Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionperformance-engineerExecute the skills CLI command in your project's root directory to begin installation:
Fetches performance-engineer from charon-fan/agent-playbook and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate performance-engineer. Access via /performance-engineer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.
Activates when you:
Define metrics
Measure current performance
# Response time
curl -w "@curl-format.txt" -o /dev/null -s https://example.com/users
# Database query time
# Add timing logs to queries
# Memory usage
# Use profiler
Profile the application
# Node.js
node --prof app.js
# Python
python -m cProfile app.py
# Go
go test -cpuprofile=cpu.prof
Common bottleneck locations:
| Layer | Common Issues |
|---|---|
| Database | N+1 queries, missing indexes, large result sets |
| API | Over-fetching, no caching, serial requests |
| Application | Inefficient algorithms, excessive logging |
| Frontend | Large bundles, re-renders, no lazy loading |
| Network | Too many requests, large payloads, no compression |
N+1 Queries:
// Bad: N+1 queries
const users = await User.findAll();
for (const user of users) {
user.posts = await Post.findAll({ where: { userId: user.id } });
}
// Good: Eager loading
const users = await User.findAll({
include: [{ model: Post, as: 'posts' }]
});
Missing Indexes:
-- Add index on frequently queried columns
CREATE INDEX idx_user_email ON users(email);
CREATE INDEX idx_post_user_id ON posts(user_id);
Pagination:
// Always paginate large result sets
const users = await User.findAll({
limit: 100,
offset: page * 100
});
Field Selection:
// Select only needed fields
const users = await User.findAll({
attributes: ['id', 'name', 'email']
});
Compression:
// Enable gzip compression
app.use(compression());
Code Splitting:
// Lazy load routes
const Dashboard = lazy(() => import('./Dashboard'));
Memoization:
// Use useMemo for expensive calculations
const filtered = useMemo(() =>
items.filter(item => item.active),
[items]
);
Image Optimization:
| Metric | Target | Critical Threshold |
|---|---|---|
| API Response (p50) | < 100ms | < 500ms |
| API Response (p95) | < 500ms | < 1s |
| API Response (p99) | < 1s | < 2s |
| Database Query | < 50ms | < 200ms |
| Page Load (FMP) | < 2s | < 3s |
| Time to Interactive | < 3s | < 5s |
| Memory Usage | < 512MB | < 1GB |
// Cache expensive computations
const cache = new Map();
async function getUserStats(userId: string) {
if (cache.has(userId)) {
return cache.get(userId);
}
const stats = await calculateUserStats(userId);
cache.set(userId, stats);
// Invalidate after 5 minutes
setTimeout(() => cache.delete(userId), 5 * 60 * 1000);
return stats;
}
// Bad: Individual requests
for (const id of userIds) {
await fetchUser(id);
}
// Good: Batch request
await fetchUsers(userIds);
// Debounce search input
const debouncedSearch = debounce(search, 300);
// Throttle scroll events
const throttledScroll = throttle(handleScroll, 100);
| Tool | Purpose |
|---|---|
| Lighthouse | Frontend performance |
| New Relic | APM monitoring |
| Datadog | Infrastructure monitoring |
| Prometheus | Metrics collection |
Profile application:
python scripts/profile.py
Generate performance report:
python scripts/perf_report.py
references/optimization.md - Optimization techniquesreferences/monitoring.md - Monitoring setupreferences/checklist.md - Performance checklistMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
performance-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: performance-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend performance-engineer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
performance-engineer reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: performance-engineer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: performance-engineer is focused, and the summary matches what you get after install.
performance-engineer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for performance-engineer matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend performance-engineer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
performance-engineer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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