Why should I audit my CLAUDE.md file?

Auditing ensures your file follows Anthropic's 'keep it lean' guidance, preventing performance degradation and ensuring Claude doesn't miss critical project instructions due to context bloat.

How does the health scoring system work?

It provides a health score from 0-100 based on security, compliance with official standards, and community best practices. Scores below 40 are considered critical and require immediate action.

Can this skill detect security vulnerabilities?

Yes, it specifically scans for hardcoded secrets, API keys, and private tokens that should never be stored in project memory files, providing a security score of 100 only when no risks are found.

Is this skill useful for CI/CD pipelines?

Absolutely. Use the JSON output mode to create automated validation gates that prevent the merging of poorly structured or insecure CLAUDE.md files into your main branch.

Claude MD Auditor

Name: Claude MD Auditor
Author: cskiro

bycskiro

Security & Testing

Validates and optimizes CLAUDE.md files against official Anthropic guidance and community best practices to ensure secure and efficient AI context management.

About

Claude MD Auditor is a specialized skill designed to maintain the integrity and performance of CLAUDE.md files, which serve as the primary memory system for Claude Code. It provides comprehensive audits that score files based on security, compliance with Anthropic’s official hierarchy, and academic research regarding LLM context optimization. By identifying anti-patterns, detecting hardcoded secrets, and refactoring bloated files, it ensures that Claude remains performant, adheres to project standards, and avoids the 'lost in the middle' phenomenon common in large context windows.

Key Features

Production-ready refactoring to generate leaner and more effective memory files
0 GitHub stars
Machine-readable JSON output for seamless CI/CD pipeline integration
Multi-tier validation against official Anthropic memory hierarchy requirements
Automated security scanning for API keys and sensitive credentials within memory files
Research-backed optimization to prevent 'lost in the middle' context issues

Use Cases

Automating quality gates in CI/CD pipelines to prevent suboptimal or insecure context commits
Onboarding new projects by validating and structuring initial memory configuration files
Troubleshooting Claude performance issues when the model fails to follow project-specific standards

About

Key Features

Production-ready refactoring to generate leaner and more effective memory files
0 GitHub stars
Machine-readable JSON output for seamless CI/CD pipeline integration
Multi-tier validation against official Anthropic memory hierarchy requirements
Automated security scanning for API keys and sensitive credentials within memory files
Research-backed optimization to prevent 'lost in the middle' context issues

Use Cases

Automating quality gates in CI/CD pipelines to prevent suboptimal or insecure context commits
Onboarding new projects by validating and structuring initial memory configuration files
Troubleshooting Claude performance issues when the model fails to follow project-specific standards