Systematic approach to measuring, analyzing, and optimizing application performance.
Works with
Covers Core Web Vitals (LCP, INP, CLS) with target thresholds and measurement stages from development through production
Provides a 4-step profiling workflow: establish baseline, identify bottleneck, apply fix, validate improvement
Includes tool selection guidance for specific problems (Lighthouse for page load, DevTools for runtime and memory, bundle analyzers for code size)
Documents common runt
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionperformance-profilingExecute the skills CLI command in your project's root directory to begin installation:
Fetches performance-profiling from sickn33/antigravity-awesome-skills 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-profiling. Access via /performance-profiling 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.
Submit your Claude Code skill and start earning
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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Measure, analyze, optimize - in that order.
Execute these for automated profiling:
| Script | Purpose | Usage |
|---|---|---|
scripts/lighthouse_audit.py |
Lighthouse performance audit | python scripts/lighthouse_audit.py https://example.com |
| Metric | Good | Poor | Measures |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | Loading |
| INP | < 200ms | > 500ms | Interactivity |
| CLS | < 0.1 | > 0.25 | Stability |
| Stage | Tool |
|---|---|
| Development | Local Lighthouse |
| CI/CD | Lighthouse CI |
| Production | RUM (Real User Monitoring) |
1. BASELINE → Measure current state
2. IDENTIFY → Find the bottleneck
3. FIX → Make targeted change
4. VALIDATE → Confirm improvement
| Problem | Tool |
|---|---|
| Page load | Lighthouse |
| Bundle size | Bundle analyzer |
| Runtime | DevTools Performance |
| Memory | DevTools Memory |
| Network | DevTools Network |
| Issue | Indicator |
|---|---|
| Large dependencies | Top of bundle |
| Duplicate code | Multiple chunks |
| Unused code | Low coverage |
| Missing splits | Single large chunk |
| Finding | Action |
|---|---|
| Big library | Import specific modules |
| Duplicate deps | Dedupe, update versions |
| Route in main | Code split |
| Unused exports | Tree shake |
| Pattern | Meaning |
|---|---|
| Long tasks (>50ms) | UI blocking |
| Many small tasks | Possible batching opportunity |
| Layout/paint | Rendering bottleneck |
| Script | JavaScript execution |
| Pattern | Meaning |
|---|---|
| Growing heap | Possible leak |
| Large retained | Check references |
| Detached DOM | Not cleaned up |
| Symptom | Likely Cause |
|---|---|
| Slow initial load | Large JS, render blocking |
| Slow interactions | Heavy event handlers |
| Jank during scroll | Layout thrashing |
| Growing memory | Leaks, retained refs |
| Priority | Action | Impact |
|---|---|---|
| 1 | Enable compression | High |
| 2 | Lazy load images | High |
| 3 | Code split routes | High |
| 4 | Cache static assets | Medium |
| 5 | Optimize images | Medium |
| ❌ Don't | ✅ Do |
|---|---|
| Guess at problems | Profile first |
| Micro-optimize | Fix biggest issue |
| Optimize early | Optimize when needed |
| Ignore real users | Use RUM data |
Remember: The fastest code is code that doesn't run. Remove before optimizing.
This skill is applicable to execute the workflow or actions described in the overview.
Make 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.
sickn33/antigravity-awesome-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: performance-profiling is focused, and the summary matches what you get after install.
Useful defaults in performance-profiling — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: performance-profiling is the kind of skill you can hand to a new teammate without a long onboarding doc.
performance-profiling has been reliable in day-to-day use. Documentation quality is above average for community skills.
performance-profiling fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in performance-profiling — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added performance-profiling from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend performance-profiling for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
performance-profiling has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for performance-profiling matched our evaluation — installs cleanly and behaves as described in the markdown.
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