[M G] Ok, nächster schritt

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Erledigt, Sir.

## Renderer v1 gebaut

Ich habe jetzt den ersten **manifest-driven Preview Renderer** implementiert.

Kurz gesagt:

```text
Render Manifest JSON
→ Pillow erzeugt vertikale Szenen-Frames
→ FFmpeg baut daraus MP4
```

Also: **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.

## Neu implementiert

Code:

```text
autoshorts/preview_renderer.py
```

CLI:

```text
scripts/render_preview.py
```

Tests:

```text
tests/test_preview_renderer.py
```

Dependency ergänzt:

```text
Pillow>=10.0
```

## Erstes Testvideo gerendert

Für:

```text
ai-004 — Stop asking AI to think for you
```

Output:

```text
data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4
```

Technische Prüfung:

```text
width=540
height=960
avg_frame_rate=24/1
duration=43.041667
```

Preview-Video:

MEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4

## Was der Renderer v1 aktuell macht

- nimmt das Render Manifest
- baut 5 visuelle Szenen
- nutzt pro Szene:
  - Asset Type
  - On-screen Text
  - Timing
  - Retention Goal
- erzeugt vertikale Preview-Cards
- exportiert daraus ein MP4 via FFmpeg

Das ist bewusst noch **Preview/Timing-Renderer**, nicht finaler Beauty-Renderer. Er zeigt uns: Struktur, Timing, Szenenfluss, Textgewicht. Erst danach lohnt sich Politur.

## Tests / Git

Full suite:

```text
24 passed in 0.88s
```

Neuer Commit:

```text
07bbc57 feat: add manifest preview renderer
```

Branch:

```text
feat/initial-growth-engine
```

Remote verifiziert:

```text
local == remote
```

PR-Link bleibt:

https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine

## Nächster sinnvoller Schritt

Jetzt würde ich als Nächstes den Renderer von „Card Preview“ zu **echter Video-Struktur v2** bringen:

- Hook text animation verbessern
- echte Caption-Overlays über alle Beats
- simple motion/pan/zoom
- optional TTS-Audio-Spur
- danach wieder ein neues MP4 zur Freigabe

Aktuell ist es der erste brauchbare Rohschnitt. Nicht hübsch. Aber kontrolliert. Und Kontrolle schlägt KI-Konfetti.


[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:
## Active Task
User asked: "ok nächster schritt"

## Goal
Der 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.

## Constraints & Preferences
- Sprache/Kommunikation: Deutsch; User wird teilweise mit „Sir“ angesprochen.
- Der User lehnt finale statische Textkarten-/KI-Bild-Slideshows ab; sie sind nur Preview/Storyboard, nicht Endprodukt.
- Finale Videos sollen bewegte Sequenzen, AI-video Assets, Screen-Recording-Simulationen, Mechanismus-Animationen, kinetic captions, Sound/SFX und schnelle visuelle Wechsel enthalten.
- Der aktuelle Card-/Preview-Renderer ist nur Timing-/Storyboard-Werkzeug.
- `static_card_limit = 0` als Qualitätsprinzip für finale Inhalte.
- Content-Richtung: kein generischer KI-Avatar-News-Kanal, kein „Was wäre wenn…“-KI-Müll, kein Reused-Content-Klon.
- Avatare höchstens als Nebenfigur/Branding-Element, nicht als Hauptformat.
- Bevorzugte Nische: **Hidden Money & AI Systems**.
- Zielserie: **Unsichtbare Systeme in 30 Sekunden**.
- Claim: „Wir zeigen Dinge, die dich Geld, Zeit oder Kontrolle kosten – bevor du sie bemerkst.“
- Erfolgsmechanik: Hook → Objekt/Problem → Schnitt ins Innere → Gefahr/Überraschung → Auflösung.
- 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.
- User erlaubt, die untracked Telegram-Review-Dateien zu ignorieren, weil sie versehentlich durch einen falschen Telegram-Kanal entstanden sind.
- Keine API-Keys, Tokens, Passwörter, Secrets, Credentials oder Connection Strings in Zusammenfassungen oder Artefakten bewahren; ggf. mit `[REDACTED]` ersetzen.

## Completed Actions
1. 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].
2. 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].
3. 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].
4. 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].
5. 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].
6. TEST full suite nach Typography-Änderung — Ergebnis: `25 passed in 0.96s` [tool: pytest claimed].
7. 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].
8. 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].
9. 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].
10. 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].
11. CREATED `scripts/generate_asset_plans.py` — CLI zum Generieren von Asset-Plänen aus vorhandenen Render-/Storyboard-Daten [tool: code claimed].
12. CREATED `tests/test_asset_plan.py` — Tests für Asset Plan Builder, u.a. Provider-Prompting und keine finalen statischen Cards [tool: code claimed].
13. 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].
14. SET Asset Pipeline `static_card_limit = 0` — finale statische Textkarten explizit ausgeschlossen; Preview bleibt Storyboard/Timing-Werkzeug [tool: code claimed].
15. 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].
16. 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].
17. TEST full suite nach Asset Plan v2 — Ergebnis: `32 passed in 0.96s` [tool: pytest claimed].
18. 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].
19. 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].
20. CREATED `autoshorts/retention_studio.py` — neue Strategiekomponente, die AutoShortsBot konzeptionell als `semi_automatic_retention_studio` statt KI-Video-Fabrik verankert [tool: code claimed].
21. CREATED `scripts/generate_retention_studio.py` — CLI zum Generieren von Retention-Studio-Strategieartefakten [tool: code claimed].
22. CREATED `tests/test_retention_studio.py` — Tests für Strategie, Dramaturgie, visuelle Bewegung, Sound Cues und Follow Trigger [tool: code claimed].
23. GENERATED Retention-Studio-Artefakte — erzeugt unter `data/retention_studio/`: `strategy.json`, `pilot_ideas.json`, `mechanism_explainers.json` [tool: terminal claimed].
24. 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].
25. EXCLUDED unerwünschte Content-Formate in Strategie — `ki_avatar_news`, `static_ai_slideshow`, `reused_content_clone`; Quality Gate `static_card_limit = 0` [tool: code claimed].
26. 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].
27. 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].
28. ADDED Format-Taxonomie — `mechanism_reveal`, `mistake_breakdown`, `hidden_cost`, `before_after`, `myth_vs_reality`, `mini_simulation`, `nobody_notices_this` [tool: code claimed].
29. 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].
30. 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].
31. SET Produktionsentscheidung — Empfehlung `produce_count = 5 von 10` Pilot-Ideen [tool: code claimed].
32. 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].
33. TEST full suite nach Retention Studio — Ergebnis: `36 passed in 0.99s` [tool: pytest claimed].
34. 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].
35. 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].
36. CREATED `autoshorts/mechanism_pipeline.py` — Adapter von Retention-Studio Mechanism Explainer zu renderer-nahem Mechanism Shot Plan [tool: code claimed].
37. CREATED `scripts/generate_mechanism_shot_plans.py` — CLI zum Generieren von Mechanism Shot Plans aus Mechanism Explainern [tool: code claimed].
38. 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].
39. GENERATED Pilot-Shot-Plan — Datei `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` erzeugt [tool: terminal claimed].
40. 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].
41. 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].
42. SET Shot-Plan-Constraints — `static_card_limit = 0`, `visual_change_every_seconds = 2`, `primary_style = mechanism_reveal_motion_explainer` [tool: code claimed].
43. TEST full suite nach Mechanism Pipeline — Ergebnis: `40 passed in 0.97s` [tool: pytest claimed].
44. 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].
45. 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].
46. 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].

## Active State
- Repository/Projekt: `/home/agent/jarvis_runtime/AutoShortsBot`.
- Branch: `feat/initial-growth-engine`.
- Remote-Status zuletzt berichtet: `local == remote`.
- Letzter bekannter Commit: `5644c8b feat: adapt mechanism explainers to shot plans`.
- Vorherige relevante Commits:
  - `3cc201b fix: improve preview renderer text sizing`
  - `b60981b feat: add motion-led asset planning`
  - `a9fe897 feat: add retention studio strategy`
- Letzter Teststatus: `40 passed in 0.97s`.
- Aktuelles Pipeline-Level erreicht:
  - Retention Studio Strategie vorhanden.
  - Pilot-Ideen gescored.
  - Mechanism Explainer vorhanden.
  - Mechanism Shot Plan für Pilot „Warum ein billiges Auto teuer sein kann“ vorhanden.
- Nächster logischer Schritt noch nicht erledigt:
  - `Mechanism Shot Plan → Render Manifest → Asset Plan` für den neuen Pilot, nicht mehr nur für alte AI-Life-Top-3.
- Untracked/zu ignorieren:
  - `autoshorts/review_package.py`
  - `data/telegram_reviews/`
  - `scripts/generate_telegram_review_packages.py`
  - `tests/test_review_package.py`
- Keine laufenden Server oder Prozesse erwähnt.
- Keine Secrets/Credentials bewahren; falls vorhanden, als `[REDACTED]`.

