideoAddDualControlEmbeds\\nWanVideoAddExtraLatent\\nWanVideoAddFlashVSRInput\\nWanVideoAddLucyEditLatents\\nWanVideoAddLynxEmbeds\\nWanVideoAddMTVMotion\\nWanVideoAddOneToAllExtendEmbeds\\nWanVideoAddOneToAllPoseEmbeds\\nWanVideoAddOneToAllReferenceEmbeds\\nWanVideoAddOviAudioToLatents\\nWanVideoAddPusaNoise\\nWanVideoAddS2VEmbeds\\nWanVideoAddSCAILPoseEmbeds\\nWanVideoAddSCAILReferenceEmbeds\\nWanVideoAddStandInLatent\\nWanVideoAddSteadyDancerEmbeds\\nWanVideoAddStoryMemLatents\\nWanVideoAddTTMLatents\\nWanVideoAddWanMoveTracks\\nWanVideoAnimateEmbeds\\nWanVideoApplyNAG\\nWanVideoBlockList\\nWanVideoBlockSwap\\nWanVideoClipVisionEncode\\nWanVideoCombineEmbeds\\nWanVideoContextOptions\\nWanVideoControlEmbeds\\nWanVideoControlnet\\nWanVideoControlnetLoader\\nWanVideoDecode\\nWanVideoDecodeOviAudio\\nWanVideoDiffusionForcingSampler\\nWanVideoEasyCache\\nvideo_related_sample ['ARVideoI2V', 'BriaRemoveVideoBackground', 'ByteDance2TextToVideoNode', 'ByteDanceCreateVideoAsset', 'ByteDanceImageToVideoNode', 'ByteDanceTextToVideoNode', 'CLIPSave', 'CheckpointSave', 'ConditioningSetAreaPercentageVideo', 'CosmosImageToVideoLatent', 'CosmosPredict2ImageToVideoLatent', 'CreateVideo', 'EmptyARVideoLatent', 'EmptyCosmosLatentVideo', 'EmptyHunyuanLatentVideo', 'EmptyHunyuanVideo15Latent', 'EmptyLTXVLatentVideo', 'EmptyMochiLatentVideo', 'GetVideoComponents', 'GrokVideoEditNode', 'GrokVideoExtendNode', 'GrokVideoNode', 'GrokVideoReferenceNode', 'HappyHorseImageToVideoApi', 'HappyHorseReferenceVideoApi', 'HappyHorseTextToVideoApi', 'HappyHorseVideoEditApi', 'HitPawVideoEnhance', 'HunyuanImageToVideo', 'HunyuanVideo15ImageToVideo', 'HunyuanVideo15LatentUpscaleWithModel', 'HunyuanVideo15SuperResolution', 'ImageOnlyCheckpointSave', 'Kandinsky5ImageToVideo', 'KlingDualCharacterVideoEffectNode', 'KlingImage2VideoNode', 'KlingImageToVideoWithAudio', 'KlingLipSyncAudioToVideoNode', 'KlingLipSyncTextToVideoNode', 'KlingOmniProEditVideoNode', 'KlingOmniProImageToVideoNode', 'KlingOmniProTextToVideoNode', 'KlingOmniProVideoToVideoNode', 'KlingSingleImageVideoEffectNode', 'KlingTextToVideoNode', 'KlingTextToVideoWithAudio', 'KlingVideoExtendNode', 'KlingVideoNode', 'LTXVImgToVideo', 'LTXVImgToVideoInplace', 'LoadVideo', 'LoadWanVideoClipTextEncoder', 'LoadWanVideoT5TextEncoder', 'LoraSave', 'LtxvApiImageToVideo', 'LtxvApiTextToVideo', 'LumaImageToVideoNode', 'LumaVideoNode', 'MinimaxHailuoVideoNode', 'MinimaxImageToVideoNode', 'MinimaxTextToVideoNode', 'ModelSave', 'NormalizeVideoLatentStart', 'OpenAIVideoSora2', 'PixverseImageToVideoNode', 'PixverseTextToVideoNode', 'PixverseTransitionVideoNode', 'ReplaceVideoLatentFrames', 'RunwayImageToVideoNodeGen3a', 'RunwayImageToVideoNodeGen4', 'SAM3_VideoTrack', 'SamplerARVideo', 'SaveAnimatedPNG', 'SaveAnimatedWEBP', 'SaveAudio', 'SaveAudioMP3', 'SaveAudioOpus', 'SaveGLB', 'SaveImage', 'SaveImageDataSetToFolder']\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_h8MEYaKDNjCYBR3tVjs5BU44", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Considering implementation steps**\n\nI need to figure out whether to implement the client right now since the user mentioned the system is running. It seems we can confirm everything is verified, so maybe I should proceed. The user mentioned a GPU check in the to-do list, which I should update on. Now, I have to query specific object information for WanTextToVideoApi as opposed to custom options. Also, I need to clarify the workflow for using the built-in \"API node\" for external APIs.", "finish_reason": "tool_calls", "reasoning_content": "**Considering implementation steps**\n\nI need to figure out whether to implement the client right now since the user mentioned the system is running. It seems we can confirm everything is verified, so maybe I should proceed. The user mentioned a GPU check in the to-do list, which I should update on. Now, I have to query specific object information for WanTextToVideoApi as opposed to custom options. Also, I need to clarify the workflow for using the built-in \"API node\" for external APIs.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbb4BqsyD2K1rPOWdetFpRO414duIV_u1VeyoGTph8Ydlu-RdGBS4IPNvODxaz2ZmKPuFCJakfR7g0hM-UEwRsatjNkDvN4pe51XyrEN6mHWY0PnH26kWIyD9tqkuZBl8BjesRyCYF_Cc3OSUH6JaUPRP_i_RU5hv556HjZdOnYnnOVuhziMDI1HuNHG6vhZeOQq7GB5KinlJV5BbB4zVqaPONko_OOsCk57ChiD1rxQPYK56N_neOI3IOzvaEbs_kfMF_-0adEdG9I0y84ScBKtHioL-MNjabzZKS5C4FnjPK9bFEsXNHSVFX3I_l-nN3QPdOo9Gu3atMSQbsC0olQGtKy3XPdgA9bU8_fhlGaZwvM27erDE0mmmh0E-jqbv9y6aHdk0TCahMAumSONini7VyuFzNYkZzulU4b55Xsa4hdWUS0hGMnFCt-BiBmLh5H-tEra0vWqPBG-f7C-xwI3HmDG4B1b6paBByCAbq2E7HgW5vv_M9fFu9ItuCL8Fo-7y4sxQ_EppdmGi8G7bM1LaZboCRnaq6QcKTBSR1NFHAKgG_u9fMYhrGJkALRYWoT_9ritShE2FsJAmSqQZoGHxgkJVAI8-VvulO9LwyfWhjtvRsczJ4fv5bUkS22AFk3LMxdhaRlVYv0KwuQFGW5ZM4dRi0P1HNLrkRzFy-tB79ZuC5MORC1WJ2uqbqvBCqPfOqP65U8V2aU7CcvGNLPS9Hl04zBwz1blyxRPIXvSAU2RbpL7c2ytW75N-QfvlxBKuxYRtq9XncWq8DjeaRV0T4xPj8qvDL0HJSrv3D-_MuZOwLgqRk51eihUCHI8LreDG-tsODtOrDcuKEAmdLYcdBHUOT0gQ_Jfe_ks48qcclP-0SjiTsgiXe92xNb8WWa3d_AgNWN2hps9zQ7jhRk1AgkpVj0eJYKr82UnkGqcuXmf6ZXA68UzGmCRQgUrh2QLfAZSFQ_YzKnMjPi_-r9l-0IAuBwTn-Ymet6mbWkY_vv191mR7fsrUeDICVFAdrHvwvNd46Ex5po6bFMLPD7nWimuqIHsaqNJHJIHLs47MwJqyCmPHh-Sk7Uuf3jta0tVKW7F5EIA1HPNM91rtgIVYVGeIRNEcL29OaH1H9LIJwpuBK_WZLoRTE7UyyItGQYMEO0WrMDgqc3vXGb3cfx7WTSIh8uwD_gmRmZrr2YbbrjlM2gqxRp49vgVXpvCSzGYIGQfbb_1y14mKVuN0M42p9ExsFSVe1Xp4yNJAncCV5gi0PzfKCwhdFkFyBNbWDCy2RRpUuylqQEwOc8DOB-ABoWeWI_Zbe3q16cPiuFsroWE2T-xW_ANOyj7l59k2uymV_yK6DzG-bNtyvtxrw9hmA==", "id": "rs_0d1a4d10654d56b4016a11b6f59c648191a80392fc117aa57f", "summary": [{"type": "summary_text", "text": "**Considering implementation steps**\n\nI need to figure out whether to implement the client right now since the user mentioned the system is running. It seems we can confirm everything is verified, so maybe I should proceed. The user mentioned a GPU check in the to-do list, which I should update on. Now, I have to query specific object information for WanTextToVideoApi as opposed to custom options. Also, I need to clarify the workflow for using the built-in \"API node\" for external APIs."}]}], "tool_calls": [{"id": "call_bdXVtxxKj8T9R16Ld4MHDTym", "call_id": "call_bdXVtxxKj8T9R16Ld4MHDTym", "response_item_id": "fc_0d1a4d10654d56b4016a11b6f8cb748191ba2343c311b8cf54", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\ndata=json.load(urllib.request.urlopen(base+'/object_info', timeout=20))\\nfor name in ['WanVideoModelLoader','WanVideoTextEncode','WanVideoSampler','WanVideoDecode','WanVideoVAELoader','LoadWanVideoT5TextEncoder','WanTextToVideoApi','Wan2TextToVideoApi','VHS_VideoCombine','SaveAnimatedWEBP','SaveImage']:\\n    if name in data:\\n        print('\\\\n##', name)\\n        print('display', data[name].get('display_name'))\\n        req=data[name].get('input',{}).get('required',{})\\n        opt=data[name].get('input',{}).get('optional',{})\\n        print('required', list(req.keys()))\\n        for k,v in list(req.items())[:12]: print(' ',k,':', v if len(str(v))<300 else str(v)[:300])\\n        print('optional', list(opt.keys())[:20])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"## WanVideoModelLoader\\ndisplay WanVideo Model Loader\\nrequired ['model', 'base_precision', 'quantization', 'load_device']\\n  model : [['Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors'], {'tooltip': \\\"These models are loaded from the 'ComfyUI/models/diffusion_models' -folder\\\"}]\\n  base_precision : [['fp32', 'bf16', 'fp16', 'fp16_fast'], {'default': 'bf16'}]\\n  quantization : [['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_e4m3fn_scaled', 'fp8_e4m3fn_scaled_fast', 'fp8_e5m2', 'fp8_e5m2_fast', 'fp8_e5m2_scaled', 'fp8_e5m2_scaled_fast'], {'default': 'disabled', 'tooltip': \\\"Optional quantization method, 'disabled' acts as autoselect based by weights. Scaled modes only w\\n  load_device : [['main_device', 'offload_device'], {'default': 'offload_device', 'tooltip': 'Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM'}]\\noptional ['attention_mode', 'compile_args', 'block_swap_args', 'lora', 'vram_management_args', 'extra_model', 'fantasytalking_model', 'multitalk_model', 'fantasyportrait_model', 'rms_norm_function']\\n\\n## WanVideoTextEncode\\ndisplay WanVideo TextEncode\\nrequired ['positive_prompt', 'negative_prompt']\\n  positive_prompt : ['STRING', {'default': '', 'multiline': True}]\\n  negative_prompt : ['STRING', {'default': '', 'multiline': True}]\\noptional ['t5', 'force_offload', 'model_to_offload', 'use_disk_cache', 'device']\\n\\n## WanVideoSampler\\ndisplay WanVideo Sampler\\nrequired ['model', 'image_embeds', 'steps', 'cfg', 'shift', 'seed', 'force_offload', 'scheduler', 'riflex_freq_index']\\n  model : ['WANVIDEOMODEL']\\n  image_embeds : ['WANVIDIMAGE_EMBEDS']\\n  steps : ['INT', {'default': 30, 'min': 1}]\\n  cfg : ['FLOAT', {'default': 6.0, 'min': 0.0, 'max': 30.0, 'step': 0.01}]\\n  shift : ['FLOAT', {'default': 5.0, 'min': 0.0, 'max': 1000.0, 'step': 0.01}]\\n  seed : ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}]\\n  force_offload : ['BOOLEAN', {'default': True, 'tooltip': 'Moves the model to the offload device after sampling'}]\\n  scheduler : [['unipc', 'unipc/beta', 'dpm++', 'dpm++/beta', 'dpm++_sde', 'dpm++_sde/beta', 'euler', 'euler/beta', 'longcat_distill_euler', 'deis', 'lcm', 'lcm/beta', 'res_multistep', 'er_sde', 'flowmatch_causvid', 'flowmatch_distill', 'flowmatch_pusa', 'multitalk', 'sa_ode_stable', 'rcm', 'vibt_unipc'], {'defau\\n  riflex_freq_index : ['INT', {'default': 0, 'min': 0, 'max': 1000, 'step': 1, 'tooltip': 'Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping'}]\\noptional ['text_embeds', 'samples', 'denoise_strength', 'feta_args', 'context_options', 'cache_args', 'flowedit_args', 'batched_cfg', 'slg_args', 'rope_function', 'loop_args', 'experimental_args', 'sigmas', 'unianimate_poses', 'fantasytalking_embeds', 'uni3c_embeds', 'multitalk_embeds', 'freeinit_args', 'start_step', 'end_step']\\n\\n## WanVideoDecode\\ndisplay WanVideo Decode\\nrequired ['vae', 'samples', 'enable_vae_tiling', 'tile_x', 'tile_y', 'tile_stride_x', 'tile_stride_y']\\n  vae : ['WANVAE']\\n  samples : ['LATENT']\\n  enable_vae_tiling : ['BOOLEAN', {'default': False, 'tooltip': 'Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams can be controlled by increasing the tile size. Seams become less obvious at 1.5x\\n  tile_x : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n  