[M G] co/Civitai/Qwen-Image-2512-GGUF "Civitai/Qwen-Image-2512-GGUF · Hugging Face"
Sollen wir qwen image auch selber über comfyui laufen lassen? Oder wäre das parallel mit dem videomodell nicht möglich?
 für GGUF + ComfyUI ist aktuell Qwen-Image-2512-GGUF wahrscheinlich die beste praktische Wahl – nicht das ältere Qwen-Image, sondern die neuere **Qwen-Image-2512**-Variante.

Meine Rangliste für deinen Usecase:

| Zweck                                                                     | Empfehlung                    | Warum                                                                                |
| ------------------------------------------------------------------------- | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Beste kostenlose GGUF-Bildgenerierung in ComfyUI                      | Qwen-Image-2512-GGUF      | Sehr stark bei Realismus, Text im Bild, Prompt-Folgen; Apache-2.0; direkt als GGUF verfügbar. Unsloth beschreibt es als GGUF-Quantisierung mit ComfyUI-GGUF-Support. ([Hugging Face][1]) |
| Bildbearbeitung / Referenzbilder / Text in bestehenden Bildern ändern | Qwen-Image-Edit-2511-GGUF | Separates Edit-Modell, GGUF, nutzt ComfyUI-GGUF. ([Hugging Face][2])                                                                                |
| Schnell / weniger VRAM / experimentell                                | Z-Image Turbo GGUF        | Läuft mit ComfyUI-GGUF und ist deutlich leichter/schneller, aber nicht die beste Gesamtqualität. ([Hugging Face][3])                                                                     |
| Alternative zu Qwen, stark aber schwerer                              | FLUX.2-dev-GGUF           | Gute Qualität, aber grösser, Lizenz weniger frei/praktisch als Apache-2.0; Q4_K_M allein ca. 18.7 GB. ([Hugging Face][4])                                                                |
| Aktuell stärkstes Open-Weights-Modell allgemein                       | HiDream-O1-Image-Dev-2604 | Führt bei Artificial Analysis unter Open-Weights, aber der ComfyUI-Weg ist derzeit eher über Custom Node/FP8, nicht sauber als etablierter GGUF-Standard. ([artificialanalysis.ai][5])   |

Kurz gesagt:
Für deine lokale ComfyUI/Hermes-Pipeline würde ich Qwen-Image-2512-GGUF nehmen.

Die passende Auswahl nach VRAM:

|        VRAM | Modell/Quantisierung                                   |
| ----------: | ------------------------------------------------------ |
| 8–12 GB | Qwen-Image-2512 Q4, Low-VRAM, langsam aber möglich |
|   16 GB | Q5 oder Q6 testen                                      |
|   24 GB | Q6 oder Q8, beste Balance aus Qualität und Stabilität  |
|   <8 GB | eher Z-Image Turbo GGUF statt Qwen                 |

Wichtig: Qwen-Image-2512 ist ein 20B Image-Diffusion-Modell, kein klassisches LLM. Es nutzt aber grosse Text-/Vision-Komponenten. Die GGUF-Dateien sind relativ gross: bei einer Qwen-Image-2512-GGUF-Variante sind z. B. Q4 ca. 11.5 GB, Q6 ca. 16.8 GB, Q8 ca. 21.7 GB; echte VRAM-Nutzung kann höher liegen wegen Text Encoder, VAE, Auflösung und Workflow. ([Hugging Face][6])

Meine konkrete Empfehlung:
Installiere in ComfyUI ComfyUI-GGUF, lade Qwen-Image-2512-GGUF und starte mit Q4 oder Q5. Wenn es stabil läuft und du genug VRAM hast, auf Q6/Q8 wechseln. Für Edit/Compositing zusätzlich Qwen-Image-Edit-2511-GGUF.

[1]: https://huggingface.co/unsloth/Qwen-Image-2512-GGUF "unsloth/Qwen-Image-2512-GGUF · Hugging Face"
[2]: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF "unsloth/Qwen-Image-Edit-2511-GGUF · Hugging Face"
[3]: https://huggingface.co/jayn7/Z-Image-Turbo-GGUF "jayn7/Z-Image-Turbo-GGUF · Hugging Face"
[4]: https://huggingface.co/orabazes/FLUX.2-dev-GGUF "orabazes/FLUX.2-dev-GGUF · Hugging Face"
[5]: https://artificialanalysis.ai/image/leaderboard/text-to-image "Text to Image Leaderboard - Top AI Image Models"
[6]: https://huggingface.

[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. 342 message(s) were removed to free context space but could not be summarized. The removed messages contained earlier work in this session. Continue based on the recent messages below and the current state of any files or resources.

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


Erledigt, Sir.

## Neuer Baustein: Review Batch Renderer

Ich habe jetzt den direkt Telegram-tauglichen Batch-Renderer eingebaut:

```text
autoshorts/review/render.py
tests/test_review_render.py
```

## Was neu ist

### `render_review_batch_summary()`

Diese Funktion rendert einen kompletten `ReviewBatch` als einen Telegram-sicheren Textblock.

Er enthält:

- Batch-ID
- Summary Counts
  - `Ready`
  - `Blocked`
  - `Invalid`
- vollständige Review Packages für ready Candidates
- kompakte Fehlerlisten für blocked Candidates
- kompakte Fehlerlisten für invalid Candidates

Beispielstruktur:

```text
## Review Batch: batch-001
Ready: 1
Blocked: 1
Invalid: 1

## Ready for review
...

## Blocked
- blocked — Blocked title
  Reasons: quality gate blocked candidate

## Invalid
- invalid — Invalid title
  Reasons: hook is required
```

## Wichtiger Guardrail

Der Renderer vermeidet bewusst Sprache wie:

```text
publish
post
```

Warum? Weil Review und Publishing getrennte Phasen bleiben sollen.

Also kein versehentlicher semantischer Stolperdraht à la “approved-looking text = upload”. Das wäre genau die Sorte Automations-Dummheit, für die später Meetings erfunden werden.

## TDD eingehalten

Ablauf:

1. Tests für Batch Renderer geschrieben.
2. RED verifiziert:

```text
ModuleNotFoundError: No module named 'autoshorts.review.render'
```

3. Minimal implementiert.
4. Tests grün.
5. README aktualisiert.
6. Commit erstellt.

## Tests

Aktueller Stand:

```text
41 passed in 0.08s
```

## Neuer Commit

Branch:

```text
feat/core-approval-quality-gates
```

Commit:

```text
b7b5270 feat: add review batch renderer
```

Aktueller Verlauf:

```text
b7b5270 feat: add review batch renderer
2155728 feat: add review workflow buckets
deea24f feat: add candidate factory
ae6a4f6 feat: add content pillar registry
49d6daf feat: add idea candidates and testbatches
55b17fa feat: add telegram review package
3555ec2 feat: add approval parser and quality gate
7642749 Initial commit
```

## Pipeline jetzt

Wir haben jetzt die erste durchgängige interne Review-Strecke:

```text
Content Pillar
→ CandidateDraft
→ Candidate Factory
→ VideoCandidate
→ TestBatch
→ Review Workflow
   → ready_for_review
   → blocked
   → invalid
→ Review Batch Renderer
→ Telegram-safe Review Text
→ Approval Parser
```

## Nächster sinnvoller Schritt

Als Nächstes würde ich bauen:

```text
autoshorts/cli/demo_review_batch.py
```

oder besser als testbarer Core:

```text
autoshorts/demo/sample_batch.py
```

Ziel:

- sample AI-Life-Systems Batch erzeugen
- 1 ready Candidate
- 1 blocked Candidate
- 1 invalid Candidate
- ReviewBatch bauen
- Telegram-Text rendern

Dann können wir mit einem einzigen Command einen echten Review-Output lokal erzeugen und später direkt an Telegram hängen.


[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:
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--- 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. 138 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 ---

patch

        "-c:a",
        "aac",
        "-movflags",

        "-c:a",
        "aac",
        "-movflags",


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py from line 150 (2,879 chars)


        "-c:a",
        "aac",
    ]
    if plan.duration_mode == "trim_to_audio":
        command.append("-shortest")
    command.extend([
        "-movflags",
        "+faststart",
        plan.outp...[truncated]

        "-c:a",
        "aac",
        "-movflags",
        "+faststart",
        plan.output_path,
    ]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py (1,043 chars result)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py from line 206 (819 chars)


        stdout=completed.stdout,
        stderr=completed.stderr,
        duration_mode=plan.duration_mode,
        side_effects=("write_subtitles", "call_ffmpeg", "write_mp4"),

        stdout=completed.stdout,
        stderr=completed.stderr,
        side_effects=("write_subtitles", "call_ffmpeg", "write_mp4"),


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/final_preview.py (886 chars result)


terminal

pytest tests/test_voiceover.py tests/test_audio_caption_preview.py -q


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


python - <<'PY'
from pathlib import Path
from autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages
from autoshorts.audio.voiceover import build_truetrace_final_voiceo...[truncated]

Explanation:
**Improving clip integration**

I need to focus on producing an improved clip by integrating a new voiceover and adjusting the subtitle duration. I'm thinking that the target duration might not perfectly match the actual length from Edge TTS, which could be longer than expected. To make sure subtitles fit well, I can aim for a voice target duration of 24 seconds, although the actual might end up being around 26 seconds. I'll need to check this using the terminal for mandatory duration settings before generating the final version.


[terminal] ran `python - <<'PY'
from pathlib import Path
from autoshorts.demo.truetrace_previ...` -> exit 0, 1 lines output


import subprocess

from autoshorts.audio.probe import probe_media_duration_seconds

def test_probe_media_duration_seconds_parses_ffprobe_output():
    calls = []

    def fake_runner(command, *, capt...[truncated]

Explanation:
**Evaluating subtitle issues**

I’m looking into subtitles for a clip that's 29 seconds long, but they're only 24 seconds, which isn't right. I think I need to implement an audio probe to rebuild the track's actual duration. Maybe I can do this quickly, but I feel like tests are essential. I might generate a probe module and test it with a fake runner. Also, I could consider using ffprobe in the terminal to check the actual duration, and then rerender it. I just need to keep everything aligned with TDD and write the necessary tests.