## In Progress
- Der User hat „ok nächster schritt“ gesagt. Gemeinter nächster Arbeitsschritt laut vorheriger Antwort:
  - Mechanism Shot Plan für „Warum ein billiges Auto teuer sein kann“
  - → Render Manifest
  - → Asset Plan
  - Ziel: neue Retention-Studio-/Mechanism-Explainer-Pipeline an die bestehende Render-/Asset-Plan-Infrastruktur anbinden.
- Noch nicht umgesetzt:
  - Adapter/CLI/Tests für `Mechanism Shot Plan → Render Manifest`.
  - Generierung eines Render Manifests für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann`.
  - Asset Plan für diesen neuen Pilot auf Basis des neuen Render Manifests.
  - Provider Job Specs für cinematic Szenen des Pilots.

## Blocked
Keine bekannten Blocker.
Keine Fehlermeldungen offen.
Einziger 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.

## Key Decisions
1. 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.
2. Finale Videos müssen motion-led sein: AI-video Clips, Screen-Recording-Simulationen, mechanism animations, kinetic captions, Voiceover, SFX und schnelle Schnitte.
3. `static_card_limit = 0` ist ein strategisches Qualitätsgate für finale Videos. Grund: User lehnt statische KI-Bilder + Text/Voiceover als KI-Müll-Massenware ab.
4. 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.
5. 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.
6. Kernformat: Mechanism Reveal / Visual Explanation / Mini-Investigation. Grund: zeigt unsichtbare Mechanismen visuell und bietet echten Erkenntniswert statt generischem Voiceover.
7. Avatare nicht als Hauptformat. Grund: viele KI-Avatare wirken generisch, halten Retention schlecht, und es gibt Disclosure-/Trust-Risiken bei realistischen synthetischen Menschen.
8. Re-Kuratierung erfolgreicher Videos ist strategisch erlaubt, aber nicht als Kopie/Neuvertontung. Grund: YouTube Reused Content vermeiden; Mechaniken adaptieren, nicht Inhalte klonen.
9. 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.
10. Untracked Telegram-Review-Dateien werden ignoriert. Grund: User schrieb versehentlich in falschen Telegram-Kanal; Änderungen sollen nicht mit dieser Pipeline vermischt werden.

## Resolved Questions
1. 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.
2. 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.
3. 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.
4. User fragte implizit, ob statische Cards reichen. Antwort: Nein, statische Cards sind nur Preview/Storyboard und als finales Format strategisch zu schwach.
5. 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.
6. User schlug Retention-Studio statt KI-Video-Content vor. Antwort: Konzept wurde als `semi_automatic_retention_studio` umgesetzt und in Strategieartefakten verankert.
7. 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.
8. User erklärte, der separate Working Tree könne ignoriert werden. Antwort: Telegram-Review-Dateien wurden nicht angefasst und nicht committet.

## Pending User Asks
- „ok nächster schritt“ ist noch offen. Gemeint ist nach aktuellem Stand der nächste Build-Schritt:
  - Mechanism Shot Plan → Render Manifest → Asset Plan für den Pilot „Warum ein billiges Auto teuer sein kann“.

## Relevant Files
- `autoshorts/asset_plan.py` — Asset-Plan-v2-Komponente für motion-led finale Asset-Auswahl pro Szene.
- `scripts/generate_asset_plans.py` — CLI zur Asset-Plan-Generierung.
- `tests/test_asset_plan.py` — Tests für Asset-Plan-Logik.
- `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.
- `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.
- `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.
- `autoshorts/retention_studio.py` — Strategie-/Pilot-/Mechanism-Explainer-Komponente für Retention Studio.
- `scripts/generate_retention_studio.py` — CLI zur Generierung von Retention-Studio-Artefakten.
- `tests/test_retention_studio.py` — Tests für Strategie, Beats, visuelle Bewegung, Sound Cues, Follow Trigger.
- `data/retention_studio/strategy.json` — Retention-Studio-Strategie.
- `data/retention_studio/pilot_ideas.json` — gescorte Pilot-Ideen.
- `data/retention_studio/mechanism_explainers.json` — Mechanism-Explainer-Daten.
- `autoshorts/mechanism_pipeline.py` — Adapter von Mechanism Explainer zu Mechanism Shot Plan.
- `scripts/generate_mechanism_shot_plans.py` — CLI zur Shot-Plan-Generierung.
- `tests/test_mechanism_pipeline.py` — Tests für Mechanism Shot Plan Adapter.
- `data/mechanism_shot_plans/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.md` — generierter Pilot-Shot-Plan.
- `data/previews/testbatch_002_top3/ai-004-stop-asking-ai-to-think-for-you.mp4` — neu gerenderter Preview.
- `data/previews/testbatch_002_top3/ai-005-the-4-box-prompt-for-mental-overload.mp4` — neu gerenderter Preview.
- `data/previews/testbatch_002_top3/attention-002-the-algorithm-does-not-need-you-happy.mp4` — neu gerenderter Preview.
- `autoshorts/review_package.py` — untracked/zu ignorieren.
- `data/telegram_reviews/` — untracked/zu ignorieren.
- `scripts/generate_telegram_review_packages.py` — untracked/zu ignorieren.
- `tests/test_review_package.py` — untracked/zu ignorieren.

## Remaining Work
- Neuen Adapter bauen: `Mechanism Shot Plan → Render Manifest`.
- Render Manifest für `hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann` erzeugen.
- Asset Plan v2 auf diesen neuen Mechanism-Render-Manifest anwenden.
- Provider Job Specs für cinematic AI-video Szenen exportieren, z.B. Kling/Hailuo/Runway-kompatible Prompts.
- Mechanism Animation Specs definieren für 3D-Car-X-Ray, Preis-/Risiko-/Marge-/Timing-Layer.
- Final Renderer erweitern, damit er echte Clip-Dateien, Screen-Recording-Simulationen, mechanism animations und kinetic captions zusammensetzt.
- Optional: Preview-Storyboard weiter nutzen, aber klar als nicht-final markieren.
- Später: Analysepipeline für Retention nach 1s/3s, durchschnittliche Wiedergabedauer, Replays, Kommentare pro 1’000 Views, Follows pro 1’000 Views.

## Critical Context
- Frühere Kontextzusammenfassung war nicht verfügbar: „Summary generation was unavailable. 125 message(s) were removed…“. Frühere Details vor den neuen Turns können fehlen.
- Branch: `feat/initial-growth-engine`.
- PR-Link bleibt: `https://github.com/Gamexgit/AutoShortsBot/pull/new/feat/initial-growth-engine`.
- Letzter bekannter Teststatus: `40 passed in 0.97s`.
- Wichtigster aktueller Pilot: `Warum ein billiges Auto teuer sein kann`.
- Pilot-Shot-Plan-Szenen:
  - 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.
  - Scene 2 — `problem`, `1.4s–5.0s`, `cinematic_ai_video`, Visual: surface layer peels away to reveal hidden system, Motion: layer peel + parallax push.
  - 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.
  - Scene 4 — `twist`, `18.0s–28.0s`, `mechanism_animation`, Visual: wrong cause fades out, real cause appears, Motion: snap zoom + wrong answer collapses.
  - Scene 5 — `follow_trigger`, `28.0s–34.0s`, `kinetic_text_overlay`, Visual: animated system map, Motion: map draws itself, final node glows.
- Strategy constraints to preserve:
  - `static_card_limit = 0`
  - `visual_change_every_seconds = 2`
  - `primary_style = mechanism_reveal_motion_explainer`
- Provider negative prompt concepts:
  - no readable text
  - no subtitles
  - no logos
  - no watermarks
  - no fake UI text
  - no slideshow
  - no static image
  - no uncanny faces
- Do not include or preserve any credentials/secrets; replace with `[REDACTED]` if encountered.

--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---


[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:
Summary 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.

--- END OF CONTEXT SUMMARY — respond to the message below, not the summary above ---


Output:
...                                                                      [100%]
3 passed in 2.50s


Explanation:
**Rerendering adjustments**

I need to think about how to adjust the rendering. Maybe I should set the caption to display fully at 540 instead of truncating it. If the layout doesn’t fit, there might be issues with the lines. It seems like I could tweak it to improve the overall presentation. I’ll go ahead and try rendering it again with these modifications in mind!


[terminal] ran `python scripts/render_free_local_motion.py --jobs data/free_production_jobs/m...` -> exit 0, 1 lines output


Question: Prüfe den gerenderten Frame nach dem Caption-Fix: Ist die Caption vollständig innerhalb des weißen Kastens und des Bildes? Sind Mechanism-Layer/Auto erkennbar? Kurze Qualitätsdiagnose.


[vision_analyze] 'Prüfe den gerenderten Frame nach dem Caption-Fix: ' (315 chars)


[search_files] content search for 'Röntgenblick' in /home/agent/jarvis_runtime/AutoShortsBot -> 6 matches


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/render_manifest.py from line 1 (9,256 chars)


Explanation:
**Inspecting display issues**

I 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!


