llm-docs-optimizer

# 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):**
1. **Question-Snippet Comparison (80%)**: How well snippets answer common developer questions
2. **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