What problem does it solve?
Performance work often fails because benchmarks are unstable, baselines are missing, or optimizations are kept without reproducible evidence. This Skill enforces a disciplined loop so every optimization is measured, verified, and regression-free.
Core Features & Use Cases
- Stable Benchmark Discipline: Preserves benchmark identity and workload so before-and-after comparisons stay valid, and requires a new benchmark identifier for materially different workloads.
- Evidence-Based Optimization: Requires profiling the hot path before editing, recording a statistically useful baseline, and re-running correctness checks after each bounded change.
- Regression Gating: Keeps a change only when the improvement is reproducible and no contract regresses, and forbids reporting gains from debug builds or incomparable fixtures.
- Use Case: While optimizing the Fallow analyzer, you profile the hot path, implement one bounded change, re-run the same benchmark and correctness checks, and only merge when the gain reproduces.
Quick Start
Use the perf-loop skill to guide my next performance optimization on this codebase with a stable benchmark and correctness checks.