What problem does it solve? After finishing code, a design document, or a Skill file, it is hard to know whether the artifact could be better beyond simply being correct. This Skill performs a systematic optimization audit that answers "can it be better and how", producing prioritized, actionable improvement suggestions instead of vague feedback. ## Core Features & Use Cases - Type-Adaptive Analysis: Automatically detects whether the input is code, a design document, or a Skill file and switches to the matching evaluation dimensions (performance/readability for code, architecture/completeness for designs, trigger accuracy/redundancy for Skills). - Prioritized Recommendations: Scores each dimension on a calibrated 1-5 scale, identifies optimization opportunities with root-cause attribution, and ranks them by a benefit-versus-difficulty matrix (P0/P1/P2) with concrete before/after improvement plans. - Guarded Execution: Before applying any optimization, it invokes a request-guard brake check to prevent over-engineering, and supports a challenger re-review of the analysis report. - Use Case: You just wrote a 500-line Python module and ask "how can this code be improved". The Skill scores it across five dimensions, lists root-caused optimization points, and outputs a phased execution plan with verification steps. ## Quick Start Ask the AI to analyze your code, design document, or Skill file for optimization opportunities and output a prioritized improvement report.