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py from line 1 (6,358 chars)


Explanation:
**Improving text truncation**

I'm realizing that the text often clips at 72 characters, and it ends awkwardly with "und." To fix this, I think adding an ellipsis could help. We want the renderer overlay to be concise, but it shouldn't dangle at the end. So, I'll test adding a mechanism to ensure it includes an ellipsis when truncated, avoiding stopwords like "und." I also need to make sure "Liquidität" is still included. Maybe I should add a test in the test mechanism pipeline for this.


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py from line 1 (3,226 chars)


patch

def test_mechanism_scene_overlay_truncation_marks_open_sentence():
    plan = build_mechanism_shot_plan(_auto_explainer())
    overlay = plan["scenes"][2]["on_screen_text"]

    assert overlay.endswit...[truncated]


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/tests/test_mechanism_pipeline.py (810 chars result)


[terminal] ran `pytest tests/test_mechanism_pipeline.py::test_mechanism_scene_overlay_truncat...` -> exit 1, 1 lines output


def _overlay_text(voiceover: str, limit: int = 72) -> str:
    cleaned = " ".join(voiceover.split())
    if len(cleaned) <= limit:
        return cleaned
    truncated = cleaned[: limit - 1].rsplit(" ...[truncated]

def _overlay_text(voiceover: str, limit: int = 72) -> str:
    cleaned = " ".join(voiceover.split())
    if len(cleaned) <= limit:
        return cleaned
    return cleaned[:limit].rsplit(" ", 1)[0].r...[truncated]


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/mechanism_pipeline.py (924 chars result)


Output:
.                                                                        [100%]
1 passed in 0.03s


Explanation:
**Assessing artifact regeneration**

I’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!


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_mechanism_render_manifests.py from line 1 (1,811 chars)


terminal

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]


[terminal] ran `python scripts/generate_mechanism_shot_plans.py --out data/mechanism_shot_pla...` -> exit 0, 1 lines output


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]

Explanation:
**Inspecting generated plans**

I 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.


[terminal] ran `ffmpeg -y -ss 00:00:08 -i data/local_motion_renders/mechanism_pilots/hidden-m...` -> exit 0, 1 lines output


Explanation:
**Considering artifact files**

I'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.

I'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.


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/.gitignore from line 1 (740 chars)


from pathlib import Path
root=Path('/home/agent/jarvis_runtime/AutoShortsBot')
pilot='hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann'
dirs=[
 'data/asset_plans/mechanism_pilots',
 'data/...[truncated]

Explanation:
**Considering code changes**

I’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.


[execute_code] `from pathlib import Path root=Path('/home/agent/jarvis_runti...` (1 lines output)


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/.gitignore (409 chars result)


[terminal] ran `git status --short` -> exit 0, 1 lines output


social-media-shorts-growth-engine

Rendering and external API calls can be integration-tested separately.

## Pitfalls

Rendering and external API calls can be integration-tested separately.

For this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_produc...[truncated]

Explanation:
**Updating skills for workflow**

I 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.


Patched SKILL.md in skill 'social-media-shorts-growth-engine' (1 replacement).


terminal

git diff --stat && git diff -- . ':!data/local_motion_renders' | grep -iE 'api[_-]?key|token|secret|password|credential' || true

ffprobe -v error -select_streams v:0 -show_entries stream=width,height,avg_frame_rate,duration -of default=nw=1:nk=1 data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4


Output:
...................................................                      [100%]
51 passed in 3.32s


[terminal] ran `git diff --stat && git diff -- . ':!data/local_motion_renders' | grep -iE 'ap...` -> exit 0, 1 lines output


Output:
540
960
15/1
34.000000


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]


[terminal] ran `git add .gitignore autoshorts/asset_plan.py autoshorts/mechanism_pipeline.py ...` -> exit 0, 1 lines output


git diff --cached --check && git diff --cached | grep -iE 'api[_-]?key|token|secret|password|credential' || true && git commit -m "feat: add free local motion renderer" && git push


[terminal] ran `git diff --cached --check && git diff --cached | grep -iE 'api[_-]?key|token|...` -> exit 128, 1 lines output


[skill_view] name=github-auth (11,442 chars)


git remote -v && gh auth status 2>/dev/null || true && git status --short


[terminal] ran `git remote -v && gh auth status 2>/dev/null || true && git status --short` -> exit 0, 1 lines output


python - <<'PY'
import os
for name in ['GITHUB_TOKEN','GH_TOKEN']:
    print(f'{name}=' + ('set' if os.environ.get(name) else 'unset'))
PY
if [ -f ~/.git-credentials ]; then grep -q github.com ~/.git-credentials && echo git_credentials_has_github || true; fi
if [ -f ~/.hermes/.env ]; then grep -q '^GITHUB_TOKEN=' ~/.hermes/.env && echo hermes_env_has_github_token || true; fi

Explanation:
**Checking commit status**

After 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!


Output:
GITHUB_TOKEN=***
GH_TOKEN=***


set +x
BRANCH=$(git branch --show-current)
TOKEN=${GITHUB_TOKEN:-${GH_TOKEN:-}}
if [ -z "$TOKEN" ]; then echo "NO_TOKEN"; exit 2; fi
AUTH_URL="https://${TOKEN}@github.com/Gamexgit/AutoShortsBot.git"
g...[truncated]


python - <<'PY'
import os
for name in ['GITHUB_TOKEN','GH_TOKEN']:
    val=os.environ.get(name)
    print(name, 'len', len(val) if val else 0, 'prefix', val[:4] if val else '')
PY
printf 'bash GITHUB len %s GH len %s\n' "${#GITHUB_TOKEN}" "${#GH_TOKEN}"


Output:
GITHUB_TOKEN len 0 prefix
GH_TOKEN len 0 prefix
bash GITHUB len 0 GH len 0


git config --global credential.helper 2>/dev/null || echo no_helper
[ -f ~/.git-credentials ] && python - <<'PY'
from pathlib import Path
p=Path.home()/'.git-credentials'
print('git_credentials_exists', p.exists(), 'size', p.stat().st_size if p.exists() else 0)
print('has_github', 'github.com' in p.read_text(errors='ignore') if p.exists() else False)
PY


ssh -T -o BatchMode=yes -o StrictHostKeyChecking=accept-new git@github.com 2>&1 | head -5; true

Explanation:
**Considering SSH options**

I’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!


Output:
Warning: Permanently added 'github.com' (ED25519) to the list of known hosts.
git@github.com: Permission denied (publickey).


git log --oneline -3 && git status --short && git rev-parse --abbrev-ref HEAD && git rev-parse HEAD

Explanation:
**Finalizing media and testing**

I need to make a final note about committing changes locally that haven't been pushed due to credential issues. I might also want to deliver the MP4 media. It seems important to inspect the final screenshot carefully, as I’ve already done that. I should probably run full tests after the commit too. Plus, checking the status and log would be a good idea to ensure everything's in order.


[terminal] ran `git log --oneline -3 && git status --short && git rev-parse --abbrev-ref HEAD...` -> exit 0, 1 lines output


todo

Local Motion Renderer v1 implementieren und Pilot-MP4 rendern

Tests, ffprobe und Git-Status/Commit/Push verifizieren


[todo] updated task list


Erledigt, Sir.

## Free Local Motion Renderer v1 ist gebaut

Der Workflow ist jetzt wirklich **gratis**:

```text
Free Production Jobs
→ Local Motion Renderer
→ FFmpeg MP4
```

Keine Hailuo/Kling/Runway/Veo/Pika/Luma Credits. Kein bezahlter Video-Provider. Nur lokal:

- Pillow
- FFmpeg
- procedural motion
- mechanism layers
- kinetic captions
- optional NVIDIA LLM nur für Text-Refinement, nicht fürs Video

## Neu implementiert

Code:

```text
autoshorts/local_motion_renderer.py
```

CLI:

```text
scripts/render_free_local_motion.py
```

Tests:

```text
tests/test_local_motion_renderer.py
```

## Pilot-Video gerendert

Für:

```text
Warum ein billiges Auto teuer sein kann
```

Output:

```text
data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4
```

Technisch geprüft:

```text
width: 540
height: 960
fps: 15
duration: 34.000000s
```

Preview:

MEDIA:/home/agent/jarvis_runtime/AutoShortsBot/data/local_motion_renders/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.mp4

## Was der Renderer v1 jetzt macht

Er erzeugt lokal bewegte Szenen:

- Scene 1/2: procedural cinematic motion mit Car/X-Ray/Preis-Risiko-Tags
- Scene 3/4: Mechanism-Layer-Reveal mit Preis, Reparatur, Standtage, Marge etc.
- Scene 5: kinetic text overlay
- Caption-Overlay mit sicherem Text-Fitting
- sichtbare Bewegung über die gesamte Laufzeit
- Metadaten-JSON neben dem Render

Also nicht mehr nur statische Preview-Cards. Noch nicht „Netflix“, aber auch nicht „PowerPoint im Witness Protection Program“.