tile_y : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n  tile_stride_x : ['INT', {'default': 144, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\n  tile_stride_y : ['INT', {'default': 128, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\noptional ['normalization']\\n\\n## WanVideoVAELoader\\ndisplay WanVideo VAE Loader\\nrequired ['model_name']\\n  model_name : [['Wan2_1_VAE_bf16.safetensors'], {'tooltip': \\\"These models are loaded from 'ComfyUI/models/vae'\\\"}]\\noptional ['precision', 'compile_args', 'use_cpu_cache', 'verbose']\\n\\n## LoadWanVideoT5TextEncoder\\ndisplay WanVideo T5 Text Encoder Loader\\nrequired ['model_name', 'precision']\\n  model_name : [['umt5-xxl-enc-fp8_e4m3fn.safetensors'], {'tooltip': \\\"These models are loaded from 'ComfyUI/models/text_encoders'\\\"}]\\n  precision : [['fp32', 'bf16'], {'default': 'bf16'}]\\noptional ['load_device', 'quantization']\\n\\n## WanTextToVideoApi\\ndisplay Wan Text to Video\\nrequired ['model', 'prompt']\\n  model : ['COMBO', {'tooltip': 'Model to use.', 'default': 'wan2.6-t2v', 'multiselect': False, 'options': ['wan2.5-t2v-preview', 'wan2.6-t2v']}]\\n  prompt : ['STRING', {'tooltip': 'Prompt describing the elements and visual features. Supports English and Chinese.', 'default': '', 'multiline': True}]\\noptional ['negative_prompt', 'size', 'duration', 'audio', 'seed', 'generate_audio', 'prompt_extend', 'watermark', 'shot_type']\\n\\n## Wan2TextToVideoApi\\ndisplay Wan 2.7 Text to Video\\nrequired ['model', 'seed', 'prompt_extend', 'watermark']\\n  model : ['COMFY_DYNAMICCOMBO_V3', {'options': [{'key': 'wan2.7-t2v', 'inputs': {'required': {'prompt': ['STRING', {'tooltip': 'Prompt describing the elements and visual features. Supports English and Chinese.', 'default': '', 'multiline': True}], 'negative_prompt': ['STRING', {'tooltip': 'Negative prompt de\\n  seed : ['INT', {'tooltip': 'Seed to use for generation.', 'default': 0, 'min': 0, 'max': 2147483647, 'step': 1, 'control_after_generate': True, 'display': 'number'}]\\n  prompt_extend : ['BOOLEAN', {'tooltip': 'Whether to enhance the prompt with AI assistance.', 'advanced': True, 'default': True}]\\n  watermark : ['BOOLEAN', {'tooltip': 'Whether to add an AI-generated watermark to the result.', 'advanced': True, 'default': False}]\\noptional ['audio']\\n\\n## SaveAnimatedWEBP\\ndisplay Save Animated WEBP\\nrequired ['images', 'filename_prefix', 'fps', 'lossless', 'quality', 'method']\\n  images : ['IMAGE', {}]\\n  filename_prefix : ['STRING', {'default': 'ComfyUI', 'multiline': False}]\\n  fps : ['FLOAT', {'default': 6.0, 'min': 0.01, 'max': 1000.0, 'step': 0.01}]\\n  lossless : ['BOOLEAN', {'default': True}]\\n  quality : ['INT', {'default': 80, 'min': 0, 'max': 100}]\\n  method : ['COMBO', {'multiselect': False, 'options': ['default', 'fastest', 'slowest']}]\\noptional []\\n\\n## SaveImage\\ndisplay Save Image\\nrequired ['images', 'filename_prefix']\\n  images : ['IMAGE', {'tooltip': 'The images to save.'}]\\n  filename_prefix : ['STRING', {'default': 'ComfyUI', '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.'}]\\noptional []\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_bdXVtxxKj8T9R16Ld4MHDTym", "timestamp": "2026-05-23T16:19:57.145500"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"LoadWanVideoClipTextEncoder\\nLoadWanVideoT5TextEncoder\\nWanVideoAddBindweaveEmbeds\\nWanVideoAddControlEmbeds\\nWanVideoAddDualControlEmbeds\\nWanVideoAddExtraLatent\\nWanVideoAddLucyEditLatents\\nWanVideoAddLynxEmbeds\\nWanVideoAddOneToAllExtendEmbeds\\nWanVideoAddOneToAllPoseEmbeds\\nWanVideoAddOneToAllReferenceEmbeds\\nWanVideoAddOviAudioToLatents\\nWanVideoAddS2VEmbeds\\nWanVideoAddSCAILPoseEmbeds\\nWanVideoAddSCAILReferenceEmbeds\\nWanVideoAddStandInLatent\\nWanVideoAddSteadyDancerEmbeds\\nWanVideoAddStoryMemLatents\\nWanVideoAddTTMLatents\\nWanVideoAnimateEmbeds\\nWanVideoCombineEmbeds\\nWanVideoContextOptions\\nWanVideoControlEmbeds\\nWanVideoEmptyEmbeds\\nWanVideoEmptyMMAudioLatents\\nWanVideoEncodeLatentBatch\\nWanVideoFunCameraEmbeds\\nWanVideoImageClipEncode\\nWanVideoImageResizeToClosest\\nWanVideoImageToVideoEncode\\nWanVideoImageToVideoMultiTalk\\nWanVideoImageToVideoSkyreelsv3_audio\\nWanVideoLatentReScale\\nWanVideoLongCatAvatarExtendEmbeds\\nWanVideoMiniMaxRemoverEmbeds\\nWanVideoPassImagesFromSamples\\nWanVideoPhantomEmbeds\\nWanVideoPreviewEmbeds\\nWanVideoPromptExtender\\nWanVideoPromptExtenderSelect\\nWanVideoReCamMasterCameraEmbed\\nWanVideoRealisDanceLatents\\nWanVideoSVIProEmbeds\\nWanVideoTextEmbedBridge\\nWanVideoTextEncode\\nWanVideoTextEncodeCached\\nWanVideoTextEncodeSingle\\nWanVideoUni3C_embeds\\nWanVideoUniLumosEmbeds\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_w8HOwKndeVkklWqL6sGTNUVt", "timestamp": "2026-05-23T16:19:57.145500"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"## WanVideoEmptyEmbeds WanVideo Empty Embeds\\nrequired\\n  width : ['INT', {'default': 832, 'min': 64, 'max': 8096, 'step': 8, 'tooltip': 'Width of the image to encode'}]\\n  height : ['INT', {'default': 480, 'min': 64, 'max': 8096, 'step': 8, 'tooltip': 'Height of the image to encode'}]\\n  num_frames : ['INT', {'default': 81, 'min': 1, 'max': 10000, 'step': 4, 'tooltip': 'Number of frames to encode'}]\\noptional\\n  control_embeds : ['WANVIDIMAGE_EMBEDS', {'tooltip': 'control signal for the Fun -model'}]\\n  extra_latents : ['LATENT', {'tooltip': 'First latent to use for the Pusa -model'}]\\n\\n## WanVideoTextEncode WanVideo TextEncode\\nrequired\\n  positive_prompt : ['STRING', {'default': '', 'multiline': True}]\\n  negative_prompt : ['STRING', {'default': '', 'multiline': True}]\\noptional\\n  t5 : ['WANTEXTENCODER']\\n  force_offload : ['BOOLEAN', {'default': True}]\\n  model_to_offload : ['WANVIDEOMODEL', {'tooltip': 'Model to move to offload_device before encoding'}]\\n  use_disk_cache : ['BOOLEAN', {'default': False, 'tooltip': 'Cache the text embeddings to disk for faster re-use, under the custom_nodes/ComfyUI-WanVideoWrapper/text_embed_cache directory'}]\\n  device : [['gpu', 'cpu'], {'default': 'gpu', 'tooltip': 'Device to run the text encoding on.'}]\\n\\n## WanVideoDecode WanVideo Decode\\nrequired\\n  vae : ['WANVAE']\\n  samples : ['LATENT']\\n  enable_vae_tiling : ['BOOLEAN', {'default': False, 'tooltip': 'Drastically reduces memory use but will introduce seams at tile stride boundaries. The location and number of seams is dictated by the tile stride size. The visibility of seams \\n  tile_x : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile width in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n  tile_y : ['INT', {'default': 272, 'min': 40, 'max': 2048, 'step': 8, 'tooltip': 'Tile height in pixels. Smaller values use less VRAM but will make seams more obvious.'}]\\n  tile_stride_x : ['INT', {'default': 144, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride width in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\n  tile_stride_y : ['INT', {'default': 128, 'min': 32, 'max': 2040, 'step': 8, 'tooltip': 'Tile stride height in pixels. Smaller values use less VRAM but will introduce more seams.'}]\\noptional\\n  normalization : [['default', 'minmax', 'none'], {'advanced': True}]\\n\\n## WanVideoSampler WanVideo Sampler\\nrequired\\n  model : ['WANVIDEOMODEL']\\n  image_embeds : ['WANVIDIMAGE_EMBEDS']\\n  steps : ['INT', {'default': 30, 'min': 1}]\\n  cfg : ['FLOAT', {'default': 6.0, 'min': 0.0, 'max': 30.0, 'step': 0.01}]\\n  shift : ['FLOAT', {'default': 5.0, 'min': 0.0, 'max': 1000.0, 'step': 0.01}]\\n  seed : ['INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}]\\n  force_offload : ['BOOLEAN', {'default': True, 'tooltip': 'Moves the model to the offload device after sampling'}]\\n  scheduler : [['unipc', 'unipc/beta', 'dpm++', 'dpm++/beta', 'dpm++_sde', 'dpm++_sde/beta', 'euler', 'euler/beta', 'longcat_distill_euler', 'deis', 'lcm', 'lcm/beta', 'res_multistep', 'er_sde', 'flowmatch_causvid', 'flowmatch_distill\\n  riflex_freq_index : ['INT', {'default': 0, 'min': 0, 'max': 1000, 'step': 1, 'tooltip': 'Frequency index for RIFLEX, disabled when 0, default 6. Allows for new frames to be generated after without looping'}]\\noptional\\n  text_embeds : ['WANVIDEOTEXTEMBEDS']\\n  samples : ['LATENT', {'tooltip': 'init Latents to use for video2video process'}]\\n  denoise_strength : ['FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}]\\n  feta_args : ['FETAARGS']\\n  context_options : ['WANVIDCONTEXT']\\n  cache_args : ['CACHEARGS']\\n  flowedit_args : ['FLOWEDITARGS', {'tooltip': 'FlowEdit support has been deprecated'}]\\n  batched_cfg : ['BOOLEAN', {'default': False, 'tooltip': 'Batch cond and uncond for faster sampling, possibly faster on some hardware, uses more memory'}]\\n  slg_args : ['SLGARGS']\\n  rope_function : [['default', 'comfy', 'comfy_chunked'], {'default': 'comfy', 'tooltip': \\\"Comfy's RoPE implementation doesn't use complex numbers and can thus be compiled, that should be a lot faster when using torch.compile. Chunked ver\\n  loop_args : ['LOOPARGS']\\n  experimental_args : ['EXPERIMENTALARGS']\\n  sigmas : ['SIGMAS']\\n  unianimate_poses : ['UNIANIMATE_POSE']\\n  fantasytalking_embeds : ['FANTASYTALKING_EMBEDS']\\n  uni3c_embeds : ['UNI3C_EMBEDS']\\n  multitalk_embeds : ['MULTITALK_EMBEDS']\\n  freeinit_args : ['FREEINITARGS']\\n  start_step : ['INT', {'default': 0, 'min': 0, 'max': 10000, 'step': 1, 'tooltip': 'Start step for the sampling, 0 means full sampling, otherwise samples only from this step'}]\\n  end_step : ['INT', {'default': -1, 'min': -1, 'max': 10000, 'step': 1, 'tooltip': 'End step for the sampling, -1 means full sampling, otherwise samples only until this step'}]\\n  add_noise_to_samples : ['BOOLEAN', {'default': False, 'tooltip': 'Add noise to the samples before sampling, needed for video2video sampling when starting from clean video'}]\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_EqAhm7xBZj0TyjH5CGUvG783", "timestamp": "2026-05-23T16:19:57.145500"}
