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llm-docs-optimizer

Optimize README and docs for LLM retrieval with c7score analysis.

1|Updated Mar 15, 2026
Search Tags:#documentation#analysis#llm#llms.txt#c7score#readme-optimization
One-click install
npx skills add https://github.com/kennyolofsson23-netizen/claude-code-config --skill llm-docs-optimizer-kennyolofsson23-netizen
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-docs-optimizer
Source: https://github.com/kennyolofsson23-netizen/claude-code-config/tree/main/skills/llm-docs-optimizer
Command: npx skills add https://github.com/kennyolofsson23-netizen/claude-code-config --skill llm-docs-optimizer-kennyolofsson23-netizen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Projects and READMEs are often written for humans and contain fragmented, import-only snippets, directory metadata, and inconsistent formatting that make them hard for LLMs and AI coding assistants to use effectively. This skill centralizes a question-driven approach to restructure documentation, add runnable examples, remove noise, and improve discoverability so LLMs provide more accurate, actionable answers.

Core Features & Use Cases

  • C7Score Optimization: Evaluate and improve documentation across question-snippet matching, LLM evaluation, formatting, metadata removal, and initialization clarity to raise Context7 benchmark scores.
  • llms.txt Generation: Build an LLM-friendly llms.txt navigation file with prioritized full-URL links and concise summaries to help agents find relevant docs quickly.
  • Automated Analysis: Optionally run the included analyze_docs.py to detect import-only snippets, installation-only blocks, duplicates, and formatting issues and produce a prioritized remediation plan.
  • Use Case: Improve a repository README so that Claude, Copilot, or retrieval-based assistants can answer common "How do I..." developer questions with copy-paste runnable examples and a linked llms.txt for fast navigation.

Quick Start

Ask llm-docs-optimizer to analyze and optimize your README for c7score and optionally generate an llms.txt navigation file for the repository.

Frequently Asked Questions about llm-docs-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize my README for LLMs and AI coding assistants?▼

To optimize README files for LLMs, you apply a question-driven approach that restructures documentation, adds runnable examples, and removes noise. This improves LLM retrieval and provides accurate answers for AI coding assistants.

What is c7score optimization for project documentation?▼

C7score optimization evaluates and improves documentation across question-snippet matching, LLM evaluation, formatting, and initialization clarity. It raises Context7 benchmark scores to make project docs more usable for AI assistants.

How do I generate an llms.txt file for my repository?▼

To generate an llms.txt file, you build an LLM-friendly navigation file containing prioritized full-URL links and concise summaries. This helps AI agents find relevant documentation quickly and improves retrieval accuracy.

Why do AI assistants give wrong answers from my documentation?▼

AI assistants give wrong answers because documentation often contains fragmented, import-only snippets and inconsistent formatting. Removing this noise and adding copy-paste runnable examples fixes LLM retrieval and provides accurate answers.

Can I automatically detect import-only snippets and formatting issues in my docs?▼

Yes, you can automatically detect import-only snippets, installation-only blocks, and duplicates by running the included analyze_docs.py script. It analyzes code snippets and produces a prioritized remediation plan for your docs.

Does this documentation optimization approach work with existing docs directories?▼

Yes, this approach works with existing docs directories, README files, and example files. It analyzes your current documentation structure and applies targeted fixes without requiring a complete rewrite of your project files.

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