Agent-skills-standard common-performance-engineering
Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language. (triggers: **/*.ts, **/*.tsx, **/*.go, **/*.dart, **/*.java, **/*.kt, **/*.swift, **/*.py, performance, optimize, profile, scalability, latency, throughput, memory leak, bottleneck)
install
source · Clone the upstream repo
git clone https://github.com/HoangNguyen0403/agent-skills-standard
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/HoangNguyen0403/agent-skills-standard "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/common/common-performance-engineering" ~/.claude/skills/hoangnguyen0403-agent-skills-standard-common-performance-engineering-22aec8 && rm -rf "$T"
manifest:
skills/common/common-performance-engineering/SKILL.mdsource content
Performance Engineering Standards
Priority: P0 (CRITICAL)
Workflow
- Baseline: Profile before changing anything — measure CPU, memory, and latency.
- Identify: Find top bottleneck (N+1 query, hot loop, memory leak).
- Fix: Apply targeted optimization from sections below.
- Verify: Re-profile to confirm improvement and check for regressions.
Resource Management
- Memory Efficiency:
- Avoid memory leaks: explicit cleanup of listeners, observers, and streams.
- Optimize data structures:
for lookups,Set
for iteration.List - Lazy Initialization: Initialize expensive objects only when needed.
- CPU Optimization:
- Aim for O(1) or O(n); avoid O(n^2) in critical paths.
- Offload heavy computations to background threads or workers.
- Memoize pure, expensive functions.
See implementation examples for memoization and batching patterns.
Network & I/O
- Payload Reduction: Use efficient serialization (Protobuf, JSON minification) and compression (gzip/br).
- Batching: Group multiple small requests into single bulk operations.
- Caching: Implement multi-level caching (Memory -> Storage -> Network) with appropriate TTL and invalidation.
- 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 offloading to workers.
- 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
- Implementation Patterns — profiling patterns, benchmark setup