Optimize documentation for AI coding assistants and LLMs. Improves docs for Claude, Copilot, and other AI tools through c7score optimization, llms.txt generation, question-driven restructuring, and automated quality scoring. Use when asked to improve, optimize, or enhance documentation for AI assistants, LLMs, c7score, Context7, or when creating llms.txt files. Also use for documentation quality analysis, README optimization, or ensuring docs follow best practices for LLM retrieval systems.
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Optimize documentation for AI coding assistants and LLMs. Improves docs for Claude, Copilot, and other AI tools through c7score optimization, llms.txt generation, question-driven restructuring, and automated quality scoring. Use when asked to improve, optimize, or enhance documentation for AI assistants, LLMs, c7score, Context7, or when creating llms.txt files. Also use for documentation quality analysis, README optimization, or ensuring docs follow best practices for LLM retrieval systems.
LLM Docs Optimizer
This skill optimizes project documentation and README files for AI coding assistants and LLMs like Claude, GitHub Copilot, and others. It improves documentation quality through multiple approaches: c7score optimization (Context7's quality benchmark), llms.txt file generation for LLM navigation, question-driven content restructuring, and automated quality scoring across 5 key metrics.
Version: 1.3.0
Understanding C7Score
C7score evaluates documentation using 5 metrics across two categories:
LLM Analysis (85% of score):
Question-Snippet Comparison (80%): How well snippets answer common developer questions
LLM Evaluation (5%): Relevancy, clarity, correctness, and uniqueness
Text Analysis (15% of score):
3. Formatting (5%): Proper structure and language tags
4. Project Metadata (5%): Absence of irrelevant content
5. Initialization (5%): Not just imports/installations
For detailed information on each metric, read references/c7score_metrics.md.
Core Workflow
Step 0: Ask About llms.txt Generation (C7Score Optimization Only)
IMPORTANT: When the user requests c7score documentation optimization, ALWAYS ask if they also want an llms.txt file:
Use the AskUserQuestion tool with this question:
Question: "Would you also like me to generate an llms.txt file for your project?"
Header: "llms.txt"
Options:
- "Yes, create both optimized docs and llms.txt"
Description: "Optimize documentation for c7score AND generate an llms.txt navigation file"
- "No, just optimize the documentation"
Description: "Only perform c7score optimization without llms.txt generation"
If user chooses "Yes":
Proceed with c7score optimization workflow (Steps 1-5)
Then follow the llms.txt generation workflow
Provide both optimized documentation AND llms.txt file
If user chooses "No":
Proceed with c7score optimization workflow only (Steps 1-5)
Note: If the user explicitly requests ONLY llms.txt generation (no c7score mention), skip this step and go directly to the llms.txt generation workflow.
Step 1: Analyze Current Documentation
When given a project or documentation to optimize:
Read the documentation files (README.md, docs/*.md, etc.)
Run the analysis script (optional but recommended) to identify issues:
Note: The script requires Python 3.7+ and is optional. You can skip it if Python is unavailable.
Review the analysis report (if script was run) to understand current state:
Count of code snippets with issues
Breakdown by metric type
Duplicate snippets
Language distribution
Step 2: Generate Developer Questions
Create a list of 15-20 questions that developers commonly ask about the project:
Focus on "How do I..." questions
Cover setup, configuration, basic usage, common operations
Include authentication, error handling, advanced features
Think about real-world use cases
Example questions:
How do I install and set up [project]?
How do I authenticate/configure [project]?
How do I [main feature/operation]?
How do I handle errors?
How do I integrate with [common tools]?
Step 3: Map Questions to Snippets
Evaluate which questions are well-answered by existing documentation:
✅ Questions with complete, working code examples
⚠️ Questions with partial or theoretical answers
❌ Questions with no answers
Prioritize filling gaps for unanswered questions.
