Automates the recording of structured performance investigation notes, evidence, and decision-making logic within the AI state directory.
This skill streamlines the performance debugging process by maintaining a detailed, persistent log of investigations within the local project environment. It ensures that every phase of a performance audit is documented with verbatim user quotes, clear phase summaries, and concrete evidence pointers such as file paths, metrics, and command outputs. By enforcing a standardized output format and adhering to local performance requirement contracts, it helps developers track the rationale behind optimizations and maintain a clear audit trail of their investigation steps for future reference.
Key Features
01Seamless integration with AI_STATE_DIR for persistent state management
02Phase-based documentation of decisions and rationales
03Structured markdown logging for performance audits
04Verbatim capture of user requirements and quotes
05Automated tracking of evidence including metrics and file pointers
06311 GitHub stars
Use Cases
01Tracking the reasoning and technical evidence behind specific code optimization decisions.
02Maintaining a persistent audit trail for complex performance regressions and debugging sessions.
03Documenting a step-by-step performance audit of a slow API endpoint or database query.