## Timing-Fix

Ich habe außerdem einen wichtigen Fehler behoben:

Vorher waren die Free-Production-Jobs auf Szenenlängen von 3–6 Sekunden gecappt. Dadurch wäre aus dem 34s-Pilot nur ein 25s-Video geworden.

Jetzt gilt:

```text
Render Manifest duration
→ Asset Plan duration
→ Free Job duration
→ Local Render duration
```

Für den Pilot jetzt korrekt:

```text
1.4s + 3.6s + 13s + 10s + 6s = 34s
```

## Caption-Fix

Beim visuellen Check war die Caption in Scene 3 zuerst zu breit und wurde seitlich abgeschnitten. Behoben:

- adaptive Caption-Layout-Berechnung
- max. Safe-Area-Breite
- Font-Fallback
- Tests gegen abgeschnittene lange deutsche Captions

Zusätzlich: gekürzte Overlay-Texte enden jetzt sauber mit `…` und nicht awkward auf „und“. Ein kleiner Sieg gegen deutsche Nebensatzmonster.

## Tests

Full suite:

```text
51 passed in 3.32s
```

## Git

Lokaler Commit erstellt:

```text
ff5df62 feat: add free local motion renderer
```

Branch:

```text
feat/initial-growth-engine
```

Wichtig: Push zu GitHub ist aktuell blockiert, weil in dieser Umgebung keine GitHub-Credentials verfügbar sind:

```text
fatal: could not read Username for 'https://github.com'
```

SSH ist ebenfalls nicht authentifiziert:

```text
Permission denied (publickey)
```

Der Commit ist lokal sauber vorhanden, aber noch **nicht remote gepusht**.

## Weiterhin ignoriert

Die versehentlichen Telegram-Review-Dateien sind weiterhin untracked und nicht angefasst:

```text
autoshorts/review_package.py
data/telegram_reviews/
scripts/generate_telegram_review_packages.py
tests/test_review_package.py
```

## Nächster sinnvoller Schritt

Jetzt würde ich den Renderer v2 bauen:

```text
Local Motion Renderer v1
→ bessere Mechanism Visual Grammar v2
```

Konkret:

- echte Objekt-Layer statt grober Rechtecke
- bessere Auto-X-Ray-Darstellung
- per-scene motion presets:
  - hard zoom
  - layer peel
  - risk meter fill
  - wrong-answer collapse
  - final map glow
- einfache lokale SFX-Spur via FFmpeg/Sine/Noise/Clicks
- danach wieder ein neues MP4 zur Freigabe.


Explanation:
**Researching video providers**

I 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.


[skill_view] name=social-media-shorts-growth-engine (8,591 chars)


[skill_view] name=web-scraping (5,754 chars)


Free/freemium KI-Video-Tools für realistischere Shorts recherchieren

Gratis-Workflow-Entscheidung für AutoShortsBot ableiten

Provider-/Free-Tier-Registry und Job-Specs im Repo ergänzen

Tests, Artefakte und Git-Status verifizieren

Explanation:
**Searching for information**

I 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.


[browser_navigate] https://www.google.com/search?q=AI+video+generator+free+tier+Pika+PixVerse+CapCut+2026 (920 chars)


[browser_navigate] https://www.perplexity.ai/search?q=AI%20video%20generator%20free%20tier%20Pika%20PixVerse%20CapCut (922 chars)


Explanation:
**Exploring alternatives for information**

I 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!


[Duplicate tool output — same content as a more recent call]


[browser_navigate] https://pixverse.ai/pricing (1,085 chars)


[browser_navigate] https://www.capcut.com/tools/ai-video-generator (8,498 chars)


Explanation:
**Considering multi-browser navigation**

I'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!


[browser_navigate] https://app.pixverse.ai/pricing (1,326 chars)


[browser_navigate] https://pixverse.ai/ (8,594 chars)


[browser_navigate] https://runwayml.com/pricing/ (6,054 chars)


[browser_navigate] https://search.brave.com/search?q=PixVerse+free+credits+pricing+AI+video (986 chars)


[browser_console] expression=document.body.innerText.match(/.{0,80}(f (2,741 chars result)


document.body.innerText.split('\n').filter(l=>/free|credit|watermark|HD quality|Is there a free/i.test(l)).slice(-50)


[browser_console] expression=document.body.innerText.split('\n').filt (2,370 chars result)


[browser_navigate] https://pika.art/pricing (8,586 chars)


Array.from(document.querySelectorAll('body *')).map(e=>e.innerText).filter(Boolean).join('\n').match(/.{0,100}(80 monthly|480p|12 credits Free|Commercial use|no watermark|Text-to-Video).{0,100}/g)?.slice(0,20)


[browser_console] expression=Array.from(document.querySelectorAll('bo (775 chars result)


[browser_navigate] https://app.pixverse.ai/ (8,447 chars)


Explanation:
**Evaluating video tool options**

PixVerse offers 90 free daily refresh credits, which is great. I suspect Pika might work without an account, but I need to check if it has an API. For CapCut, I need to figure out the manual process since there are no credit details. It seems I should establish a process for using these free providers effectively, including a workflow for exports and external AI video job specifications. Additionally, I need to look into my code repository to track changes and implement new tests, maybe using TDD again.


[skill_view] name=test-driven-development (11,198 chars)


terminal

git status --short && git log --oneline -3

Explanation:
**Inspecting the repo**

I need to implement something, but first, it seems like a good idea to inspect the repository. I wonder if there are any important details or documentation I've missed. By checking it out first, I can ensure I'm on the right track and avoid potential issues later on. It’s always better to gather all necessary information before diving into an implementation. Let's see what I can find!


[terminal] ran `git status --short && git log --oneline -3` -> exit 0, 1 lines output


Explanation:
**Considering video provider options**

I'm thinking about implementing a provider registry. It seems I might need to compile research into data. I'll create tests for the free AI video providers to select those suitable for realistic scenes. It would be helpful to generate external handoff specs and use source URLs with observed details. I also need to follow test-driven development along with adding options like CapCut, Pika, PixVerse, and Runway. These providers have usable free tiers, which the user wants.


[browser_navigate] https://lumalabs.ai/pricing (8,871 chars)


[browser_navigate] https://hailuoai.video/ (8,379 chars)


todo

Free/freemium KI-Video-Tools für realistischere Shorts recherchieren

Gratis-Workflow-Entscheidung für AutoShortsBot ableiten

Explanation:
**Planning a video project**

I think I need to create a realistic video plan that combines AI-generated clips from a free tier with a local renderer. That's a good hybrid approach! I should start coding now and make sure to use fixtures to streamline the process. And, of course, writing tests will be essential to ensure everything runs smoothly. I’ll document this step-by-step so that I can keep track of my progress!


[todo] updated task list


import json
from pathlib import Path

from autoshorts.free_ai_video_providers import (
    FREE_AI_VIDEO_PROVIDERS,
    build_free_ai_video_handoff,
    recommended_provider_order,
    write_free_ai_v...[truncated]


[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/tests/test_free_ai_video_providers.py (67 lines)


[terminal] ran `pytest tests/test_free_ai_video_providers.py -q` -> exit 2, 1 lines output


Provider-/Free-Tier-Registry und Job-Specs im Repo ergänzen


"""Free-tier AI video provider handoff specs.

This module does not automate paid video generation. It creates manual browser
handoff packets for free/freemium AI video tools, then keeps final assembl...[truncated]


"""Generate manual free-tier AI video handoff packets."""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve()....[truncated]


Output:
....                                                                     [100%]
4 passed in 0.05s


terminal

python scripts/generate_free_ai_video_handoffs.py --jobs data/free_production_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json --out data/free_ai_video_handoffs/mechanism_pilots && pytest tests/ -q


[terminal] ran `python scripts/generate_free_ai_video_handoffs.py --jobs data/free_production...` -> exit 0, 1 lines output


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/data/free_ai_video_handoffs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json from line 1 (12,947 chars)


patch

    assert all("no readable text" in job["negative_prompt"].lower() for job in ai_jobs)
    assert all("local procedural" not in job["prompt"].lower() for job in ai_jobs)
    assert all("paid ai-video...[truncated]

    assert all("no readable text" in job["negative_prompt"].lower() for job in ai_jobs)

    local_jobs = handoff["local_jobs"]


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/tests/test_free_ai_video_providers.py (964 chars result)


[terminal] ran `pytest tests/test_free_ai_video_providers.py::test_free_ai_video_handoff_crea...` -> exit 1, 1 lines output


def _free_ai_prompt(job: dict[str, Any]) -> str:
    intent = job.get("source_prompt", "")
    for marker in [
        "Use local procedural animation/parallax rather than paid AI-video credits; keep ...[truncated]

def _free_ai_prompt(job: dict[str, Any]) -> str:
    return (
        f"Vertical 9:16 realistic cinematic short video, {job['source_prompt']} "
        "No captions, no readable text, no logos, no wat...[truncated]


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/autoshorts/free_ai_video_providers.py (1,308 chars result)


terminal

pytest tests/test_free_ai_video_providers.py -q && python scripts/generate_free_ai_video_handoffs.py --jobs data/free_production_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json --out data/free_ai_video_handoffs/mechanism_pilots


[terminal] ran `pytest tests/test_free_ai_video_providers.py -q && python scripts/generate_fr...` -> exit 0, 1 lines output


[patch] replace in /home/agent/jarvis_runtime/AutoShortsBot/.gitignore (412 chars result)


Explanation:
**Considering skill updates**

I feel like I might need to think about updating my skills later. I should definitely run some full tests and check on everything to see where I stand. It's always good to ensure I'm up to date and functioning at my best. Keeping my abilities sharp will help me serve the user better, so I'll keep that in mind as I move forward!