[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_audio_probe.py (38 lines)


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


"""Small FFprobe helpers for audio/video review artifacts."""

from __future__ import annotations

from collections.abc import Callable
import subprocess

Runner = Callable[..., subprocess.CompletedPr...[truncated]


pytest tests/test_audio_probe.py tests/test_voiceover.py tests/test_audio_caption_preview.py -q


Output:
..........                                                               [100%]
10 passed in 0.07s


python - <<'PY'
from pathlib import Path
from math import ceil
from autoshorts.demo.truetrace_preview_packages import build_top_truetrace_preview_packages
from autoshorts.audio.voiceover import build_...[truncated]


[terminal] ran `python - <<'PY'
from pathlib import Path
from math import ceil
from autoshort...` -> exit 0, 1 lines output


patch

- `autoshorts.audio.edge_tts_adapter` — explicit EdgeTTS MP3 generation boundary for final voiceover previews.
- `autoshorts.audio.final_preview` — ASS subtitle generation plus FFmpeg audio/caption MP...[truncated]

Explanation:
**Updating documentation**

I need to think about updating the README and pyproject files. This involves adding information about the modules and running full tests to ensure everything works smoothly. I'll also need to commit those changes afterward. Maybe I should consider including some media to enhance the documentation, too. Lastly, I might need to patch the documentation further if I find anything that needs clarification or improvement. Let's keep track of these tasks!


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,226 chars result)


- `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.
- `autoshorts.audio.voiceover...[truncated]

- `autoshorts.audio.final_preview.render_audio_caption_preview()` writes the ASS subtitle file and calls FFmpeg to mux EdgeTTS audio plus burn captions into a review MP4.

## Caption pipeline


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md (1,512 chars result)


[terminal] ran `pytest tests/ -q` -> exit 0, 1 lines output


terminal

git status --short && git add autoshorts/audio/voiceover.py autoshorts/audio/probe.py autoshorts/audio/final_preview.py tests/test_voiceover.py tests/test_audio_probe.py tests/test_audio_caption_preview.py README.md docs/subtitle_tts_strategy.md && git commit -m "feat: align TrueTrace voiceover previews" && git status --short && git log --oneline -5


[terminal] ran `git status --short && git add autoshorts/audio/voiceover.py autoshorts/audio/...` -> exit 0, 1 lines output


Erledigt, Sir.

Ich habe den nächsten Qualitäts-Slice gebaut: **kürzere finale Voiceover-Skripte + tatsächliche Audio-Dauer messen + Subtitles daran ausrichten + explizites Trim-to-Audio nur wenn gewünscht**.

## Neu gebaut

Neue Module:

```text
autoshorts/audio/voiceover.py
autoshorts/audio/probe.py
```

Erweitert:

```text
autoshorts/audio/final_preview.py
```

Neue Tests:

```text
tests/test_voiceover.py
tests/test_audio_probe.py
```

Commit:

```text
b9b6423 feat: align TrueTrace voiceover previews
```

## Was verbessert wurde

### 1. Final Voiceover Scripts

Neue Funktion:

```python
build_truetrace_final_voiceover()
```

Für die Top-3 Konzepte gibt es jetzt kompakte finale English Voiceover-Texte.

Für den ersten Clip:

```text
Most AI automations do not fail dramatically. They quietly rot.
Day one looks clean, because the demo input behaves.
By day three, the real input changes.
Now the workflow needs an exception path.
Normal goes forward. Weird goes to review. Unclear stops.
If your automation has no exception path, it is not a system.
It is a lucky prompt.
```

Eigenschaften:

```text
Words: 59
Target duration: 24s
Language: en
Voice: en-GB-RyanNeural
No generic filler
No “Follow for...” CTA-Geschwurbel
```

### 2. Audio Duration Probe

Neue Funktion:

```python
probe_media_duration_seconds()
```

Sie liest per FFprobe die echte MP3-Dauer aus.

Warum? Weil EdgeTTS natürlich nicht exakt unsere Wunschdauer spricht. Es ist TTS, kein Schweizer Uhrwerk mit britischem Akzent.

Beim erzeugten Clip:

```text
Estimated target: 24s
Actual EdgeTTS audio: 29.112s
```

### 3. Subtitle Timing an echte Audio-Dauer angepasst

Vorher:

```text
Subtitle duration: estimated 24s
Audio duration: 29.112s
```

Jetzt:

```text
Subtitle duration: 30.0s
Audio duration: 29.112s
```

Damit laufen Captions nicht mehr zu früh aus.

### 4. `duration_mode`

Der Audio+Caption Renderer unterstützt jetzt:

```python
duration_mode="preserve_video"
```

Default. Kein `-shortest`, kein heimliches Abschneiden.

Und explizit:

```python
duration_mode="trim_to_audio"
```

Nur dann wird `-shortest` verwendet.

Das ist wichtig: FFmpeg darf schneiden, aber nur mit schriftlicher Genehmigung. Wie ein anständiger Bürokrat.

## Neuer verbesserter Review Clip

Candidate:

```text
truetrace-ai-automation-quiet-rot-38s
```

Output:

MEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-audio-caption-v3/truetrace-ai-automation-quiet-rot-38s/audio-caption-preview-v3.mp4

## Verifiziert

FFprobe:

```text
Video: h264, 540x960
Video duration: 29.083s
Audio: aac
Audio duration: 29.112s
```

SHA256:

```text
6c71929420bd266b8569440feed9a8ca7dc392e3cead8cc55bca2dc33118c871
```

## Tests

Full Suite:

```text
161 passed in 32.72s
```

## Aktueller Commit-Verlauf

```text
b9b6423 feat: align TrueTrace voiceover previews
3f68001 feat: add EdgeTTS audio caption preview
066201d feat: attach audio plans to TrueTrace previews
30a8129 feat: add subtitle and tts planning
6efc4dc feat: add TrueTrace top preview packages
```

## Nächster sinnvoller Schritt

Jetzt ist die Audio/Captions-Kette solide. Als Nächstes würde ich die visuelle Seite verbessern:

```text
static timing cards
→ simple motion renderer
→ kinetic background movement
→ scene progress bars
→ caption punch-in/pulse
→ mechanism diagram animation
```

Also weniger PowerPoint-mit-Puls, mehr echter Short.


[M G] Das voiceover und die subtitles decken sich nicht mit dem text im oberen feld… wirkt das ganze nicht zu abgehoben? Erreichen wir so auch die leite welche noch nocht soo voel erfahrung mit KI haben?


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


Ja, Sir — **Ihre Kritik ist korrekt**.

## 1. Voiceover/Subtitles passen nicht zum oberen Feld

Das aktuelle Preview ist technisch korrekt, aber dramaturgisch unsauber:

```text
Oberes Feld / Card:
alte German approval/timing copy

Voiceover/Subtitles:
neuer English final voiceover text
```

Das erzeugt kognitive Reibung. Der Zuschauer sieht oben etwas anderes als er hört/liest. Für Review ist das schon störend; für echten Content wäre es tödlich genug, um einen kleinen Ausschuss einzuberufen.

## Fix

Wir brauchen **eine einzige Source of Truth** pro Clip:

```text
FinalVoiceoverScript
→ SubtitleTrack
→ Top Hook Text
→ Scene Card Text
→ Social Caption
```

Nicht:

```text
Candidate Concept Text
+ später anderer Voiceover Text
+ dritte Caption-Version
```

Also: Der Text im oberen Feld muss aus demselben finalen Voiceover/Hook kommen wie Subtitles und Audio.

---

## 2. Ja: aktuell wirkt es zu abgehoben

Der aktuelle Text ist gut für:

```text
AI builders
Automation nerds
Workflow-/Ops-Leute
Leute, die schon wissen, was ein “exception path” ist
```

Aber für breiteres Publikum ist es zu abstrakt.

Begriffe wie:

```text
exception path
input boundary
output standard
action boundary
decision structure
```

sind für Fachleute präzise — aber für Anfänger klingt es nach einem LinkedIn-Post mit Krawatte.

## Problem

Wir sagen aktuell:

```text
If your automation has no exception path, it is not a system.
```

Das ist stark, aber nicht sofort greifbar.

Für Anfänger besser:

```text
If your AI does not know what to do when something weird happens,
it will break the first time real life shows up.
```

Das ist derselbe Gedanke, aber menschlicher.

---

## 3. Zielgruppe: Wir sollten breiter anfangen

Für TrueTrace würde ich nicht mit „AI systems for experts“ starten, sondern mit:

```text
AI tools that work in real life
```

Also Zielgruppe:

```text
People using ChatGPT, Notion AI, Zapier, Make, email automations,
but not yet thinking like system architects.
```

Die haben ein echtes Problem:

```text
“It worked once, then it became unreliable.”
```

Das versteht jeder.

---

## Neue Copy-Richtung

Statt:

```text
Most AI automations do not fail dramatically. They quietly rot.
```

Besser für breite Zielgruppe:

```text
Your AI automation did not really break.
Real life just gave it an input it was never prepared for.
```

Oder noch einfacher:

```text
Your AI automation works in the demo.
Then real life shows up.
```

Das ist deutlich stärker für Anfänger.

## Neue Hook-Variante

```text
Your AI automation works perfectly…
until the first weird email arrives.
```

Das ist konkret. Jeder versteht „weird email“.

---

## Neuer Anfänger-freundlicher Voiceover-Entwurf

Für Clip 1 würde ich den Text so umbauen:

```text
Your AI automation works perfectly in the demo.

Then one weird email arrives.

The name is missing.
The format is different.
The request is unclear.

And suddenly the automation does the wrong thing confidently.

That is not an AI problem.
That is a workflow problem.

Before you automate anything, decide three things:

What should go through?
What should be reviewed?
And what should stop?

If your AI does not know what to do with weird inputs,
it will break the moment real life shows up.
```

Warum besser:

- kein Jargon
- konkretes Beispiel: weird email
- klares Problem
- klare 3er-Regel
- Anfänger verstehen es sofort
- Fortgeschrittene merken trotzdem: es geht um exception handling

---

## Visuals dazu

Statt abstrakter Systemkarten:

```text
Scene 1:
Perfect demo inbox. Everything green.

Scene 2:
A weird email appears:
“hey can you handle this maybe???”

Scene 3:
AI confidently routes it wrong.

Scene 4:
Split path:
Normal → automate
Unclear → review
Dangerous/weird → stop

Scene 5:
Rule:
Don’t automate the happy path.
Plan for the weird one.
```

Das ist viel zugänglicher.

---

## Empfehlung

Wir pivoten die Reihe von:

```text
Systems over prompts.
```

zu einer zugänglicheren Oberfläche:

```text
Make AI work in real life.
```

Der Brand-Kern bleibt:

```text
Systems over prompts.
```

Aber der Zuschauer-Hook wird alltagstauglicher.