Step 4: Optimize Documentation
Apply optimizations based on priority:
Priority 1: Question Coverage (80% of score)
Add complete code examples for unanswered questions
Transform API references into usage examples
Ensure each major snippet answers at least one common question
Make examples self-contained and runnable
Priority 2: Remove Duplicates
Identify similar or identical snippets
Consolidate into comprehensive examples
Ensure each snippet provides unique value
Priority 3: Fix Formatting
Use proper language tags (python, javascript, typescript, bash, etc.)
Follow TITLE / DESCRIPTION / CODE structure
Avoid very short (<3 lines) or very long (>100 lines) snippets
Don't use descriptive strings as language tags
Priority 4: Remove Metadata
Remove or minimize licensing snippets
Remove directory structure listings
Remove citations and BibTeX entries
Keep only usage-relevant content
Priority 5: Enhance Initialization Snippets
Combine import-only snippets with usage examples
Add context to installation commands
Always show what comes after setup
For detailed transformation patterns, read references/optimization_patterns.md.
Step 5: Validate Optimizations
Before finalizing, verify each optimized snippet:
✅ Can run standalone (copy-paste works)
✅ Answers a specific developer question
✅ Provides unique information
✅ Uses proper format and language tag
✅ Focuses on practical usage
✅ Includes necessary imports/setup
✅ No licensing, citations, or directory trees
✅ Syntactically correct code
Step 6: Evaluate C7Score Impact
After optimization, provide a c7score evaluation comparing the original and optimized documentation:
Evaluation Process:
Analyze Original Documentation against c7score metrics:
Question-Snippet Matching (80%): How well do code examples answer developer questions?
LLM Evaluation (10%): Clarity, correctness, unique information
Formatting (5%): Proper markdown structure and language tags
Metadata Removal (2.5%): Absence of licenses, citations, directory trees
Initialization (2.5%): More than just imports/installation
Analyze Optimized Documentation using the same metrics
Calculate Scores (0-100 for each metric):
For Question-Snippet Matching:
90-100: Excellent - Complete, practical answers with context
70-89: Good - Most questions answered with working examples
50-69: Fair - Partial answers, missing context
30-49: Poor - Vague or incomplete answers
0-29: Very Poor - Questions not addressed
For LLM Evaluation:
90-100: Unique, clear, syntactically perfect
70-89: Mostly unique and clear, minor issues
50-69: Some duplicates or clarity issues
30-49: Significant duplicates or syntax errors
0-29: Major quality problems
For Formatting:
100: All snippets properly formatted with language tags
Base scores on concrete evidence from the documentation
Explain reasoning for each score
Highlight specific improvements made
Final score is weighted average: (Q×0.8) + (L×0.1) + (F×0.05) + (M×0.025) + (I×0.025)
Note: These are estimated scores based on c7score methodology. For official scores, users can submit to Context7's benchmark.
Common Transformation Patterns
Transform API Reference → Complete Example
Before:
## authenticate(api_key)
Authenticates the client.
After:
## Authentication
```python
from library import Client
client = Client(api_key="your_key")
client.authenticate()
# Now ready to make requests
result = client.get_data()
Score impact estimate - Expected improvement by metric
Recommendations - Further improvement suggestions
Save the optimized documentation files in the user's working directory or a designated output location. You can ask the user where they'd like the files saved if unclear.
Examples
For c7score optimization: See examples/sample_readme.md for before/after transformations
For llms.txt generation: See examples/sample_llmstxt.md for different project types
Creating llms.txt Files
What is llms.txt?
llms.txt is a standardized markdown file format designed to provide LLM-friendly content summaries and documentation navigation. It helps language models and AI agents quickly understand project structure and find relevant documentation.
Key purposes:
Provides brief background information and guidance
Links to detailed markdown documentation
Optimized for consumption by language models
Helps LLMs navigate documentation efficiently
Used at inference time when users request information
See examples/sample_llmstxt.md for complete examples of each type.
