task-flow-tool:ai-optimizer

Auditing AI-awareness rules involves scanning configuration files across a project to detect redundancies, conflicts, and stale examples. The tool generates an actionable cleanup plan that reduces token bloat while preserving the original rule intent.

Deduplicating conflicting AI configuration files requires categorizing AI-awareness rules across the codebase to identify overlapping instructions. A structured cleanup workflow then harmonizes these files by enforcing a token-efficient plan before applying edits.

Cleaning up AI rules without losing intent requires an audit tool that analyzes existing AI-awareness files and generates a concrete change plan. This plan targets redundancies and conflicts while explicitly preserving the original configuration intent.

Yes, you can review changes before any modifications occur because the tool enforces an explicit approval workflow. It generates a concrete cleanup plan ready for your review and requires your explicit approval before applying any edits to the codebase.

You should run an AI-awareness file audit during periodic maintenance, after adding new AI rules, or as a post-setup validation step. This ensures your AI configurations remain clean, compliant, and free from token bloat throughout the project lifecycle.