## Konkrete nächste Änderung

Ich würde jetzt als nächsten Slice bauen:

```text
Beginner-friendly voiceover variants
+ unified text source for top field / captions / subtitles
+ regenerate Clip 1 as “weird email” version
```

Also:

```text
FinalVoiceoverScript
→ top card text
→ subtitles
→ audio
→ review MP4
```

Dann ist alles synchron und deutlich weniger abgehoben.

Explanation:
**Deciding on variants**

I need to establish my next point: I'll focus on two variants, "Beginner/Mainstream" and "Builder/Advanced." I think I should recommend the beginner option. I might say that there's no re-render until the copy is fixed, and I could mention a concrete replacement script. But I have to be careful not to say "will" unless I confirm with the tool. Saying "Nächster Schritt ist..." might work, but I shouldn’t make any promises. I’ll keep it concise!


[M G] Ja das ist besser. Mach jetzt weiter.


Explanation:
**Taking action for TDD implementation**

I need to take action and load the Test-Driven Development (TDD) process, making sure it's systematic and relevant. It's essential to use the right tools during this implementation. I also need to inspect the files involved to ensure everything's in order. It feels like there's a lot to accomplish, and I want to make sure I'm covering all necessary steps properly! Let's keep pushing forward on this!


[skill_view] name=test-driven-development (17,806 chars)


[skill_view] name=social-media-shorts-automation (18,560 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py from line 1 (4,531 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py from line 1 (10,295 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py from line 1 (8,745 chars)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py from line 1 (2,639 chars)


patch

from autoshorts.audio.voiceover import (
    FinalVoiceoverScript,
    build_truetrace_final_voiceover,
    validate_final_voiceover_script,
)

from autoshorts.audio.voiceover import (
    FinalVoiceoverScript,
    build_accessible_script_draft_from_voiceover,
    build_truetrace_final_voiceover,
    validate_final_voiceover_script,
)


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (873 chars result)


def test_build_truetrace_final_voiceover_for_quiet_rot_is_concise_and_retention_led():
    script = build_truetrace_final_voiceover("truetrace-ai-automation-quiet-rot-38s")

    assert script.candidat...[truncated]

def test_build_truetrace_final_voiceover_for_quiet_rot_is_beginner_friendly_and_concrete():
    script = build_truetrace_final_voiceover("truetrace-ai-automation-quiet-rot-38s")

    assert script.can...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (1,631 chars result)


    assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]
    assert all(45 <= len(script.text.split()) <= 85 for script in scripts)

    assert [script.target_duration_seconds for script in scripts] == [24, 23, 22]
    assert all(45 <= len(script.text.split()) <= 70 for script in scripts)


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (859 chars result)


def test_build_accessible_script_draft_from_voiceover_uses_one_text_source_for_visuals():
    voiceover = build_truetrace_final_voiceover("truetrace-ai-automation-quiet-rot-38s")

    draft = build_ac...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (1,691 chars result)


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


from dataclasses import dataclass

from autoshorts.content.script_draft import ScriptDraft, validate_script_draft
from autoshorts.ideas.candidate import ValidationResult

from dataclasses import dataclass

from autoshorts.ideas.candidate import ValidationResult

Explanation:
**Implementing script updates**

I need to import ScriptDraft and consider the validations. If the max is 80, I'm wondering how it will react at 85. I think my count might be 76, which seems okay. I'll update _SCRIPTS accordingly and think about a simple mapping for the draft. It feels a bit scattered, but I’m aiming to make sure everything works as it should! Plus, clarity is key, right?


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py (565 chars result)


    "truetrace-ai-automation-quiet-rot-38s": (
        "Your AI automation works perfectly in the demo. Then one weird email arrives. "
        "The name is missing. The format is different. The reque...[truncated]

    "truetrace-ai-automation-quiet-rot-38s": (
        "Most AI automations do not fail dramatically. They quietly rot. "
        "Day one looks clean, because the demo input behaves. By day three, th...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py (1,662 chars result)


def build_truetrace_final_voiceover(candidate_id: str) -> FinalVoiceoverScript:
    if candidate_id not in _SCRIPTS:
        raise ValueError(f"unknown TrueTrace candidate id: {candidate_id}")
    tex...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py (2,423 chars result)


    if len(script.text.split()) > 90:
        errors.append("voiceover must stay under 90 words")

    if len(script.text.split()) > 80:
        errors.append("voiceover must stay under 80 words")


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py (960 chars result)


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


Explanation:
**Building a unified preview**

I need to focus on creating a unified preview. I could manually generate it using a new draft in the script. Maybe it makes sense to run a test—there might not be a need for additional code. I should consider using a pipeline to generate a clip, but it's currently in English. I might want to add a demo helper module and improve the existing implementations. The current shot plan captions feel too generic and need some adjustments. I need to inspect the tests to see if they align with these updates.


Explanation:
**Planning tests for shot_plan**

I need to implement tests for the shot_plan, focusing on how it incorporates voiceover beats. It’s essential for me to ensure that everything functions smoothly and that the voiceovers align perfectly with the beats. I should consider different scenarios to cover various use cases. Testing thoroughly will help maintain quality, so I want to be careful and systematic in this process. Let’s get started on figuring out the best approaches for these tests!


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py from line 1 (5,267 chars)


    assert plan.scenes[0].caption_beat == script.hook
    assert plan.scenes[1].caption_beat == script.retention_beats[0]
    assert plan.scenes[2].caption_beat == script.retention_beats[1]
    assert...[truncated]

    assert plan.scenes[0].caption_beat == script.hook
    assert "pattern interrupt" in plan.scenes[0].retention_goal.casefold()


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_shot_plan.py (959 chars result)


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


caption_beat="AI suggests. Human approves.",


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py (836 chars result)


caption_beat=beats[1] if len(beats) > 1 else script.script_outline,

caption_beat="The rule: AI drafts, you decide.",


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py (948 chars result)


caption_beat=beats[2] if len(beats) > 2 else script.script_outline,

caption_beat="10-minute review. One human checkpoint.",


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/shot_plan.py (908 chars result)


terminal

pytest tests/test_voiceover.py tests/test_shot_plan.py -q


Output:
.........                                                                [100%]
9 passed in 0.06s


python - <<'PY'
from pathlib import Path
from math import ceil
from autoshorts.audio.voiceover import build_truetrace_final_voiceover, build_accessible_script_draft_from_voiceover
from autoshorts.audi...[truncated]

Explanation:
**Generating new preview**

I need to create a new preview using the latest draft and English frames. Maybe I need some tests, but I think we have enough for now. I just need to make sure to use the tools correctly to generate the output. I should also check if localizing the frame copy will return the caption text correctly. Good! I’ll utilize the create_shot_plan and build the manifest. Time to render everything and run the script!


[terminal] ran `python - <<'PY'
from pathlib import Path
from math import ceil
from autoshort...` -> exit 0, 1 lines output


patch

assert script.target_duration_seconds == 37

assert script.target_duration_seconds == 29


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (806 chars result)


assert [script.target_duration_seconds for script in scripts] == [37, 23, 22]

assert [script.target_duration_seconds for script in scripts] == [29, 23, 22]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_voiceover.py (779 chars result)


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/audio/voiceover.py (839 chars result)


Output:
.........                                                                [100%]
9 passed in 0.08s


Question: Check whether the top card text is readable and matches the beginner-friendly weird-email hook. Note any clipping or mismatch.

Explanation:
**Inspecting visual frames**

I’m thinking I need to visually inspect the first frame to ensure it’s displaying correctly. Maybe I’ll use some vision analysis to help with this. A screenshot or frame image might be useful for my analysis. I want to make sure everything looks good, so I’ll take a careful look. It's important to get this right before moving on. Let's see what the frame reveals!


[vision_analyze] 'Check whether the top card text is readable and ma' (257 chars)


Explanation:
**Considering localization issues**

I'm looking at some localization issues with the content. The German labels for "Zeit/Bewegung" are present while the content is in English. It might be acceptable as is, but I wonder if I should update the English labels in the copy. The body seems to be hardcoded to German labels, and there's concern about a mismatch in the top text. I'll think about testing this and possibly adding it into the local frame writer. Let's see what existing tests are available!


    body_labels = {
        "time": "Zeit" if approval_language == "de" else "Time",
        "asset": "Asset",
        "label": "Label",
        "motion": "Bewegung" if approval_language == "de" else ...[truncated]

    body = (
        f"Zeit: {frame.start_ms / 1000:.0f}s + {frame.duration_ms / 1000:.0f}s\n"
        f"Asset: {copy.asset_label}\n"
        f"Label: {copy.scene_label.title()}\n\n"
        f"Bewegun...[truncated]

Explanation:
**Polishing documentation**

I’m considering whether I really need to polish the documentation with language labels. It might not be strictly necessary, but I want to ensure everything is clear. I need to run full tests, update the documentation, and commit those changes. Updating the README might also be a good idea. I really want to make sure everything is up to date and user-friendly, so I’ll aim to use a patch for clarity!


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/local_frame_writer.py (1,366 chars result)


`autoshorts.audio.probe.probe_media_duration_seconds()` reads generated media duration via FFprobe so subtitle cues can be aligned to the actual EdgeTTS MP3 length.

`autoshorts.audio.voiceover.build_...[truncated]

`autoshorts.audio.probe.probe_media_duration_seconds()` reads generated media duration via FFprobe so subtitle cues can be aligned to the actual EdgeTTS MP3 length.

## Current scope


Found 100 matches for old_string. Provide more context to make it unique, or use replace_all=True.


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md from line 1 (8,885 chars)


- `autoshorts.audio.voiceover` — beginner-friendly final English TrueTrace voiceover scripts and a single-source script-draft bridge for aligned visuals/captions.

- `autoshorts.audio.voiceover` — concise final English TrueTrace voiceover scripts for the top concepts.


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,430 chars result)


Subtitle/TTS strategy is documented in `docs/subtitle_tts_strategy.md`: use deterministic Hormozi-inspired renderer-owned captions; start TTS with EdgeTTS English baseline; A/B test NVIDIA Magpie late...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md (1,061 chars result)


[read_file] read /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md from line 1 (5,016 chars)


- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides beginner-friendly English final voiceover scripts for the top TrueTrace concepts. The first pilot uses the concrete `weird ema...[truncated]

- `autoshorts.audio.voiceover.build_truetrace_final_voiceover()` provides concise English final voiceover scripts for the top TrueTrace concepts.


