Implementation spec: governance, decision intelligence, theme system, and E2E bug fixes

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)

skills/pipelines/talking-head/asset-director.md

@@ -16,6 +16,16 @@ You have a scene plan and script. Your job is to generate the supporting assets
 
 ## Process
 
+### Step 0: Hero Scene Sample (Mandatory)
+
+Before batch asset generation:
+1. Identify the hero scene (the visual peak of the video)
+2. Generate ONE sample asset for that scene (subtitle style, overlay, or background)
+3. Present it: "This is the visual direction for the most important scene. Does this match what you're imagining? I'll generate the rest in this style."
+4. 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.
+
 ### Step 1: Generate Subtitles
 
 Use the transcription data from the script stage to create:
@@ -167,3 +177,32 @@ Document all generated assets with paths, types, and tool references:
 ### Step 7: Submit
 
 Validate the asset_manifest against the schema and persist via checkpoint.
+
+### Mid-Production Fact Verification
+
+If you encounter uncertainty during asset generation:
+- Use `web_search` to verify visual accuracy of subjects (e.g. what does this building actually look like?)
+- Use `web_search` to 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.
+
+## When You Do Not Know How
+
+If you encounter a generation technique, provider behavior, or prompting pattern you are unsure about:
+
+1. **Search the web** for current best practices — models and APIs change frequently, and the agent's training data may be stale
+2. **Check `.agents/skills/`** for existing Layer 3 knowledge (provider-specific prompting guides, API patterns)
+3. **If neither helps**, write a project-scoped skill at `projects/<project-name>/skills/<name>.md` documenting what you learned
+4. **Reference source URLs** in the skill so the knowledge is traceable
+5. **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.

skills/pipelines/talking-head/script-director.md

@@ -54,3 +54,14 @@ Assemble the structured script with:
 ### Step 6: Submit
 
 Validate the script against the schema and persist via checkpoint.
+
+### Mid-Production Fact Verification
+
+If you encounter uncertainty during script writing:
+- Use `web_search` to verify factual claims before committing them to the script
+- Use `web_search` to find reference images for visual accuracy
+- Log verification in the decision log: `category="visual_accuracy_check"`
+
+Every factual claim in the script should be traceable to the `research_brief`.
+If you make a claim that isn't in the research, do additional research and
+add the source. Do not invent statistics, dates, or attributions.