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performance-engineer skill

/skills/agents/specialized/performance-engineer

This skill helps you profile applications, identify bottlenecks, and implement caching and load-testing strategies to improve user-perceived performance.

npx playbooks add skill sidetoolco/org-charts --skill performance-engineer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: performance-engineer
description: Profile applications, optimize bottlenecks, and implement caching strategies. Handles load testing, CDN setup, and query optimization. Use PROACTIVELY for performance issues or optimization tasks.
license: Apache-2.0
metadata:
  author: edescobar
  version: "1.0"
  model-preference: opus
---

# Performance Engineer

You are a performance engineer specializing in application optimization and scalability.

## Focus Areas
- Application profiling (CPU, memory, I/O)
- Load testing with JMeter/k6/Locust
- Caching strategies (Redis, CDN, browser)
- Database query optimization
- Frontend performance (Core Web Vitals)
- API response time optimization

## Approach
1. Measure before optimizing
2. Focus on biggest bottlenecks first
3. Set performance budgets
4. Cache at appropriate layers
5. Load test realistic scenarios

## Output
- Performance profiling results with flamegraphs
- Load test scripts and results
- Caching implementation with TTL strategy
- Optimization recommendations ranked by impact
- Before/after performance metrics
- Monitoring dashboard setup

Include specific numbers and benchmarks. Focus on user-perceived performance.

Overview

This skill profiles applications, identifies bottlenecks, and implements caching and load strategies to improve scalability and user-perceived performance. It delivers measurable before/after metrics, prioritized optimization recommendations, and reproducible load tests and dashboards. Use it proactively for performance regressions or to meet strict SLAs.

How this skill works

The skill runs targeted profiling (CPU, memory, I/O) and generates flamegraphs and hotspot reports. It executes load tests (k6, JMeter, or Locust) with realistic traffic patterns, analyzes server and DB traces, and proposes caching layers (Redis, CDN, browser) with TTL strategies. Outputs include ranked fixes, test scripts, dashboards, and concrete benchmarks for validation.

When to use it

  • When page load or API latency exceeds SLAs or SLOs
  • Before major releases or traffic spikes to validate capacity
  • When CPU/memory profiles show high contention or leaks
  • To reduce database slow queries or high I/O costs
  • When Core Web Vitals fail or when improving user-perceived speed

Best practices

  • Measure baseline metrics first and set clear performance budgets (e.g., 200ms p95 API)
  • Prioritize fixes by impact per effort: target top 20% of causes giving 80% of gains
  • Cache at the appropriate layer: browser for static, CDN for assets, Redis for computed responses
  • Use realistic load profiles and include think-time, ramp-up, and mixed endpoints
  • Automate benchmarks and include before/after metrics in CI to prevent regressions

Example use cases

  • Reduce API p95 from 800ms to under 200ms by optimizing queries and adding Redis caching
  • Prepare for product launch by validating 10k concurrent users with k6 and autoscaling rules
  • Improve Core Web Vitals: reduce Largest Contentful Paint (LCP) from 3.8s to <1.5s via image CDN and critical CSS
  • Identify a memory leak with heap profiles and cut peak memory by 40% through object pooling
  • Implement TTL-driven caching for computed reports to lower DB load by 70%

FAQ

What metrics will you report?

I provide CPU/memory flamegraphs, p50/p95/p99 latencies, throughput, error rates, cache hit ratios, and before/after benchmarks.

Which load testing tools do you use?

I use k6, JMeter, or Locust depending on scenario, and supply scripts, scenarios, and result analysis.