UitbreidenOS llms.txt Skill
When to activate
- Creating documentation that AI agents can navigate efficiently
- Setting up Claude Code to use your project's latest docs without hallucinating APIs
- Building a library or framework and wanting to make it AI-legible
- Reducing context window usage when working with large documentation sets
- Enabling Claude to retrieve only the specific docs it needs for a task
When NOT to use
- Small projects where pasting the docs directly is fine (< 5,000 tokens)
- When no documentation exists yet — write the docs first
- Private internal tools with no external documentation site
What is llms.txt
The llms.txt standard (llmstxt.org) is a simple markdown file at the root of a documentation site that serves as a machine-readable index. Instead of stuffing hundreds of thousands of tokens of raw documentation into a prompt (which increases hallucination risk by creating context confusion), an agent reads the concise index, reasons about which specific pages it needs, and fetches only those.
Without llms.txt: agent reads everything → context bloat → mixed-up APIs → hallucinations
With llms.txt: agent reads index → reasons → fetches 3 specific pages → accurate generation
Research shows 60%+ reduction in API hallucination errors vs raw context stuffing.
Instructions
Basic llms.txt format
<!-- /llms.txt — place at your docs site root -->
# My Library
> TypeScript-first HTTP client for the Acme API
## Docs
- [Getting Started](/docs/getting-started): Installation, first request, authentication setup
- [API Reference](/docs/api-reference): Complete method signatures with parameters and return types
- [Authentication](/docs/auth): API key, OAuth2, JWT token patterns and refresh logic
- [Error Handling](/docs/errors): Error codes, retry logic, exception types
- [Pagination](/docs/pagination): Cursor and offset pagination, iterating large result sets
- [Rate Limiting](/docs/rate-limiting): Limits per tier, backoff strategies, header interpretation
- [Webhooks](/docs/webhooks): Signature verification, event types, retry behaviour
- [Examples](/docs/examples): Common patterns: CRUD, streaming, batch operations
## Optional
- [Migration Guide](/docs/migration): Breaking changes and upgrade paths between major versions
- [Changelog](/docs/changelog): Version history
Format rules:
# Title— library name (one line)> Description— one sentence about what it does## Docs— required pages (agent fetches these when relevant)## Optional— supplementary pages (agent fetches only if needed)- Each line:
- [Page Name](URL): one-sentence description - Descriptions must be semantic — the agent matches these to user requests
Generate llms.txt for your project
# Use the llms-txt CLI to generate from existing docs
npx llms-txt-generator --source ./docs --output ./public/llms.txt
# Or generate from a live docs site
npx llms-txt-generator --url https://docs.myproject.com --output ./llms.txt
Manually write llms.txt for your CLAUDE.md project
For projects you're building with Claude Code, add an llms.txt that points to the external documentation Claude should use:
<!-- llms.txt for a Next.js + Drizzle + Better Auth project -->
# Project Tech Stack
> SaaS app using Next.js 16, Drizzle ORM, Neon Postgres, Better Auth, shadcn/ui
## Framework Docs
- [Next.js App Router](https://nextjs.org/docs/app): Server Components, Server Actions, route handlers, middleware
- [Next.js Data Fetching](https://nextjs.org/docs/app/building-your-application/data-fetching): fetch patterns, caching, revalidation
## Database
- [Drizzle ORM Docs](https://orm.drizzle.team/docs): Schema definition, queries, migrations, relations
- [Neon Serverless](https://neon.tech/docs): Branching, connection pooling, pgvector
## Auth
- [Better Auth Docs](https://www.better-auth.com/docs): Setup, providers, session, plugins, 2FA
## UI
- [shadcn/ui Components](https://ui.shadcn.com/docs/components): Available components, usage, theming
## Optional
- [Drizzle Kit](https://orm.drizzle.team/docs/kit-overview): Migration CLI commands
- [Neon pgvector](https://neon.tech/docs/extensions/pgvector): Vector search setup and queries
Connect llms.txt to Claude Code via MCP
// .claude/settings.json — add a URL-loading MCP server
{
"mcpServers": {
"docs": {
"command": "npx",
"args": [
"-y",
"@llmstxt/mcp-server",
"--llms-txt", "https://docs.myproject.com/llms.txt"
]
}
}
}
Or use the simple fetch approach in a CLAUDE.md:
<!-- CLAUDE.md — point Claude to your llms.txt -->
## Documentation
When you need to reference external library docs, first fetch and read:
https://docs.myproject.com/llms.txt
Then use the links in that file to fetch only the specific pages relevant to the task.
