ai-video-generation

Route across 10+ video models (HappyHorse, Kling, Seedance, Veo, Wan, Hailuo, Dreamina) for text-to-video, image-to-video, and video extension.

- Supports text-to-video (t2v), image-to-video (i2v), and Veo's video-extend endpoint with model selection logic optimized for intent (quality tier, multi-shot identity, physics accuracy, audio sync, speed)

- Includes 10+ production models with documented prompting patterns: HappyHorse 1.0 (Arena #1, in-pass audio), Kling 3.0 (4K, multi-shot), Seedance v2 (multi-modal, cinematic), Veo 3-1 (physics-respecting), Wan 2-7 (audio-driven lip-sync), plus Hailuo, Dreamina, and legacy tiers

- Each model route ships its exact schema, invoke command, and prompting tips (e.g., "lead with subject and motion verb", "describe audio inline", "Veo respects physics")

- Invoked via runcomfy run <vendor>/<model>/<endpoint> with JSON input; triggers on "generate video", "make a video

AI Video Generation

Generate videos with the full RunComfy video-model catalog through one CLI - text-to-video, image-to-video, and Veo's video-extend. This skill picks the right model for the user's intent and ships the documented prompt patterns + the exact runcomfy run invoke for each.

runcomfy.com · Video models · CLI docs

Powered by the RunComfy CLI

# 1. Install (see runcomfy-cli skill for details)
npm i -g @runcomfy/cli      # or:  npx -y @runcomfy/cli --version

# 2. Sign in
runcomfy login              # or in CI: export RUNCOMFY_TOKEN=<token>