Implements the 2026-04-02 transformation spec (Phases 1-8) and fixes all critical bugs found during 5-pipeline E2E testing. Governance & Decision Intelligence: - Pipeline-specific stage order in checkpoint (replaces global STAGES list) - Provider scoring engine (lib/scoring.py) with 7-dimension weighted ranking - Decision log artifact enforced at proposal/idea stage across all 10 pipelines - Delivery promise classifier prevents silent motion-to-still downgrades - Structured shot language in scene_plan schema (camera, lens, lighting, DOF) - Variation checker and slideshow risk scorer block samey output before render - Creative intake, capability extension, and creative-intake meta skills - Final self-review artifact with 5 mandatory checks before presenting output - Source media review contract for user-supplied footage Render & Theme System: - Remotion AnimatedBackground now derives colors from playbook (no more hardcoded dark blue fintech gradient on every video) - video_compose builds custom ThemeConfig from playbook YAML colors/fonts — custom playbooks flow through to Remotion automatically - Explainer component wires theme to all child components (charts, cards, etc.) - resolveAsset() handles absolute paths on Windows/Unix via file:// URIs - RENDERER_FAMILY_MAP synced with actual Remotion compositions Critical Bug Fixes: - Windows npx subprocess: run_command() resolves .cmd wrappers via shutil.which() - Silent renderer downgrade: Remotion failure now returns explicit error with options instead of silently falling back to FFmpeg - .env inline comment parsing strips trailing # comments from API keys - concat_path UnboundLocalError in video_compose finally block - audio_mixer and showcase_card capture=True kwarg bug - Selector estimate_cost() calls fixed (_select_tool -> _select_best_tool) - asset_manifest schema expanded with provider, license, subtype fields - screen-demo subtitle_gen moved from required to optional tools - Duration drift detection in post-render final review (>25% warns)
4.2 KiB
Asset Director - Podcast Repurpose Pipeline
When To Use
This stage builds the reusable kit for podcast-derived video assets: subtitles, speaker cards, quote cards, optional topic art, and optional music support.
Prerequisites
| Layer | Resource | Purpose |
|---|---|---|
| Schema | schemas/artifacts/asset_manifest.schema.json |
Artifact validation |
| Prior artifacts | state.artifacts["scene_plan"]["scene_plan"], state.artifacts["script"]["script"], state.artifacts["idea"]["brief"] |
Deliverable plan and transcript truth |
| Tools | subtitle_gen, image_selector, diagram_gen, music_gen, audio_enhance |
Asset generation |
| Playbook | Active style playbook | Brand consistency |
Process
1. Start With Mandatory Assets
Highest priority:
- subtitles for every clip,
- clean audio where needed,
- speaker attribution assets if multiple speakers appear,
- quote-card templates for quote-led outputs.
1b. Hero Scene Sample (Mandatory)
Before batch asset generation:
- Identify the hero clip (the most important or impactful clip in the batch)
- Generate ONE sample asset for that clip (subtitle style, speaker card, or quote card)
- Present it: "This is the visual direction for the most important clip. Does this match what you're imagining? I'll generate the rest in this style."
- Wait for approval before proceeding to batch generation
This prevents the most expensive mistake: generating 10+ assets in a direction the user doesn't like.
2. Treat Topic Graphics As Optional
Generated graphics should support the batch, not dominate it. Use them only when:
- the topic truly benefits from a clarifying image,
- the episode companion needs chapter separation,
- the budget can support consistent outputs.
3. Use Templates, Not Reinvention
Prefer reusable templates for:
- speaker cards,
- quote cards,
- end cards,
- brand containers.
4. Store Rich Asset Truth In Metadata
Recommended metadata keys:
speaker_assetssubtitle_assetsquote_card_assetstopic_graphicsmusic_assets
5. Quality Gate
- all clips have subtitle assets,
- speaker identity is visually consistent,
- quote-card text remains mobile-readable,
- optional generated art stays within budget and style constraints.
Mid-Production Fact Verification
If you encounter uncertainty during asset generation:
- Use
web_searchto verify visual accuracy of subjects (e.g. what does this building actually look like?) - Use
web_searchto find reference images before generating illustrations - Log verification in the decision log:
category="visual_accuracy_check"
Visual accuracy matters. If the script mentions a specific place, person, or object, verify what it actually looks like before generating images. Don't rely on the AI model's training data — it may be wrong or outdated.
Common Pitfalls
- Spending budget on optional art before subtitles and attribution assets are complete.
- Creating inconsistent speaker cards across the same episode.
- Overproducing topic graphics for long-form companion videos.
When You Do Not Know How
If you encounter a generation technique, provider behavior, or prompting pattern you are unsure about:
- Search the web for current best practices — models and APIs change frequently, and the agent's training data may be stale
- Check
.agents/skills/for existing Layer 3 knowledge (provider-specific prompting guides, API patterns) - If neither helps, write a project-scoped skill at
projects/<project-name>/skills/<name>.mddocumenting what you learned - Reference source URLs in the skill so the knowledge is traceable
- Log it in the decision log:
category: "capability_extension",subject: "learned technique: <name>"
This is especially important for:
- Video generation prompting — models respond to specific vocabularies that change with each version
- Image model parameters — optimal settings for FLUX, DALL-E, Imagen differ and evolve
- Audio provider quirks — voice cloning, music generation, and TTS each have model-specific best practices
- Remotion component patterns — new composition techniques emerge as the framework evolves
Do not rely on stale knowledge. When in doubt, search first.