performance-optimization
Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck,
SKILL.mdskills/performance-optimization/SKILL.md
GitHubPerformance Optimization
Measure the bottleneck before optimizing it.
Workflow
- Define the user or system target: p50/p95/p99 latency, throughput, startup, memory ceiling, frame budget, bundle size, query cost, or error budget.
- Build a representative and repeatable workload with realistic data, concurrency, cache state, network, and device assumptions.
- Capture a baseline with profiler, trace, query plan, browser performance panel, bundle analyzer, or resource metrics. Record variance and warm/cold state.
- Rank bottlenecks by user impact and cost. Form one falsifiable hypothesis.
- Make the smallest change, rerun the same workload, and compare effect and regressions. Keep only improvements that meet the target without violating correctness, accessibility, cost, or operability.
- Add a regression budget/check where the risk is likely to recur.
Read measurement.md. Do not optimize a benchmark that does not represent the user path, hide work by weakening correctness, or claim improvement from one noisy run.
Completion condition
The bottleneck, baseline, change, before/after result, variance, trade-offs, and remaining limits are documented.