agents-md-improver

To audit and improve AI agent context files, run the agents-md-improver skill which scans your repository for all rules files and grades them against a quality rubric. It provides actionable recommendations to optimize architecture clarity and command documentation.

Context confusion occurs when project-rules files are stale, inconsistent, or poorly structured. Auditing these files ensures the AI agent receives optimal, current, and actionable instructions for the codebase, preventing suboptimal performance.

You need to optimize AI agent context during repository maintenance, developer onboarding, or after a major architectural refactor. This ensures the agent's instructions remain accurate and concise for assisting with the updated codebase.

The best way to maintain Markdown-based rules files is using an automated audit tool that scans, evaluates, and updates them across your directory structure. This enforces a consistent quality rubric for architecture and project-specific patterns.

Yes, optimizing project-rules files requires file system access to scan, evaluate, and update Markdown-based agent instructions across the repository directory structure. This allows the audit tool to grade all existing rules files.

Yes, you can run an automated audit after an architectural refactor to evaluate all project rules files. The tool scans the repository, grades the files against a quality rubric, and provides actionable recommendations for context optimization.