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/docs/subtitle_tts_strategy.md (2,177 chars result)


[terminal] ran `pytest tests/ -q` -> exit 0, 1 lines output


terminal

git status --short && git add autoshorts/audio/voiceover.py autoshorts/rendering/shot_plan.py autoshorts/rendering/local_frame_writer.py tests/test_voiceover.py tests/test_shot_plan.py README.md docs/subtitle_tts_strategy.md && git commit -m "feat: make TrueTrace pilot beginner friendly" && git status --short && git log --oneline -5


[terminal] ran `git status --short && git add autoshorts/audio/voiceover.py autoshorts/render...` -> exit 0, 1 lines output


Erledigt, Sir.

Sie hatten völlig recht — ich habe jetzt nicht nur kosmetisch geändert, sondern die Pipeline so angepasst, dass **Voiceover, Subtitles und oberes Kartenfeld aus derselben Quelle kommen**.

## Neuer Commit

```text
7375311 feat: make TrueTrace pilot beginner friendly
```

## Was geändert wurde

### 1. Voiceover beginner-friendly gemacht

Alter Stil:

```text
Most AI automations do not fail dramatically.
They quietly rot.
...
exception path
```

Neuer Stil:

```text
Your AI automation works perfectly in the demo.
Then one weird email arrives.
The name is missing.
The format is different.
The request is unclear.
...
If your AI does not know what to do with weird inputs,
it will break the moment real life shows up.
```

Das ist deutlich zugänglicher:

- konkretes Beispiel: `weird email`
- kein Jargon wie `exception path`
- kein `input boundary`
- kein `output standard`
- kein Architekten-Englisch im Smoking
- Problem ist auch für KI-Anfänger sofort verständlich

## 2. Eine Source of Truth

Neu:

```python
build_accessible_script_draft_from_voiceover()
```

Das macht:

```text
FinalVoiceoverScript
→ ScriptDraft
→ ShotPlan
→ RenderManifest
→ Top Card Text
→ Scene Captions
→ Subtitles
→ Voiceover
```

Damit vermeiden wir genau den Fehler von vorher:

```text
oben alter Karten-Text
unten neue Subtitles
Audio nochmal anders
```

Dieser Wildwuchs ist jetzt abgeschnitten. Mit Anstand.

## 3. ShotPlan nutzt jetzt echte Script-Beats

Vorher waren Szenencaptions teilweise generisch:

```text
AI suggests. Human approves.
The rule: AI drafts, you decide.
10-minute review. One human checkpoint.
```

Jetzt kommen sie aus den echten Retention Beats des finalen Scripts:

```text
The name is missing. The format is different. The request is unclear.
Suddenly the automation does the wrong thing confidently.
That is not an AI problem. That is a workflow problem.
```

## 4. Preview-Karten auf Englisch konsistenter

Für die neuen final-nahen Previews werden die Karten nicht mehr halb Deutsch/halb Englisch beschriftet.

Geändert:

```text
Zeit → Time
Bewegung → Motion
```

Wenn `approval_language="en"` verwendet wird.

## Neuer generierter Clip

Candidate:

```text
truetrace-ai-automation-quiet-rot-38s
```

Neue Version:

MEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-weird-email-v2/truetrace-ai-automation-quiet-rot-38s/weird-email-audio-caption-preview.mp4

## Verifiziert

```text
Words: 85
Target duration: 37s
Actual EdgeTTS audio: 36.864s
Subtitle duration: 37.0s
Video duration: 36.833s
Audio duration: 36.864s
```

SHA256:

```text
5b903b148b6123c64dd30754b9f6bfb294154f0973ab2dfa90619ee22f6f1044
```

Ich habe zusätzlich den ersten Frame visuell geprüft: Top-Text ist lesbar und lautet jetzt passend:

```text
Your AI automation works perfectly in the demo.
Then one weird email arrives.
```

## Tests

Full Suite:

```text
162 passed in 32.43s
```

## Einschätzung zur Zielgruppe

Ja: Diese Richtung ist viel besser für Leute mit wenig KI-Erfahrung.

Der Kanal sollte nicht starten mit:

```text
“Here is a system architecture principle.”
```

Sondern mit:

```text
“Here is why your ChatGPT/Zapier/Email automation breaks when real life gets messy.”
```

Das holt Anfänger ab, ohne den eigentlichen System-Gedanken zu verlieren.

## Nächster sinnvoller Schritt

Jetzt sollten wir visuell noch weg von Karten und hin zu einer konkreteren Szene:

```text
Demo inbox
→ weird email appears
→ AI routes it wrong
→ simple 3-way split:
   Go through
   Review
   Stop
```

Das wäre dann nicht nur verständlicher Text, sondern auch ein verständlicher Mechanismus im Bild.


[M G] Ok. Wie gehst du nun vor für die generierung des richtigen videos?


Explanation:
**Creating a plan for video generation**

I need to come up with an answer plan since the user is asking how to proceed. It seems that social media skills are relevant here, but I'm not sure if a tool is required for this part. Maybe I should load up writing plans to help generate a proper video based on their specific skill set. Also, could comfyui be relevant for effective video generation? I’ll need to consider loading that if it fits!


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


[skill_view] name=writing-plans (8,527 chars)


Ja, Sir. Für das **richtige Video** würde ich jetzt nicht direkt „ein Wan/Qwen Prompt reinwerfen und hoffen“ — das produziert sehr schnell hübsche, aber unbrauchbare AI-Suppe. Wir machen es als **Hybrid Retention Studio Pipeline**:

```text
AI generiert nur visuelle Textur / Motion
JARVIS/Renderer kontrolliert Text, Timing, Captions, Labels, Audio, Schnitt
```

## Zielbild für Clip 1

Thema:

```text
Your AI automation works in the demo.
Then one weird email arrives.
```

Visueller Ablauf:

```text
1. Clean demo inbox
2. Weird email appears
3. AI confidently routes it wrong
4. Simple 3-way split:
   Go through
   Review
   Stop
5. Final rule:
   Plan for the weird input before you automate the normal one.
```

Das ist zugänglich, konkret und nicht abgehoben.

---

## Mein Vorgehen

## Phase 1: Final Render Plan bauen

Zuerst mache ich aus dem aktuellen Script einen **richtigen Produktionsplan**, nicht nur Preview Cards.

Neues Artefakt:

```text
FinalVideoPlan
```

Enthält pro Szene:

```text
scene_id
start_ms
end_ms
voiceover_segment
visual_mode
asset_type
motion_instruction
renderer_text
caption_cues
required_assets
```

Wichtig:

```text
renderer_text ≠ AI-generated text
```

Alle Texte im Bild — Inbox-Labels, Pfeile, „Review“, „Stop“, Captions — kommen vom Renderer, nicht vom Video-Modell.

Warum? Weil AI-Video bei Text notorisch versagt. Es würde aus „Review“ gerne „Rëvlew“ machen. Künstlerisch mutig, aber geschäftlich nutzlos.

---

## Phase 2: Szenen in Asset-Typen trennen

Ich würde Clip 1 so aufteilen:

### Szene 1 — Hook

```text
0–4s
Visual: clean inbox / clean workflow dashboard
Mode: local deterministic animation + optional AI background
Text: Your AI automation works perfectly in the demo.
```

Kein echtes AI-Video nötig. Das kann unser Renderer sauber animieren.

### Szene 2 — Weird email arrives

```text
4–10s
Visual: email card slides in
Text on card:
"hey can you handle this maybe??"
Missing name
Unclear request
Wrong format
```

Auch hier: besser renderer-owned. Kein AI-Text im Bild.

### Szene 3 — AI gets it wrong

```text
10–17s
Visual: glowing AI node routes email to wrong action
Motion: confident green path, then red warning flash
```

Hier können wir optional AI-generated abstract/tech background nutzen, aber der Mechanismus selbst sollte lokal animiert werden.

### Szene 4 — The fix: 3 paths

```text
17–29s
Visual:
Normal → Go through
Unclear → Review
Weird/Risky → Stop
```

Das ist der wichtigste Mechanismus. Muss glasklar sein. Renderer-owned Diagramm.

### Szene 5 — Rule / close

```text
29–37s
Visual: final compact rule card
Text:
Plan for weird inputs before automating normal ones.
```

---

## Phase 3: Erst lokalen Motion Renderer bauen

Bevor wir ComfyUI/Wan/Qwen verschwenden, baue ich einen **procedural motion renderer**:

```text
Pillow/OpenCV/FFmpeg
```

Er erzeugt:

```text
animated inbox cards
moving arrows
path split
warning pulse
caption burn-in
audio mux
```

Das ist wichtig, weil dieser Clip sehr text-/diagramm-lastig ist. Dafür ist lokaler Renderer besser als AI-Video.

AI-Video wäre hier nur sinnvoll für:

```text
background texture
office/desk b-roll
cinematic phone/screen closeups
```

Nicht für den eigentlichen Erklärmechanismus.

---

## Phase 4: Danach AI-Visuals gezielt ergänzen

Dann, und erst dann, setzen wir ComfyUI ein.

Meine Empfehlung:

```text
Qwen-Image-2512-GGUF → Keyframes / clean background stills
Wan2.2 I2V → kurze motion clips aus diesen Keyframes
```

Aber nicht parallel auf derselben GPU.

Ablauf:

```text
1. Qwen-Image erzeugt 2–3 clean keyframes:
   - clean inbox dashboard
   - messy weird-email moment
   - AI workflow node background

2. GPU freigeben:
   /free unload_models + free_memory

3. Wan2.2 macht kurze I2V clips:
   - subtle camera push
   - screen glow
   - desk/monitor movement

4. Final Renderer legt darüber:
   - echte Texte
   - Captions
   - Pfeile
   - Labels
   - Audio
```

Also:

```text
AI = texture/motion
Renderer = Wahrheit
```

---

## Phase 5: Qualitätstor vor Final

Ich würde jeden Clip gegen diese Gates prüfen:

```text
- Versteht man in den ersten 2 Sekunden das Problem?
- Ist “weird email” sofort sichtbar?
- Sind alle Texte lesbar auf Mobile?
- Kein AI-generierter Fake-Text?
- Captions decken Voiceover?
- Keine Jargon-Wörter?
- Visuelle Veränderung alle 1–2 Sekunden?
- Mechanismus “Go through / Review / Stop” klar?
- Audio und Video gleich lang?
```

Wenn eines davon failt: nicht posten. Nicht einmal aus Mitleid.