Do NOT load the entire documentation site — use the index to reason about what you need.
llms.txt for a library you're publishing
If you're publishing a library and want Claude (and other agents) to use it correctly:
<!-- public/llms.txt on your docs site -->
# acme-client
> Official TypeScript SDK for the Acme REST API. Supports Node.js 18+ and Edge Runtime.
## Docs
- [Installation](https://docs.acme.com/sdk/install): npm install, env setup, first call
- [Authentication](https://docs.acme.com/sdk/auth): API keys, OAuth, token refresh
- [Customers](https://docs.acme.com/sdk/customers): CRUD operations, search, pagination
- [Payments](https://docs.acme.com/sdk/payments): Charge, refund, subscription management
- [Webhooks](https://docs.acme.com/sdk/webhooks): Signature verification, event types
- [Error Handling](https://docs.acme.com/sdk/errors): Error classes, retry logic, HTTP status codes
- [TypeScript Types](https://docs.acme.com/sdk/types): Full type definitions and interfaces
## Optional
- [Migration from v1](https://docs.acme.com/sdk/migration): Breaking changes in v2
- [Examples](https://docs.acme.com/sdk/examples): Common patterns and recipes
- [Changelog](https://docs.acme.com/sdk/changelog): Version history
Submit your llms.txt to the directory at llmstxt.org so Claude and other agents can discover it.
Writing good page descriptions
The one-sentence description per link is critical — it's what the agent uses to decide whether to fetch the page.
# Bad descriptions (too vague — agent can't reason about relevance)
- [Authentication](/auth): Auth docs
- [API Reference](/api): API reference
# Good descriptions (semantic — agent can match to user request)
- [Authentication](/auth): API key setup, OAuth2 PKCE flow, token refresh with retry logic
- [API Reference](/api): Complete method signatures for all endpoints with TypeScript types, parameters, return types, and error codes
Verify your llms.txt works
# Test: does Claude fetch only what it needs?
# Prompt: "Show me how to implement pagination using the cursor pattern"
# Good: Claude fetches only /docs/pagination
# Bad: Claude fetches all 20 documentation pages
Example
User: Create an llms.txt for a project using Vercel AI SDK + Anthropic + Next.js so Claude Code doesn't hallucinate SDK method names.
Expected output:
# AI Chat App
> Next.js 16 + Vercel AI SDK + Anthropic Claude — streaming chat with tool calls
## Docs
- [Vercel AI SDK: useChat](https://sdk.vercel.ai/docs/reference/ai-sdk-ui/use-chat): Hook API, message format, streaming, tool invocations
- [Vercel AI SDK: streamText](https://sdk.vercel.ai/docs/reference/ai-sdk-core/stream-text): Server-side streaming, tool definitions, maxSteps
- [Anthropic Claude Models](https://platform.claude.com/docs/about-claude/models/overview.md): Current model IDs, context windows, pricing
- [Vercel AI SDK: generateObject](https://sdk.vercel.ai/docs/reference/ai-sdk-core/generate-object): Structured output with Zod schemas
## Optional
- [Vercel AI SDK: Providers](https://sdk.vercel.ai/docs/foundations/providers-and-models): Switching between Claude, OpenAI, Gemini
- [Anthropic Prompt Caching](https://platform.claude.com/docs/build-with-claude/prompt-caching.md): cache_control setup, token savings