Common performance engineering
Skill ComeOnOliver/skillshub/skills/HoangNguyen0403/agent-skills-standard/common-performance-engineering
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Universal standards for high-performance development. Use when optimizing, reducing latency, fixing memory leaks, profiling, or improving throughput. (triggers: **/*.ts, **/*.tsx, **/*.go, **/*.dart, **/*.java, **/*.kt, **/*.swift, **/*.py, performance, optimize, profile, scalability, latency, throughput, memory leak, bottleneck)
SKILL.md
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Performance Engineering Standards
Priority: P0 (CRITICAL)
🚀 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.
Anti-Patterns
- No premature optimization: Profile first, fix proven bottlenecks only.
- No N+1 queries: Always batch and paginate data-access operations.
- No synchronous I/O on main thread: Async all file/network access.
References
- Performance Examples — profiling patterns, benchmark setup