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# performance-optimizer ## Purpose During Development Use this skill when Codex and similar AI coding agents are reviewing or implementing performance-related changes such as latency reduction, duplicate-work removal, scaling improvements, batching, caching, or throughput improvements. This is a reusable development-time reasoning framework. It is not runtime code and must not be coupled into the product being reviewed. ## When To Apply - performance-focused features or refactors - PRs that claim to speed up code paths - bugs involving timeouts, duplicate work, memory growth, or slow orchestration - changes that introduce caching, batching, concurrency, or reuse ## What To Inspect - performance goals or claims - instrumentation, traces, or timing evidence - high-cost paths and repeated calls - cache or reuse semantics - tests or benchmarks that verify the claim ## Rules To Enforce - require evidence for performance claims - remove duplicate work before speculative micro-optimization - keep performance behavior visible and debuggable - do not weaken safety, correctness, or contract clarity for speed - make caching and reuse explicit to callers and reviewers ## Common Anti-Patterns To Catch - performance claims without measurement - hidden or stale caches - performance shortcuts that skip validation or safety controls - optimizing low-cost code while ignoring dominant bottlenecks - scattering reuse logic across unrelated modules ## Expected Output From The Agent - bottleneck summary - evidence review - optimization options with trade-offs - regression risks - verification guidance ## Output Structure - Summary - Key Findings - Risks - Affected Files - Evidence Reviewed - Suggested Improvements - Suggested Tests - Skills applied ## Example Usage "Review this PR that claims to reduce latency and check whether the evidence supports the claim." "Use this skill to plan safe duplicate-work reduction in a multi-stage workflow."