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Use when fixing latency, throughput, or resource saturation.\n---\n\n# Performance Tuning (Deep Workflow)\n\nPerformance work is **measurement-driven**. **Profile** before optimizing; **verify** after changes; **guard** against regressions with benchmarks or production metrics.\n\n## When to Offer This Workflow\n\n**Trigger conditions:**\n\n- High **CPU**, **memory**, **p99** latency, **GC** pauses\n- **Cost** reduction via efficiency\n- **Premature** optimization requests—need **evidence** first\n\n**Initial offer:**\n\nUse **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).\n\n---\n\n## Stage 1: Frame Goals & SLOs\n\n**Goal:** **Numeric** targets: p95 latency, throughput, max memory—**not** “faster.”\n\n### Questions\n\n1. Which **workloads** matter most (batch vs interactive)?\n2. **Correctness** constraints (approximation allowed or not)?\n3. **Cost** budget for hardware vs engineering time?\n\n**Exit condition:** One-page success criteria and out-of-scope areas.\n\n---\n\n## Stage 2: Measure Baseline\n\n**Goal:** **Reproducible** benchmark or **RUM** segment—same inputs, same conditions.\n\n### Practices\n\n- **Warm** caches when prod is always warm\n- **Statistical** repeat (multiple runs, discard outliers methodology)\n\n**Exit condition:** Baseline numbers + environment fingerprint (versions, flags).\n\n---\n\n## Stage 3: Profile & Hypothesize\n\n**Goal:** Find **dominant cost**: CPU bound, I/O bound, lock contention, allocation rate.\n\n### Tools (examples)\n\n- **CPU** flame graphs; **async** wait profiling\n- **Alloc** profiling for GC pressure\n- **DB** query plans and lock waits\n\n**Exit condition:** Hypothesis tied to evidence (e.g., “40% time in JSON parse”).\n\n---\n\n## Stage 4: Implement Changes\n\n**Goal:** **Smallest** change that addresses the hotspot; **avoid** **clever** without proof.\n\n### Levers\n\n- **Algorithm** / data structure\n- **Caching** with **invalidation** discipline\n- **Batching** I/O; **connection** pooling\n- **Parallelism** where safe—watch **locks**\n\n---\n\n## Stage 5: Verify & Compare\n\n**Goal:** **A/B** or before/after with **same** workload; **watch** **tail** latency **not** only mean.\n\n### Production\n\n- **Canary** with **error** rate and **latency** gates\n\n---\n\n## Stage 6: Prevent Regression\n\n**Goal:** **Micro-benchmarks** in CI (optional), **budgets**, or **synthetic** checks.\n\n---\n\n## Final Review Checklist\n\n- [ ] Goals and baseline documented\n- [ ] Root cause supported by profiler/trace evidence\n- [ ] Change scoped; trade-offs explicit\n- [ ] Verification on realistic load\n- [ ] Regression guard where feasible\n\n## Tips for Effective Guidance\n\n- **Little’s Law** intuition: queues blow **latency**—often **fix** **concurrency** **before** **micro-opts**.\n- **Avoid** optimizing **cold** paths **first**.\n- **GC** languages: **allocation** **rate** often **is** the **enemy**.\n\n## Handling Deviations\n\n- **Embedded** / **mobile**: **battery** and **thermal** **constraints** **matter** **too**.\n- **Distributed** systems: **local** **opt** **may** **hurt** **system** **(see** **load-testing**).\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn736js06krgvrxx1g1jkqw0h9831z63\",\n  \"slug\": \"performance-tuning\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1774397228875\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nDeep performance tuning workflow-goals and measurement, profiling, hotspots, caching and concurrency trade-offs, system-specific tuning (DB, GC, network), and verification.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[mikeclaw007](https://clawhub.ai/user/mikeclaw007)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineers use this skill to investigate latency, throughput, resource saturation, and efficiency issues with a measurement-driven tuning workflow.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Applying performance recommendations directly to production systems can affect reliability or user experience.\n\nMitigation: Use baselines, realistic load tests, canaries, and operational metrics before broad rollout.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown]\n\n**Output Format:** [Markdown checklist and structured workflow guidance]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Documentation-only guidance; no tools, scripts, credentials, or runtime integrations are declared.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.","readmeExcerpt":"Skill: Performance Tuning Owner: mikeclaw007 Summary: Deep performance tuning workflow—goals and measurement, profiling, hotspots, caching and concurrency trade-offs, system-specific tuning (DB, GC, network), an... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-03-25T00:07:08.875Z | auto Initial release of the performance-tuning skill. - Introduces a six-stage, measurement-driven workflow for deep performance tuni","codeSnippets":[],"executableExamples":[],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: performance-tuning\ndescription: 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.\n---\n\n# Performance Tuning (Deep Workflow)\n\nPerformance work is **measurement-driven**. **Profile** before optimizing; **verify** after changes; **guard** against regressions with benchmarks or production metrics.\n\n## When to Offer This Workflow\n\n**Trigger conditions:**\n\n- High **CPU**, **memory**, **p99** latency, **GC** pauses\n- **Cost** reduction via efficiency\n- **Premature** optimization requests—need **evidence** first\n\n**Initial offer:**\n\nUse **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).\n\n---\n\n## Stage 1: Frame Goals & SLOs\n\n**Goal:** **Numeric** targets: p95 latency, throughput, max memory—**not** “faster.”\n\n### Questions\n\n1. 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