create-movie

> STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT. # create-movie Orchestrated movie creation for Horus persona. Creates mockumentaries, short films, music videos, and educational content through a phased workflow. ## Philosophy > "AI isn't the artist, it's the amplifier" - Nobody & The Computer Horus uses AI to turn imagination into audiovisual reality. He doesn't just use pre-built tools - he writes code to create his own tools. ## Phases ``` HARDWARE CHECK → RESEARCH → SCRIPT → CASTING → EXPERT REVIEW → GENERATE → ASSEMBLE → LEARN ``` **Note:** BUILD TOOLS is optional and only triggered when custom effects are needed. ### Phase 0: Hardware Detection (Automatic) Before any generation, the orchestrator automatically detects hardware via `/ops-workstation`: ```bash # Automatic hardware check on startup ./run.sh create "prompt" # → Calls /ops-workstation gpu to detect VRAM # → Calls /ops-workstation memory to detect RAM # → Auto-selects optimal model variant ``` **Auto-Selection Logic:** | Detected VRAM | Model Selected | Settings | |---------------|----------------|----------| | ≥24GB | LTX-2 19B FP8 | 720p/1080p, audio on, batch=1 | | 16-23GB | LTX-2 19B FP4 | 720p only, audio on, batch=1 | | 12-15GB | LTX-2 Distilled 2B | 720p, audio optional, batch=1 | | <12GB | **RunPod suggested** | Prompts to use `/ops-runpod` | **RAM-Based Optimizations:** | Detected RAM | Optimization | |--------------|--------------| | ≥128GB | Weight streaming enabled (offload to RAM) | | 64-127GB | Partial offloading | | <64GB | No offloading, strict VRAM limits | **Override Auto-Detection:** ```bash # Force specific model variant ./run.sh create "prompt" --model ltx2-fp4 ./run.sh create "prompt" --model ltx2-distilled ./run.sh create "prompt" --runpod # Force cloud generation ``` ### Phase 1: Research (Library-First) 1. **Check Horus's Library First:** - `horus-filmmaking` scope (past techniques, learnings) - `horus_lore` scope (YouTube transcripts, film analysis) - Ingested movies with emotion tags - Episodic archive (past filmmaking sessions) 2. **Search for New Resources:** - `/ingest-movie search` for films to watch - `/ingest-youtube search` for tutorials 3. **Deep Web Research:** - `/dogpile` for comprehensive multi-source search - `/surf` for specific tutorials/references ### Phase 2: Script (via /create-story) - Integrates with `/create-story` skill for screenplay generation - Uses Chutes models (chimera, qwen, deepseek-r1) for creative writing - Parses INT./EXT. headings, dialogue, action, audio cues - Outputs structured scene breakdown with visual descriptions **Format Options:** - `screenplay` (default) - Standard INT./EXT. scene headings - `mockumentary` - Interview segments with talking heads + B-roll - `reconstruction` - Historical recreation with narrator framing ### Phase 2.5: Casting (via /create-cast) - Multi-round collaborative character casting - Extracts characters from screenplay via script analysis - Optional reference actor discovery via `/discover-talent` (TMDB) - Generates identity packs: front, 3/4, full-body reference images - Voice casting from existing TTS models or queues training - Bridge attributes extracted via Federated Taxonomy **Output:** ``` characters/ ├── casting_session.json ├── SARAH/ │ ├── character_bible.yaml │ └── identity_pack/ │ ├── front.png │ ├── three_quarter.png │ └── full_body.png └── voice_assignments.yaml ``` ### Phase 2.7: Expert Review (NON-NEGOTIABLE) **When a creative team has multiple personas**, this phase enforces a feedback loop before generation: ``` 1. DIRECTOR creates vision (shot list, style notes) 2. TECHNICAL EXPERT reviews for AI video execution - e.g., Dan Kieft for Kling, video model specialist for Veo 3. EXPERT sends formal notes to DIRECTOR - Technical limitations, proposed changes, questions 4. DIRECTOR approves, revises, or escalates 