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
+138
-34
@@ -96,47 +96,150 @@ a11y = validate_accessibility(playbook)
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print(f" Playbook a11y: pass={a11y['pass']}, errors={a11y['error_count']}, warnings={a11y['warning_count']}")
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# ===================================================================
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# Stage 1: idea -> brief
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# Stage 0: research -> research_brief (minimal, to satisfy pipeline order)
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# ===================================================================
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print("\n--- Stage 1: idea ---")
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brief = {
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print("\n--- Stage 0: research ---")
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research_brief = {
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"version": "1.0",
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"title": "AI Video Production in 60 Seconds",
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"hook": "What if you could create a professional video in 60 seconds with just a text prompt?",
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"key_points": [
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"Traditional video production takes days or weeks",
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"AI can automate scripting, visuals, narration, and editing",
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"OpenMontage orchestrates the full pipeline",
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"topic": "AI Video Production",
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"research_date": "2026-04-02",
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"landscape": {
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"existing_content": [
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{"title": "AI Video Tools Explained", "source": "youtube", "angle": "tutorial", "what_it_covers": "Tool comparison", "what_it_misses": "End-to-end pipeline"},
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{"title": "Making Videos with AI", "source": "blog", "angle": "overview", "what_it_covers": "Market landscape", "what_it_misses": "Hands-on workflow"},
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{"title": "AI Content Creation Guide", "source": "youtube", "angle": "how-to", "what_it_covers": "Individual tools", "what_it_misses": "Orchestration concept"},
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],
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"saturated_angles": ["basic tool demos", "AI will replace editors"],
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"underserved_gaps": ["End-to-end orchestration pipeline", "Cost vs quality tradeoffs"],
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},
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"data_points": [
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{"claim": "AI video market growing 25% YoY", "source_url": "https://example.com/report", "credibility": "primary_source"},
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{"claim": "70% of creators want automated editing", "source_url": "https://example.com/survey", "credibility": "primary_source"},
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{"claim": "Average production time drops 80% with AI", "source_url": "https://example.com/study", "credibility": "secondary_source"},
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],
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"tone": "confident, energetic",
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"style": "clean-professional",
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"target_platform": "youtube",
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"target_duration_seconds": 60,
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"target_audience": "content creators and developers",
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"cta": "Try OpenMontage today",
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"angle_options": [
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{"name": "democratization", "description": "AI makes video creation accessible to everyone"},
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{"name": "workflow", "description": "AI automates the tedious parts of video production"},
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{"name": "quality", "description": "AI-generated content is reaching professional quality"},
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"audience_insights": {
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"common_questions": [
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"Can AI really make professional-looking videos?",
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"How much does AI video production cost?",
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"What tools do I need to get started?",
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],
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"misconceptions": [
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{"myth": "AI videos always look robotic", "reality": "Modern AI produces broadcast-quality output"},
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],
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"knowledge_level": "Familiar with basic editing but not AI-specific tools",
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},
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"angles_discovered": [
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{"name": "60-Second Studio", "hook": "Your entire production team in a single prompt", "type": "trending", "why_now": "AI orchestration tools just reached production quality"},
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{"name": "Quality Democratization", "hook": "Pro-grade video without pro skills", "type": "evergreen", "why_now": "Creator economy growing, skill gap remains"},
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{"name": "The Hidden Cost Myth", "hook": "AI video costs pennies, not thousands", "type": "contrarian", "why_now": "Most creators still think AI video is expensive"},
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],
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"sources": [
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{"url": "https://example.com/ai-video", "title": "AI Video Production Overview", "used_for": "landscape"},
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{"url": "https://example.com/ai-tools", "title": "Top AI Video Tools 2026", "used_for": "data_points"},
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{"url": "https://example.com/workflow", "title": "Automated Video Workflows", "used_for": "angles"},
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{"url": "https://example.com/creators", "title": "Creator Economy Report", "used_for": "audience_insights"},
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{"url": "https://example.com/market", "title": "AI Video Market Analysis", "used_for": "landscape"},
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],
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"selected_angle": "workflow",
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}
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try:
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validate_artifact("brief", brief)
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check("Brief validates against schema", True)
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validate_artifact("research_brief", research_brief)
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check("Research brief validates against schema", True)
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except Exception as e:
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check("Brief validates against schema", False, str(e))
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check("Research brief validates against schema", False, str(e))
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cp_path = write_checkpoint(
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PIPELINE_DIR, PROJECT_ID, "idea", "completed",
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artifacts={"brief": brief},
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PIPELINE_DIR, PROJECT_ID, "research", "completed",
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artifacts={"research_brief": research_brief},
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pipeline_type="animated-explainer",
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style_playbook="clean-professional",
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)
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check("Idea checkpoint written", cp_path.exists())
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# Next uncompleted stage in global STAGES order (research/proposal come before idea)
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check("Next stage after idea", get_next_stage(PIPELINE_DIR, PROJECT_ID) == "research")
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check("Research checkpoint written", cp_path.exists())
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# ===================================================================
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# Stage 1: proposal -> proposal_packet
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# ===================================================================
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print("\n--- Stage 1: proposal ---")
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proposal_packet = {
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"version": "1.0",
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"concept_options": [
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{
