Performance engineering
Skill ComeOnOliver/skillshub/skills/HoangNguyen0403/agent-skills-standard/performance-engineering
🧠 The right skill, one API call. AI agent skills registry with token-efficient skill resolution. 5,000+ skills from 500+ top repos.
npx -y skills add ComeOnOliver/skillshub --skill performance-engineeringAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Universal standards for high-performance development. Use when optimizing, reducing latency, fixing memory leaks, profiling, or improving throughput.
SKILL.md
2.8 KB, 468 tokens by cl100k_base, as published. Nobody here has run it
Performance Engineering Standards
Universal standards for high-performance software development across all frameworks.
Priority: P0 (CRITICAL)
Universal standards for building high-performance software across all frameworks and languages.
🚀 Core Principles
- Efficiency by Design: Minimize resource consumption (CPU, Memory, Network) without sacrificing readability.
- Measure First: Never optimize without a baseline. Use profiling tools before and after changes.
- Scalability: Design systems to handle increased load by optimizing time and space complexity.
💾 Resource Management
- Memory Efficiency:
- Avoid memory leaks: explicit cleanup of listeners, observers, and streams.
- Optimize data structures: use the most efficient collection for the use case (e.g.,
Setfor lookups,Listfor iteration). - Lazy Initialization: Initialize expensive objects only when needed.
- CPU Optimization:
- Algorithm Complexity: Aim for $O(1)$ or $O(n)$ where possible; avoid $O(n^2)$ in critical paths.
- Offload Work: Move heavy computations to background threads or workers.
- Minimize Re-computation: Use memoization for pure, expensive functions.
🌐 Network & I/O
- Payload Reduction: Use efficient serialization (JSON minification, Protobuf) and compression.
- Batching: Group multiple small requests into single bulk operations.
- Caching Strategy:
- Implement multi-level caching (Memory -> Storage -> Network).
- Use appropriate TTL (Time To Live) and invalidation strategies.
- Non-blocking I/O: Always use asynchronous operations for file system and network access.
⚡ UI/UX Performance
- Minimize Main Thread Work: Keep animations and interactions fluid by keeping the main thread free.
- Virtualization: Use lazy loading or virtualization for long lists/large datasets.
- Tree Shaking: Ensure build tools remove unused code and dependencies.
📊 Monitoring & Testing
- Benchmarking: Write micro-benchmarks for performance-critical functions.
- SLIs/SLOs: Define Service Level Indicators (latency, throughput) and Objectives.
- Load Testing: Test system behavior under peak and stress conditions.