5. BOTH sign off before generation proceeds ``` **Required Documents:** | Document | Purpose | |----------|---------| | `{EXPERT}_REVIEW.md` | Technical review with recommendations | | `{EXPERT}_TO_{DIRECTOR}_FEEDBACK.md` | Formal notes requiring director decision | | `{DIRECTOR}_APPROVAL.md` | Director sign-off on technical changes | | `*_V2_APPROVED.md` | Final approved instructions for generation | **Expert Personas (queryable via /memory):** - **Dan Kieft** (scope: `dan-kieft`) - Kling AI, multi-shot prompting, character consistency - **Video model specialists** - Add via `/ingest-youtube` + `/memory learn` **Workflow:** ```bash # 1. Create initial instructions # (output: KLING_INSTRUCTIONS_V1.md) # 2. Query expert persona ./run.sh recall --q "multi-shot prompting" --scope dan-kieft # 3. Generate expert review # (output: DAN_KIEFT_REVIEW.md) # 4. Send to director for approval # (output: DAN_TO_WILSON_FEEDBACK.md) # 5. Director approves # (output: WILSON_APPROVAL.md) # 6. Final approved instructions # (output: KLING_INSTRUCTIONS_V2_APPROVED.md) # 7. ONLY NOW proceed to generation ``` **Skip Conditions:** - Single-persona projects (no creative team) - `--skip-expert-review` flag (use with caution) ### Phase 3: Build Tools (Optional) - Write code in Docker-isolated sandbox - Create custom tools for specific effects - Iterate on approaches ### Phase 4: Generate - Use ComfyUI, Stable Diffusion for images - Use **auto-selected video model** based on hardware (LTX-2 FP8/FP4/Distilled) - Use Whisper, IndexTTS2 for audio - If hardware insufficient, automatically suggests `/ops-runpod` ### Phase 5: Assemble - Combine assets with FFmpeg - Output MP4 video or interactive HTML ### Phase 6: Learn - Store successful techniques in /memory - Remember what worked for future movies ## Quick Start ```bash cd .pi/skills/create-movie # Full orchestrated workflow (recommended) ./run.sh create "A 30-second film about discovering colors" # With options ./run.sh create "film noir detective" \ --duration 60 \ --style "high contrast, shadows, venetian blinds" \ --format mp4 \ --work-dir ./noir_project # Individual phases (for manual control) ./run.sh research "film noir lighting techniques" ./run.sh script --from-research research.json --duration 30 --use-create-story ./run.sh build-tools --script script.json ./run.sh generate --tools ./tools --script script.json --style "cinematic" ./run.sh assemble --assets ./assets --output movie.mp4 --format mp4 ./run.sh learn --project-dir ./movie_project ``` ## CLI Commands ### create Full orchestrated workflow through all phases. ```bash ./run.sh create PROMPT [OPTIONS] --output, -o Output file (default: movie.mp4) --work-dir, -w Working directory (default: ./movie_project) --duration, -d Target duration in seconds (default: 30) --style, -s Visual style (e.g., 'cinematic', 'film noir') --format, -f Output format: mp4 or html (default: mp4) --store-learnings Store learnings in memory (default: true) --skip-research Skip research phase if research.json exists --skip-casting Skip casting phase (no identity packs) ``` ### research Library-first research: checks Horus's memory and ingested content before external search. ```bash ./run.sh research TOPIC [OPTIONS] --output, -o Output file (default: research.json) --skip-external Only search library, skip external sources ``` ### script Generate screenplay with scene breakdown. Integrates with `/create-story`. ```bash ./run.sh script [OPTIONS] --from-research, -r Research JSON file (required) --prompt, -p Override topic from research --duration, -d Target duration in seconds --use-create-story Use /create-story skill for screenplay --model, -m LLM model (default: chimera) --output, -o Output file (default: script.json) ``` ### build-tools Generate custom tools in Docker sandbox. ```bash ./run.sh build-tools [OPTIONS] --script, -s Script JSON file (required) --output-dir, -o Output directory (default: ./tools) --skip-docker Use host instead of Docker sandbox ``` ### generate Create images, video, and audio assets. ```bash ./run.sh generate [OPTIONS] --tools, -t Tools directory (default: ./tools) --script, -s Script JSON file (required) --output-dir, -o Assets output directory (default: ./assets) --style Visual style to apply ``` ### assemble Combine assets into final output. ```bash ./run.sh assemble [OPTIONS] --assets, -a Assets directory (required) --output, -o Output file/directory (required) --format, -f Output format: mp4 or html (default: mp4) --fps Frames per second for MP4 (default: 24) ``` ### learn Store filmmaking insights in memory after a project. ```bash ./run.sh learn [OPTIONS] --project-dir, -p Project directory (required) --scope Memory scope (default: horus-filmmaking) --dry-run Show learnings without storing ``` ### study Pre-phase: Learn filmmaking topics BEFORE creating movies. Targeted /dogpile with internal (memory) + external (web) search, then stores via `/memory learn`. ```bash ./run.sh study TOPIC [OPTIONS] --scope Memory scope (default: horus-filmmaking) --deep/--quick Deep research (dogpile) vs quick (YouTube search) --list-topics Show suggested filmmaking topics # Examples: ./run.sh study "cinematography lighting techniques" --deep ./run.sh study "camera framing composition" --deep ./run.sh study --list-topics ``` ### study-all Comprehensive learning session - studies all core filmmaking topics. ```bash ./run.sh study-all [OPTIONS] --scope Memory scope (default: horus-filmmaking) ``` ## Output Formats ### MP4 Video Standard video file, playable anywhere. ### Interactive HTML Web-based experience with: - Frame-by-frame navigation - Audio controls - Scene metadata viewer ## Shot Specification (HorusShotSpec v0.1) HorusShotSpec is a YAML-based shot specification format that replaces KSML for video generation. ### Schema Overview ```yaml shot_id: "ACT1_SC02_SHOT03" prompt: text: "A tense noir interrogation in a dim room. Slow dolly push toward suspect." negative: "text overlays, watermarks, shaky camera" duration_s: 8 # Valid: 4, 8, 16 seconds aspect_ratio: "16:9" # Valid: 16:9, 9:16, 1:1 resolution: "1080p" # Valid: 720p, 1080p references: subject_images: - path: "./assets/detective.png" weight: 0.7 controls: seed: 42 safety: "default" renderer: name: "veo" model: "veo-3.1-generate-preview" metadata: scene: "SC02" act: "ACT1" sequence_order: 3 ``` ### Compilation The `shot_compiler` module validates and compiles YAML to Veo API JSON: ```python from core.shot_compiler import compile_yaml_to_veo_json veo_request = compile_yaml_to_veo_json(yaml_content) # → Returns dict ready for Veo API ``` ### Validation Rules | Field | Constraint | |-------|------------| | `duration_s` | Must be 4, 8, or 16 seconds | | `aspect_ratio` | Must be 16:9, 9:16, or 1:1 | | `references.subject_images` | Max 6 images | | `references.*.weight` | 0.0 to 1.0 | | `prompt.text` | Max 4000 characters | ### Migration from KSML > **DEPRECATED**: KSML is deprecated in favor of HorusShotSpec YAML. > See `docs/KSML_TO_YAML_MIGRATION.md` for migration guide. **Quick comparison:** | Feature | KSML (deprecated) | HorusShotSpec (recommended) | |---------|-------------------|----------------------------| | Renderer | Kling | Veo (or any) | | Schema | Kling-specific | Renderer-neutral | | Validation | Manual | Built-in constraints | | Compilation | Export only | YAML → Veo JSON | See [MODELS.md](references/MODELS.md) for the video model selection guide, VRAM requirements, camera controls, WAN 2.2, and performance expectations. See [EXAMPLES.md](references/EXAMPLES.md) for workflow patterns, multi-model collaboration, and example sessions. See [REFERENCE.md](references/REFERENCE.md) for available skills, free/open-source tools, memory integration, and dependencies.