Output:
.......................................................                  [100%]
55 passed in 3.25s


[terminal] ran `git diff --stat && git status --short` -> exit 0, 1 lines output


[read_file] read /home/agent/jarvis_runtime/AutoShortsBot/data/free_ai_video_handoffs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json from line 49 (6,024 chars)


social-media-shorts-growth-engine

For this AutoShortsBot/Retention-Studio workflow, prefer a free-first renderer path before any paid video provider: `free_production_jobs → local_motion_renderer → FFmpeg MP4`. The local renderer shou...[truncated]

Explanation:
**Considering git commits and .gitignore**

I'm thinking about the diff, which only shows changes in the gitignore because untracked files aren't listed. I should commit the new files, especially if I'm also updating my skills with an external provider. It's a good idea to check if `.gitignore` includes `data/free_ai_video_handoffs` since that JSON artifact should be committed. I'll need to ensure I handle credentials properly to push. I want to add and commit everything to avoid untracked files in my review.


terminal

git add .gitignore autoshorts/free_ai_video_providers.py scripts/generate_free_ai_video_handoffs.py tests/test_free_ai_video_providers.py data/free_ai_video_handoffs/mechanism_pilots/hidden-money-syst...[truncated]


[terminal] ran `git add .gitignore autoshorts/free_ai_video_providers.py scripts/generate_fre...` -> exit 0, 1 lines output


git status --short && git log --oneline -4


Output:
.......................................................                  [100%]
55 passed in 3.55s


[terminal] ran `git status --short && git log --oneline -4` -> exit 0, 1 lines output


Output: fatal: could not read Username for 'https://github.com': Kein passendes Gerät bzw. keine passende Adresse gefunden


todo

Tests, Artefakte und Git-Status verifizieren


[todo] updated task list


Ja, Sir — exakt: **das aktuelle MP4 ist noch Konzept/Animatic**, kein fertiges realistisches Video.

Es 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.

Ich habe recherchiert und den Workflow entsprechend weitergebaut.

## Recherche: gratis / free-tier KI-Video-Tools

## 1. PixVerse

Gefunden auf:

```text
https://app.pixverse.ai/
```

Die App zeigt aktuell:

```text
90
Täglich aktualisieren
Jetzt registrieren und kostenlose Credits erhalten
```

Einschätzung:

- sehr interessant als **Primary Free-Tier Candidate**
- offenbar tägliche Free-Credits
- geeignet für realistische 9:16 Short-Clips
- manuelle Browser-Nutzung/Login nötig
- keine paid Top-ups erlaubt

Priorität:

```text
#1 PixVerse
```

## 2. Pika

Gefunden auf offizieller Pricing-Seite:

```text
https://pika.art/pricing
```

Free Basic:

```text
$0
80 monthly video credits
Access to Pika 2.5, 480p only
Download videos with no watermark
Commercial use
```

Für Text/Image-to-Video laut Pricing:

```text
480p / 5s = 12 credits Free
```

Einschätzung:

- sehr brauchbarer Fallback
- Free-Output nur 480p, aber für Shorts mit lokalem Upscale/Compositing akzeptabel
- no-watermark + commercial-use Claim ist stark
- monatlich begrenzter als PixVerse

Priorität:

```text
#2 Pika
```

## 3. CapCut AI Video Maker

Gefunden auf:

```text
https://www.capcut.com/tools/ai-video-generator
```

CapCut sagt u.a.:

```text
Free AI Video Generator
Export the finished video in HD quality for free
```

Einschätzung:

- gut für manuelle Politur, Templates, Captions, Schnitt
- Free-Quota öffentlich weniger klar
- CapCut kann schnell in Pro-Assets/Upsell laufen
- trotzdem nützlich als kostenloser Editor-/Assembler-Kandidat

Priorität:

```text
#3 CapCut
```

## 4. Hailuo

Gefunden auf:

```text
https://hailuoai.video/
```

Die Seite zeigt einen Generator mit:

```text
768p
6s
25 credits
```

Einschätzung:

- nur verwenden, falls kostenlose vorhandene Credits da sind
- keine Käufe, keine Top-ups
- weil du Hailuo explizit als „kein Geld für Credits“ genannt hast: nur Free-Quota-Fallback, nicht Hauptpfad

Priorität:

```text
#4 Hailuo, nur falls Free-Credits verfügbar
```

## Nicht als Free-First geeignet

Aktuell nicht als Primary Free Workflow:

```text
Runway
Luma
Kling
Veo
Sora API
```

Grund:

- entweder paid-first
- Free-Kontingent unklar/klein
- API/Automation meist bezahlt
- oder nicht stabil genug für unseren kostenlosen Produktionspfad

## Neue Workflow-Entscheidung

Der Zielworkflow ist jetzt:

```text
1. Hermes/ChatGPT/NVIDIA LLM:
   Shot plan, prompts, captions, scene specs

2. Free AI Video Provider:
   PixVerse / Pika / CapCut / Hailuo-Free
   erzeugen realistische kurze Clips manuell im Browser

3. Import:
   Clips lokal speichern unter data/imported_ai_clips/...

4. Local Renderer:
   Mechanism-Layer, Captions, SFX, Timing, Assembly

5. FFmpeg:
   finales 9:16 MP4
```

Das heißt:
**Die realistischen Teile kommen aus kostenlosen KI-Video-Free-Tiers.**
**Die System-Erklärung, Captions und Montage bleiben lokal kontrolliert.**

Das 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.

## Im Repo neu gebaut

Neue Komponente:

```text
autoshorts/free_ai_video_providers.py
```

Neues CLI:

```text
scripts/generate_free_ai_video_handoffs.py
```

Neue Tests:

```text
tests/test_free_ai_video_providers.py
```

Neues Artefakt:

```text
data/free_ai_video_handoffs/mechanism_pilots/
  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json
```

## Was dieses Handoff enthält

Für den Pilot:

```text
Warum ein billiges Auto teuer sein kann
```

werden jetzt **2 realistische AI-Video-Jobs** erzeugt:

```text
scene_01 → realistische KI-Video-Szene
scene_02 → realistische KI-Video-Szene
```

Provider-Reihenfolge:

```text
1. PixVerse
2. Pika
3. CapCut
4. Hailuo nur Free-Credits
```

Guardrails:

```text
paid_topups_allowed: false
paid_subscriptions_allowed: false
do_not_buy_credits: true
avoid_pro_templates_or_paid_assets: true
export_without_baked_captions: true
final_captions_added_locally: true
```

Die Mechanismus-Szenen bleiben vorerst lokal:

```text
scene_03 → local mechanism animation
scene_04 → local mechanism animation
scene_05 → local kinetic text
```

Warum? Weil KI-Video-Tools bei präzisen Layer-Erklärungen oft halluzinieren. Realistische Clips: ja. Exakte Erklärgrafik: lieber lokal kontrolliert.

## Beispiel Prompt für Scene 1

Das Handoff erzeugt provider-ready Prompts wie:

```text
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.
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.
```

Wichtig: Captions kommen später lokal drauf. Kein KI-Gibberish im Bild.

## Tests

Full suite:

```text
55 passed in 3.55s
```

## Git

Neuer lokaler Commit:

```text
9f41a9f feat: add free ai video handoff specs
```

Davor liegt noch:

```text
ff5df62 feat: add free local motion renderer
```

Push ist weiterhin blockiert, weil GitHub-Credentials in dieser Umgebung fehlen:

```text
fatal: could not read Username for 'https://github.com'
```

## Antwort auf deine Kernfrage

Ja: **Das Endprodukt soll realistischer werden.**

Was du aktuell gesehen hast, ist:

```text
Animatic / Motion Concept / Local Placeholder
```

Nicht das finale Video.

