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Stop your coding agent from shipping the same Inter-and-purple-gradient page every time. hash-design rolls five directions (or any number from 1 to 12), freezes each one as a design brief, lists them one line each, renders them side by side when you ask, and builds the one you pick. Any direction can be rebuilt from its ref.
Live examples · Mapping spec · MCP reference · Changelog
Showcase · Quick start · How it works · Reference · FAQ
Tell your agent (Claude Code, Cursor, Codex, Windsurf, VS Code Copilot…):
Install the hash-design skill for this agent: run `npx -y hash-design skill install --dir .`
and register its MCP server with `npx -y hash-design mcp`, following
https://github.com/qybaihe/hash-design#quick-start.
Four full runs, each from its own fact sheet and a fixed seed that only makes it reproducible: the seed is the example's folder name, used once with no re-rolls. All four are hash-design/v2 runs, so every brief is exactly what its ref reproduces today (examples.test.ts checks it byte for byte). Every preview was rendered by its own sub-agent that saw only its brief and the fact sheet; all 20 score 100/100 on critique_page, with brief adherence 100. All of them are on the examples index. Each plate below is one roll: its five directions side by side.
Seed fourth-lane-bakery · critique_page 100 · brief adherence 100 · example folder: facts, briefs, previews and the process
Seed sluice · critique_page 100 · brief adherence 100 · example folder: facts, briefs, previews and the process
Seed bridge-two-bikes · critique_page 100 · brief adherence 100 · example folder: facts, briefs, previews and the process
Seed causeway-festival · critique_page 100 · brief adherence 100 · example folder: facts, briefs, previews and the process
Ask an LLM for "a beautiful page" and it almost always collapses into the same modal aesthetic: Inter, a purple-to-blue gradient, a centered headline with two buttons, three identical feature cards, glassmorphism, emoji icons, and copy like Unlock, Seamless, Supercharge. The reason is simple: whenever the model gets to choose, it picks the most common thing in its training data.
hash-design does not ask the model to have better taste. It takes the choice away:
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