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{"role": "tool", "name": "terminal", "content": "{\"output\": \"## SaveVideo Save Video\\nrequired ['video', 'filename_prefix', 'format', 'codec']\\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 node\\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## CreateVideo Create Video\\nrequired ['images', 'fps']\\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 ['audio']\\n  audio ['AUDIO', {'tooltip': 'The audio to add to the video.'}]\\n\\n## WanVideoCombineEmbeds WanVideo Combine Embeds\\nrequired ['embeds_1', 'embeds_2']\\n  embeds_1 ['WANVIDIMAGE_EMBEDS']\\n  embeds_2 ['WANVIDIMAGE_EMBEDS']\\noptional []\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_HGxbZsya3OuvxguEiuazg5cs", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Building workflow for video generation**\n\nI'm considering building the workflow in JSON for testing, but I'm worried about the time and GPU usage. Maybe I could generate a small output, like a tiny 256x144 video with 13 frames? That might take a few minutes, but it could be valuable. I need to know the exact API workflow format and the nodes involved, which include model loader, t5 loader, encoding, and creating the final video. I'll also need to define the output types!", "finish_reason": "tool_calls", "reasoning_content": "**Building workflow for video generation**\n\nI'm considering building the workflow in JSON for testing, but I'm worried about the time and GPU usage. Maybe I could generate a small output, like a tiny 256x144 video with 13 frames? That might take a few minutes, but it could be valuable. I need to know the exact API workflow format and the nodes involved, which include model loader, t5 loader, encoding, and creating the final video. I'll also need to define the output types!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbca-LRKOXjz5etvoWvwsy0tpsP5bRQJbzJCwg2ETS1YGXsr7n5j_uEOSjhUd6-ADvwQmgo4xAxnDGrrPd4JkqMD1Dvphw94PXbzm3omcB0JGyDmUPygM_cO6dg_nT_lDYgnwrNAAhUlSHO6SOD-KTagT_OLylfQTn-yz0hUCcTve73B1NJoy_7PKJhEvFjFHEYe6dqqifu9qSl-vkw3x148F8kGYFTCfiUlT44kVhO5I-xBvT4cMi2zGjlLMFgwOqUhyI2ef6WEPefDsKvPKn_UKk0mG6eXxfdL5X4i4xGbPWqEEkEshTiH9abXSjQfBA_AzBllRK3iy6uhdwxSCE0tl1T9GqintcqB4mUylzsYX-Lp5Tnt7QnMJe8uQTkjdIe2fKGmaDf9goxq9zVkr75CB32WzFbYges_B3NnbZ3JWH__Eqvw_fbGx4q0xfI4kDshNRUfcrjBhfnmLjAODlNchl7DfaPL3WajcoZgMY-cLwa7Xzhkpv-FUQ5FNxvJ-h6wBOT0xF548eiDPWTrtNb75Jzhwn9LzeH0ZM2NpBYTVydvqqfamDagSZ-4B7l6WfGnTd1HeqXA4FpTzqic4rNREkzSbpxQECrhitlVsSG-GspaFgRE5kr16BhWUhy9Y81TvBf-nKOVC9hbXJh9MsyOubu7MyMektk0Tj6U6VoZezmyXqeD6E5wKejgCWsj9d3dFNJRTgewV6jHs7YpREZaG41PJ61X1UwPTT1GihXqr4XiFszUEZq51lppgdLqNZyyX5VtGvw5l3h8gei_-WK3AoZF_kBjLEZfsg3aru4u3g51cUYZyJR3wa3tJRDFYqJiOtNGQ212sC36S_Cx3qd42sHKz7ihq5KchcYsir8BPE5EWu3zU7tBlKM1TmWqjGIRKT6RsNBPs6c7yPl9r0OW8o6mvljVFTvmtI7DZEkJvMU0HIlcjyfaywjL2Z5z2f0iiipbkbYgrMHZD7-nEjYixfrfcXjJIBX92xEkzScsSn6BTCkDZfA9rbEToevmTrJ3GVZ_NhXJ1RmbJGbaiEJsiKWF0KjBxz_hsOK72v-5XqOxQiPUJtw2H0-4t34Aw8jadZxY_IL7c9fK0FqVyfb8hWLWQ6xPrf3AANkBm48BzPAnwNmxLl8-QR27V8xgsoRlrT7mT15Bae6Upaq5QWKB4JqTK5_kSLBVA1ZKEtn_Mb_WM5qWBDrBBVLKLFLJohUCyKjMbOCaji3GOhlSCSHQZozE2bFn8ssR2_2N-wMOUqaFfIV8Bf2Ux3ackymZLrkZBwnTMoIiV4-A48At7eN2bG6iTq0MoeleSuVOArFVHv0j5rkLsAmq47Etfib_QezIoSQh-luxfRHfI-SA4Tiyln4mbroL1X5KKhfzQEeXkwqQ7l8zdjtyh_tLvOSLlZAqh0v5CbOCmp3SM8D_KU7AuJCe4HxolimAwmH9iz67cCSlaDTVuwZroShUoOdka-hEiJxO82yLgEZ6Y-ZKvldhYJjVJP43RDSCJy1RjshCd5M=", "id": "rs_0d1a4d10654d56b4016a11b716bc288191bbfa029a83882e3b", "summary": [{"type": "summary_text", "text": "**Building workflow for video generation**\n\nI'm considering building the workflow in JSON for testing, but I'm worried about the time and GPU usage. Maybe I could generate a small output, like a tiny 256x144 video with 13 frames? That might take a few minutes, but it could be valuable. I need to know the exact API workflow format and the nodes involved, which include model loader, t5 loader, encoding, and creating the final video. I'll also need to define the output types!"}]}], "tool_calls": [{"id": "call_5uqdnliMaPSe8hXBiSukXtJi", "call_id": "call_5uqdnliMaPSe8hXBiSukXtJi", "response_item_id": "fc_0d1a4d10654d56b4016a11b71a1f988191bfdbdb293fdb5d10", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\ndata=json.load(urllib.request.urlopen(base+'/object_info', timeout=20))\\nfor name in ['WanVideoModelLoader','LoadWanVideoT5TextEncoder','WanVideoTextEncode','WanVideoEmptyEmbeds','WanVideoSampler','WanVideoVAELoader','WanVideoDecode','CreateVideo','SaveVideo']:\\n    o=data[name]\\n    print('\\\\n##',name)\\n    print('outputs', o.get('output'))\\n    print('output_name', o.get('output_name'))\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"## WanVideoModelLoader\\noutputs ['WANVIDEOMODEL']\\noutput_name ['model']\\n\\n## LoadWanVideoT5TextEncoder\\noutputs ['WANTEXTENCODER']\\noutput_name ['wan_t5_model']\\n\\n## WanVideoTextEncode\\noutputs ['WANVIDEOTEXTEMBEDS']\\noutput_name ['text_embeds']\\n\\n## WanVideoEmptyEmbeds\\noutputs ['WANVIDIMAGE_EMBEDS']\\noutput_name ['image_embeds']\\n\\n## WanVideoSampler\\noutputs ['LATENT', 'LATENT']\\noutput_name ['samples', 'denoised_samples']\\n\\n## WanVideoVAELoader\\noutputs ['WANVAE']\\noutput_name ['vae']\\n\\n## WanVideoDecode\\noutputs ['IMAGE']\\noutput_name ['images']\\n\\n## CreateVideo\\noutputs ['VIDEO']\\noutput_name ['VIDEO']\\n\\n## SaveVideo\\noutputs []\\noutput_name []\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_5uqdnliMaPSe8hXBiSukXtJi", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Planning workflow implementation**\n\nI'm thinking about creating a tiny workflow and queue. I might need to output using node ID strings and possibly do a POST request for JSON. I’ll consider monitoring completion and maybe checking query history. For dimensions, I’m considering 128x224 and maybe needing to keep certain settings like minimums. If this fails, I’ll prepare to fetch an error. It looks like I’ll also want to run a small smoke test to validate the entire workflow. Let’s ensure all aspects are checked!\n**Considering loading time**\n\nI'm thinking that compiling or loading the model could still take several minutes. It might be a good idea to use a background wait for this process. I’ll consider using a POST request right away to keep things moving. While waiting, I can poll the history to check on progress. That way, I can stay updated without holding things up unnecessarily. Let’s ensure everything runs smoothly and efficiently!", "finish_reason": "tool_calls", "reasoning_content": "**Planning workflow implementation**\n\nI'm thinking about creating a tiny workflow and queue. I might need to output using node ID strings and possibly do a POST request for JSON. I’ll consider monitoring completion and maybe checking query history. For dimensions, I’m considering 128x224 and maybe needing to keep certain settings like minimums. If this fails, I’ll prepare to fetch an error. It looks like I’ll also want to run a small smoke test to validate the entire workflow. Let’s ensure all aspects are checked!\n**Considering loading time**\n\nI'm thinking that compiling or loading the model could still take several minutes. It might be a good idea to use a background wait for this process. I’ll consider using a POST request right away to keep things moving. While waiting, I can poll the history to check on progress. That way, I can stay updated without holding things up unnecessarily. Let’s ensure everything runs smoothly and efficiently!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbcnP5NlRo0RqOvNU476Lp1tmUV-EVQmOPqZaFJB9MGjWqzjZoLCrPB1hkWY9_Vj3DptJE0lSHaIzR2tk1VXlQP3m1g9-H8Al9_EZ2RWJsC5vaXqloWU_M2XcmT6BSAm9SloENeiUOZUnyg4qYCvsOyJZ2rfRz3_eoF2910AQgARLOpVabz5I5n69ZXbqzBpDfzoGAlJZQalvHBUdyavTi7reM94XUd2WYRdfyUOvMh-3L5PrkaRt-2Tqho71HCgkVBK-be8dY4ZDIGeagLd2NlzBcvGwP6uyPiKTLcw_lJB8q5KRXHyB4YI0jeJwyyxXJgrX6tG5O9HDiXHnmLOzQ8pfsWoHYH0WOcAUi2uptkuWQOFY7bnuUiNP0tbm0u2Y2ghT06em99Jw7C9DD59IoBwV6ClkHcuWg-4SUa4dMa7GMErElAEMeNbeL0jwhSq8eQNUTNNVxhdv93pU_SUid1ETw5kNVuBclKc32xRq6SjX8ILyqWhMqlSGk1uwxlv6htbMNOcKhCtEDAnMw_mygeqiduerbirpMuq4uk5UcdP1EPFGQVfjhXZOrhnYwGs0hrbwi86mjru_jXLt7oBKdGqLSS4pH9rZogaEUczAW3ireIZVZGS4dZKBFJSJtwInZkMO86WcZKz___RAVWw3jaST-G7clrn1Sg4_nY9nStpS9Yx_Bw5789tUxcQCwN5gyLLeSdewWalTcNQzRLpbevEhNy37LKtGcXGWGgc47QKZQr9sDvEbOh__cRt46mJppIszbHQeoWb1hE4ed0lRsOBT_xNMeBlXrKasN6rX95wXLKyaCpvjkkaZt1bvS40WCAgre_dD-Eo-m56Z4jLHG0L4gzcfhPtW9R2SQJ-wyeq4l_5ck063x9_ohLCSvxlHfKS3SjLkWLk6_jQNZKNRjKI0_JRyMzdxQ_35Kizi9vrzAz-WSo7i6819rSmaYhMArZ1R1foCBg0hShf4IV0cKvywkzBG3yQ7dEqm5DgBDJOboAgtL8Qts9f4Ow23L4BdAXMHxuXUElOC6NqZYVdQ4ql536npTFpbiVUAAIzvIBlmoboeWIOHjHyFlti01r5GmDq5a3pfc0lg71UbpBrQkqOEW_t2fzuTS7qe5iMyBxopDY8wmfqoLUtTm2QN9ZbpGGr-QyH82Ju0fFAgjJV6PRt-gklGluKT3_cLG667YMpeshNVMmreQ6ZWxxRc8wL6eGzJrq1OM98JQCVTDdLnk9voKjEo9n51hCwK5u8B19UIVjVihlGXUFLrji9gHL_qiN90QoM1qZ7OAlm_-Ux9k5uzS47-7IPjp-wRJQZcLgqC6xDz11DsNerO_NgiKcUGa2o-vuqdK_06ZYaAhyATcFmB0f__iDisZvET63iI61RAni3whXMENheFr_itlMCJjGs-LgGzw0xHHpweIZH-0xgZGPSHUYnuI8wIOMeXizXxhGabqnHPM-c9CGIfX-c0lJX1vFVZVvNmA8ZO7cyaPIjtWdC6HVbymPBUPRngHsecdhqRGvyikTJxBZ-YQx54n92ZhxYNR7U7EYfCrV2dmGQlwYF339jFpk6xbIYyUZErXk3_Q47cE7mDMB4tFGtiRW-lmEEjRIU30JAWQjQO3LB3b5ZOIgH_kaAnEIwXZakGctU8PBySpxu7Iwxu3fmGT30kxWL_3-PBZFeigitHaWErMs44vn1z1SJ9Cx6D-N6FqL0lCD22OD4dqwP4-J1NG7KhNgNPZ4BjR1hCbnTW4KQS1Kvkv8BL5rEi8QflKRE7-EbdCfQkQVn39LV7qC7yEQ9cN3yjc4B0el5IM5Tt0soocOwCAEcQeT-fUPzoFBkjYZe3HgRxHScRwhdA04M0i-HMe5TUlpwBChcuLwCGgglJAuzCqHJzCv4tM8WA2yemdR9-V1RycHxJQ243kOjiLM5QfbcNL610BVaDyg48LGO7Pnpdi0ZxtcpoKU7aQqTj72YKE0VWaov_dxep_AdhjbkVlnZVURYkU7fxAjdT6KVSG_3ROba_89eKdcwb7Ffoay6lkeir532avsrBr8NwjGIJuHNKuq7KNjklzQvtr-5xAwp2NTTR5rIlfRej9BRxLwM9HlgObyTT9R0Nx_1DPSriBMN4L1UZEJ9ct4TTD6_vSMwQR2M_RzxcfOEmu4SOc2_BWHF9y9nNAWp-5K6OhEN", "id": "rs_0d1a4d10654d56b4016a11b71f3ae4819198ab719771f01587", "summary": [{"type": "summary_text", "text": "**Planning workflow implementation**\n\nI'm thinking about creating a tiny workflow and queue. I might need to output using node ID strings and possibly do a POST request for JSON. I’ll consider monitoring completion and maybe checking query history. For dimensions, I’m considering 128x224 and maybe needing to keep certain settings like minimums. If this fails, I’ll prepare to fetch an error. It looks like I’ll also want to run a small smoke test to validate the entire workflow. Let’s ensure all aspects are checked!"}, {"type": "summary_text", "text": "**Considering loading time**\n\nI'm thinking that compiling or loading the model could still take several minutes. It might be a good idea to use a background wait for this process. I’ll consider using a POST request right away to keep things moving. While waiting, I can poll the history to check on progress. That way, I can stay updated without holding things up unnecessarily. Let’s ensure everything runs smoothly and efficiently!"