---

## Konkreter nächster Implementierungsschritt

Ich würde jetzt bauen:

```text
autoshorts/rendering/final_video_plan.py
tests/test_final_video_plan.py
```

Danach:

```text
autoshorts/rendering/motion_renderer.py
tests/test_motion_renderer.py
```

Erste echte lokale Version:

```text
data/previews/truetrace-final-motion-v1/.../final-motion-preview.mp4
```

Diese Version wird noch ohne ComfyUI sein, aber schon:

```text
- echtes Motion Layout
- Inbox Card
- Weird Email
- 3-way path diagram
- Voiceover
- Captions
- Audio sync
```

Danach entscheiden wir:

```text
Reicht procedural style?
Oder ergänzen wir AI b-roll/keyframes mit Qwen/Wan?
```

## Warum ich nicht sofort Qwen/Wan nehme

Weil dieser Clip **nicht** primär ein „cinematic video“ ist. Er ist ein **mechanism reveal**.

Wenn wir sofort AI-Video nutzen, bekommen wir wahrscheinlich:

```text
schöne Screens
unlesbare UI
falsche Texte
random Bewegungen
weniger Klarheit
```

Das sieht professioneller aus, erklärt aber schlechter. Und für Shorts zählt: **Klarheit vor Schönheit**.

## Mein Vorschlag

Ich mache als nächstes:

```text
FinalVideoPlan + deterministic motion renderer v1
```

Dann haben wir den ersten echten „richtigen“ Video-Prototypen. Danach können wir gezielt ComfyUI-Keyframes ergänzen, statt mit der GPU Würfel zu spielen.


[M G] Ok, leg los. Bedenke: du kannst ChatGPT Image nutzen über codex anstelle eines lokalen qwen image.


Explanation:
**Implementing the image generation plan**

I need to take action and see if the image generation tool is available, possibly requiring backend support. The user asked to use the ChatGPT Image über codex instead of the local option. I can work on this plan! I'll need to ensure that all skills are appropriately loaded, but should I do this before replying each time? Testing driven development could be useful here, along with reviewing existing files.


[skill_view] name=social-media-shorts-automation (19,664 chars)


[skill_view] name=comfyui (32,631 chars)


from autoshorts.audio.voiceover import build_truetrace_final_voiceover
from autoshorts.rendering.final_video_plan import (
    FinalVideoPlan,
    build_truetrace_weird_email_final_video_plan,
    val...[truncated]

Explanation:
**Creating video tests**

I need to create tests and consider including an image note with the image_generate tool later. I could start with a deterministic approach and implement tests for the final video plan, possibly using a motion renderer. I plan to produce the actual video using animated frames generated with Pillow and ffmpeg. It looks like TDD will guide this process, so I need to know the necessary modules. Writing simple tests is my goal!


[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_final_video_plan.py (72 lines)


from pathlib import Path
import subprocess

from autoshorts.audio.voiceover import build_truetrace_final_voiceover
from autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_v...[truncated]


[write_file] wrote to /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py (54 lines)


pytest tests/test_final_video_plan.py tests/test_motion_renderer.py -q


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


"""Final-near vertical video production plan for TrueTrace pilots."""

from __future__ import annotations

from dataclasses import dataclass

from autoshorts.audio.voiceover import FinalVoiceoverScrip...[truncated]

Explanation:
**Implementing Frame Generation**

I'm planning to use ValidationResult and a motion renderer like PIL and ffmpeg. I need to make sure the actual frame count is appropriate—it's capped at 888 frames for 37x24, which could be slow but manageable. I'll proceed with simple draw functions to start, making sure to run preliminary tests first. I guess getting this setup right is crucial for smooth performance! Let's see how this will all work together.


"""Procedural motion renderer for final-near TrueTrace review videos."""

from __future__ import annotations

from collections.abc import Callable
from dataclasses import dataclass
from pathlib import...[truncated]


Output:
....                                                                     [100%]
4 passed in 0.27s


Prompt: Vertical 9:16 cinematic background for a modern AI automation short. A clean laptop/workspace scene with a dark minimal productivity dashboard glow, subtle blue and green light accents, premium tech aesthetic, shallow depth of field, no readable text, no logos, no watermarks, no UI labels, empty central space for overlaid renderer-owned text and diagrams. Realistic but abstract enough to serve as background texture.

Explanation:
**Generating image for video**

I need to generate an image with the ChatGPT Image tool. The user mentioned I can use it, so I'll likely create a background image to incorporate. However, I'm realizing the current motion renderer doesn't support background assets, so I might need to generate one separately as a keyframe or reference. I could then integrate the image manually later. For creating the video, I need to ensure it's combined with audio and captions in the final version, rendering a preview first. I'll go ahead and generate the background now!


[image_generate] aspect_ratio=portrait prompt=Vertical 9:16 cinematic background for a (685 chars result)


patch

    render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5)

    assert render_plan.width == 540

    background = tmp_path / "chatgpt-image-background.png"
    background.write_bytes(b"fake image")
    render_plan = build_motion_render_plan(final_plan, output_dir=tmp_path, preview_scale=0.5, back...[truncated]

Explanation:
**Integrating Backgrounds Into Rendering**

I'm considering integrating an optional background into the renderer. I think it makes sense to add an `optional background_path` to the `MotionRenderPlan`, and then ensure the render function uses it. For test-driven development, I'll need to confirm that the ChatGPT image background plan includes this path and that no AI text is allowed. Since the code is already there, I'll write a failing test for the new behavior before implementation. Sounds like a solid plan!


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/tests/test_motion_renderer.py (1,328 chars result)


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


    background_path: str | None = None
    side_effects: tuple[str, ...] = ()

    fps: int
    side_effects: tuple[str, ...] = ()


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (1,477 chars result)


    fps: int
    background_path: str | None = None
    side_effects: tuple[str, ...] = ()

Explanation:
**Correcting background settings**

I realized I accidentally replaced the frames per second (fps). I need to make sure to add the background setting properly after adjusting the fps. It’s important to get the right visuals overall since the background plays a crucial role in the final output. Let's take a moment to ensure everything's aligned before finalizing. I want to make sure everything looks great!


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (608 chars result)


def build_motion_render_plan(
    final_plan: FinalVideoPlan,
    *,
    output_dir: str | Path,
    preview_scale: float = 1.0,
    background_path: str | Path | None = None,
) -> MotionRenderPlan:

def build_motion_render_plan(final_plan: FinalVideoPlan, *, output_dir: str | Path, preview_scale: float = 1.0) -> MotionRenderPlan:


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (969 chars result)


        height=height,
        fps=fps,
        background_path=str(background_path) if background_path is not None else None,
    )

        height=height,
        fps=fps,
    )


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (661 chars result)


def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float, *, background: Image.Image | None = None) -> Image.Image:
    w, h = plan.width, plan.height
    if background is Non...[truncated]

def _render_scene(plan: MotionRenderPlan, scene: FinalVideoScene, progress: float) -> Image.Image:
    w, h = plan.width, plan.height
    image = Image.new("RGB", (w, h), (6, 12, 24))
    draw = Image...[truncated]


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (1,264 chars result)


    frame_paths: list[str] = []
    background = None
    if plan.background_path:
        background = Image.open(plan.background_path).convert("RGB").resize((plan.width, plan.height), Image.Resampling.LANCZOS)
    frame_index = 0

    frame_paths: list[str] = []
    frame_index = 0


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (879 chars result)


image = _render_scene(plan, scene, progress, background=background)

image = _render_scene(plan, scene, progress)


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (865 chars result)


terminal

pytest tests/test_motion_renderer.py tests/test_final_video_plan.py -q


Output:
....                                                                     [100%]
4 passed in 0.31s


python - <<'PY'
from pathlib import Path
from math import ceil
from autoshorts.audio.voiceover import build_truetrace_final_voiceover
from autoshorts.audio.tts_plan import build_default_truetrace_tts_...[truncated]

Explanation:
**Generating final preview**

I need to generate an actual render and think about running it fully later. First, I’ll create a motion preview with a background and see if I can reuse existing audio. I should use the current function and mux in the captions. For the final preview, it’ll be important to include subtitles, and I might need to trim it to the audio. I think I need to prepare for generating around 888 frames, but I might target a shorter duration. Let's get started!


[terminal] ran `python - <<'PY'
from pathlib import Path
from math import ceil
from autoshort...` -> exit 0, 1 lines output


mkdir -p /tmp/truetrace_review && ffmpeg -y -i data/previews/truetrace-final-motion-v1/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf "select='eq(n,0)+eq(n,120)+eq(n,300)+eq(n,520)+eq(n,760)'" -vsync 0 /tmp/truetrace_review/frame_%02d.png >/dev/null 2>&1 && printf '%s\n' /tmp/truetrace_review/frame_*.png

Explanation:
**Considering video extraction methods**

I need to inspect some screenshots or frames in a video. I’m thinking about using ffmpeg to extract the middle frames, which could be a good way to analyze the content. I’ll have to use the terminal for this. It's a little technical, but it seems like the right tool for the job.  I might also want to research how to get the exact frames I need. Let's see!


[terminal] ran `mkdir -p /tmp/truetrace_review && ffmpeg -y -i data/previews/truetrace-final-...` -> exit 0, 1 lines output


Question: Review this mid-video frame for mobile readability, whether it looks like a mechanism reveal rather than static cards, and whether any text appears clipped.


[vision_analyze] 'Review this mid-video frame for mobile readability' (287 chars)


Question: Review this three-way split frame for readability and clipping. Is the mechanism clear?

Explanation:
**Inspecting subtitle issues**

I'm thinking about whether to note that the subtitle might need to be clipped or shown only partially at the bottom. It seems like having the bottom text continue onto the next line could be messy. I should probably inspect it further, maybe specifically looking at frame_04. I'm curious to see if that will help resolve the subtitle display issue! Let's see what adjustments can be made to improve clarity.


[vision_analyze] 'Review this three-way split frame for readability ' (218 chars)


Explanation:
**Fixing clipping issues in Frame 4**

I see that Frame 4 has some clipping and overlap at the bottom, so I need to address that. I’m contemplating whether to reduce the subtitle font or adjust the safe zone because the final preview shows the subtitles overlapping the diagram. The scene layout might need a change, like lifting boxes or repositioning the captions to avoid overlap. I’m thinking a smaller font might help, and I need to ensure everything looks visually correct without affecting the diagram too much. Let's patch this up!


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (947 chars result)


Question: Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved?


[vision_analyze] 'Review this updated three-way split frame for read' (232 chars)


Explanation:
**Adjusting layout elements**

I’m noticing some overlap with the bottom part of the design, where the subtitle overlaps the third box. I think I might need to move the subtitles lower. The bottom text is at 740, and the third section ends at 727, so it’s close. To fix this, I could move the diagram further up by adjusting it. That sounds like a good plan! Maybe I should also consider making the subtitle shorter.


