Skill: mikeclaw007/performance-tuning (version 1.0.0)

--- name: performance-tuning description: Deep performance tuning workflow—goals and measurement, profiling, hotspots, caching and concurrency trade-offs, system-specific tuning (DB, GC, network), and verification. Use when fixing latency, throughput, or resource saturation. --- # Performance Tuning (Deep Workflow) Performance work is **measurement-driven**. **Profile** before optimizing; **verify** after changes; **guard** against regressions with benchmarks or production metrics. ## When to Offer This Workflow **Trigger conditions:** - High **CPU**, **memory**, **p99** latency, **GC** pauses - **Cost** reduction via efficiency - **Premature** optimization requests—need **evidence** first **Initial offer:** Use **six stages**: (1) frame goals & SLOs, (2) measure baseline, (3) profile & hypothesize, (4) implement changes, (5) verify & compare, (6) prevent regression). Confirm **language/runtime** and **environment** (prod-like data volume). --- ## Stage 1: Frame Goals & SLOs **Goal:** **Numeric** targets: p95 latency, throughput, max memory—**not** “faster.” ### Questions 1. Which **workloads** matter most (batch vs interactive)? 2. **Correctness** constraints (approximation allowed or not)? 3. **Cost** budget for hardware vs engineering time? **Exit condition:** One-page success criteria and out-of-scope areas. --- ## Stage 2: Measure Baseline **Goal:** **Reproducible** benchmark or **RUM** segment—same inputs, same conditions. ### Practices - **Warm** caches when prod is always warm - **Statistical** repeat (multiple runs, discard outliers methodology) **Exit condition:** Baseline numbers + environment fingerprint (versions, flags). --- ## Stage 3: Profile & Hypothesize **Goal:** Find **dominant cost**: CPU bound, I/O bound, lock contention, allocation rate. ### Tools (examples) - **CPU** flame graphs; **async** wait profiling - **Alloc** profiling for GC pressure - **DB** query plans and lock waits **Exit condition:** Hypothesis tied to evidence (e.g., “40% time in JSON parse”). --- ## Stage 4: Implement Changes **Goal:** **Smallest** change that addresses the hotspot; **avoid** **clever** without proof. ### Levers - **Algorithm** / data structure - **Caching** with **invalidation** discipline - **Batching** I/O; **connection** pooling - **Parallelism** where safe—watch **locks** --- ## Stage 5: Verify & Compare **Goal:** **A/B** or before/after with **same** workload; **watch** **tail** latency **not** only mean. ### Production - **Canary** with **error** rate and **latency** gates --- ## Stage 6: Prevent Regression **Goal:** **Micro-benchmarks** in CI (optional), **budgets**, or **synthetic** checks. --- ## Final Review Checklist - [ ] Goals and baseline documented - [ ] Root cause supported by profiler/trace evidence - [ ] Change scoped; trade-offs explicit - [ ] Verification on realistic load - [ ] Regression guard where feasible ## Tips for Effective Guidance - **Little’s Law** intuition: queues blow **latency**—often **fix** **concurrency** **before** **micro-opts**. - **Avoid** optimizing **cold** paths **first**. - **GC** languages: **allocation** **rate** often **is** the **enemy**. ## Handling Deviations - **Embedded** / **mobile**: **battery** and **thermal** **constraints** **matter** **too**. - **Distributed** systems: **local** **opt** **may** **hurt** **system** **(see** **load-testing**).


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