performance-optimisation
Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency.
When & Why to Use This Skill
This Claude skill is a comprehensive performance optimization toolkit designed to analyze and enhance full-stack applications. It provides a systematic workflow to identify and resolve bottlenecks across frontend interfaces, backend logic, and database interactions. By leveraging profiling tools and data-driven analysis, it helps developers improve application speed, reduce latency, and optimize resource consumption for high-performance software delivery.
Use Cases
- Database Performance Tuning: Using EXPLAIN ANALYZE to identify slow queries and implementing indexing or schema optimizations to reduce data retrieval lag.
- Backend Resource Profiling: Utilizing tools like cProfile for Python or Node.js profilers to detect CPU-intensive functions and memory leaks in server-side code.
- Frontend Web Optimization: Analyzing Lighthouse reports to improve Core Web Vitals, minimize render-blocking resources, and enhance overall user experience scores.
- Algorithm & Logic Refinement: Identifying inefficient data structures or redundant computations within the codebase and replacing them with optimized, high-efficiency alternatives.
| name | performance-optimisation |
|---|---|
| description | Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency. |
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:
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
# 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.