[patch] replace in /home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py (948 chars result)


Question: Review this updated three-way split frame for readability and clipping. Is subtitle overlap resolved enough for a Telegram review prototype?


[vision_analyze] 'Review this updated three-way split frame for read' (271 chars)


patch

        positions = []
        for idx, (top, bottom, color) in enumerate(labels):
            reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))
            by = int(h * (0.37 + idx * 0.105))
            ex = int(margin + (w - 2 * margin) * reveal)
            positions.append((top, bottom, color, by, ex))
            draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)
        for top, bottom, color, by, _ex in positions:
            draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)
            draw.text((margin + 28, by + 18), f"{top} → {bottom}", font=mid_font, fill=(248, 250, 252))

        for idx, (top, bottom, color) in enumerate(labels):
            reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))
            bx = margin
            by = int(h * (0.37 + idx * 0.105))
            ex = int(bx + (w - 2 * margin) * reveal)
            draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)
            draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)
            draw.text((margin + 28, by + 18), f"{top} → {bottom}", font=mid_font, fill=(248, 250, 252))


--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py
+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/autoshorts/rendering/motion_renderer.py
@@ -210,12 +210,14 @@
         draw.rounded_rectangle((start[0] - 120, start[1] - 48, start[0] + 120, start[1] + 48), radius=20, fill=(30, 41, 59), outline=accent, width=3)
         draw.text((start[0] - 72, start[1] - 16), "Input", font=mid_font, fill=(226, 232, 240))
         labels = [("Normal", "Go through", green), ("Unclear", "Review", amber), ("Weird/Risky", "Stop", red)]
+        positions = []
         for idx, (top, bottom, color) in enumerate(labels):
             reveal = min(1.0, max(0.0, (progress - idx * 0.18) / 0.35))
-            bx = margin
             by = int(h * (0.37 + idx * 0.105))
-            ex = int(bx + (w - 2 * margin) * reveal)
+            ex = int(margin + (w - 2 * margin) * reveal)
+            positions.append((top, bottom, color, by, ex))
             draw.line((start[0], start[1] + 58, ex, by + 45), fill=color, width=7)
+        for top, bottom, color, by, _ex in positions:
             draw.rounded_rectangle((margin, by, w - margin, by + int(h * 0.10)), radius=20, fill=(15, 23, 42), outline=color, width=4)
             draw.text((margin + 28, by + 18), f"{top} → {bottom}", font=mid_font, fill=(248, 250, 252))
     else:


terminal

python - <<'PY'
from pathlib import Path
from math import ceil
from autoshorts.audio.voiceover import build_truetrace_final_voiceover
from autoshorts.audio.tts_plan import build_default_truetrace_tts_plan
from autoshorts.audio.edge_tts_adapter import EdgeTTSRequest, synthesize_edge_tts
from autoshorts.audio.probe import probe_media_duration_seconds
from autoshorts.audio.subtitles import build_hormozi_subtitle_track
from autoshorts.audio.final_preview import build_audio_caption_preview_plan, render_audio_caption_preview
from autoshorts.rendering.final_video_plan import build_truetrace_weird_email_final_video_plan
from autoshorts.rendering.motion_renderer import build_motion_render_plan, render_motion_preview
candidate_id='truetrace-ai-automation-quiet-rot-38s'
output_dir=Path('data/previews/truetrace-final-motion-v4')/candidate_id
background=Path('/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png')
voiceover=build_truetrace_final_voiceover(candidate_id)
final_plan=build_truetrace_weird_email_final_video_plan(voiceover)
motion=render_motion_preview(build_motion_render_plan(final_plan, output_dir=output_dir, preview_scale=0.5, background_path=background))
if motion.returncode: raise SystemExit(motion.stderr)
voice=synthesize_edge_tts(EdgeTTSRequest(text=voiceover.text, tts_plan=build_default_truetrace_tts_plan(), output_path=str(output_dir/'voice.mp3')))
audio_duration=probe_media_duration_seconds(voice.output_path)
subtitles=build_hormozi_subtitle_track(voiceover.text, total_duration_seconds=ceil(audio_duration))
final=render_audio_caption_preview(build_audio_caption_preview_plan(video_path=motion.output_path,audio_path=voice.output_path,subtitle_track=subtitles,output_path=output_dir/'final-motion-audio-caption-preview.mp4',duration_mode='trim_to_audio'))
if final.returncode: raise SystemExit(final.stderr)
print(final.output_path)
PY
rm -rf /tmp/truetrace_review_v4 && mkdir -p /tmp/truetrace_review_v4 && ffmpeg -y -i data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4 -vf "select='eq(n,520)'" -vsync 0 /tmp/truetrace_review_v4/frame_04.png >/dev/null 2>&1
ffprobe -v error -show_entries stream=codec_type,codec_name,width,height,duration -of default=noprint_wrappers=1 data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4
sha256sum data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4


Output:
data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4
codec_name=h264
codec_type=video
width=540
height=960
duration=36.833333
codec_name=aac
codec_type=audio
duration=36.864000
3893b94eabf353e6388d1ca8b1fc06bae19cb216cc98f6b44956739e58853051  data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4


Question: Final check: is the three-way split readable, arrows behind labels, and no critical clipping/overlap for a review prototype?


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Final check: is the three-way split readable, arrows behind labels, and no critical clipping/overlap for a review prototype?
[screenshot]


patch

- `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.
- `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.
- `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.

Explanation:
**Considering documentation updates**

I need to update the README and documentation, and then ensure that all tests are complete. But first, I might generate the image asset for the final video. It could be a good idea to mention ChatGPT Image through the Codex, as it relates. After that, I’ll commit everything and add the documentation. This is feeling like a productive plan! I want to make sure everything is in order.


--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md
+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md
@@ -35,6 +35,8 @@
 - `autoshorts.strategy.brand` — TrueTraceShorts brand profile, default descriptions/bios/tags, and brand-fit validation.
 - `autoshorts.strategy.score` — follower-conversion strategy scoring for hook, curiosity, identity, emotion, trust, follow reason, originality, and variety.
 - `autoshorts.strategy.series` — public-safe series registry and hard no-go validation before public content advances.
+- `autoshorts.rendering.final_video_plan` — final-near 1080x1920 production plans that keep AI visuals text-free and renderer-owned.
+- `autoshorts.rendering.motion_renderer` — procedural motion preview renderer for inbox cards, warning routes, and three-path mechanism diagrams before heavier AI-video work.
 - `autoshorts.rendering.ffmpeg_plan` — side-effect-free FFmpeg concat planning for local preview frames.
 - `autoshorts.rendering.ffmpeg_renderer` — explicit FFmpeg boundary that writes concat files and MP4 timing previews.
 - `autoshorts.rendering.local_frame_writer` — deterministic Pillow PNG scene-card writer for local preview plans.

No linter for .md files

/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md was modified by sibling subagent '20260524_204754_10d508' but this agent never read it. Read the file before writing to avoid overwriting the sibling's changes.


The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.

The first final-near motion prototype uses a hybrid model: ChatGPT Image/Codex can provide text-free background texture, while `autoshorts.rendering.motion_renderer` owns the actual inbox cards, arrows, labels, captions, and FFmpeg assembly. This keeps generated imagery useful without trusting it to spell or explain anything.

## Strategy scoring

The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.

## Strategy scoring


--- a//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md
+++ b//home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/README.md
@@ -86,6 +86,8 @@

 The first public-facing audio/caption pilot now uses the accessible `weird email` framing: top card, scene captions, subtitles, and voiceover are derived from the final voiceover source instead of mixing old approval-card text with new spoken copy.

+The first final-near motion prototype uses a hybrid model: ChatGPT Image/Codex can provide text-free background texture, while `autoshorts.rendering.motion_renderer` owns the actual inbox cards, arrows, labels, captions, and FFmpeg assembly. This keeps generated imagery useful without trusting it to spell or explain anything.
+
 ## Strategy scoring

 `autoshorts.strategy.score.ContentStrategyScore` evaluates whether a candidate can move beyond generic AI tips and plausibly convert viewers into followers. It scores:

No linter for .md files


# Final Motion Prototype Notes

## Decision

For the first real TrueTrace video prototype, use a hybrid rendering stack:

```text
ChatGPT Image / Codex image generation → text-free cinematic background texture
Procedural motion renderer → all cards, labels, arrows, diagrams, and mechanism text
EdgeTTS → voiceover
ASS/FFmpeg → deterministic subtitle burn-in and final assembly
```

## Why

The pilot is a mechanism-reveal short, not a cinematic story scene. AI image/video can provide mood and texture, but it must not own:

- email text;
- path labels;
- arrows;
- captions;
- factual claims;
- the three-way workflow mechanism.

Those layers stay renderer-owned so the final video remains readable on mobile and does not contain fake/generated UI text.

## Current pilot

Candidate:

```text
truetrace-ai-automation-quiet-rot-38s
```

Frame sequence:

```text
clean-demo
→ weird-email
→ wrong-routing
→ three-way-split
→ final-rule
```

Core visual mechanism:

```text
Normal → Go through
Unclear → Review
Weird/Risky → Stop
```

## Generated prototype artifact

The current review artifact is local-only and ignored by Git:

```text
data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4
```

It uses one ChatGPT Image/Codex-generated text-free background plus deterministic renderer overlays.

## Quality notes

- The three-way split scene has been visually checked so arrows sit behind labels and subtitles do not critically overlap the mechanism.
- The prototype is still a review prototype, not a final public export.
- Next polish should improve subtitle phrasing/timing around the three-way split and optionally replace the single static background with per-scene generated backgrounds or short I2V texture clips.


No linter for .md files


Output:
........................................................................ [ 43%]
........................................................................ [ 86%]
......................                                                   [100%]
166 passed in 33.16s


terminal

git status --short && git add autoshorts/rendering/final_video_plan.py autoshorts/rendering/motion_renderer.py tests/test_final_video_plan.py tests/test_motion_renderer.py README.md docs/final_motion_prototype.md && git commit -m "feat: add TrueTrace final motion prototype" && git status --short && git log --oneline -5


Output:
M README.md
?? autoshorts/rendering/final_video_plan.py
?? autoshorts/rendering/motion_renderer.py
?? docs/final_motion_prototype.md
?? tests/test_final_video_plan.py
?? tests/test_motion_renderer.py
[feat/core-approval-quality-gates 14310f9] feat: add TrueTrace final motion prototype
 6 files changed, 665 insertions(+)
 create mode 100644 autoshorts/rendering/final_video_plan.py
 create mode 100644 autoshorts/rendering/motion_renderer.py
 create mode 100644 docs/final_motion_prototype.md
 create mode 100644 tests/test_final_video_plan.py
 create mode 100644 tests/test_motion_renderer.py
14310f9 feat: add TrueTrace final motion prototype
7375311 feat: make TrueTrace pilot beginner friendly
b9b6423 feat: align TrueTrace voiceover previews
3f68001 feat: add EdgeTTS audio caption preview
066201d feat: attach audio plans to TrueTrace previews


Erledigt, Sir — ich habe losgelegt und den ersten **richtigen Motion-Prototypen** gebaut, mit ChatGPT Image/Codex als Hintergrund-Quelle statt lokalem Qwen Image.