Der nächste Produktionsschritt ist jetzt konkret:

```text
PixVerse oder Pika öffnen
→ scene_01 + scene_02 mit Free Credits generieren
→ Clips herunterladen
→ lokal importieren
→ Local Renderer ersetzt Placeholder durch echte KI-Clips
→ Mechanism Layers + Captions + SFX darüber
→ finales MP4
```

## Nächster sinnvoller Schritt

Ich würde als Nächstes bauen:

```text
Imported AI Clip Assembler
```

Damit das System automatisch prüft:

```text
data/imported_ai_clips/<video_id>/scene_01.mp4
data/imported_ai_clips/<video_id>/scene_02.mp4
```

Wenn Clips vorhanden sind:

```text
echte AI-Clips verwenden
```

Wenn nicht:

```text
local placeholder verwenden
```

Dann können wir Free-Tier-Clips manuell aus PixVerse/Pika holen und sofort in denselben Renderprozess einbauen.


Aktuelle ChatGPT Pro Video-/Sora-Leistungen prüfen

TDD für Imported AI Clip Assembler schreiben und RED verifizieren

Imported AI Clip Assembler implementieren

Tests/Artefakte/Git-Status verifizieren

Explanation:
**Assessing ChatGPT Pro features**

I 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.


[browser_navigate] https://openai.com/chatgpt/pricing/ (8,785 chars)


[browser_navigate] https://help.openai.com/en/articles/9957612-generating-videos-on-sora (892 chars)


[terminal] ran `git status --short && git log --oneline -3` -> exit 0, 1 lines output


We use cookies and similar technologies to deliver, maintain, improve our services and for security purposes.


[browser_navigate] https://chatgpt.com/pricing/ (8,602 chars)


[browser_console] expression=document.body.innerText (12,763 chars result)


Explanation:
**Searching for pricing details**

I’m trying to find specific pricing information for Sora, especially regarding video generation. It seems that the current pricing details don’t cover video generation explicitly, just mentioning voice and video in the context of chat. I think my best bet is to check directly on their website, sora.com. I’ll use my browser for that. Let’s see what I can find!


[browser_navigate] https://openai.com/sora/ (3,249 chars)


Explanation:
**Considering discontinued product features**

I’ve noted that Sora has been discontinued in 2026, and I need to mention that fact. Utilizing an article about it would be helpful, but there’s an issue since a browser snapshot hides some paragraphs from view. I think I can work around this by using the console's innerText to extract the information I need. It feels like a small challenge, but I’m sure I can get the full details this way!


[browser_console] expression=document.body.innerText (2,482 chars result)


[browser_console] expression=document.body.innerText (2,682 chars result)


Explanation:
**Investigating navigation issues**

It looks like the click didn't lead to navigation. I wonder if the link reference is stale or maybe something's off in the destination. Should I try a search instead? It's interesting how sometimes things just don’t work as expected. Let’s explore options to figure this out. I'll keep checking to find a way to get the navigation working smoothly!


[browser_navigate] https://help.openai.com/en/?q=Using%20Credits%20for%20Flexible%20Usage%20in%20ChatGPT (7,569 chars)


[browser_console] expression=document.body.innerText (4,800 chars result)


Explanation:
**Exploring click issues**

I need to address a click issue, and I think using JavaScript to find the href might be the right way to go. That's a straightforward way to pull the link associated with the click, ensuring I get the right target. I wonder if I need to consider edge cases where multiple links may exist or how to handle any potential errors. I'll dig into the specifics more deeply to troubleshoot effectively!


[browser_console] expression=Array.from(document.querySelectorAll('a' (949 chars result)


[browser_navigate] https://help.openai.com/en/articles/12642688-using-credits-for-flexible-usage-in-chatgpt-freegopluspro (8,099 chars)


[browser_console] expression=document.body.innerText (5,954 chars result)


from pathlib import Path

from autoshorts.imported_clip_assembler import build_imported_clip_assembly_plan, discover_imported_ai_clips

VIDEO_ID = "hidden-money-systems-warum-ein-billiges-auto-teuer-...[truncated]

Explanation:
**Considering video provider integration**

I'm thinking about adding a video provider since the ChatGPT Pro video isn't currently available. Maybe I could incorporate a module like openai_subscription_video.py or find a simpler way to augment the registry. It seems important to include any unavailable providers, and testing this in new tests sounds like a good plan. Using Test-Driven Development (TDD) could help ensure everything functions correctly while I work on this.


[write_file] wrote to /home/agent/jarvis_runtime/AutoShortsBot/tests/test_imported_clip_assembler.py (91 lines)


[terminal] ran `pytest tests/test_imported_clip_assembler.py -q` -> exit 2, 1 lines output


"""Assemble imported free-tier AI clips with local renderer fallbacks.

The module builds a deterministic assembly plan. It does not buy credits, call
paid APIs, or automate browser accounts. Human-ge...[truncated]


"""Generate imported/free-tier AI clip assembly plans."""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve()....[truncated]


Output:
...                                                                      [100%]
3 passed in 0.03s


Aktuelle ChatGPT Pro Video-/Sora-Leistungen prüfen

TDD für Imported AI Clip Assembler schreiben und RED verifizieren

Imported AI Clip Assembler implementieren


[todo] updated task list


terminal

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


[terminal] ran `python scripts/generate_imported_clip_assembly_plans.py --jobs data/free_prod...` -> exit 0, 1 lines output


[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)


Need maybe maybe need implement actual hybrid render? But time, OK. Need final include answer. Need git commit. Check.

git diff -- . ':!data/local_motion_renders' | grep -iE 'api[_-]?key|token|secret|password|credential' || true


[terminal] ran `git diff --stat && git status --short` -> exit 0, 1 lines output


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


[terminal] ran `git add autoshorts/imported_clip_assembler.py scripts/generate_imported_clip_...` -> exit 0, 1 lines output


Output: fatal: could not read Username for 'https://github.com': Kein passendes Gerät bzw. keine passende Adresse gefunden


todo

Tests/Artefakte/Git-Status verifizieren


Weitergebaut, Sir.

## Kurzantwort zu ChatGPT Pro / Video

Nach aktuellem OpenAI-Stand: **Nein, dein ChatGPT Pro Abo ist derzeit kein brauchbarer Video-Generator für unseren Workflow.**

Ich habe die aktuellen OpenAI-Seiten geprüft:

## ChatGPT Pro enthält laut Pricing

Auf der ChatGPT-Pricing-Seite steht bei Pro u.a.:

- 5x oder 20x mehr Nutzung
- GPT-5.5 Pro
- maximale Codex-Aufgaben
- unbegrenztes GPT-5.3 und Datei-Uploads
- unbegrenzte und schnellere Bilderstellung
- maximale Deep Research und Agentenmodus
- maximale Erinnerung / maximaler Kontext
- neue Funktionen als experimentelle Vorschau

Aber: **keine aktive neue Videogeneration als ChatGPT-Pro-Feature**.

## Sora-Status

OpenAI Help Center sagt aktuell:

```text
The Sora web and app experiences were discontinued on April 26, 2026.
The Sora API will be discontinued on September 24, 2026.
```

Zusätzlich steht im Credits-Artikel:

```text
Credits currently can only be used with Codex and ChatGPT for Excel.
```

Bedeutung für uns:

```text
ChatGPT Pro → sehr gut für Planung, Scripting, Prompts, Code, Bilder
ChatGPT Pro → aktuell nicht als Video-Generator für neue Clips einplanen
```

Also: **ChatGPT Pro bleibt unser Gehirn, nicht unsere Kamera.**
Eine sehr teure Kamera wäre es ohnehin gewesen. Charmant, aber unpraktisch.

## Workflow-Entscheidung aktualisiert

Für realistischere Clips bleibt der Free-Tier-Hybrid:

```text
ChatGPT Pro / Hermes:
  Script, Shot Plan, Prompts, Captions, Renderlogik

PixVerse / Pika / CapCut / ggf. Hailuo-Free:
  manuell erzeugte realistische 9:16 Clips

Local Renderer:
  Mechanismus-Layer, Captions, SFX, Timing

FFmpeg:
  finales MP4
```

OpenAI/Sora wird im System ausdrücklich markiert als:

```text
do_not_use_for_new_video_generation
```

## Weitergebaut: Imported AI Clip Assembler

Neu implementiert:

```text
autoshorts/imported_clip_assembler.py
```

Neues CLI:

```text
scripts/generate_imported_clip_assembly_plans.py
```

Neue Tests:

```text
tests/test_imported_clip_assembler.py
```

Neues Artefakt:

```text
data/imported_clip_assembly_plans/mechanism_pilots/
  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json
```

## Was der neue Assembler macht

Er prüft künftig diesen Import-Pfad:

```text
data/imported_ai_clips/
  hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/
    scene_01.mp4
    scene_02.mp4
```

Wenn Clips vorhanden sind:

```text
scene_01 → imported_ai_clip
scene_02 → imported_ai_clip
```

Wenn nicht:

```text
scene_01 → local_renderer_fallback
scene_02 → local_renderer_fallback
```

Mechanismus-Szenen bleiben lokal:

```text
scene_03 → local_renderer
scene_04 → local_renderer
scene_05 → local_renderer
```

Aktueller Assembly-Plan sagt erwartungsgemäß:

```text
imported_ai_clip_count: 0
local_fallback_count: 2
local_renderer_count: 3
```

Weil noch keine PixVerse/Pika-Clips importiert wurden.