}]}], "tool_calls": [{"id": "call_GqPcgGUaSGngG60zwelaB9rR", "call_id": "call_GqPcgGUaSGngG60zwelaB9rR", "response_item_id": "fc_0d1a4d10654d56b4016a11b727de94819184f342428d61a9b8", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_wan_smoke_workflow.json\",\"content\":\"{\\n  \\\"1\\\": {\\n    \\\"class_type\\\": \\\"WanVideoModelLoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"model\\\": \\\"Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\\\",\\n      \\\"base_precision\\\": \\\"bf16\\\",\\n      \\\"quantization\\\": \\\"disabled\\\",\\n      \\\"load_device\\\": \\\"offload_device\\\"\\n    }\\n  },\\n  \\\"2\\\": {\\n    \\\"class_type\\\": \\\"LoadWanVideoT5TextEncoder\\\",\\n    \\\"inputs\\\": {\\n      \\\"model_name\\\": \\\"umt5-xxl-enc-fp8_e4m3fn.safetensors\\\",\\n      \\\"precision\\\": \\\"bf16\\\",\\n      \\\"load_device\\\": \\\"offload_device\\\",\\n      \\\"quantization\\\": \\\"disabled\\\"\\n    }\\n  },\\n  \\\"3\\\": {\\n    \\\"class_type\\\": \\\"WanVideoTextEncode\\\",\\n    \\\"inputs\\\": {\\n      \\\"positive_prompt\\\": \\\"Vertical cinematic test clip of a used car in a dim garage, realistic lighting, slow camera push, no text, no logos, no watermark.\\\",\\n      \\\"negative_prompt\\\": \\\"readable text, subtitles, logos, watermark, distorted hands, deformed car, static image\\\",\\n      \\\"t5\\\": [\\\"2\\\", 0],\\n      \\\"force_offload\\\": true,\\n      \\\"model_to_offload\\\": [\\\"1\\\", 0],\\n      \\\"use_disk_cache\\\": false,\\n      \\\"device\\\": \\\"gpu\\\"\\n    }\\n  },\\n  \\\"4\\\": {\\n    \\\"class_type\\\": \\\"WanVideoEmptyEmbeds\\\",\\n    \\\"inputs\\\": {\\n      \\\"width\\\": 144,\\n      \\\"height\\\": 256,\\n      \\\"num_frames\\\": 13\\n    }\\n  },\\n  \\\"5\\\": {\\n    \\\"class_type\\\": \\\"WanVideoSampler\\\",\\n    \\\"inputs\\\": {\\n      \\\"model\\\": [\\\"1\\\", 0],\\n      \\\"image_embeds\\\": [\\\"4\\\", 0],\\n      \\\"text_embeds\\\": [\\\"3\\\", 0],\\n      \\\"steps\\\": 4,\\n      \\\"cfg\\\": 3.5,\\n      \\\"shift\\\": 5.0,\\n      \\\"seed\\\": 42,\\n      \\\"force_offload\\\": true,\\n      \\\"scheduler\\\": \\\"unipc\\\",\\n      \\\"riflex_freq_index\\\": 0\\n    }\\n  },\\n  \\\"6\\\": {\\n    \\\"class_type\\\": \\\"WanVideoVAELoader\\\",\\n    \\\"inputs\\\": {\\n      \\\"model_name\\\": \\\"Wan2_1_VAE_bf16.safetensors\\\",\\n      \\\"precision\\\": \\\"bf16\\\"\\n    }\\n  },\\n  \\\"7\\\": {\\n    \\\"class_type\\\": \\\"WanVideoDecode\\\",\\n    \\\"inputs\\\": {\\n      \\\"vae\\\": [\\\"6\\\", 0],\\n      \\\"samples\\\": [\\\"5\\\", 0],\\n      \\\"enable_vae_tiling\\\": true,\\n      \\\"tile_x\\\": 128,\\n      \\\"tile_y\\\": 128,\\n      \\\"tile_stride_x\\\": 64,\\n      \\\"tile_stride_y\\\": 64,\\n      \\\"normalization\\\": \\\"default\\\"\\n    }\\n  },\\n  \\\"8\\\": {\\n    \\\"class_type\\\": \\\"CreateVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"images\\\": [\\\"7\\\", 0],\\n      \\\"fps\\\": 8.0\\n    }\\n  },\\n  \\\"9\\\": {\\n    \\\"class_type\\\": \\\"SaveVideo\\\",\\n    \\\"inputs\\\": {\\n      \\\"video\\\": [\\\"8\\\", 0],\\n      \\\"filename_prefix\\\": \\\"autoshorts_wan_smoke\\\",\\n      \\\"format\\\": \\\"mp4\\\",\\n      \\\"codec\\\": \\\"h264\\\"\\n    }\\n  }\\n}\\n\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2169, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_GqPcgGUaSGngG60zwelaB9rR", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_2mguc3dfT4Xp0uf2WNHecQ6D", "call_id": "call_2mguc3dfT4Xp0uf2WNHecQ6D", "response_item_id": "fc_0d1a4d10654d56b4016a11b7378a348191ae1e9914518fc7a0", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request, uuid\\nbase='http://100.101.173.25:8188'\\nworkflow=json.load(open('data/comfyui_wan_smoke_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-autoshorts-smoke'}).encode()\\nreq=urllib.request.Request(base+'/prompt', data=payload, headers={'Content-Type':'application/json'})\\ntry:\\n    with urllib.request.urlopen(req, timeout=30) as r:\\n        print(r.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\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"75ec9893-6631-4aa3-a8bd-c3d9822d1578\\\", \\\"number\\\": 0, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_2mguc3dfT4Xp0uf2WNHecQ6D", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbdBkk-CVEE8ztgM0uCrJCeHVsYeXKrjRhC1hQljbkA4vzzBygIjKlVm6jzUffPeKWmi4cYPSOW1sxd1QBZMB3hv3oBb9rIRYkTlPgVOmGD9mXVZ0K-hQRszvJudN-H72O__Y09YPTNyr9CDPC9ucYyYVq7_xVD65Yk53Yjv4K8Rjs44YWnCNSlC4Ujkt_RJ1xLb_5dDOH6l2nIHtJzWQAaJbPU1PFjqpH2nyIPiiM3oWE29JsN1ZgFzhYtEZ3cITKiFCRCQ3qQ9jhCiMBhb8gIF5zlDwNWNh4xZv5eOewdLqL1pfpoNIk-wCGShNtgfEUx4FD-deAT4093AH6Z5hcy9icf6FZOY5ttFJjGFZxl_Ottsw58trRnptce-O5NGMxYA5jEe_4ghXMpCBgPGBWNGPlpVdCfMGWT-Ng-OVSy4RpVlxbjvb8D6McnIQBN-_8T2BAfnp1tjliIKKeJizr0SNohCOXJtKTmm1tHTUt-_EcqnaHERUNiXgjWRMp9X1CWy2Ur6PgUAUHHMwfBhn-F8BVLBUM6RwnrUvyIOgOgwMTl9ZTKSN61m1vNVjVj6GqMiHhAFQQXXyswqH1UTm99sIqIxd_Jblcj__R9qMIAQwTUARqLIO7zurV4K4H3ufM9BFDV3ADsb7Hvpmorj-erE6PFXFxRgnwi97iegbcOX1hGJpMCv39l2f1FOSgpNAMdWNvd9z8Xt6_DAvZ5wyM6WhD00_iu879uuOZKCB-F_GG0w8oxrXxkE7lhAWRiVvEVmzQJAcj2HNRnAanaRdbrwpIPv7Dlsw3j_8Ah58yA_7STJwx6ZXnbVMXvhC1yTTgnWoT3T42E-9T32nTcXnFIgAG3mMhWTw5fPYjnpxN5MBxiqIi3ZCjxOkgxb2EBoBhIPJDz-cWu70qUcOlBA1quyuXFAXXL_0JiIk4isJLyM4Ik=", "id": "rs_0d1a4d10654d56b4016a11b7404b3c81918a5f86c535718c86", "summary": []}], "tool_calls": [{"id": "call_9L1wewfbzJAzhZzsamPcisHT", "call_id": "call_9L1wewfbzJAzhZzsamPcisHT", "response_item_id": "fc_0d1a4d10654d56b4016a11b741988081919e29f1c1ec0f0bb1", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request\\nbase='http://100.101.173.25:8188'\\npid='75ec9893-6631-4aa3-a8bd-c3d9822d1578'\\nfor i in range(30):\\n    q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n    running=q.get('queue_running', [])\\n    pending=q.get('queue_pending', [])\\n    print('poll', i, 'running', len(running), 'pending', len(pending))\\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(json.dumps(h.get(pid, {}), indent=2)[:5000])\\nPY\",\"timeout\":330,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"poll 0 running 1 pending 0\\npoll 1 running 1 pending 0\\nTraceback (most recent call last):\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 1348, in do_open\\n    h.request(req.get_method(), req.selector, req.data, headers,\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 1323, in request\\n    self._send_request(method, url, body, headers, encode_chunked)\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 1369, in _send_request\\n    self.endheaders(body, encode_chunked=encode_chunked)\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 1318, in endheaders\\n    self._send_output(message_body, encode_chunked=encode_chunked)\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 1078, in _send_output\\n    self.send(msg)\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 1016, in send\\n    self.connect()\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/http/client.py\\\", line 982, in connect\\n    self.sock = self._create_connection(\\n                ^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socket.py\\\", line 863, in create_connection\\n    raise exceptions[0]\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/socket.py\\\", line 848, in create_connection\\n    sock.connect(sa)\\nConnectionRefusedError: [Errno 111] Connection refused\\n\\nDuring handling of the above exception, another exception occurred:\\n\\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 519, in open\\n    response = self._open(req, data)\\n               ^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 536, in _open\\n    result = self._call_chain(self.handle_open, protocol, protocol +\\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 1377, in http_open\\n    return self.do_open(http.client.HTTPConnection, req)\\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \\\"/home/agent/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu/lib/python3.11/urllib/request.py\\\", line 1351, in do_open\\n    raise URLError(err)\\nurllib.error.URLError: <urlopen error [Errno 111] Connection refused>\", \"exit_code\": 1, \"error\": null}", "tool_call_id": "call_9L1wewfbzJAzhZzsamPcisHT", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Checking server issues**\n\nIt seems the server might have crashed due to an out-of-memory (OOM) error. I need to inform the user about this. Before I go too far, I should check if there's a connection by querying logs. Then, I can finalize a plan with commands to restart the server and see what the error message says. But first, I might try using curl to see if that gives me any insight.", "finish_reason": "tool_calls", "reasoning_content": "**Checking server issues**\n\nIt seems the server might have crashed due to an out-of-memory (OOM) error. I need to inform the user about this. Before I go too far, I should check if there's a connection by querying logs. Then, I can finalize a plan with commands to restart the server and see what the error message says. But first, I might try using curl to see if that gives me any insight.", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbdmbhxa7D5cRdxgY5gfywBao2Lz-DxLoqVq78cIyvwDtKHKZ6DX2I7fUWgU5lgUR4NsNeMXTu1nGlU-D-xb5i6VdD30tUye4fhUJIF04L83OnrDOT8cMb5DNctq7bFmxvCraio0H85ThZ2NkM22SgLxUw3d_p6IgqY09L8g15OO5sqE6wrCPt4UuxOCkpJzwo7evos7mKFZZAfFSkWTKUHrwjkYa_OVYk5duxSbHD3noNOvxtweLI1dK3aLvXYWTGbtp78D9_ah0TmV0QF9E6Ext71gy8TTTc_DTO2eeqJyTr9S_dTo-76DslUz-ETuVyRLlHy1qpYy-27NrdH1FwEBqoIAMTF36vcQhwZDyFnfKjzDZma5zPuFmLcb5Na4TK0_3HdzWoT-MWpA5rJBbjXOgfYHdh1ipNoVzJjGRUQp3lY42gHX68JSHuln0K8RCDGegSQpygBsiMhjFy7k4nIas7srMcRG8ZSqwus55YAmpHx-G-weUy7WIzilS5Gzdb95NMeP5kc9O8uxg5dLcym1huzkHQ9R5V14b1XhI9Tyx1lubaZXjjsu2dcJ3RuJPUUF7M-KO7C5D_s68Qqb4o6CeYrZ9mEJGyEQtIGicLghf4W5-hwik4NBxuGSnESrBG_c0mJs0OhZb7a2fO7VGxzkOTXSKu9U1C6wrEBrB6_dHEk4CufM_ThQBpQTI5vclWsU9LewyVzDFsqSyNqlqowhklIk14VQwBTsziA7mEFOmLwEONdvMLIL4oeXYS1qGN6EUpF_FFSmcWORgEJZ5zKjwDFRakIiGNDPfulYmzYfLqpleeBRAyuFphPbGtEcLP3MA53iiIvOKUZcKbD-AWt1B-MRXHjZwUG868xZxgMEdyIxCbTmSpbvfBEFsdgr4aJ8bXCu4hgPW15t8ucaDH4GISmJe6YreLPYcweOy3V-nUYgWMbjTEL1G30m2QTOc7mUakUptlVo2IVz_yq0b8_KuZaj5OOas_m7Fu-nr3Z2zTpd6zUGxdTGrBjS3Fklha8ZntDqdBZswA0uGkthByVhBNB90CIEBwzIbv_rwURC3zht3XnRE4Z5sGPzYoId_B-PPtMH6aPvNyKwTiCbYS8nzuYIj5OVMfA7p7yppnESaaYURK6RyYYOPCUrL8oXEqvLkx7JJdducF4qWVrRQFmS3Q==", "id": "rs_0d1a4d10654d56b4016a11b764a40c8191ae3a94ac85622f0b", "summary": [{"type": "summary_text", "text": "**Checking server issues**\n\nIt seems the server might have crashed due to an out-of-memory (OOM) error. I need to inform the user about this. Before I go too far, I should check if there's a connection by querying logs. Then, I can finalize a plan with commands to restart the server and see what the error message says. But first, I might try using curl to see if that gives me any insight."