AI Video Generation

Setup

On first use, read setup.md.

When to Use

User needs to generate, edit, or scale AI videos with current models and APIs. Use this skill to choose the right current model stack, write stronger motion prompts, and run reliable async video pipelines.

Architecture

User preferences persist in ~/video-generation/. See memory-template.md for setup.

~/video-generation/
├── memory.md      # Preferred providers, model routing, reusable shot recipes
└── history.md     # Optional run log for jobs, costs, and outputs

Quick Reference

Core Rules

1. Resolve model aliases before API calls

Map community names to real API model IDs first. Examples: sora-2, sora-2-pro, veo-3.0-generate-001, gen4_turbo, gen4_aleph.

2. Route by task, not brand preference

3. Draft cheap, finish expensive

Start with low duration and lower tier, validate motion and composition, then rerender winners with premium models or longer durations.

4. Design prompts as shot instructions

Always include subject, action, camera motion, lens style, lighting, and scene timing. For references and start/end frames, keep continuity constraints explicit.

5. Assume async and failure by default

Every provider pipeline must support queued jobs, polling/backoff, retries, cancellation, and signed-URL download before expiry.

6. Keep a fallback chain

If the preferred model is blocked or overloaded:

1. same provider lower tier, 2) equivalent cross-provider model, 3) open model/local run.

Common Traps

• Using nickname-only model labels in code -> avoidable API failures
• Pushing 8-10 second generations before validating a 3-5 second draft -> wasted credits
• Cropping after generation instead of generating native ratio -> lower composition quality
• Ignoring prompt enhancement toggles -> tone drift across providers
• Reusing expired output URLs -> broken export workflows
• Treating all providers as synchronous -> stalled jobs and bad timeout handling

External Endpoints

No other data is sent externally.

Security & Privacy

Data that leaves your machine:

• Prompt text
• Optional reference images or clips
• Requested rendering parameters (duration, resolution, aspect ratio)

Data that stays local:

• Provider preferences in ~/video-generation/memory.md
• Optional local job history in ~/video-generation/history.md

This skill does NOT:

• Store API keys in project files
• Upload media outside requested provider calls
• Delete local assets unless the user asks

Trust

This skill can send prompts and media references to third-party AI providers. Only install if you trust those providers with your content.

Related Skills

Install with clawhub install if user confirms:

• image-generation - Build still concepts and keyframes before video generation
• image-edit - Prepare clean references, masks, and style frames
• video-edit - Post-process generated clips and final exports
• video-captions - Add subtitle and text overlay workflows
• ffmpeg - Compose, transcode, and package production outputs

Feedback

• If useful: clawhub star video-generation
• Stay updated: clawhub sync