## OpenAI/Sora-Hinweis im Artefakt

Der Assembly-Plan enthält jetzt explizit:

```json
{
  "provider": "chatgpt_pro_sora",
  "available_for_new_video_generation": false,
  "decision": "do_not_use_for_new_video_generation"
}
```

Mit Quellenhinweis auf:

```text
Sora web/app discontinued: 2026-04-26
Sora API discontinued: 2026-09-24
Credits currently only for Codex and ChatGPT for Excel
```

## Tests

Full suite:

```text
58 passed in 3.39s
```

## Git

Neuer lokaler Commit:

```text
85ec56a feat: add imported clip assembly plans
```

Aktueller lokaler Commit-Verlauf:

```text
85ec56a feat: add imported clip assembly plans
9f41a9f feat: add free ai video handoff specs
ff5df62 feat: add free local motion renderer
```

Push weiterhin blockiert wegen fehlender GitHub-Credentials:

```text
fatal: could not read Username for 'https://github.com'
```

Die versehentlichen Telegram-Review-Dateien bleiben untracked und unberührt.

## Nächster praktischer Produktionsschritt

Jetzt brauchen wir konkret zwei manuell generierte Clips:

```text
scene_01.mp4
scene_02.mp4
```

Am besten zuerst in PixVerse, sonst Pika.

Ablage danach:

```text
data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4
data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4
```

Dann kann ich den nächsten Baustein bauen:

```text
Hybrid Final Renderer:
imported AI clips
+ local mechanism scenes
+ local kinetic captions
+ FFmpeg assembly
→ finales MP4
```


[M G] ok, wie gehen wir also weiter vor? generierst du die Videos?


Explanation:
**Evaluating steps for prompts**

I 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!


[skill_view] name=social-media-shorts-growth-engine (9,161 chars)


Ja — aber mit einer wichtigen Trennung, Sir:

## Was ich direkt generieren kann

Ich kann hier vollständig selbst erzeugen:

- Shot Plans
- Prompts
- Captions
- Voiceover-Text
- lokale Animatics / Placeholder-Videos
- Mechanism-Animationen
- Kinetic Captions
- finale FFmpeg-Montage
- Review-Pakete
- Tests / Code / Render-Pipeline

Also alles, was **lokal** läuft.

## Was ich nicht sauber allein generieren kann

Die **realistischen KI-Videoclips** aus PixVerse/Pika/CapCut kann ich nicht zuverlässig komplett autonom erzeugen, weil:

- Login im Browser nötig ist
- evtl. CAPTCHA / E-Mail / Account-Session kommt
- Free Credits accountgebunden sind
- ich keine Käufe/Top-ups machen darf
- externe Webseiten häufig Export/Download manuell absichern

Ich kann es theoretisch im Browser versuchen, aber praktisch ist der stabile Workflow:

```text
Ich generiere Prompt + genaue Anleitung
du / wir erzeugen Clips in PixVerse oder Pika
du lädst sie herunter
ich importiere sie in den Renderer
ich baue daraus das finale Video
```

## Konkreter Ablauf ab jetzt

## Schritt 1 — Ich liefere dir 2 Clip-Aufträge

Für den Pilot brauchen wir zuerst nur:

```text
scene_01.mp4
scene_02.mp4
```

Diese zwei Clips sind realistische B-Roll / Hook-Szenen.

## Schritt 2 — Du erzeugst sie gratis

Empfohlen:

```text
1. PixVerse
2. Pika
3. CapCut
4. Hailuo nur falls Free Credits vorhanden
```

Keine paid Credits. Keine Subscription. Keine Top-ups. Natürlich. Wir sind nicht hier, um Kapitalismus zu füttern, bevor das Video überhaupt Views hat.

## Schritt 3 — Clips ablegen

Nach Download müssen die Dateien hier landen:

```text
data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.mp4
data/imported_ai_clips/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.mp4
```

## Schritt 4 — Ich rendere final hybrid

Dann mache ich:

```text
importierte realistische Clips
+ lokale Mechanism-Layer
+ Captions
+ SFX
+ Timing
+ FFmpeg Assembly
→ finales MP4
```

## Soll ich die Videos generieren?

Präzise Antwort:

```text
Die lokalen Teile: ja, vollständig.
Die Free-Tier KI-Clips: ich bereite sie vor; generieren/Download braucht wahrscheinlich dich im Browser/Login.
```

Falls du willst, kann ich beim nächsten Schritt **sofort** ein Telegram-taugliches Clip-Generation-Paket liefern:

- PixVerse-Link
- Pika-Link
- Prompt für scene_01
- Prompt für scene_02
- Negative Prompt
- Export-Einstellungen
- Dateinamen
- Qualitätscheckliste

Dann erzeugst du die zwei Clips, lädst sie herunter, und ich baue daraus das finale Video.

Explanation:
**Considering browser options**

I 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!


[M G] ok mach das. recherchiere zudem wo welche solche tools du per API ansteuern kannst, oder per gratis weboberfläche bedienen? irgendwelche opensource lösungen?


Explanation:
**Looking into web research and packages**

I need to act and research current web trends. Maybe I should generate a package in the repository as well. Skills are loaded, so that’s good. Is Test-Driven Development (TDD) necessary? The user is asking for research and the creation of a package, so using the web makes sense. I could also look into open-source video models like Wan2.1, HunyuanVideo, and others for local feasibility and relevant APIs. I'll need to create a concise plan and package file using the right tools.

Tools/API/Web/Open-Source Optionen für kostenlose KI-Video-Clips recherchieren

Clip-Generation-Paket für scene_01/scene_02 erzeugen

Artefakt im AutoShortsBot Repo ablegen und prüfen

Vorgehensplan und Empfehlungen zusammenfassen


[browser_navigate] https://huggingface.co/spaces?sort=trending&search=text%20to%20video (8,720 chars)


[browser_navigate] https://github.com/Wan-Video/Wan2.1 (8,666 chars)


[browser_navigate] https://github.com/Tencent-Hunyuan/HunyuanVideo (8,720 chars)


Operation timed out. The page may still be loading or the element may not exist.


[browser_navigate] https://github.com/genmoai/models (8,692 chars)


terminal

python - <<'PY'
import urllib.request
urls={
'wan':'https://raw.githubusercontent.com/Wan-Video/Wan2.1/main/README.md',
'hunyuan':'https://raw.githubusercontent.com/Tencent-Hunyuan/HunyuanVideo/main/R...[truncated]


[terminal] ran `python - <<'PY'
import urllib.request
urls={
'wan':'https://raw.githubusercon...` -> exit 0, 1 lines output


Explanation:
**Searching for API options**

I'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?


[browser_navigate] https://build.nvidia.com/explore/discover?search=video (8,513 chars)


[browser_navigate] https://replicate.com/collections/text-to-video (8,387 chars)


[browser_navigate] https://fal.ai/models?categories=video (8,082 chars)


Explanation:
**Planning clip package creation**

I 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.


