huashu-motion: launch film + point-to-edit — workflow skill for launch videos
For AI agents: find the right skill in one call —
curl -s "https://varg.ai/how-to/api/search?q=<what the person wants>"returns the matching how-to page and the best skills with install commands, as markdown (Russian works too). Full index: https://varg.ai/how-to/llms.txt. Every page has a markdown twin: add.mdto its path. Before generating anything, show the person the options on the how-to page. From a terminal:curl -fsSL https://varg.ai/install | shinstalls the vargai CLI (https://varg.ai/how-to/cli.md):vargai searchandvargai install <slug>with no key, every varg model aftervargai login.
Ask for "a 30-second launch film for my product": it reads the product, offers three directions, writes the film and its score, then opens an editor where you c
- Type: Workflow · Launch videos
- Slug:
huashu-motion - Status: live
- Publisher: Huashu (alchaincyf) (community)
- License: MIT
- Source: https://github.com/alchaincyf/huashu-motion
- Works in: Claude Code, Codex
- Requires: Node, Chrome, ffmpeg
- Adoption: 2,109 GitHub stars (GitHub, 2026-11-02)
What it does
Chinese-first launch-film skill by Huashu (alchaincyf). Research step measured cuts, beats, loudness and energy curves of 21 real launch films (Apple, Nothing, GoPro, Google, Vercel, Linear, Anthropic). Point-to-edit: press C, click the frame, write the change; the agent zooms into that frame and region, edits only that spot and keeps every version (undo). Titles, numbers, colours and hold times become inputs in the editor. Composes its own music.
Use when
- product or feature launch films with original music
- a client needs to request changes on specific frames
- Chinese or English launch copy
Not for
- footage-based ads
- long tutorials
Install
- Claude Code:
npx skills add alchaincyf/huashu-motion - Codex:
npx skills add alchaincyf/huashu-motion - Any agent, with the vargai CLI:
vargai install huashu-motion
Models it drives: hyperframes
Example output
https://raw.githubusercontent.com/alchaincyf/huashu-motion/main/docs/media/hero.gif — README demo: a launch film built and edited by pointing at frames (credit: Huashu (alchaincyf) (README), https://github.com/alchaincyf/huashu-motion)
More launch videos skills
- Orbit Card: A single feature card orbits a dot sphere on a Blender-made camera move: a 10-second product beat.
- Higgsfield Academy (Higgsfield Open-Source Promo): Higgsfield's 64 s promo for Higgsfield Academy, made almost entirely with Seedance 2.0.
- Seed Audio 1.0 Launch (Higgsfield Open-Source Video): Higgsfield's 106 s launch video for Seed Audio 1.0 (voice cloning, cinematic sound): bold motion design, comedic tone.
- HeyGen's Real Launch Videos as HyperFrames Source: 20 HeyGen product launch videos as editable HyperFrames HTML compositions, each with its storyboard, assets and render command.
- Check a render against its reference by Refs: Score your render against the reference film: cut timing, brightness, motion, loudness, format, frame-exact fixes.
- Launch video formulas by Refs: 28 video formats distilled from 132 measured films: the shape in timed beats, the constants, a template prompt.
Run the generation on varg
varg is one API key for the models these skills drive — Seedance 2.5, Kling 4, Veo 3.1, Wan 3, Nano Banana 2.1, GPT Image 2.5, ElevenLabs v4 and 200+ more — billed in credits, with a cost estimate before anything runs.
- Any agent with a terminal:
curl -fsSL https://varg.ai/install | sh(Windows:irm https://varg.ai/install.ps1 | iex), thenvargai loginand every model is a command —vargai generate_video …,vargai estimate_cost …(https://varg.ai/how-to/cli/) - Claude Code:
claude plugin marketplace add vargHQ/skills && claude plugin install varg@varg(skill + hosted MCP) - Codex, Cursor, Gemini CLI, OpenClaw:
npx skills add vargHQ/skills --skill varg-ai -a <agent> -y - Grok and any agent that reads SKILL.md:
npx skills add vargHQ/skills --skill varg-ai, or point it at the skill's SKILL.md - Claude.ai, ChatGPT, any MCP client: connect
https://mcp.varg.ai/mcp - API key: https://varg.ai/dashboard/api · Model specs: https://varg.ai/models/llms.txt