performance-tuning-expert
Application performance specialist for profiling, optimization, and scaling strategies
Best use case
performance-tuning-expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Application performance specialist for profiling, optimization, and scaling strategies
Teams using performance-tuning-expert should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performance-tuning-expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performance-tuning-expert Compares
| Feature / Agent | performance-tuning-expert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Application performance specialist for profiling, optimization, and scaling strategies
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Performance Tuning Expert > **Status**: ✅ Research complete > **Last validated**: 2025-11-08 > **Confidence**: 🟡 Medium — Research-backed tuning playbook – review semi-annually ## How to use this skill 1. Triage performance goals via [modules/core-guidance.md](modules/core-guidance.md). 2. Instrument and profile using [modules/profiling-and-metrics.md](modules/profiling-and-metrics.md). 3. Apply backend optimizations from [modules/backend-optimization.md](modules/backend-optimization.md). 4. Improve user-facing performance using [modules/frontend-and-client.md](modules/frontend-and-client.md). 5. Tune runtime and infrastructure with [modules/infrastructure-and-runtime.md](modules/infrastructure-and-runtime.md). 6. Lock improvements through [modules/regression-testing-and-guardrails.md](modules/regression-testing-and-guardrails.md). 7. Track additional research in [modules/known-gaps.md](modules/known-gaps.md) and follow the cadence in [modules/research-checklist.md](modules/research-checklist.md). ## Module overview - [Core guidance](modules/core-guidance.md) — intake checklist, prioritization criteria, ROI framing. - [Profiling & metrics](modules/profiling-and-metrics.md) — observability setup, profiling stacks, benchmarking. - [Backend optimization](modules/backend-optimization.md) — language/runtime tuning, data access improvements. - [Frontend & client](modules/frontend-and-client.md) — Core Web Vitals, mobile optimization, offline strategies. - [Infrastructure & runtime](modules/infrastructure-and-runtime.md) — caching, autoscaling, hardware selection. - [Regression testing & guardrails](modules/regression-testing-and-guardrails.md) — load testing, CI integration, alerting. - [Known gaps](modules/known-gaps.md) — ongoing research backlog. - [Research checklist](modules/research-checklist.md) — semi-annual review plan. ## Research status - Material incorporates latest Web Vitals, Netflix/AWS performance engineering guidance, and mechanical sympathy insights. - Next validation due 2026-05-01 or earlier if significant runtime updates land (e.g., JVM, Node, Rust). - Known gaps cover ML/AI workloads and eBPF observability patterns pending deeper study.
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