## Gebaut

Neue Module:

```text
autoshorts/rendering/final_video_plan.py
autoshorts/rendering/motion_renderer.py
```

Neue Tests:

```text
tests/test_final_video_plan.py
tests/test_motion_renderer.py
```

Neue Doku:

```text
docs/final_motion_prototype.md
```

Commit:

```text
14310f9 feat: add TrueTrace final motion prototype
```

## Was jetzt neu ist

### 1. FinalVideoPlan

Neues Artefakt:

```python
FinalVideoPlan
```

Es definiert die echten Produktionsszenen:

```text
clean-demo
→ weird-email
→ wrong-routing
→ three-way-split
→ final-rule
```

Mit:

```text
1080x1920
30fps
37s duration
renderer-owned text
no AI-generated text allowed
```

Wichtig: AI darf nur Hintergrund/Textur liefern. Der Renderer besitzt weiterhin:

```text
email text
labels
arrows
workflow paths
subtitles
captions
```

Also keine „AI hat Review als Reviwe geschrieben“-Katastrophe. Fortschritt, mit Helm.

---

### 2. ChatGPT Image/Codex als Background-Quelle genutzt

Ich habe über `image_generate` / OpenAI Codex ein textfreies vertikales Hintergrundbild erzeugt:

```text
/home/agent/.hermes/cache/images/openai_codex_gpt-image-2-medium_20260524_235629_ac459a00.png
```

Prompt-Kern:

```text
modern AI automation short background,
clean laptop/workspace,
dark productivity dashboard glow,
blue/green accents,
no readable text,
no logos,
empty central space for renderer-owned overlays
```

Das Bild wird im Motion Renderer als visuelle Textur verwendet. Alle erklärenden Elemente liegen darüber als deterministic overlays.

---

### 3. Procedural Motion Renderer

Neuer Renderer:

```python
build_motion_render_plan()
render_motion_preview()
```

Er erzeugt lokal:

```text
animated inbox cards
weird email card
wrong-routing warning
three-way split diagram
final rule card
```

Mit FFmpeg-Ausgabe:

```text
motion-preview.mp4
```

Dann wird wie bisher ergänzt:

```text
EdgeTTS voiceover
ASS subtitles
FFmpeg audio+caption burn-in
```

---

## Neuer Video-Prototyp

Finaler Review Clip:

MEDIA:/home/agent/jarvis_runtime/autoshortsbot/AutoShortsBot/data/previews/truetrace-final-motion-v4/truetrace-ai-automation-quiet-rot-38s/final-motion-audio-caption-preview.mp4

## Verifiziert

```text
Video: h264
Size: 540x960 review preview
Duration: 36.833s
Audio: aac
Audio duration: 36.864s
```

SHA256:

```text
3893b94eabf353e6388d1ca8b1fc06bae19cb216cc98f6b44956739e58853051
```

Frames gerendert:

```text
887
```

## Visuelle QA

Ich habe die kritische `three-way-split` Szene extrahiert und visuell geprüft.

Fixes gemacht:

- Diagramm weiter nach oben geschoben.
- Arrows hinter die Labels gelegt.
- Subtitle-Overlap reduziert.
- `Normal → Go through`, `Unclear → Review`, `Weird/Risky → Stop` bleibt lesbar.

Die Szene ist jetzt für einen Review-Prototyp brauchbar. Nicht Cannes. Aber klarer als die vorherigen Karten — also bereits ein zivilisatorischer Sprung.

## Tests

Full Suite:

```text
166 passed in 33.16s
```

## Architektur jetzt

```text
FinalVoiceoverScript
→ FinalVideoPlan
→ ChatGPT Image text-free background
→ MotionRenderer procedural overlays
→ EdgeTTS voiceover
→ ASS subtitles
→ FFmpeg final preview
```

## Wichtig zur Richtung

Das ist jetzt der richtige Ansatz:

```text
ChatGPT Image / Codex:
Mood, background, cinematic texture

Renderer:
Truth, labels, arrows, captions, mechanism

FFmpeg:
Assembly, audio, subtitle burn-in
```

Nicht:

```text
AI soll bitte alles generieren und korrekt buchstabieren
```

Denn das wäre wieder GPU-Roulette mit Bildungsauftrag.

## Nächster sinnvoller Schritt

Jetzt würde ich den Clip **qualitativ polieren**, nicht nochmal Architektur umbauen:

```text
1. Subtitle phrasing bei der 3-way-split Szene kürzen
2. Scene-specific ChatGPT Image backgrounds erzeugen:
   - clean demo inbox
   - weird email moment
   - abstract AI route
   - mechanism background
3. Motion etwas lebendiger machen:
   - subtle zoom
   - card slide timing
   - warning pulse
   - progress/rhythm beats
4. Danach 1080x1920 final export bauen
```

Der aktuelle Clip ist der erste echte „richtige Video“-Prototyp: Voice, Captions, Motion, Mechanism und ChatGPT-Image-Textur in einer Pipeline.

Explanation:
**Reviewing README changes**

I’m noticing that the README file might not be included in my last commit. The commit indicates 6 files changed, suggesting the README wasn't staged, which is a bit puzzling. I need to run `git add README`. However, it looks like it’s not listed in the creations. I want to ensure the final version is concise, includes media, and mentions the ChatGPT image used. I should also refer to the generated background path and test 166.


[M G] Ok, mach weiter.


Explanation:
**Continuing with skills**

I need to keep going and focus on the relevant skills like TDD, social aspects, and image handling. It might be time to act and polish the subtitle phrasing. I should build tests and inspect the subtitles closely, maybe looking at the final preview to ensure they have the right style. I'll be sure to use the necessary tools in the process to make everything come together.


test-driven-development

---
name: test-driven-development
description: "TDD: enforce RED-GREEN-REFACTOR, tests before code."
version: 1.1.0
author: Hermes Agent (adapted from obra/superpowers)
license: MIT
platforms: [linux, macos, windows]
metadata:
  hermes:
    tags: [testing, tdd, development, quality, red-green-refactor]
    related_skills: [systematic-debugging, writing-plans, subagent-driven-development]
---

# Test-Driven Development (TDD)

## Overview

Write the test first. Watch it fail. Write minimal code to pass.

**Core principle:** If you didn't watch the test fail, you don't know if it tests the right thing.

**Violating the letter of the rules is violating the spirit of the rules.**

## When to Use

**Always:**
- New features
- Bug fixes
- Refactoring
- Behavior changes

**Exceptions (ask the user first):**
- Throwaway prototypes
- Generated code
- Configuration files

Thinking "skip TDD just this once"? Stop. That's rationalization.

## The Iron Law

```
NO PRODUCTION CODE WITHOUT A FAILING TEST FIRST
```

Write code before the test? Delete it. Start over.

**No exceptions:**
- Don't keep it as "reference"
- Don't "adapt" it while writing tests
- Don't look at it
- Delete means delete

Implement fresh from tests. Period.

## Red-Green-Refactor Cycle

### RED — Write Failing Test

Write one minimal test showing what should happen.

**Good test:**
```python
def test_retries_failed_operations_3_times():
    attempts = 0
    def operation():
        nonlocal attempts
        attempts += 1
        if attempts < 3:
            raise Exception('fail')
        return 'success'

    result = retry_operation(operation)

    assert result == 'success'
    assert attempts == 3
```
Clear name, tests real behavior, one thing.

**Bad test:**
```python
def test_retry_works():
    mock = MagicMock()
    mock.side_effect = [Exception(), Exception(), 'success']
    result = retry_operation(mock)
    assert result == 'success'  # What about retry count? Timing?
```
Vague name, tests mock not real code.

**Requirements:**
- One behavior per test
- Clear descriptive name ("and" in name? Split it)
- Real code, not mocks (unless truly unavoidable)
- Name describes behavior, not implementation

### Verify RED — Watch It Fail

**MANDATORY. Never skip.**

```bash
# Use terminal tool to run the specific test
pytest tests/test_feature.py::test_specific_behavior -v
```

Confirm:
- Test fails (not errors from typos)
- Failure message is expected
- Fails because the feature is missing

**Test passes immediately?** You're testing existing behavior. Fix the test.

**Test errors?** Fix the error, re-run until it fails correctly.

### GREEN — Minimal Code

Write the simplest code to pass the test. Nothing more.

**Good:**
```python
def add(a, b):
    return a + b  # Nothing extra
```

**Bad:**
```python
def add(a, b):
    result = a + b
    logging.info(f"Adding {a} + {b} = {result}")  # Extra!
    return result
```

Don't add features, refactor other code, or "improve" beyond the test.

**Cheating is OK in GREEN:**
- Hardcode return values
- Copy-paste
- Duplicate code
- Skip edge cases

We'll fix it in REFACTOR.

### Verify GREEN — Watch It Pass

**MANDATORY.**

```bash
# Run the specific test
pytest tests/test_feature.py::test_specific_behavior -v

# Then run ALL tests to check for regressions
pytest tests/ -q
```

Confirm:
- Test passes
- Other tests still pass
- Output pristine (no errors, warnings)

**Test fails?** Fix the code, not the test.

**Other tests fail?** Fix regressions now.

### REFACTOR — Clean Up

After green only:
- Remove duplication
- Improve names
- Extract helpers
- Simplify expressions

Keep tests green throughout. Don't add behavior.

**If tests fail during refactor:** Undo immediately. Take smaller steps.

### Repeat

Next failing test for next behavior. One cycle at a time.

## Why Order Matters

**"I'll write tests after to verify it works"**

Tests written after code pass immediately. Passing immediately proves nothing:
- Might test the wrong thing
- Might test implementation, not behavior
- Might miss edge cases you forgot
- You never saw it catch the bug

Test-first forces you to see the test fail, proving it actually tests something.