Step 3: Create the Structure
Build the llms.txt file following this structure:
1. H1 Title (Required)
# Project Name
2. Blockquote Summary (Highly Recommended)
> Brief description of what the project does, its main purpose, and key value proposition.> Should be 1-3 sentences that give LLMs essential context.
3. Key Features/Principles (Optional but Helpful)
Key features:
- Main feature or capability
- Another important aspect
- Third key point
Project follows these principles:
- Design principle 1
- Design principle 2
4. Documentation Sections (Core Content)
Organize links into H2-headed sections:
## Documentation- [Link Title](https://full-url): Brief description of what this contains
- [Another Doc](https://full-url): What developers will find here
## API Reference- [Core API](https://full-url): Main API documentation
- [Configuration](https://full-url): Configuration options
## Examples- [Basic Usage](https://full-url): Simple getting-started examples
- [Advanced Patterns](https://full-url): Complex use cases
## Optional- [Blog](https://full-url): Latest updates and tutorials
- [Community](https://full-url): Where to get help
Step 4: Format Links Properly
Each link must follow this exact format:
- [Descriptive Title](https://full-url): Optional helpful notes about the resource
Requirements:
Use markdown bullet lists (-)
Use markdown hyperlinks [text](url)
Use full URLs with protocol (https://), not relative paths
Add : followed by helpful description (optional but recommended)
Prefer linking to .md files when possible
Examples:
✅ Good:
- [Quick Start](https://github.com/user/repo/blob/main/docs/quickstart.md): Get running in 5 minutes
- [API Reference](https://github.com/user/repo/blob/main/docs/api.md): Complete function documentation
❌ Bad:
- [Guide](../docs/guide.md): A guide
- Guide: docs/guide.md
- [Click here](guide)
Step 5: Organize Sections by Priority
Order sections from most to least important:
High Priority (First):
Documentation / Getting Started
Core API / Commands
Examples
Medium Priority (Middle):
Guides / Tutorials
Configuration
Development / Contributing
Low Priority (Last - Optional Section):
Blog posts
Community links
Changelog
Extended tutorials
Background reading
The "Optional" section has special meaning: LLMs can skip this when shorter context is needed.
✅ Descriptive notes added after colons where helpful
✅ Content is concise and clear
✅ No complex markdown (tables, images, code blocks in the llms.txt itself)
Common Section Templates
For Python Libraries
# LibraryName> Brief description of what the library does and its main use case.## Documentation- Getting started, installation, core concepts
## API Reference- Module/class/function documentation
## Examples- Usage examples, patterns, recipes
## Development- Contributing, testing, development setup
## Optional- Changelog, blog, community
For CLI Tools
# ToolName> Brief description of what the tool does.## Getting Started- Installation, quickstart
## Commands- Command reference and examples
## Configuration- Config files, environment variables
## Examples- Common workflows and patterns
## Optional- Advanced usage, plugins, troubleshooting
For Web Frameworks
# FrameworkName> Brief description and key features.## Documentation- Core concepts, routing, data fetching
## Guides- Authentication, deployment, testing
## API Reference- Configuration, CLI, components
## Examples- Sample applications
## Integrations- Third-party tools and services
## Optional- Blog, showcase, community
For Claude Skills
# skill-name> Brief description of what the skill does.## Documentation- README, SKILL.md, usage guide
## Reference Materials- Specifications, patterns, formats
## Examples- Usage examples, before/after
## Development- Scripts, contributing guide
## Optional- External resources, related tools
Tips for High-Quality llms.txt Files
Be Concise: Use clear, brief language in descriptions
Think Like a New User: What would they want to find first?
Descriptive Links: Use meaningful link text, not "click here"
Add Context: Notes after colons help LLMs understand what each link contains
Stable URLs: Link to versioned or permanent documentation
Progressive Detail: Start with essentials, end with optional resources
Test Comprehension: Read it yourself - does it make sense quickly?
Keep Updated: Update as documentation structure evolves