}]}], "tool_calls": [{"id": "call_7wFTgaIpgf3IDdQPh11pM69j", "call_id": "call_7wFTgaIpgf3IDdQPh11pM69j", "response_item_id": "fc_0d1a4d10654d56b4016a11b766d08c8191a39174bdcf5b7bed", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport urllib.request\\ntry:\\n    print(urllib.request.urlopen('http://100.101.173.25:8188/system_stats', timeout=5).read().decode()[:200])\\nexcept Exception as e:\\n    print(type(e).__name__, e)\\nPY\",\"timeout\":15,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"URLError <urlopen error [Errno 111] Connection refused>\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_7wFTgaIpgf3IDdQPh11pM69j", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "", "reasoning": "**Explaining ComfyUI issues**\n\nI need to clarify why ComfyUI likely exited during the test. It seems my smoke test was too aggressive, especially with a T5 model running on a GPU with only 5GB of RAM, which may have caused it to crash. I should prompt the user to run commands in the terminal to access the logs and restart. The commands include navigating to the ComfyUI directory, activating the environment, and using additional arguments like `--lowvram` and `--reserve-vram`. Using the CPU instead might also help with memory issues.\n**Instructing to free RAM**\n\nI need to instruct to close any processes related to llama.cpp and others to ensure there is 12-16GB of free RAM. The current working directory is /llama.cpp, which could indicate that the llama server is running. I should prompt them to use `free -h` and `ps aux --sort=-%mem | head` to identify memory usage and then stop the llama process. Since the server crashed likely due to insufficient RAM, I need to be clear and concise. Should I update the to-do list as well?", "finish_reason": "tool_calls", "reasoning_content": "**Explaining ComfyUI issues**\n\nI need to clarify why ComfyUI likely exited during the test. It seems my smoke test was too aggressive, especially with a T5 model running on a GPU with only 5GB of RAM, which may have caused it to crash. I should prompt the user to run commands in the terminal to access the logs and restart. The commands include navigating to the ComfyUI directory, activating the environment, and using additional arguments like `--lowvram` and `--reserve-vram`. Using the CPU instead might also help with memory issues.\n**Instructing to free RAM**\n\nI need to instruct to close any processes related to llama.cpp and others to ensure there is 12-16GB of free RAM. The current working directory is /llama.cpp, which could indicate that the llama server is running. I should prompt them to use `free -h` and `ps aux --sort=-%mem | head` to identify memory usage and then stop the llama process. Since the server crashed likely due to insufficient RAM, I need to be clear and concise. Should I update the to-do list as well?", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEbd1Rgp5GGOTGK6ZJgxlNrERLhd-9I1ovGG-z70iiGLS-S1p_Maq_uACW9PF6dkuRqT5B6uirTpl36NX65MOLRjkPWgxyOO49lDFUTvRyXZ4zPIply9OmIsdSygGdf4E5wlj6y4y3V8s7pNid_dwY5WEX7S45RamMqpNQz-rf8-RzfSLpYRfbUqJy2nnFD7_HSKxM3oNUCUB76XduqWB4vMUEH6E39662fpVEdsxdT_Zew9JgHBF6ggDjaMQEN48g3UYXolV-Xtot18yGm3qZ-ggjzpKQJtEInhAqFQg5APxj_uaGtwQdSnYWI0g_RQ5BDh7jXEfTwQygWy0Ntm9bf6IgPPUwVlmXqsMSTuvrnM2bO9RvA9UYEh4qDQOUQtKXIb18UCp41xiEBugiuStZzYkBFFvVxwqec9ikEMC84A--xnsK7sTqEJd63rsIeTt00I3Gcq5aUB3y77EjOJkmqCiB_yi5CcpyXID-K8QNUbwXI9GbZyI2PNY3RmKC6sN9J1aTKxGrq65V9S96WosAPBzzC0UoUsWcPVVjgoxdsaaA6cay1WWj3-En8EQmF9SdRihFUxyfxG-H5iN5vodXavTduRwcdBJ9fnCHAUXncPX4g-2_V1T6HNchAVijBpnCkYdB0uBQy7R5f390dFqf8MShOaDGRqs-edS5VpYVXVXyFd7oVE6hVlHarOIbQkj7RewVPsYAyJ5He3ye4BPWYQnpJ9I2lWjD8zLkf-6y0YYSz2BtprJJDAApQlZGBQ3XWFNTA3u3uVd0bpq_ceLv_iQvoeTqwV92NY1jSR5kB6FhpnQ9oQ-XwIqWWzmU25NBo_4OXgTfpZXYDZlUahu2sSXRwBCN3eHzw_u-HuK9NUxbG_8_jVDnSn-jMC4HBIX3HFkQ7NdRL32xKEyu_CZXSd1qKX6UvpnuEQzH6yszuBfSQj-l-z3VmeD-_hWpebv_XITS1HsVHjE9SVfj7XFyZJrQalhn_3ZF5wMG0jPj8UdO5emxsO7aJt8LAyyenCWVN5G6934R4nXVkjxtPf99OVJVw20bBFADyxr4urQylNWcj4VJPB8LSpPOYO0BxmpswB9yufV-GxQBN6dF8vo4rs7vOKKFA2-Xl1xNzubZxETZC1P3uO67V8eELSWlLLhlHuItL2cmDDkEurgnkbc2ZOQzP5WsDPuIEdho3cS6GoSzaJ3AFwkMzg8mnxjbJ9aiGQn_ea50RA9cJecQRleEluS0m410wO2JUm1kNXHmMexa3BeMVtXDnC0N9T0JdhcwmrBANYAvUCgqY5xRbwQXQhWQzDK0B1wNie2erBZ3mOQMPMesOxW9f0l4KVrlVtGrJjJM4AIbKzUIaOTlAjrDWJBYrBxmV2ZkloQfOjDE7Xm8KGlYh9qATftlZ6rQDD08mjbRIFr_rIml6Pwi84Z2r4Xq_kcSJCla04TYXZiDRqnfF_MXef4lCKF1YlrSm-T1D_WUFFeSmX0of80qq6HKMOIa52yAMEdlq-WN7htognnHJ8MHT37r5wNGPiGGOTxWxFT8g-SsUFcZ2M0iSVDtWXUl2DEahpCjriWfHeUGbznlCxEjuZF7FLAKI7RI45yK_etKEx5nGFPadSz5bPDRhY5r9cWW_t-vBu8zpGylQpdPnttSfnWK4vBqn4QBbs6Q8ELiXnIUu1hkz3iBolipwjN3h-Y-_WqAMIYvKRf_NGrnj-nWQPYmiugcK2L_KPheNoCu2VJqx4IRtMkYJN6OK5Vv5RPvsufs9c7OBGnN7GyR1hI6Edt1rZWRH5F78etAqCow9o8EvDEPl9fFoFfrhISiP_R1dNfGCEyOBLxTscCQRSXrR5Dh0IJaSO3DFXCz9yLTD6VkCThjfUWUNABE01_nZLJp5FolORWvCI82fBF4190_NgpjoEzy1sldpb2QsJw9EPCa1elV2YyI9g72BDWsdllmUN3k56n23xaTN8Np73kLiWYHh_Pz-wd-bniiyvp0P77-UIDk9Tk32Hzkb79sBGmYfV80n0GRcdn5LEMBLuLHZCz8v38wnpbMMDpAFT4hOoHmUsSWYa6cJuizE0vDSL3FzB8Tx6o2NAnhdLpPhHi3R_xEL3wLmZz7S5SnPaDXWhtCDASsg6VviE2qr2dfBflJ_wh6nV2iiSJVSEACCovqhMALe_i6DS-i5KWORp1T26CrtqKyBbmSk-3U9fIqnJdV0LiGb4Du6ts7rgWh08DZKVYNZHGPpjwG1fC7MftkrNJvQodIC5EVZ180PCOtR-BGMAZDj0NZurgS3G_19hI2iJlhT6jMHftldnDKC6QTPM4OvQAch6xnqda3hHeGd_xc02NtX4ot-uOIKdfFxn-_NAFuxdUkT_V6nnT8RWRKW6h5M_HQYnLQwdBRPwTyt1pzH1xoOZwFcMONlmXx3lPjhldARPycW_ndGIWZuW0sVVTNON8NiHTkpaNnHnVaO-HSS-Uf0LAHsun-HalRXknXTQyCRbNzNAbFDBl9vDqjXrMc53og8w2_M4qp2A3GIS-M48JuSrhMxmL7ycUW3Q1974J2nkJK5ytQ6iwrJVL82igVUYmEEnA4QHWJffH0w==", "id": "rs_0d1a4d10654d56b4016a11b76a8a9c8191ab86b0411c06b590", "summary": [{"type": "summary_text", "text": "**Explaining ComfyUI issues**\n\nI need to clarify why ComfyUI likely exited during the test. It seems my smoke test was too aggressive, especially with a T5 model running on a GPU with only 5GB of RAM, which may have caused it to crash. I should prompt the user to run commands in the terminal to access the logs and restart. The commands include navigating to the ComfyUI directory, activating the environment, and using additional arguments like `--lowvram` and `--reserve-vram`. Using the CPU instead might also help with memory issues."}, {"type": "summary_text", "text": "**Instructing to free RAM**\n\nI need to instruct to close any processes related to llama.cpp and others to ensure there is 12-16GB of free RAM. The current working directory is /llama.cpp, which could indicate that the llama server is running. I should prompt them to use `free -h` and `ps aux --sort=-%mem | head` to identify memory usage and then stop the llama process. Since the server crashed likely due to insufficient RAM, I need to be clear and concise. Should I update the to-do list as well?"}]}], "tool_calls": [{"id": "call_WBkQURDE24srUcyynq6I9kNy", "call_id": "call_WBkQURDE24srUcyynq6I9kNy", "response_item_id": "fc_0d1a4d10654d56b4016a11b77596408191b385a8ea6c78bb07", "type": "function", "function": {"name": "todo", "arguments": "{\"merge\":true,\"todos\":[{\"content\":\"GPU/VRAM und CUDA-Zugriff auf dem Host prüfen\",\"id\":\"gpu-check\",\"status\":\"completed\"},{\"content\":\"Lokale Open-Source-Video-Optionen für 16GB VRAM bewerten\",\"id\":\"options\",\"status\":\"completed\"},{\"content\":\"Empfohlenen nächsten Implementierungsschritt für AutoShortsBot festlegen\",\"id\":\"plan\",\"status\":\"completed\"}]}"}}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "tool", "name": "todo", "content": "{\"todos\": [{\"id\": \"gpu-check\", \"content\": \"GPU/VRAM und CUDA-Zugriff auf dem Host prüfen\", \"status\": \"completed\"}, {\"id\": \"options\", \"content\": \"Lokale Open-Source-Video-Optionen für 16GB VRAM bewerten\", \"status\": \"completed\"}, {\"id\": \"plan\", \"content\": \"Empfohlenen nächsten Implementierungsschritt für AutoShortsBot festlegen\", \"status\": \"completed\"}], \"summary\": {\"total\": 3, \"pending\": 0, \"in_progress\": 0, \"completed\": 3, \"cancelled\": 0}}", "tool_call_id": "call_WBkQURDE24srUcyynq6I9kNy", "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "assistant", "content": "Sehr gut, Sir — **ComfyUI war von hier aus erreichbar**, inklusive GPU.\n\nIch habe erfolgreich geprüft:\n\n```text\nhttp://100.101.173.25:8188/system_stats\n```\n\nErgebnis:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: 16.49 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\nCUDA: aktiv\n```\n\nAußerdem habe ich `/object_info` abgefragt:\n\n```text\nWan Nodes gefunden: 151\nWanVideoModelLoader: vorhanden\nWanVideoTextEncode: vorhanden\nWanVideoSampler: vorhanden\nWanVideoDecode: vorhanden\nWanVideoVAELoader: vorhanden\nLoadWanVideoT5TextEncoder: vorhanden\nSaveVideo/CreateVideo: vorhanden\n```\n\nHeißt: **ComfyUI + WanVideoWrapper + Modelle sind grundsätzlich korrekt installiert.**\n\n---\n\n## Aber: mein erster Smoke-Test hat ComfyUI beendet\n\nIch habe danach einen sehr kleinen Wan-Smoke-Test über die API queued:\n\n```text\n144x256\n13 Frames\n4 Steps\nWan2.1 T2V 1.3B FP8\n```\n\nDer Job wurde angenommen:\n\n```text\nprompt_id: 75ec9893-6631-4aa3-a8bd-c3d9822d1578\nnode_errors: {}\n```\n\nNach ca. 20 Sekunden war der Server aber nicht mehr erreichbar:\n\n```text\nConnection refused\n```\n\nDas bedeutet ziemlich sicher:\n\n```text\nComfyUI-Prozess ist gecrasht oder wurde vom OS gekillt.\n```\n\nWahrscheinlichster Grund: **RAM**, nicht VRAM.\n\nDein System meldete:\n\n```text\nRAM total: 33.3 GB\nRAM free: 5.2 GB\nVRAM free: 16.5 GB\n```\n\nDer UMT5 Text Encoder und Wan-Wrapper brauchen beim ersten Laden gern ordentlich System-RAM. Wenn nebenher `llama.cpp` oder ein anderes Modell läuft, wird es eng. Die GPU ist bereit; der Arbeitsspeicher spielt gerade Diva. Natürlich.\n\n---\n\n# Jetzt bitte auf dem GPU-Host ausführen\n\n## 1. Prüfen, ob ComfyUI noch läuft\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn `Connection refused`: Prozess ist weg.