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)
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calesthio
@@ -55,17 +55,71 @@ FLUX.2 supports up to 4 references (klein) or 8 references (pro/max/flex). Refer
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Use the same `seed` parameter across generations with similar prompts. Produces similar compositions but is fragile to prompt changes — use as supplement, not primary strategy.
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## Prompt Template
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## Prompt Construction — 3-Part Contextual Approach
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**Do NOT copy the playbook's `image_prompt_prefix` verbatim into every prompt.** That's what makes all scenes look the same. Instead, build each prompt from 3 contextual layers:
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### Part 1: Scene-Specific Style Direction (from shot_language + texture_keywords)
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Use the scene's `shot_language` fields to set camera and lighting:
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```
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[SHOT SIZE from shot_language.shot_size, e.g., "medium close-up"].
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[LIGHTING from shot_language.lighting_key, e.g., "golden hour warm light"].
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[DEPTH from shot_language.depth_of_field, e.g., "shallow depth of field with bokeh"].
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[TEXTURE from scene.texture_keywords, e.g., "film grain, warm tones"].
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```
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If the scene has no shot_language, fall back to the template below.
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### Part 2: Playbook Consistency Anchor (adapted, not verbatim)
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Extract the ESSENCE of the playbook's visual language — don't copy the prefix. For example:
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- Playbook says "Clean, minimal illustration with soft shadows, muted color palette" → Adapt to: "muted color palette, soft shadows"
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- Playbook says "Bold flat motion graphics, vibrant gradients" → Adapt to: "vibrant flat style"
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The anchor keeps scenes visually coherent without making them identical.
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### Part 3: Scene Description
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The actual content of the scene. Be specific — replace generic words with concrete details.
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**BAD:** "A person using a computer in a modern office"
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**GOOD:** "Software developer in a dimly lit home office, blue monitor glow reflecting off glasses, desk cluttered with energy drinks and sticky notes"
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### Full Prompt Example (with shot_language)
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```
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[STYLE PREFIX from playbook].
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[SCENE DESCRIPTION: subject, action, environment].
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Medium close-up, golden hour warm lighting, shallow depth of field.
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Muted earth tones, soft shadows.
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Beekeeper in white protective gear lifting a frame dripping with honey,
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late afternoon sun catching golden droplets, lavender field blurred
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in the background. Film grain, warm amber tones.
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16:9 aspect ratio.
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```
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### Fallback Template (when no shot_language is available)
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```
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[ADAPTED STYLE ANCHOR from playbook — 5-10 words, not the full prefix].
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[SCENE DESCRIPTION: specific subject, action, environment].
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[LIGHTING: golden hour / overcast / studio softbox / dramatic side-light].
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[COMPOSITION: wide shot / medium shot / close-up / overhead / isometric].
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[CAMERA: Shot on [camera] with [lens] at [aperture]] (for photorealistic only).
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16:9 aspect ratio.
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```
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### Using lib/shot_prompt_builder.py
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For programmatic prompt construction, use the shot prompt builder which automates the 3-part approach:
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```python
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from lib.shot_prompt_builder import build_shot_prompt
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prompt = build_shot_prompt(scene, style_context=playbook_data)
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```
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This converts the structured shot_language fields into natural-language prompts
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optimized for image/video generation providers.
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### Style-Specific Prompt Patterns
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| Style | Prompt Pattern |
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Reference in New Issue
Block a user