# Performance Optimisation

> Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency.

- Skill: `majiayu000/performance-optimisation` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/performance-optimisation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/performance-optimisation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/performance-optimisation

---


# Performance Optimisation Skill

## Tooling Notes

This skill should only use read-only commands and avoid modifying files.

## Workflow

Copy this checklist and use it to track your progress through the performance optimisation process:

```markdown
Performance Optimisation Checklist

- [ ] Measure Baseline Performance
  - [ ] Use profiling tools to gather performance metrics.
  - [ ] Identify slow functions, database queries, and network requests.
- [ ] Identify Bottlenecks
  - [ ] Analyse profiling data to pinpoint performance issues.
  - [ ] Prioritise issues based on impact and ease of resolution.
- [ ] Implement Optimisations
  - [ ] Optimise algorithms and data structures.
  - [ ] Improve database query efficiency.
  - [ ] Reduce network latency and payload sizes.
  - [ ] Implement caching strategies where appropriate.
- [ ] Validate Improvements
  - [ ] Re-measure performance after optimisations.
  - [ ] Ensure that optimisations have led to measurable improvements.
- [ ] Document Changes
  - [ ] Update documentation to reflect performance changes.
  - [ ] Provide explanations for significant optimisations.
```

### Profiling Commands

```bash
# Node.js profiling
node --prof app.js
node --prof-process isolate-0x*.log > processed.txt

# Python profiling
python -m cProfile -o profile.out app.py
snakeviz profile.out

# Database query analysis (PostgreSQL example)
EXPLAIN ANALYZE SELECT * FROM your_table WHERE condition;

# Web performance analysis
lighthouse https://yourwebsite.com --output html --output-path report.html
```

### Common Bottlenecks and Ways to Fix Them

- **Inefficient Algorithms**: Replace with more efficient algorithms or data structures.
- **Database Query Performance**: Optimize queries, add indexes, or denormalize data.
- **Network Latency**: Minimize requests, use CDNs, and compress payloads.
- **Unnecessary Computations**: Cache results of expensive operations.
- **Memory Leaks**: Identify and fix memory leaks to improve performance over time.