\n\n---\n\n## 2. RAM prüfen\n\n```bash\nfree -h\n```\n\nDann die größten RAM-Verbraucher:\n\n```bash\nps aux --sort=-%mem | head -20\n```\n\nWenn dort `llama.cpp`, `ollama`, `vllm`, Browser, IDE oder ein anderer Modellserver viel RAM zieht: bitte temporär stoppen.\n\nFalls du llama.cpp gerade laufen hast:\n\n```bash\npkill -f llama\n```\n\nFalls Ollama läuft:\n\n```bash\nollama stop --all 2>/dev/null || true\nsudo systemctl stop ollama 2>/dev/null || true\n```\n\nDanach nochmal:\n\n```bash\nfree -h\n```\n\nFür den nächsten Test würde ich gern mindestens sehen:\n\n```text\navailable RAM: 12–16 GB+\n```\n\n---\n\n## 3. ComfyUI neu starten — diesmal lowvram\n\nBitte im Host-Terminal:\n\n```bash\ncd ~/ai/ComfyUI\nsource .venv/bin/activate\n\nTS_IP=\"$(tailscale ip -4 | head -n1)\"\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188 \\\n  --lowvram\n```\n\nFalls `--lowvram` unbekannt wäre, dann ohne:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188\n```\n\n---\n\n## 4. Nach Start prüfen\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: wieder bereit.\n\n---\n\n# Wichtig: Logs vom Crash\n\nFalls du das Terminal noch offen hast, in dem ComfyUI lief: bitte die letzten 30–80 Zeilen posten.\n\nOder falls du es als Service gestartet hast:\n\n```bash\njournalctl -u comfyui -n 120 --no-pager\n```\n\nWenn manuell gestartet, einfach Terminal-Ausgabe kopieren.\n\nIch suche konkret nach:\n\n```text\nCUDA out of memory\nKilled\nRuntimeError\nImportError\nMemoryError\n```\n\n---\n\n# Meine Anpassung für den nächsten Test\n\nIch werde den nächsten Smoke-Test konservativer fahren:\n\n```text\nText Encoder eher CPU/offload\nkleinstmögliche Framezahl\nlow VRAM settings\nggf. WanVideo TextEncode device=cpu\n```\n\nDer erste Test hat immerhin gezeigt:\n\n```text\nAPI erreichbar\nNodes vorhanden\nModelle werden von ComfyUI erkannt\nJob validiert ohne node_errors\n```\n\nDas ist gut. Jetzt müssen wir nur noch verhindern, dass der Prozess beim Laden vom System erschlagen wird. Ein sehr würdeloser Tod für ein Videomodell, aber immerhin diagnostizierbar.", "reasoning": null, "finish_reason": "stop", "codex_message_items": [{"type": "message", "role": "assistant", "status": "completed", "content": [{"type": "output_text", "text": "Sehr gut, Sir — **ComfyUI war von hier aus erreichbar**, inklusive GPU.\n\nIch habe erfolgreich geprüft:\n\n```text\nhttp://100.101.173.25:8188/system_stats\n```\n\nErgebnis:\n\n```text\nGPU: NVIDIA RTX A5000 Laptop GPU\nVRAM total: 16.76 GB\nVRAM free: 16.49 GB\nComfyUI: 0.22.0\nPyTorch: 2.12.0+cu126\nCUDA: aktiv\n```\n\nAußerdem habe ich `/object_info` abgefragt:\n\n```text\nWan Nodes gefunden: 151\nWanVideoModelLoader: vorhanden\nWanVideoTextEncode: vorhanden\nWanVideoSampler: vorhanden\nWanVideoDecode: vorhanden\nWanVideoVAELoader: vorhanden\nLoadWanVideoT5TextEncoder: vorhanden\nSaveVideo/CreateVideo: vorhanden\n```\n\nHeißt: **ComfyUI + WanVideoWrapper + Modelle sind grundsätzlich korrekt installiert.**\n\n---\n\n## Aber: mein erster Smoke-Test hat ComfyUI beendet\n\nIch habe danach einen sehr kleinen Wan-Smoke-Test über die API queued:\n\n```text\n144x256\n13 Frames\n4 Steps\nWan2.1 T2V 1.3B FP8\n```\n\nDer Job wurde angenommen:\n\n```text\nprompt_id: 75ec9893-6631-4aa3-a8bd-c3d9822d1578\nnode_errors: {}\n```\n\nNach ca. 20 Sekunden war der Server aber nicht mehr erreichbar:\n\n```text\nConnection refused\n```\n\nDas bedeutet ziemlich sicher:\n\n```text\nComfyUI-Prozess ist gecrasht oder wurde vom OS gekillt.\n```\n\nWahrscheinlichster Grund: **RAM**, nicht VRAM.\n\nDein System meldete:\n\n```text\nRAM total: 33.3 GB\nRAM free: 5.2 GB\nVRAM free: 16.5 GB\n```\n\nDer UMT5 Text Encoder und Wan-Wrapper brauchen beim ersten Laden gern ordentlich System-RAM. Wenn nebenher `llama.cpp` oder ein anderes Modell läuft, wird es eng. Die GPU ist bereit; der Arbeitsspeicher spielt gerade Diva. Natürlich.\n\n---\n\n# Jetzt bitte auf dem GPU-Host ausführen\n\n## 1. Prüfen, ob ComfyUI noch läuft\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn `Connection refused`: Prozess ist weg.\n\n---\n\n## 2. RAM prüfen\n\n```bash\nfree -h\n```\n\nDann die größten RAM-Verbraucher:\n\n```bash\nps aux --sort=-%mem | head -20\n```\n\nWenn dort `llama.cpp`, `ollama`, `vllm`, Browser, IDE oder ein anderer Modellserver viel RAM zieht: bitte temporär stoppen.\n\nFalls du llama.cpp gerade laufen hast:\n\n```bash\npkill -f llama\n```\n\nFalls Ollama läuft:\n\n```bash\nollama stop --all 2>/dev/null || true\nsudo systemctl stop ollama 2>/dev/null || true\n```\n\nDanach nochmal:\n\n```bash\nfree -h\n```\n\nFür den nächsten Test würde ich gern mindestens sehen:\n\n```text\navailable RAM: 12–16 GB+\n```\n\n---\n\n## 3. ComfyUI neu starten — diesmal lowvram\n\nBitte im Host-Terminal:\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\nFalls `--lowvram` unbekannt wäre, dann ohne:\n\n```bash\npython main.py \\\n  --listen \"$TS_IP\" \\\n  --port 8188\n```\n\n---\n\n## 4. Nach Start prüfen\n\nIn zweitem Terminal:\n\n```bash\ncurl \"http://$(tailscale ip -4 | head -n1):8188/system_stats\"\n```\n\nWenn JSON kommt: wieder bereit.\n\n---\n\n# Wichtig: Logs vom Crash\n\nFalls du das Terminal noch offen hast, in dem ComfyUI lief: bitte die letzten 30–80 Zeilen posten.\n\nOder falls du es als Service gestartet hast:\n\n```bash\njournalctl -u comfyui -n 120 --no-pager\n```\n\nWenn manuell gestartet, einfach Terminal-Ausgabe kopieren.\n\nIch suche konkret nach:\n\n```text\nCUDA out of memory\nKilled\nRuntimeError\nImportError\nMemoryError\n```\n\n---\n\n# Meine Anpassung für den nächsten Test\n\nIch werde den nächsten Smoke-Test konservativer fahren:\n\n```text\nText Encoder eher CPU/offload\nkleinstmögliche Framezahl\nlow VRAM settings\nggf. WanVideo TextEncode device=cpu\n```\n\nDer erste Test hat immerhin gezeigt:\n\n```text\nAPI erreichbar\nNodes vorhanden\nModelle werden von ComfyUI erkannt\nJob validiert ohne node_errors\n```\n\nDas ist gut. Jetzt müssen wir nur noch verhindern, dass der Prozess beim Laden vom System erschlagen wird. Ein sehr würdeloser Tod für ein Videomodell, aber immerhin diagnostizierbar."}], "id": "msg_0d1a4d10654d56b4016a11b778d7bc819187b3696004e6b6ba", "phase": "final_answer"}], "timestamp": "2026-05-23T16:19:57.145500"}
{"role": "user", "content": "[M G] probier es jetzt nochmals. ich habe Comfy UI gestartet. jetzt noch verfügbar: free -h\n               gesamt       benutzt     frei      gemns.  Puffer/Cache verfügbar\nSpeicher:       31Gi        13Gi        15Gi       1.1Gi       3.7Gi        17Gi\nAuslager:      1.9Gi       1.2Gi       680Mi zudem: gam@PC-1:~$ nvidia-smi\nSat May 23 17:57:50 2026       \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |\n+-----------------------------------------+------------------------+----------------------+\n| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n|                                         |                        |               MIG M. |\n|=========================================+========================+======================|\n|   0  NVIDIA RTX A5000 Laptop GPU    Off |   00000000:01:00.0  On |                  N/A |\n| N/A   67C    P8             21W /   80W |     255MiB /  16384MiB |      5%      Default |\n|                                         |                        |                  N/A |\n+-----------------------------------------+------------------------+----------------------+\n\n+-----------------------------------------------------------------------------------------+\n| Processes:                                                                              |\n|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n|        ID   ID                                                               Usage      |\n|=========================================================================================|\n|    0   N/A  N/A            1617      G   /usr/lib/xorg/Xorg                       69MiB |\n|    0   N/A  N/A          269216      C   python                                  160MiB |\n+---------------", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": "**Planning a workflow improvement**\n\nI need to take action here. Maybe we can try a smaller workflow using the device CPU for text encoding to reduce the risk of crashing. I should ensure the Comfy endpoint is reachable and consider using the terminal for this. Starting a background long poll with a timeout can also help. I have to be careful while submitting with CPU text, specifying width 128, height 224, 9 frames, and 2 steps. Monitoring will be important, so I’ll keep an eye on things to inspect if a crash occurs. Let’s do this!", "finish_reason": "tool_calls", "reasoning_content": "**Planning a workflow improvement**\n\nI need to take action here. Maybe we can try a smaller workflow using the device CPU for text encoding to reduce the risk of crashing. I should ensure the Comfy endpoint is reachable and consider using the terminal for this. Starting a background long poll with a timeout can also help. I have to be careful while submitting with CPU text, specifying width 128, height 224, 9 frames, and 2 steps. Monitoring will be important, so I’ll keep an eye on things to inspect if a crash occurs. Let’s do this!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEc6gHxOh4FafU-t9yb6iW-OxdFpS0b-HprfHd7A2RdFBtbgQJTkMI2atBxKoBG6HN0tRhhlrBewmOaoFra89j_zZFzzL4tv-d-QBqhVBKnH73leU0UfHzz2SjNbfSBUZGfnHX-t6vFphEGd10hUa5c7tu71xzPfDj-6zl5eBaJM9og2N_n90xPbPjmLH_JJYwqJfyaxZdKASvJfpHAH52M3beWRKq3srzWD9fj-NIB2-prKDRwDZ9IhzQe5qyUcZrYkzj6bNjhzyXhGKtDI50IwCisu-bkH8NdBW-AlqM9Pr0LZuxzkDmxiQZX4gZLqGHM7GdZiLlEtS44iP-Y2vJwMf8xis3SpD86ss2dHulTLi1VITfTJvxkqTksm1v7Z2CU-34aiZo-K2YaoTdvc79-iOe8KsphklnkNZkfwpAxmfnbVdPuHpOxgJOvFFotaWMMEq4sCuiwI7XaaAwvijZILzmcYhW6gu9O6WRsUQpqaf4H2TJxVxg8c5z5um-yFkd_CQVCYfplMDde8gajSEeA4CkaJG7l-XHnSBs_yWHy517ySSM9_iUPBfYZpviS-5YblceQHNLwbVL_bTKkZxhWMfVC8KGelxVJxhi8fv0aki6TZ0PE7ZmT9aLfbmwUZNMMZe5MxQvRJJ0J8sJM1jVQH7Vo_X28PKv717JX-osizB3xhpc8SGm313ciLAlIJivamwwGz6jI-u-XWkG4Nvqu-ZDo_DII_NXDZjLbsWa49NCW37SeePvIJpnkm53diss8QwU3OB0oF5k6lbEjOWQAYBKIBeXycWam4XzzYKX42uNlcQHJYZ1JgnjGofz1AeAaE6Z9GDn2Wlk-3qf3I8E6sXyqpGkGCFkenSVnY-5TL0xOkvJ55uHB6uZNNKyZkQUHO3IdyAriYjDDDl0km23v5HL7mqTVVBCvfVi2kt6mDwPVWUMu18SF4Uz94sUzOmb_2dWTkaGE3HlLySR7CQ9_j3Pt2Kzwcn_1DKxo0dJ3rofz02z-Wf80hA8fhm4fNKAq6JkwQzbMd_VY6cCmoRNfT8uQvHV1N_ENGQD4xhTLofmxNp0JV9qiI9KTB5G51lQsEjhZPFqcHFX6En8SDn7lsSbtSOCN0gcX5rDlnjfNO1Su3gpwRy5qzSTFmzKBkPq68OpJC2u9DLYzndh5Nsj0C5ZjfvtQ_wQ4JUCZPmfWbAkqJS1IzAHjBRJyTAw-mQuuIiPcByzxOAUEO6kJLt2C7zOqOc3xkwyXGgbpzCuC9cM5H7wSqjm2aKnIFmPw5iWm_oPJmLhVUNteW5663PBO07sd2v6AtGUtVyFKLXvKc8Na3u-pf9aH7IpWrJVMxnDlYAmi_7pSSQnqsCTxIk19CXeQ==", "id": "rs_0d1a4d10654d56b4016a11ce9ca2a0819190ada7e71036f79a", "summary": [{"type": "summary_text", "text": "**Planning a workflow improvement**\n\nI need to take action here. Maybe we can try a smaller workflow using the device CPU for text encoding to reduce the risk of crashing. I should ensure the Comfy endpoint is reachable and consider using the terminal for this. Starting a background long poll with a timeout can also help. I have to be careful while submitting with CPU text, specifying width 128, height 224, 9 frames, and 2 steps. Monitoring will be important, so I’ll keep an eye on things to inspect if a crash occurs. Let’s do this!"