llm-docs-optimizer

A Claude Code skill that makes your documentation work seamlessly with AI coding assistants. Transform generic docs into AI-optimized documentation with measurable quality improvements—scored by Context7's c7score benchmark, structured around developer questions, and enhanced with LLM-friendly navigation.

  

Why Use This?

The Problem: Standard documentation often fails with AI coding assistants. Generic API references, incomplete examples, and poor structure make it hard for tools like Claude and GitHub Copilot to help developers effectively.

The Solution: This skill transforms your documentation to work seamlessly with AI tools by:

• •Measurable Quality: Score your docs against Context7's c7score benchmark and track improvements (e.g., 45 → 89/100)
• •Better AI Retrieval: Organize content around developer questions so LLMs can find the right answers
• •Complete Examples: Transform API references into runnable, practical code snippets
• •LLM-Friendly Navigation: Generate standardized llms.txt files that help AI understand your project structure
• •Dual Benefits: Better for human developers AND AI assistants

Real Impact:

• •Developers get answers faster when using AI coding assistants
• •Your documentation becomes more practical and question-driven
• •AI tools can better understand and recommend your project
• •Measurable before/after quality metrics prove the improvement

Demo video

https://github.com/user-attachments/assets/3ad0ff3f-69bc-4553-bbfd-654b0880e3b5

Overview

This skill provides comprehensive documentation optimization for AI tools:

1. C7Score Optimization: Transform documentation to score highly on Context7's benchmark - the leading quality metric for AI-assisted coding documentation
2. llms.txt Generation: Create standardized navigation files that help LLMs quickly understand and navigate your project's documentation
3. Automated Quality Scoring: Get before/after evaluation across 5 key metrics to measure improvement
4. Question-Driven Restructuring: Organize content around developer questions for better AI retrieval

What is c7score?

c7score is a benchmark created by Context7 that measures documentation quality for AI-assisted coding. It evaluates:

• •Question-Snippet Matching (80%): How well code snippets answer common developer questions
• •LLM Evaluation (10%): Overall comprehensiveness and clarity
• •Formatting (5%): Proper markdown structure and code block formatting
• •Metadata Removal (2.5%): Elimination of irrelevant timestamps and badges
• •Initialization Examples (2.5%): Quality of setup and quickstart examples

What is llms.txt?

llms.txt is a standardized markdown file format from llmstxt.org that provides LLM-friendly documentation navigation. It helps AI tools quickly understand your project structure and find relevant information.

Features

C7Score Optimization

• •📊 Documentation Analysis: Automatically identifies quality issues and gaps
• •✨ Smart Optimization: Applies proven patterns to improve LLM retrieval
• •📝 Code Snippet Enhancement: Transforms examples to better answer developer questions
• •🎯 Question-Driven Structure: Organizes content around common developer queries
• •📈 Automated Scoring: Claude-based evaluation across all 5 c7score metrics with before/after comparison
• •🔍 Python Analysis Tool: Automated script to scan documentation for issues
• •📚 Reference Patterns: Library of transformation examples and best practices

llms.txt Generation

• •🗺️ Project Analysis: Understands your documentation structure
• •📋 Smart Templates: Uses appropriate format for your project type (library, CLI, framework, etc.)
• •🔗 Proper Formatting: Follows official llmstxt.org specification
• •📝 Complete Examples: Includes before/after examples and templates
• •🎯 Priority Organization: Structures content from essential to optional

Installation

Quick Install (Recommended)

Install directly from the marketplace using Claude Code:

# Step 1: Add the marketplace (one-time setup)
/plugin marketplace add alonw0/llm-docs-optimizer

# Step 2: Install the plugin
/plugin install llm-docs-optimizer@llm-docs-optimizer-marketplace

The plugin will be automatically available in Claude Code after installation.

Alternative: Manual Installation

If you prefer manual installation:

1. Clone the repository:

``bash git clone https://github.com/alonw0/llm-docs-optimizer.git ~/.claude/plugins/llm-docs-optimizer ``

1. Restart Claude Code or run:

``bash /plugin reload ``

Prerequisites

• •Claude Code: Latest version with plugin support
• •Python 3.7+: Required for the analysis script (optional but recommended)
• •Project with documentation: README.md or similar files to optimize

Usage

Activating the Skill

The skill automatically activates when you ask Claude Code to:

• •"Optimize my documentation for c7score"
• •"Improve my README for AI coding assistants"
• •"Make my docs better for LLM retrieval"
• •"Analyze my documentation quality"

The skill activates when you ask:

• •"Create an llms.txt file for my project"
• •"Generate llms.txt"
• •"Add llmstxt navigation to my docs"
• •"Help LLMs understand my project structure"

Example Workflows

1. Start with analysis:

`` Optimize my README.md for c7score ``

1. Claude will:

- Analyze your current documentation - Run the Python analysis script (if available) - Identify common developer questions - Transform code snippets to answer those questions - Restructure content for better LLM retrieval - Generate an optimized version - Evaluate c7score impact with before/after metrics across all 5 dimensions

1. Review and iterate:

- Review the optimized documentation and score improvements (e.g., 45 → 89/100) - See detailed breakdowns across Question-Snippet Matching, LLM Evaluation, Formatting, Metadata Removal, and Initialization metrics - Request specific improvements - Test with your AI coding assistant

1. Request generation:

`` Create an llms.txt file for my project ``

1. Claude will:

- Analyze your project structure and documentation - Identify project type (library, CLI tool, framework, etc.) - Select appropriate template and sections - Generate properly formatted llms.txt with full URLs - Save to repository root

1. Use the file:

- LLMs can now navigate your documentation efficiently - Update URLs if needed when publishing - Keep updated as documentation evolves

Python Analysis Script

The included script helps identify documentation issues:

cd scripts
python analyze_docs.py /path/to/your/project

This will generate a report highlighting:

• •Missing code examples
• •Unclear explanations
• •Poor question-answer alignment
• •Formatting issues

What Gets Optimized

Before Optimization

# MyProject

A cool library for data processing.

