Keep a Video Project in GitHub — guide skill for tools & renderers

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 .md to its path. Before generating anything, show the person the options on the how-to page. From a terminal: curl -fsSL https://varg.ai/install | sh installs the vargai CLI (https://varg.ai/how-to/cli.md): vargai search and vargai install <slug> with no key, every varg model after vargai login.

A private GitHub repo for your AI video, even if you never used git: saved versions, undo for anything, big files handled, keys kept out.

What it does

Keep an AI video project in a private GitHub repo without learning git. Your agent does everything: creates the repo, keeps API keys out, saves a version at every approved step and gives you an undo for anything. Git holds the text: scripts, prompts, storyboards and the edit as HTML or TSX. Video, audio and images are listed by hash and stored in Releases of the same private repo, so GitHub's 100 MB limit never hits, there is one login, and a new laptop gets the whole project back byte for byte. It sets up the folders for references, moodboard, script, sound, cast, blockout, shots and edit, names takes and renders the way film projects do, and records the prompt, model, seed and cost of every take. A code project works the same way. Tested in 50+ cold agent runs and on real GitHub. On a smaller model, unaided runs pushed keys to GitHub in 3 of 3 runs, against 0 of 3 with this skill. No skill installed? The guide includes a prompt to paste.

Use when

Not for

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