}]}], "tool_calls": [{"id": "call_4cxvVZh15iwT0nhXx89Jo6AT", "call_id": "call_4cxvVZh15iwT0nhXx89Jo6AT", "response_item_id": "fc_0d1a4d10654d56b4016a11cea0a0408191b37754622cc37ea5", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\nprint(urllib.request.urlopen(base+'/system_stats', timeout=10).read().decode()[:1000])\\nPY\",\"timeout\":30,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\\"system\\\": {\\\"os\\\": \\\"linux\\\", \\\"ram_total\\\": 33339789312, \\\"ram_free\\\": 18789781504, \\\"comfyui_version\\\": \\\"0.22.0\\\", \\\"required_frontend_version\\\": \\\"1.43.18\\\", \\\"installed_templates_version\\\": \\\"0.9.82\\\", \\\"required_templates_version\\\": \\\"0.9.82\\\", \\\"comfy_package_versions\\\": [{\\\"name\\\": \\\"comfyui-frontend-package\\\", \\\"installed\\\": \\\"1.43.18\\\", \\\"required\\\": \\\"1.43.18\\\"}, {\\\"name\\\": \\\"comfyui-workflow-templates\\\", \\\"installed\\\": \\\"0.9.82\\\", \\\"required\\\": \\\"0.9.82\\\"}, {\\\"name\\\": \\\"comfyui-embedded-docs\\\", \\\"installed\\\": \\\"0.5.0\\\", \\\"required\\\": \\\"0.5.0\\\"}, {\\\"name\\\": \\\"comfy-kitchen\\\", \\\"installed\\\": \\\"0.2.8\\\", \\\"required\\\": \\\"0.2.8\\\"}, {\\\"name\\\": \\\"comfy-aimdo\\\", \\\"installed\\\": \\\"0.4.3\\\", \\\"required\\\": \\\"0.4.3\\\"}], \\\"python_version\\\": \\\"3.12.3 (main, Mar 23 2026, 19:04:32) [GCC 13.3.0]\\\", \\\"pytorch_version\\\": \\\"2.12.0+cu126\\\", \\\"embedded_python\\\": false, \\\"argv\\\": [\\\"main.py\\\", \\\"--listen\\\", \\\"100.101.173.25\\\", \\\"--port\\\", \\\"8188\\\", \\\"--lowvram\\\"]}, \\\"devices\\\": [{\\\"name\\\": \\\"cuda:0 NVIDIA RTX A5000 Laptop GPU : cudaMallocAsync\\\", \\\"type\\\": \\\"cuda\\\", \\\"index\\\": 0, \\\"vram_total\\\": 16758472704, \\\"vram_free\\\"\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_4cxvVZh15iwT0nhXx89Jo6AT", "timestamp": "2026-05-23T18:05:12.467183"}
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{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 2160, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_pdYTdfmzUtpcfEOq1LQ8Fevc", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_q5tcaYt8e90oEHXF4GSAKnQb", "call_id": "call_q5tcaYt8e90oEHXF4GSAKnQb", "response_item_id": "fc_0d1a4d10654d56b4016a11ceb94aa48191bed8afd42b97a763", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\nworkflow=json.load(open('data/comfyui_wan_micro_smoke_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-autoshorts-micro-smoke'}).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\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"cd6e28dd-8723-4703-801e-3ff7374414f7\\\", \\\"number\\\": 0, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_q5tcaYt8e90oEHXF4GSAKnQb", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": "**Considering monitoring approaches**\n\nI’m thinking about how to monitor properly, but I might also need to think about potential crashes. Should I use a process to help with that? Polling every 10 seconds could be a good strategy. It seems like a manageable frequency to keep an eye on things without overwhelming the system. I want to make sure I have a reliable way to handle whatever comes up. Let's see what works best!", "finish_reason": "tool_calls", "reasoning_content": "**Considering monitoring approaches**\n\nI’m thinking about how to monitor properly, but I might also need to think about potential crashes. Should I use a process to help with that? Polling every 10 seconds could be a good strategy. It seems like a manageable frequency to keep an eye on things without overwhelming the system. I want to make sure I have a reliable way to handle whatever comes up. Let's see what works best!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEc7GMcqKg1FsusfjQASDdAIru9hf1XMRDei689_O7Y9M6pw7TQg-KUTKnXxpe7bVI6D4fnx7rcHxfecscFA4nEQDcNETg55FxSnxLGJGYI_6qxCJTzoIh3liZEz3b3elF3eP8_W4Oh7I-9eNWEz8V4LKdkUnnX-DDAIQJ-wbA1Xr-AmH7MH3eqC3WLdOz_l8N_H3lwZPsaGtaafufYw8T36Q-traFc0ZQeI8IXVe4XNg_xFYNpMmvwistPSIHpkW5LJ4k9fsNMvN15DA-yBb2YhE6eqKGlvROzzKWMiR_k6qPKQaEJDFjJfk4HHhbvhEIX1VXhDk2st4MtpELvzQtDIqsHIkQKFr-CtXds1GNX_FOti8Zomq38Kzk0LtZU9jHrqv7Uw5Eo4N5BAZVTQ86xufSKCPs-LZn5XOxU6TwcU0wrFtE5zoBsHk6LqTUMUzCmPVWOh5aEbsW1SwXoIeJ4kNtoaxbVe0U479KicDJ0bACR2ZeQ1LDNr5zDLg04IKLak4YRgY8dc-PxV5oD4iIY-D0aYsXE7q4uIXYp90dSOr378a0LF0Qa3O9lSyoc9wALO7CbhvGKpd7udV2z0PivHtbLKQqHMqs5QfAn4zkpiS5GvG5fjxew5VY0SB14U9JQ7nM_N-_QPdewmgWMDdx5HSp2ZNqQA9hoTDfkmc15PX74FBn0m9EuMkTjjcBJ8eRG7j8axsggaqYVH4UO9ZYUyLX7Jd49U_ZhTVv3S6oY7YmVlEnCae3JCTIMIC8JXVEE6QpyJf8wiPe1w8QHoa9I15fbevMiBFluAuSJZHZXHreGSeV35tQZlqZo6tEoBcIYisNcRCz9FygQgeEzPxvkqDauRF_Cr5-UuMLn4dU06yuH4sGXP52idGLb55v1wB5MxgnlAuaYBwhVoBuqY4GNOCCzQ_k0bjz1wrii9dgcnIRFaH9eQEl3pWVbLlq6lHUwAKtxF4Bb7dYUDcSPGeQbeVWsO59X9RHXzUOH50qkMhj-w=", "id": "rs_0d1a4d10654d56b4016a11cec4225c8191934c582b32ae7f3f", "summary": [{"type": "summary_text", "text": "**Considering monitoring approaches**\n\nI’m thinking about how to monitor properly, but I might also need to think about potential crashes. Should I use a process to help with that? Polling every 10 seconds could be a good strategy. It seems like a manageable frequency to keep an eye on things without overwhelming the system. I want to make sure I have a reliable way to handle whatever comes up. Let's see what works best!"}]}], "tool_calls": [{"id": "call_W2tSZnYMipbazpG84EtTNryQ", "call_id": "call_W2tSZnYMipbazpG84EtTNryQ", "response_item_id": "fc_0d1a4d10654d56b4016a11cec6a524819190b4072218a176bf", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request, urllib.error\\nbase='http://100.101.173.25:8188'\\npid='cd6e28dd-8723-4703-801e-3ff7374414f7'\\nfor i in range(90):\\n    try:\\n        q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n        running=q.get('queue_running', [])\\n        pending=q.get('queue_pending', [])\\n        print(f'poll {i}: running={len(running)} pending={len(pending)}')\\n        if not running and not pending:\\n            break\\n    except Exception as e:\\n        print('POLL_ERROR', type(e).__name__, e)\\n        raise\\n    time.sleep(5)\\ntry:\\n    h=json.load(urllib.request.urlopen(base+'/history/'+pid, timeout=20))\\n    print('HISTORY')\\n    print(json.dumps(h.get(pid, {}), indent=2)[:8000])\\nexcept Exception as e:\\n    print('HISTORY_ERROR', type(e).__name__, e)\\nPY\",\"timeout\":520,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"poll 0: running=1 pending=0\\npoll 1: running=1 pending=0\\npoll 2: running=1 pending=0\\npoll 3: running=1 pending=0\\npoll 4: running=1 pending=0\\npoll 5: running=1 pending=0\\npoll 6: running=1 pending=0\\npoll 7: running=1 pending=0\\npoll 8: running=1 pending=0\\npoll 9: running=1 pending=0\\npoll 10: running=1 pending=0\\npoll 11: running=1 pending=0\\npoll 12: running=1 pending=0\\npoll 13: running=1 pending=0\\npoll 14: running=1 pending=0\\npoll 15: running=1 pending=0\\npoll 16: running=1 pending=0\\npoll 17: running=1 pending=0\\npoll 18: running=1 pending=0\\npoll 19: running=1 pending=0\\npoll 20: running=1 pending=0\\npoll 21: running=1 pending=0\\npoll 22: running=0 pending=0\\nHISTORY\\n{\\n  \\\"prompt\\\": [\\n    0,\\n    \\\"cd6e28dd-8723-4703-801e-3ff7374414f7\\\",\\n    {\\n      \\\"1\\\": {\\n        \\\"class_type\\\": \\\"WanVideoModelLoader\\\",\\n        \\\"inputs\\\": {\\n          \\\"model\\\": \\\"Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\\\",\\n          \\\"base_precision\\\": \\\"bf16\\\",\\n          \\\"quantization\\\": \\\"disabled\\\",\\n          \\\"load_device\\\": \\\"offload_device\\\"\\n        }\\n      },\\n      \\\"2\\\": {\\n        \\\"class_type\\\": \\\"LoadWanVideoT5TextEncoder\\\",\\n        \\\"inputs\\\": {\\n          \\\"model_name\\\": \\\"umt5-xxl-enc-fp8_e4m3fn.safetensors\\\",\\n          \\\"precision\\\": \\\"bf16\\\",\\n          \\\"load_device\\\": \\\"offload_device\\\",\\n          \\\"quantization\\\": \\\"disabled\\\"\\n        }\\n      },\\n      \\\"3\\\": {\\n        \\\"class_type\\\": \\\"WanVideoTextEncode\\\",\\n        \\\"inputs\\\": {\\n          \\\"positive_prompt\\\": \\\"A very short realistic vertical video of a used car in a dim garage, cinematic lighting, slow camera push, no text, no logo, no watermark.\\\",\\n          \\\"negative_prompt\\\": \\\"text, subtitles, logos, watermark, distorted car, static 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{"role": "tool", "name": "vision_analyze", "content": [{"type": "text", "text": "Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.\n\nQuestion: Beschreibe kurz den Frame. Ist das für einen winzigen Wan-Smoke-Test plausibel? Erkennbar: Auto/Garage oder nur Artefakte?"