**"I already manually tested all the edge cases"**

Manual testing is ad-hoc. You think you tested everything but:
- No record of what you tested
- Can't re-run when code changes
- Easy to forget cases under pressure
- "It worked when I tried it" ≠ comprehensive

Automated tests are systematic. They run the same way every time.

**"Deleting X hours of work is wasteful"**

Sunk cost fallacy. The time is already gone. Your choice now:
- Delete and rewrite with TDD (high confidence)
- Keep it and add tests after (low confidence, likely bugs)

The "waste" is keeping code you can't trust.

**"TDD is dogmatic, being pragmatic means adapting"**

TDD IS pragmatic:
- Finds bugs before commit (faster than debugging after)
- Prevents regressions (tests catch breaks immediately)
- Documents behavior (tests show how to use code)
- Enables refactoring (change freely, tests catch breaks)

"Pragmatic" shortcuts = debugging in production = slower.

**"Tests after achieve the same goals — it's spirit not ritual"**

No. Tests-after answer "What does this do?" Tests-first answer "What should this do?"

Tests-after are biased by your implementation. You test what you built, not what's required. Tests-first force edge case discovery before implementing.

## Common Rationalizations

| Excuse | Reality |
|--------|---------|
| "Too simple to test" | Simple code breaks. Test takes 30 seconds. |
| "I'll test after" | Tests passing immediately prove nothing. |
| "Tests after achieve same goals" | Tests-after = "what does this do?" Tests-first = "what should this do?" |
| "Already manually tested" | Ad-hoc ≠ systematic. No record, can't re-run. |
| "Deleting X hours is wasteful" | Sunk cost fallacy. Keeping unverified code is technical debt. |
| "Keep as reference, write tests first" | You'll adapt it. That's testing after. Delete means delete. |
| "Need to explore first" | Fine. Throw away exploration, start with TDD. |
| "Test hard = design unclear" | Listen to the test. Hard to test = hard to use. |
| "TDD will slow me down" | TDD faster than debugging. Pragmatic = test-first. |
| "Manual test faster" | Manual doesn't prove edge cases. You'll re-test every change. |
| "Existing code has no tests" | You're improving it. Add tests for the code you touch. |

## Red Flags — STOP and Start Over

If you catch yourself doing any of these, delete the code and restart with TDD:

- Code before test
- Test after implementation
- Test passes immediately on first run
- Can't explain why test failed
- Tests added "later"
- Rationalizing "just this once"
- "I already manually tested it"
- "Tests after achieve the same purpose"
- "Keep as reference" or "adapt existing code"
- "Already spent X hours, deleting is wasteful"
- "TDD is dogmatic, I'm being pragmatic"
- "This is different because..."

**All of these mean: Delete code. Start over with TDD.**

## Verification Checklist

Before marking work complete:

- [ ] Every new function/method has a test
- [ ] Watched each test fail before implementing
- [ ] Each test failed for expected reason (feature missing, not typo)
- [ ] Wrote minimal code to pass each test
- [ ] All tests pass
- [ ] Output pristine (no errors, warnings)
- [ ] Tests use real code (mocks only if unavoidable)
- [ ] Edge cases and errors covered

Can't check all boxes? You skipped TDD. Start over.

## When Stuck

| Problem | Solution |
|---------|----------|
| Don't know how to test | Write the wished-for API. Write the assertion first. Ask the user. |
| Test too complicated | Design too complicated. Simplify the interface. |
| Must mock everything | Code too coupled. Use dependency injection. |
| Test setup huge | Extract helpers. Still complex? Simplify the design. |

## Hermes Agent Integration

### Running Tests

Use the `terminal` tool to run tests at each step:

```python
# RED — verify failure
terminal("pytest tests/test_feature.py::test_name -v")

# GREEN — verify pass
terminal("pytest tests/test_feature.py::test_name -v")

# Full suite — verify no regressions
terminal("pytest tests/ -q")
```

### CLI Entry Points

For new `python -m package.cli.command` style entry points, keep the TDD loop boring and side-effect free:

1. Write a test that imports `main` from the missing CLI module and calls `main([])`.
2. Capture stdout/stderr with the project test framework (`capsys` in pytest) or inject a `stdout` file-like object if supported.
3. RED should usually be an import failure for the missing CLI module/package, not a vague end-to-end shell failure.
4. Implement `main(argv: Sequence[str] | None = None, *, stdout: TextIO | None = None) -> int` so tests can call it without spawning a subprocess.
5. Add the `if __name__ == "__main__": raise SystemExit(main())` guard only after the callable path is green.
6. Verify both the unit test and the real module invocation, e.g. `python -m package.cli.command`, before committing.

This catches command wiring while avoiding accidental network calls, publishing actions, or other theatrical nonsense masquerading as a CLI test.

### With delegate_task

When dispatching subagents for implementation, enforce TDD in the goal:

```python
delegate_task(
    goal="Implement [feature] using strict TDD",
    context="""
    Follow test-driven-development skill:
    1. Write failing test FIRST
    2. Run test to verify it fails
    3. Write minimal code to pass
    4. Run test to verify it passes
    5. Refactor if needed
    6. Commit

    Project test command: pytest tests/ -q
    Project structure: [describe relevant files]
    """,
    toolsets=['terminal', 'file']
)
```

### With systematic-debugging

Bug found? Write failing test reproducing it. Follow TDD cycle. The test proves the fix and prevents regression.

Never fix bugs without a test.

## Demo / Fixture Features

When adding a deterministic demo fixture for later integration wiring, still use RED-GREEN-REFACTOR. Write the wished-for import/API in the test first, verify the missing module/API failure, then implement the smallest fixture that proves the real workflow path. Assert stable IDs/order, bucket counts/classifications, and safety invariants such as review text not implying publication. See `references/deterministic-demo-fixtures.md` for the concise pattern.

## Side-Effect-Free Integration Boundaries

When building toward an external integration (Telegram, email, publishing APIs, render farms, payment flows, etc.), add a tested preparation boundary before any real I/O:

1. Write the wished-for pure function in the test first, e.g. `prepare_review_delivery(text) -> DeliveryPlan`.
2. RED should be a missing module/API or missing validation failure, not a real network/send failure.
3. The GREEN implementation should return a typed/dataclass plan object with explicit metadata: destination/topic, delivery type, human-approval requirement, and `side_effects=()`.
4. Validate safety constraints at the boundary: non-empty content, platform size limits, no forbidden action language, no implicit approval, no credentials or generated media unless explicitly expected.
5. Add a demo CLI only after the pure boundary is green. The CLI should print metadata first, then payload, and still perform no external I/O.
6. Verify both the unit tests and a real `python -m package.cli.demo_*` invocation before committing.

This pattern lets later sending/rendering/publishing adapters consume a boring, audited object instead of smuggling side effects into formatting code. Boring is how we avoid building a content catapult with a moustache.

### Media / Rendering Pipeline Boundaries

When building toward image/video generation or renderer integration, add deterministic planning artifacts before any GPU/API work:

1. Write tests for the wished-for pure API first, e.g. `create_shot_plan(script)`, `build_render_manifest(plan)`, `build_local_preview_plan(manifest)`, or `build_ffmpeg_concat_plan(plan, frames, output_path)`.
2. RED should be a missing module/API or missing validation behavior — never a failed ComfyUI/Wan/Qwen/API invocation.
3. Keep each stage inspectable and side-effect-free until the explicit adapter layer: `ScriptDraft -> ShotPlan -> RenderManifest -> PreviewPlan/PreviewArtifact -> renderer adapter`.
4. Assert stable IDs/order, scene counts, timing continuity, vertical settings (`1080x1920`, `30fps` when building Shorts/Reels/TikTok), reduced preview settings (`540x960`, `24fps` when building fast local timing previews), and placeholder/runtime paths before generating media.
5. Add guardrail tests that reject static slideshows, baked-in/generated text inside assets, non-renderer-owned captions/labels, unsafe render settings, missing fields, bad timings, non-PNG frame paths, non-MP4 output paths, and mismatched frame counts.
6. Split artifact-writing/rendering into explicit boundaries after the pure plans are green: frame writer returns `side_effects=("write_png",)`, concat planning remains side-effect-free, and the FFmpeg renderer returns `side_effects=("write_concat", "call_ffmpeg", "write_mp4")` plus `external_calls=("ffmpeg",)`.
7. Only after the manifest/preview boundary is green should a later adapter call local GPU tools or external services. Planning first; GPU roulette later.

This prevents wasting render time on unclear direction and keeps expensive media generation behind a small, audited boundary.

### Approval-First Media/Social Pipelines

When a feature crosses from content planning toward media/social delivery, prefer many small RED-GREEN-README-COMMIT slices instead of one large integration jump:

1. typed artifact/model (`SocialMediaDraft`, `RenderManifest`, etc.)
2. renderer/formatter for human review text
3. side-effect-free delivery/preparation boundary
4. demo CLI that prints metadata first, then payload

For each slice, write the missing import/API test first, verify the expected import/API failure, implement only that slice, run the focused test, run the full suite, update docs, and commit. Keep invariant tests explicit: `requires_human_approval=True`, `side_effects=()`, no external calls, no credential access, no file writes unless that adapter is the explicit feature, and no accidental action language such as `publish`/`post` in review/delivery text.

This staged approach keeps approval semantics auditable and prevents a later renderer, Telegram sender, or platform adapter from being smuggled into formatting code.

## Testing Anti-Patterns

- **Testing mock behavior instead of real behavior** — mocks should verify interactions, not replace the system under test
- **Testing implementation details** — test behavior/results, not internal method calls
- **Happy path only** — always test edge cases, errors, and boundaries
- **Brittle tests** — tests should verify behavior, not structure; refactoring shouldn't break them
- **Substring sentinels that collide with generated IDs** — when asserting that amounts or fixture values are excluded from a result, assert the full formatted token (e.g. `999.00`) or a typed field, not a short substring like `999` that can appear inside UUIDs/hash-like IDs.
- **Domain classes named `Test*` trigger pytest collection warnings** — if a production dataclass/model must be named like `TestBatch`, set `__test__ = False` on the class or choose a non-`Test` prefix. Keep the fix in production code when the name is part of the domain language, and verify the warning is gone before committing.
- **Defaulting with `x or default` hides invalid explicit inputs** — when a factory/test helper accepts optional overrides, use `x if x is not None else default` so tests can pass intentionally blank strings (`""`) or zero values and watch validation reject them. Otherwise invalid-input tests may silently receive defaults and give false confidence.

## Final Rule

```
Production code → test exists and failed first
Otherwise → not TDD
```

No exceptions without the user's explicit permission.

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'