     1|{
     2|  "schema_version": "free_ai_video_handoff.v1",
     3|  "video": {
     4|    "id": "hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann",
     5|    "title": "Warum ein billiges Auto teuer sein kann",
     6|    "pillar": "hidden_money_systems",
     7|    "duration_ms": 34000,
     8|    "aspect_ratio": "9:16",
     9|    "resolution": {
    10|      "width": 1080,
    11|      "height": 1920
    12|    },
    13|    "fps": 30
    14|  },
    15|  "budget_policy": {
    16|    "paid_topups_allowed": false,
    17|    "paid_subscriptions_allowed": false,
    18|    "allowed_strategy": "free_tier_manual_generation_plus_local_assembly",
    19|    "local_fallback_required": true
    20|  },
    21|  "researched_sources": [
    22|    {
    23|      "provider": "pixverse",
    24|      "source_url": "https://app.pixverse.ai/",
    25|      "evidence": "App page showed 90 credits with daily refresh and signup prompt for free credits."
    26|    },
    27|    {
    28|      "provider": "pika",
    29|      "source_url": "https://pika.art/pricing",
    30|      "evidence": "Pricing page showed Basic $0 with 80 monthly video credits, Pika 2.5 at 480p only, no-watermark downloads, and commercial use."
    31|    },
    32|    {
    33|      "provider": "capcut",
    34|      "source_url": "https://www.capcut.com/tools/ai-video-generator",
    35|      "evidence": "CapCut tool page describes a free AI video generator and says final videos can be exported in HD quality for free."
    36|    },
    37|    {
    38|      "provider": "hailuo",
    39|      "source_url": "https://hailuoai.video/",
    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."
    41|    }
    42|  ],
    43|  "hybrid_strategy": {
    44|    "realistic_clip_source": "free_tier_ai_video_tools_manual_handoff",
    45|    "mechanism_explainer_source": "local_renderer",
    46|    "captions": "local_kinetic_renderer_overlay",
    47|    "final_assembly": "local_ffmpeg"
    48|  },
    49|  "ai_video_jobs": [
    50|    {
    51|      "job_id": "scene_01_free_ai_video_handoff",
    52|      "scene_id": "scene_01",
    53|      "asset_mode": "cinematic_ai_video",
    54|      "status": "manual_generation_required",
    55|      "duration_seconds": 1.4,
    56|      "aspect_ratio": "9:16",
    57|      "preferred_resolution": "480p_or_best_free_available",
    58|      "provider_order": [
    59|        "pixverse",
    60|        "pika",
    61|        "capcut",
    62|        "hailuo"
    63|      ],
    64|      "providers": [
    65|        {
    66|          "provider": "pixverse",
    67|          "display_name": "PixVerse",
    68|          "url": "https://app.pixverse.ai/",
    69|          "free_tier": {
    70|            "daily_refresh_credits": 90,
    71|            "signup_required": true,
    72|            "best_use": "realistic text/image-to-video short clips"
    73|          },
    74|          "risks": [
    75|            "terms and quotas can change",
    76|            "manual account/login required",
    77|            "possible watermark depending export path"
    78|          ]
    79|        },
    80|        {
    81|          "provider": "pika",
    82|          "display_name": "Pika",
    83|          "url": "https://pika.art/",
    84|          "free_tier": {
    85|            "monthly_credits": 80,
    86|            "max_resolution": "480p",
    87|            "text_to_video_5s_credits": 12,
    88|            "watermark_free_claimed": true,
    89|            "commercial_use_claimed": true,
    90|            "signup_required": true
    91|          },
    92|          "risks": [
    93|            "low 480p free resolution",
    94|            "monthly quota is small",
    95|            "manual account/login required"
    96|          ]
    97|        },
    98|        {
    99|          "provider": "capcut",
   100|          "display_name": "CapCut AI Video Maker",
   101|          "url": "https://www.capcut.com/tools/ai-video-generator",
   102|          "free_tier": {
   103|            "hd_export_claimed": true,
   104|            "credit_amount_publicly_clear": false,
   105|            "best_use": "editorial assembly, templates, captions, quick drafts",
   106|            "signup_required": true
   107|          },
   108|          "risks": [
   109|            "free quota not clearly exposed on public page",
   110|            "may upsell Pro assets",
   111|            "manual export required"
   112|          ]
   113|        },
   114|        {
   115|          "provider": "hailuo",
   116|          "display_name": "Hailuo AI",
   117|          "url": "https://hailuoai.video/",
   118|          "free_tier": {
   119|            "default_visible_generation_cost_credits": 25,
   120|            "signup_required": true,
   121|            "use_only_existing_free_credits": true
   122|          },
   123|          "risks": [
   124|            "do not buy credits",
   125|            "free quota may vary",
   126|            "user explicitly wants no paid Hailuo credits"
   127|          ]
   128|        }
   129|      ],
   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.",
   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",
   132|      "overlay_text": "Dieser Occasion-Käufer denkt, er spart 4'000 Franken.",
   133|      "free_tier_guardrails": {
   134|        "do_not_buy_credits": true,
   135|        "use_only_free_daily_or_monthly_quota": true,
   136|        "avoid_pro_templates_or_paid_assets": true,
   137|        "export_without_baked_captions": true,
   138|        "final_captions_added_locally": true
   139|      },
   140|      "manual_steps": [
   141|        "Open the first available provider in provider_order.",
   142|        "Use text-to-video or image-to-video with the prompt below; choose vertical 9:16 if available.",
   143|        "Set duration to the requested scene duration or the nearest free option, usually 5s.",
   144|        "Do not buy credits, subscribe, or select paid-only models/templates.",
   145|        "Download the clip, then place it under data/imported_ai_clips/<video_id>/<scene_id>.mp4 for local assembly."
   146|      ],
   147|      "expected_clip_path": "data/imported_ai_clips/{video_id}/scene_01.mp4"
   148|    },
   149|    {
   150|      "job_id": "scene_02_free_ai_video_handoff",
   151|      "scene_id": "scene_02",
   152|      "asset_mode": "cinematic_ai_video",
   153|      "status": "manual_generation_required",
   154|      "duration_seconds": 3.6,
   155|      "aspect_ratio": "9:16",
   156|      "preferred_resolution": "480p_or_best_free_available",
   157|      "provider_order": [
   158|        "pixverse",
   159|        "pika",
   160|        "capcut",
   161|        "hailuo"
   162|      ],
   163|      "providers": [
   164|        {
   165|          "provider": "pixverse",
   166|          "display_name": "PixVerse",
   167|          "url": "https://app.pixverse.ai/",
   168|          "free_tier": {
   169|            "daily_refresh_credits": 90,
   170|            "signup_required": true,
   171|            "best_use": "realistic text/image-to-video short clips"
   172|          },
   173|          "risks": [
   174|            "terms and quotas can change",
   175|            "manual account/login required",
   176|            "possible watermark depending export path"
   177|          ]
   178|        },
   179|        {
   180|          "provider": "pika",
   181|          "display_name": "Pika",
   182|          "url": "https://pika.art/",
   183|          "free_tier": {
   184|            "monthly_credits": 80,
   185|            "max_resolution": "480p",
   186|            "text_to_video_5s_credits": 12,
   187|            "watermark_free_claimed": true,
   188|            "commercial_use_claimed": true,
   189|            "signup_required": true
   190|          },
   191|          "risks": [
   192|            "low 480p free resolution",
   193|            "monthly quota is small",
   194|            "manual account/login required"
   195|          ]
   196|        },
   197|        {
   198|          "provider": "capcut",
   199|          "display_name": "CapCut AI Video Maker",
   200|          "url": "https://www.capcut.com/tools/ai-video-generator",
   201|          "free_tier": {
   202|            "hd_export_claimed": true,
   203|            "credit_amount_publicly_clear": false,
   204|            "best_use": "editorial assembly, templates, captions, quick drafts",
   205|            "signup_required": true
   206|          },
   207|          "risks": [
   208|            "free quota not clearly exposed on public page",
   209|            "may upsell Pro assets",
   210|            "manual export required"
   211|          ]
   212|        },
   213|        {
   214|          "provider": "hailuo",
   215|          "display_name": "Hailuo AI",
   216|          "url": "https://hailuoai.video/",
   217|          "free_tier": {
   218|            "default_visible_generation_cost_credits": 25,
   219|            "signup_required": true,
   220|            "use_only_existing_free_credits": true
   221|          },
   222|          "risks": [
   223|            "do not buy credits",
   224|            "free quota may vary",
   225|            "user explicitly wants no paid Hailuo credits"
   226|          ]
   227|        }
   228|      ],
   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.",
   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",
   231|      "overlay_text": "Das sieht harmlos aus, bis du den Mechanismus dahinter siehst.",
   232|      "free_tier_guardrails": {
   233|        "do_not_buy_credits": true,
   234|        "use_only_free_daily_or_monthly_quota": true,
   235|        "avoid_pro_templates_or_paid_assets": true,
   236|        "export_without_baked_captions": true,
   237|        "final_captions_added_locally": true
   238|      },
   239|      "manual_steps": [
   240|        "Open the first available provider in provider_order.",
   241|        "Use text-to-video or image-to-video with the prompt below; choose vertical 9:16 if available.",
   242|        "Set duration to the requested scene duration or the nearest free option, usually 5s.",
   243|        "Do not buy credits, subscribe, or select paid-only models/templates.",
   244|        "Download the clip, then place it under data/imported_ai_clips/<video_id>/<scene_id>.mp4 for local assembly."
   245|      ],
   246|      "expected_clip_path": "data/imported_ai_clips/{video_id}/scene_02.mp4"
   247|    }
   248|  ],
   249|  "local_jobs": [
   250|    {
   251|      "scene_id": "scene_03",
   252|      "asset_mode": "mechanism_animation",
   253|      "provider": "local_renderer",
   254|      "execution_mode": "mechanism_reveal_animation",
   255|      "duration_seconds": 13,
   256|      "role": "local_mechanism_or_caption_layer"
   257|    },
   258|    {
   259|      "scene_id": "scene_04",
   260|      "asset_mode": "mechanism_animation",
   261|      "provider": "local_renderer",
   262|      "execution_mode": "mechanism_reveal_animation",
   263|      "duration_seconds": 10,
   264|      "role": "local_mechanism_or_caption_layer"
   265|    },
   266|    {
   267|      "scene_id": "scene_05",
   268|      "asset_mode": "kinetic_text_overlay",
   269|      "provider": "local_ffmpeg",
   270|      "execution_mode": "kinetic_typography",
   271|      "duration_seconds": 6,
   272|      "role": "local_mechanism_or_caption_layer"
   273|    }
   274|  ]
   275|}
   276|


Explanation:
**Considering prompt improvements**

It 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."