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"id": "c1",
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"title": "AI Video Production in 60 Seconds",
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"hook": "What if you could create a professional video in 60 seconds?",
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"narrative_structure": "problem_solution",
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"visual_approach": "Clean motion graphics with side-by-side comparisons",
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"suggested_playbook": "clean-professional",
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"target_audience": "content creators and developers",
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"target_platform": "youtube",
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"target_duration_seconds": 60,
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"why_this_works": "Direct problem/solution framing with clear visual proof points",
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},
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{
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"id": "c2",
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"title": "The 60-Second Studio",
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"hook": "Your entire production team, in a single prompt.",
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"narrative_structure": "journey",
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"visual_approach": "Animated workflow diagram that builds step by step",
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"suggested_playbook": "flat-motion-graphics",
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"target_audience": "content creators and developers",
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"target_platform": "youtube",
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"target_duration_seconds": 60,
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"why_this_works": "Journey structure creates natural pacing for a process walkthrough",
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},
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{
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"id": "c3",
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"title": "From Prompt to Premiere",
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"hook": "Traditional video production takes weeks. This takes seconds.",
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"narrative_structure": "comparison",
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"visual_approach": "Split-screen timeline comparison: old way vs AI way",
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"suggested_playbook": "clean-professional",
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"target_audience": "content creators and developers",
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"target_platform": "youtube",
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"target_duration_seconds": 60,
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"why_this_works": "Comparison structure makes the value proposition immediately tangible",
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},
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],
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"selected_concept": {"concept_id": "c1", "rationale": "Direct problem/solution framing is most effective for this audience"},
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"production_plan": {
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"pipeline": "animated-explainer",
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"playbook": "clean-professional",
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"stages": [
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{"stage": "script", "tools": [{"tool_name": "tts_selector", "role": "narration", "available": True}], "approach": "AI-written script with TTS narration"},
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{"stage": "scene_plan", "tools": [], "approach": "5 scenes with motion graphics"},
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{"stage": "assets", "tools": [{"tool_name": "image_selector", "role": "visuals", "available": True}], "approach": "AI-generated images"},
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{"stage": "edit", "tools": [], "approach": "Automated edit decisions"},
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{"stage": "compose", "tools": [{"tool_name": "video_compose", "role": "render", "available": True}], "approach": "Remotion render"},
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],
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},
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"cost_estimate": {
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"total_estimated_usd": 0.50,
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"line_items": [
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{"tool": "tts_selector", "operation": "narration", "estimated_usd": 0.10},
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{"tool": "image_selector", "operation": "5 images", "estimated_usd": 0.30},
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{"tool": "music_gen", "operation": "background track", "estimated_usd": 0.10},
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],
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"budget_verdict": "within_budget",
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},
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"approval": {
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"status": "approved",
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"approved_budget_usd": 2.00,
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},
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}
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try:
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validate_artifact("proposal_packet", proposal_packet)
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check("Proposal packet validates against schema", True)
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except Exception as e:
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check("Proposal packet validates against schema", False, str(e))
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cp_path = write_checkpoint(
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PIPELINE_DIR, PROJECT_ID, "proposal", "completed",
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artifacts={"proposal_packet": proposal_packet},
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pipeline_type="animated-explainer",
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style_playbook="clean-professional",
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)
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check("Proposal checkpoint written", cp_path.exists())
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check("Next stage after proposal", get_next_stage(PIPELINE_DIR, PROJECT_ID, "animated-explainer") == "script")
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# ===================================================================
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# Stage 2: script
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@@ -183,7 +286,7 @@ write_checkpoint(
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artifacts={"script": script},
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pipeline_type="animated-explainer",
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)
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check("Completed stages", get_completed_stages(PIPELINE_DIR, PROJECT_ID) == ["idea", "script"]) # idea and script appear in STAGES order
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check("Completed stages", get_completed_stages(PIPELINE_DIR, PROJECT_ID) == ["research", "proposal", "script"]) # research, proposal and script in pipeline order
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# ===================================================================
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# Stage 3: scene_plan
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@@ -520,13 +623,14 @@ write_checkpoint(
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# ===================================================================
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print("\n--- Final validation ---")
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E2E_STAGES = ["research", "proposal", "script", "scene_plan", "assets", "edit", "compose", "publish"]
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completed = get_completed_stages(PIPELINE_DIR, PROJECT_ID)
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check("All 7 stages completed", len(completed) == 7, f"completed={completed}")
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check("Next stage is None (done)", get_next_stage(PIPELINE_DIR, PROJECT_ID) is None)
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check("Stages in correct order", completed == STAGES, f"{completed}")
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check("All 8 stages completed", len(completed) == 8, f"completed={completed}")
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check("Next stage is None (done)", get_next_stage(PIPELINE_DIR, PROJECT_ID, "animated-explainer") is None)
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check("Stages in correct order", completed == E2E_STAGES, f"{completed}")
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# Verify all checkpoints are readable
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for stage in STAGES:
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for stage in E2E_STAGES:
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cp = read_checkpoint(PIPELINE_DIR, PROJECT_ID, stage)
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check(f"Checkpoint {stage} readable", cp is not None)
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if cp:
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