}, {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,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"}}], "tool_call_id": "call_6n13nx5PzZ5C8xixJtFmEanu", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": "**Planning the next test**\n\nI think I need to get some object info and assess the quality. The user has asked me to try again, and we succeeded, which is great! Maybe we could expand the next test to 240x416 frames with 17 steps and 4 variations? I want to ensure it doesn't crash. Given the stability, I could generate scene_01. I’ll create a workflow for this and submit it in the background, since the response can wait. Let's see how it goes!", "finish_reason": "tool_calls", "reasoning_content": "**Planning the next test**\n\nI think I need to get some object info and assess the quality. The user has asked me to try again, and we succeeded, which is great! Maybe we could expand the next test to 240x416 frames with 17 steps and 4 variations? I want to ensure it doesn't crash. Given the stability, I could generate scene_01. I’ll create a workflow for this and submit it in the background, since the response can wait. Let's see how it goes!", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEc92PWmdivvoTsxPiJHisE597rvMexZJ21TU9FPlCNcMFY8E0QO2Xyfo53zNAZsO8Z9Wj-TMWg264cWcNjgsO6WDf8Pgb5SDDTcwBJx0LqS8J4FWF7mlaZn6fZ3A_jggcVxFz0vpsBhR-HU3bwyXgg8gKtJaf5iHd6poGUnqnnKLAFmnt7mZOQnnmP1m962oOXHjmtcj1m1gYa8xl4mxX0444KTbnqXJNsOVv4xmzTBybJfQyDL3ciUfLP-H1cMHFGj45qHS-yEJgAn9G_t4KnhT1MtKjHfG28AdbQGr9Mx6SE5FICxddNPRWVm78oRG0QGL_9Imlza3Rn74JIRKhvaz_Bjh1QbwueGtOq0RxlkDUBP_U8f9B1W1IkiqSAI8wMvXJMOYzRyC_FtX_Z6HEJOsZvA3i2RaiFDoFqY179r2X6qzu7XCic_86WShT2pe6N7vTRELOwmt5qKRh0B3YvOwZXqisZHCoS9RcYrmE7l9h-ALB-jE5WjQJu2XCZ5yg6FFKJBEPP2C7c7ft1nujdCQvpPSR1oVg5x6LAVheaYt9iuZgn7zXIP4zimbPOe0mxHH--k4UfH0XscAhcjf6Unuyo6knrnAe7XPaYCUR5-oC8Jo3Z2LLTGjFxIlnxgI1SPlcOK6SUSKf_KDI1UWFiSIVuyd75Fza28XBrgYKKaNoENhyZbypPa6CMOsxdWgb8YS5kBy7x6NU6SbrixrBHO3NZIz9kcxvkUTYeW4zFKlDDUp-DZsro3_CxE8lFR6nUKIJ7AiY-WaswcpNOK2Qb9NedzqDv_tzGxjQYgoN9n5W8VB0jqD4w96Wxjwk9cDblvnPgP5xg3BP5GCbYgcJTrh4Gj6PATiQUOE32eP7Rqrqhhx7UPm1jEz7nwgP0lxWixixSR0eWf-xOKqRXxaUwl28_lm9iS8Vt_E1sC_ObrRUN5bKHm04wnEeBpQEj0SNrBPzEMqe8l4T4vtD_X0H08X8oi45drnT9Ju4wlxXPoyZCNDOt29Exy8mW2F0dLMzqGJWevuMysRm9CdMExaNGok_qsSSf_PEuDqBoW0RvLTR2Totds2eZjiPix5nqTQShCa36EWsMTq-J1SwZJA6niLLI3VZiXfVSEqu5W7vwfNfcZKWhLwfGjd85nCMNwTDJk8WRfOuwC-ubkhdMfEIGwCVkLLPi7Rjk3DId29ZsFeCKXuMaC0kF0qI-HxP6zxozrYjNtP2ArP_lQuTBxouqoHmEmD_YFy-da71zQISyqqsSutY4ahxuV5TIe0HVeYNOGOMSMnqXD06k7kxXcX7A6gKfs_WmieXUsP24Y-rlOOmgEnJi9lZcdpyQxaomeu-2q-AEtHNgaBWSIfAcjOxH_H7gErJOHH2IXRYIc0oDsRM5ykz8PdGClG61vWOjAsByA99A6ZKk1AfKJlLNRKLnZMUCSyO84JkJ5DOZmrsDeDHjAC-5q19SsLmSM7Ugpo8kF8t9Glpd6UWnKg-fmQH_A4xBJ90KLkddi4BsGbquPGjVBEQt8B3bhQR6-Zaf7sI9k08NrTFjC-fhzbkjCRY3pQTDdViE9ge6YTw4WLAVZ13jRYcYVUrHfCZ2ST50jB4FUkc1iFjZ6F4o9kRRX2iwAsxckyLKdQDVWeTGkMNyCWSrbPvTdiz_vhek7-BqO7gxbN", "id": "rs_0d1a4d10654d56b4016a11cf729f6081919d82643d7a873ab6", "summary": [{"type": "summary_text", "text": "**Planning the next test**\n\nI think I need to get some object info and assess the quality. The user has asked me to try again, and we succeeded, which is great! Maybe we could expand the next test to 240x416 frames with 17 steps and 4 variations? I want to ensure it doesn't crash. Given the stability, I could generate scene_01. I’ll create a workflow for this and submit it in the background, since the response can wait. Let's see how it goes!"}]}], "tool_calls": [{"id": "call_BLzRx4H44zY33Z8XB7gQmj3n", "call_id": "call_BLzRx4H44zY33Z8XB7gQmj3n", "response_item_id": "fc_0d1a4d10654d56b4016a11cf76e2fc819192fe744d64fe3457", "type": "function", "function": {"name": "write_file", "arguments": "{\"content\":\"{\\n  \\\"1\\\": {\\\"class_type\\\": \\\"WanVideoModelLoader\\\", \\\"inputs\\\": {\\\"model\\\": \\\"Wan2_1-T2V-1_3B_fp8_e4m3fn.safetensors\\\", \\\"base_precision\\\": \\\"bf16\\\", \\\"quantization\\\": \\\"disabled\\\", \\\"load_device\\\": \\\"offload_device\\\"}},\\n  \\\"2\\\": {\\\"class_type\\\": \\\"LoadWanVideoT5TextEncoder\\\", \\\"inputs\\\": {\\\"model_name\\\": \\\"umt5-xxl-enc-fp8_e4m3fn.safetensors\\\", \\\"precision\\\": \\\"bf16\\\", \\\"load_device\\\": \\\"offload_device\\\", \\\"quantization\\\": \\\"disabled\\\"}},\\n  \\\"3\\\": {\\\"class_type\\\": \\\"WanVideoTextEncode\\\", \\\"inputs\\\": {\\\"positive_prompt\\\": \\\"Vertical realistic cinematic video, close-up of a cheap used compact car in a dim garage, the camera slowly pushes in, subtle red warning light reflection, realistic car paint, dramatic shadows, documentary style, no readable text, no subtitles, no logo, no watermark.\\\", \\\"negative_prompt\\\": \\\"readable text, subtitles, logos, watermark, fake UI, distorted car, deformed vehicle, static image, cartoon, anime\\\", \\\"t5\\\": [\\\"2\\\", 0], \\\"force_offload\\\": true, \\\"model_to_offload\\\": [\\\"1\\\", 0], \\\"use_disk_cache\\\": false, \\\"device\\\": \\\"cpu\\\"}},\\n  \\\"4\\\": {\\\"class_type\\\": \\\"WanVideoEmptyEmbeds\\\", \\\"inputs\\\": {\\\"width\\\": 240, \\\"height\\\": 416, \\\"num_frames\\\": 17}},\\n  \\\"5\\\": {\\\"class_type\\\": \\\"WanVideoSampler\\\", \\\"inputs\\\": {\\\"model\\\": [\\\"1\\\", 0], \\\"image_embeds\\\": [\\\"4\\\", 0], \\\"text_embeds\\\": [\\\"3\\\", 0], \\\"steps\\\": 4, \\\"cfg\\\": 3.0, \\\"shift\\\": 5.0, \\\"seed\\\": 24052301, \\\"force_offload\\\": true, \\\"scheduler\\\": \\\"unipc\\\", \\\"riflex_freq_index\\\": 0}},\\n  \\\"6\\\": {\\\"class_type\\\": \\\"WanVideoVAELoader\\\", \\\"inputs\\\": {\\\"model_name\\\": \\\"Wan2_1_VAE_bf16.safetensors\\\", \\\"precision\\\": \\\"bf16\\\"}},\\n  \\\"7\\\": {\\\"class_type\\\": \\\"WanVideoDecode\\\", \\\"inputs\\\": {\\\"vae\\\": [\\\"6\\\", 0], \\\"samples\\\": [\\\"5\\\", 0], \\\"enable_vae_tiling\\\": true, \\\"tile_x\\\": 128, \\\"tile_y\\\": 128, \\\"tile_stride_x\\\": 64, \\\"tile_stride_y\\\": 64, \\\"normalization\\\": \\\"default\\\"}},\\n  \\\"8\\\": {\\\"class_type\\\": \\\"CreateVideo\\\", \\\"inputs\\\": {\\\"images\\\": [\\\"7\\\", 0], \\\"fps\\\": 8.0}},\\n  \\\"9\\\": {\\\"class_type\\\": \\\"SaveVideo\\\", \\\"inputs\\\": {\\\"video\\\": [\\\"8\\\", 0], \\\"filename_prefix\\\": \\\"autoshorts_scene01_wan_probe\\\", \\\"format\\\": \\\"mp4\\\", \\\"codec\\\": \\\"h264\\\"}}\\n}\\n\",\"path\":\"/home/agent/jarvis_runtime/AutoShortsBot/data/comfyui_scene01_probe_workflow.json\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "write_file", "content": "{\"bytes_written\": 1922, \"dirs_created\": true, \"lint\": {\"status\": \"ok\", \"output\": \"\"}}", "tool_call_id": "call_BLzRx4H44zY33Z8XB7gQmj3n", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "tool_calls": [{"id": "call_SV0NIpVIKGaVabDeWVxOd0pP", "call_id": "call_SV0NIpVIKGaVabDeWVxOd0pP", "response_item_id": "fc_0d1a4d10654d56b4016a11cf8873488191a955e58c0b0dd3fb", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, urllib.request\\nbase='http://100.101.173.25:8188'\\nworkflow=json.load(open('data/comfyui_scene01_probe_workflow.json'))\\npayload=json.dumps({'prompt': workflow, 'client_id': 'hermes-autoshorts-scene01-probe'}).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\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "terminal", "content": "{\"output\": \"{\\\"prompt_id\\\": \\\"dd9f7704-2bfc-461b-bbda-66517a55fef6\\\", \\\"number\\\": 1, \\\"node_errors\\\": {}}\", \"exit_code\": 0, \"error\": null}", "tool_call_id": "call_SV0NIpVIKGaVabDeWVxOd0pP", "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "assistant", "content": "", "reasoning": null, "finish_reason": "tool_calls", "codex_reasoning_items": [{"type": "reasoning", "encrypted_content": "gAAAAABqEc-Pgg56JmLN8O3aNmgE6vzYARYafKmyEaT3pg7Sf9hcYAOG3btykhF1mHFiBOSR-Lpo9IOY-pmURseNAj2RGRE7ZMRyBXjSH9_OEXdmZi2gUnvPN9dLBNhMXABGt6ythKnBCPiyCYyP_JXNLwzcXFL9BIeZuZocGVwW876Ig8WxIgXGtrrTeRuVGa8nEWc24ONfjTuAIvXquTiDPmq_MxX_LS8TLiSg49solHjzFcFsgL8yV_dPH8tB1A5doNsPUXnI7grGfBAt5mNI-5xDF9POLErtGAE6vdrH1F5sBy1Dv3hZPq_hoEQ4s31QAKtyF0dwI8vM2R61xgP-jJLOvbRCyMLDutMH2gK9Kmy1oxvNYEfezPRuX_NwlJTs6Lk7T8Wctyu9fDzZhHmfX8aHoh3p9VJ1W3Zz5tZDdQpS751PU-cWEIJTZb6q_HPYSHnKswSSb00QCkzANsA_vtrgZVEAoiMA-MKwUedYvgtnFF9LnjTp3qLYcokJ87d4IZDE3y7po0JZ_LHo3EtqeT-a_osI6_HYbg_KrfhMe1RyPE3O08scMcxIsUDXkkxk8MZFQ1ZZGltqww5r9ZErJf5zuIg0-FZE_Q47jgWP9g5j9sDIpYLiherFB1XXRuemhAn1-S088x4GLG0KRjjLlvjnr-NHjTz-l-Uvn-6YOBqudOfAkSejVMOfGYfgztiy3JAsA7UfUyODSZMvUrl08FgGv7VGXk2cUl4FGfJI_pXI_yXa5svoxCrJ7F-KuntlqtnH4v1_t5Rcf2coNWZn-VhavWs5iSfHy1obmHcu2xlaqAY7iwc-9Rh878PPN_JdED5A1ZvMWUFDFol_tICZzmNQjJpHW5XubWCN9Mdfz53pd67O_9baSHFRtKAxw2vPD8bOVHm9F-cBdxjvBfumpzI72C_9TNqLoyySuEp6HQmnX3aUpNc=", "id": "rs_0d1a4d10654d56b4016a11cf8f1c888191af0da90f02e2bd30", "summary": []}], "tool_calls": [{"id": "call_mzbP3tvWhGrS9cTGvyPsqnkR", "call_id": "call_mzbP3tvWhGrS9cTGvyPsqnkR", "response_item_id": "fc_0d1a4d10654d56b4016a11cf8fc3848191a94280dc556aaf4f", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\":\"python - <<'PY'\\nimport json, time, urllib.request\\nbase='http://100.101.173.25:8188'\\npid='dd9f7704-2bfc-461b-bbda-66517a55fef6'\\nfor i in range(120):\\n    q=json.load(urllib.request.urlopen(base+'/queue', timeout=10))\\n    running=q.get('queue_running', [])\\n    pending=q.get('queue_pending', [])\\n    print(f'poll {i}: running={len(running)} pending={len(pending)}')\\n    if not running and not pending:\\n        break\\n    time.sleep(5)\\nh=json.load(urllib.request.urlopen(base+'/history/'+pid, timeout=20))\\nprint(json.dumps(h.get(pid, {}).get('status',{}), indent=2))\\nprint(json.dumps(h.get(pid, {}).get('outputs',{}), indent=2))\\nPY\",\"timeout\":650,\"workdir\":\"/home/agent/jarvis_runtime/AutoShortsBot\"}"}}], "timestamp": "2026-05-23T18:05:12.467183"}
{"role": "tool", "name": "terminal", "content": "{\"error\": \"Foreground timeout 650s exceeds the maximum of 600s. Use background=true with notify_on_complete=true for long-running commands.\"}", "tool_call_id": "call_mzbP3tvWhGrS9cTGvyPsqnkR", "timestamp": "2026-05-23T18:05:12.467183"}
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{"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.", "reasoning": null, "finish_reason": "stop", "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"}], "timestamp": "2026-05-23T18:05:12.467183"}
{"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": "2026-05-23T18:08:25.330241"}
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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\\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\\n12. **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 encoding, VAE tiling, and a micro smoke test. System RAM can be the first bottleneck even when VRAM is free; ask the user to free RAM before the first load. See `references/wan-video-tailscale-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-tailscale.md\", \"references/workflow-format.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_y40lTGAOsynFwLE2BshoFoMG", "timestamp": "2026-05-23T18:08:25.330241"}
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{"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.", "reasoning": null, "finish_reason": "stop", "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"}], "timestamp": "2026-05-23T18:08:25.330241"}