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/generating-llms-txt

Generating or auditing a repository's llms.txt per the llmstxt.org spec, and creating CLAUDE.md / AGENTS.md when missing, by analyzing README, build files, docs, and API surface. Use when creating an llms.txt, auditing an existing one for spec compliance and live links, or

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ring
20577 skills42 agents1 command
Install
$ npx -y skills add LerianStudio/ring --skill generating-llms-txt --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/generating-llms-txt

Context preview

The summary Claude sees to decide when to auto-load this skill.

Generating or auditing a repository's llms.txt per the llmstxt.org spec, and creating CLAUDE.md / AGENTS.md when missing, by analyzing README, build files, docs, and API surface. Use when creating an llms.txt, auditing an existing one for spec compliance and live links, or

SKILL.md

generating-llms-txt.SKILL.md
name: ring:generating-llms-txt
description: "Generating or auditing a repository's llms.txt per the llmstxt.org spec, and creating CLAUDE.md / AGENTS.md when missing, by analyzing README, build files, docs, and API surface. Use when creating an llms.txt, auditing an existing one for spec compliance and live links, or improving a repo's AI readability. Skip when the llms.txt is current, the task is code-only with no doc scope, or the repo needs no LLM discoverability."

LLMs.txt & AI Documentation Generator

When to use

  • Creating a new llms.txt for a repository
  • Auditing an existing llms.txt for completeness
  • Generating CLAUDE.md or AGENTS.md for AI coding agents
  • Improving AI readability of a repository

Skip when

  • Repository already has a complete, up-to-date llms.txt
  • Task is code implementation with no documentation scope
  • Repository is private/internal with no LLM discoverability need

Related

**Complementary:** ring:running-dev-cycle, ring:implementing-tasks

Generates `llms.txt`, `CLAUDE.md`, and `AGENTS.md` for repositories.

Step 1: Analyze Repository

1. Read README.md — project name, description, purpose
2. Read CONTRIBUTING.md — build, test, lint instructions (if exists)
3. Read Makefile / package.json / go.mod — build system, language, dependencies
4. Scan /docs/ — available documentation
5. Scan /api/ or OpenAPI specs — API surface
6. Read existing llms.txt / CLAUDE.md / AGENTS.md (if mode=audit)
7. Identify: language, architecture, test framework

Step 2: Generate llms.txt

Follow llmstxt.org specification exactly:

# {Project Name}

> {One-line description: language, what it does, license.}

{Optional: architecture, key concepts, domain terminology needed to work with this project.}

## Docs

- [{Doc title}]({url}): {Brief description}

## API Reference

- [{API name}]({url}): {What this covers}

## Code

- [{Key module}]({path}): {What this module does}

## Optional

- [{Secondary resource}]({url}): {Description}

**Rules:**

  • One H1 (project name), required
  • Blockquote summary — required, include language and license
  • H2 sections only (no H3+)
  • Links: `[title](url): description` format
  • File in repo root: `/llms.txt`
  • Target: fits in ~2K tokens

**MUST include:** name, architecture overview, key domain concepts, links to README/CONTRIBUTING/API docs/key modules.

**MUST NOT include:** internal-only docs, CI/CD details, issue tracker, full dependency lists, changelog.

Step 3: Generate CLAUDE.md

Read by Claude Code at session start. Must be actionable with exact commands:

# {Project Name}

## Quick Start
{How to build and run locally — exact copy-pasteable commands}

## Testing
{How to run tests — exact commands including single-test}

## Linting & Formatting
{Lint/format commands, CI expectations}

## Architecture
{Brief: layers, key directories, patterns}
e.g., "Business logic in /internal/domain/, HTTP handlers in /internal/adapters/http/"

## Key Conventions
{Naming conventions, error handling, logging patterns with examples}
e.g., "Functions use camelCase: processTransaction()"

## Common Pitfalls
{What trips up new contributors or AI agents}

**Rules:**

  • Commands must be copy-pasteable (no placeholders)
  • Architecture must name actual directories
  • Conventions must have inline examples
  • Keep under 3K tokens

Step 4: Generate AGENTS.md

Same structure as CLAUDE.md but vendor-neutral language. If CLAUDE.md exists: `AGENTS.md` can reference it:

# {Project Name} — AI Agent Context

See [CLAUDE.md](./CLAUDE.md) for complete setup and conventions.

## Additional Notes
{Any agent-specific guidance not in CLAUDE.md}

Audit Mode (mode=audit)

For existing files, check:

| Check | Pass Condition | |-------|----------------| | llms.txt has H1 + blockquote | Required fields present | | All links resolve | No 404s | | Spec compliance | No H3+, no non-list content in sections | | CLAUDE.md commands valid | All commands runnable, no stale references | | Under token budget | llms.txt < 2K tokens, CLAUDE.md < 3K tokens |

Output

## LLM Documentation Report

Mode: create | audit | full
Repository: {repo_path}

### Files Generated/Updated
| File | Action | Tokens |
|------|--------|--------|
| llms.txt | Created/Updated/OK | ~{N} |
| CLAUDE.md | Created/Updated/OK | ~{N} |
| AGENTS.md | Created/Updated/OK | ~{N} |

### Audit Results (audit mode)
| Check | Status | Details |
|-------|--------|---------|
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