LLM Documentation

AI-native documentation endpoints.

Give AI coding agents exact context so they write accurate @quinkit/ui imports, prop signatures, and theme configurations without hallucination.

Step 01

Compact Endpoint (llms.txt)

A token-efficient manifest containing package imports, theme attributes, and key component APIs for rapid LLM prompt context.

Step 02

Full Inventory (llms-full.txt)

Complete untruncated documentation including all component prop interfaces, variant definitions, and complex JSX patterns.

Step 03

Zero-Hallucination Workflow

Give your AI assistant (Antigravity, Cursor, Claude, Copilot) the exact static URL to generate 100% valid @quinkit/ui imports.

1. Compact LLM Summary (`llms.txt`)Static Endpoint
txt
https://quinkit-ui.vercel.app/llms.txt
2. Full Component Inventory (`llms-full.txt`)Static Endpoint
txt
https://quinkit-ui.vercel.app/llms-full.txt
3. Recommended AI Prompt SnippetPrompt Context
markdown
Please reference https://quinkit-ui.vercel.app/llms.txt for all @quinkit/ui component imports, prop signatures, and theme attributes.

Recommended Agentic AI Workflow

  • Package API as Source of Truth: Always prefer importing primitives directly from `@quinkit/ui`.
  • Theme & Palette Scoping: Use `data-theme="..."` and `data-palette="..."` attributes on containers rather than inline hardcoded styles.
  • Static Asset Serving: Both `llms.txt` and `llms-full.txt` are served directly from the public root directory with standard plain-text headers.