## Installation
pip install myproject

After Optimization

# MyProject

A Python library for efficient data transformation and processing pipelines.

## Quick Start

### How do I install MyProject?

pip install myproject

### How do I process my first dataset?

from myproject import DataProcessor

Load and process data

processor = DataProcessor() result = processor.transform(data) print(result)

### How do I handle errors during processing?

from myproject import DataProcessor, ProcessingError

try: processor = DataProcessor() result = processor.transform(data) except ProcessingError as e: print(f"Processing failed: {e}")

Key Optimization Patterns

1. Question-Driven Headers: Transform "Installation" → "How do I install X?"
2. Complete Code Examples: Add imports, setup, and context to snippets
3. Error Handling: Show common error scenarios and solutions
4. Progressive Complexity: Start simple, then show advanced usage
5. Remove Noise: Strip timestamps, badges, and metadata
6. Concrete Examples: Replace abstract descriptions with working code

How It Works

C7Score Optimization Workflow

The skill follows a structured workflow:

1. Analysis Phase

- Reads existing documentation - Runs automated quality checks - Identifies developer questions - Maps content to questions

1. Optimization Phase

- Restructures content around questions - Enhances code snippets with context - Improves LLM-friendly formatting - Adds missing examples

1. Review Phase

- Generates optimized documentation - Provides quality score estimates - Suggests further improvements

llms.txt Generation Workflow

The skill analyzes and generates:

1. Project Analysis

- Explores documentation structure - Identifies project type - Catalogs available resources

1. Template Selection

- Chooses appropriate format (library, CLI, framework, skill, etc.) - Determines section organization - Plans content hierarchy

1. File Generation

- Creates properly formatted llms.txt - Includes descriptive links with context - Follows official llmstxt.org specification - Saves to repository root

Reference Materials

The skill includes:

C7Score Resources

• •c7score_metrics.md: Detailed explanation of benchmark metrics
• •optimization_patterns.md: 20+ transformation patterns with examples
• •sample_readme.md: Before/after optimization examples

llms.txt Resources

• •llmstxt_format.md: Complete llms.txt specification and guidelines
• •sample_llmstxt.md: Examples for different project types (library, CLI, framework, skill)

Tools

• •analyze_docs.py: Automated Python script for documentation quality scanning

Contributing

Contributions are welcome! Areas for improvement:

• •Additional optimization patterns
• •Support for more documentation formats
• •Integration with c7score API (if available)
• •Multi-language documentation support
• •Automated testing examples
• •More llms.txt templates for different project types
• •Enhanced project type detection

Tips for Best Results

For C7Score Optimization

1. Start with your README: It's usually the most important doc
2. Focus on getting started: Installation and quickstart sections matter most
3. Think like a new user: What questions would they ask?
4. Test with AI tools: Use Claude Code or other AI assistants to verify improvements
5. Iterate: Run the skill multiple times as your project evolves

For llms.txt Files

1. Keep it updated: Regenerate when you add major documentation
2. Use descriptive links: Add helpful notes after colons to explain each resource
3. Prioritize content: Essential docs first, optional resources last
4. Test with LLMs: Ask Claude Code to navigate your docs using the llms.txt
5. Follow the spec: Check against llmstxt.org for compliance

Version

Current version: 1.3.0

What's New in 1.3.0

• •📈 Automated C7Score Evaluation: Claude-based scoring across all 5 metrics with before/after comparison
• •📊 Comprehensive Scoring Rubrics: Detailed evaluation guidelines in c7score_metrics.md
• •🎯 Measurable Improvement: Clear numeric feedback on documentation quality gains

Previous Versions

v1.2.0 - Rename and Plugin Format

• •🎯 Renamed to llm-docs-optimizer (formerly c7score-optimizer)

- Broader positioning as general LLM documentation toolkit - Better discoverability and clearer purpose

• •📦 Plugin Format: Converted to Claude Code plugin for marketplace distribution

- Easy installation: /plugin marketplace add owner/llm-docs-optimizer - Professional structure with .claude-plugin/plugin.json

v1.1.0 - llms.txt Generation

• •✨ llms.txt Generation: Create LLM-friendly navigation files for projects
• •📋 Multiple Templates: Support for libraries, CLI tools, frameworks, and skills
• •🎯 Smart Analysis: Automatic project type detection

v1.0.0 - Initial Release

• •📊 C7Score Optimization: Transform documentation for AI coding assistants
• •📝 Question-Driven Structure: Organize content around developer questions
• •🔍 Analysis Tools: Python script for documentation scanning

License

MIT License - See LICENSE file for details

Links

• •Context7 c7score Benchmark
• •llmstxt.org Official Specification
• •Claude Code Documentation
• •Claude Skills Documentation

Support

For issues or questions:

• •Check the reference materials in /references/
• •Review example patterns in /references/optimization_patterns.md
• •Open an issue in the repository

Made for developers who want their documentation to work seamlessly with AI coding assistants.