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.md
source content

Performance Engineering Standards

Priority: P0 (CRITICAL)

Workflow

  1. Baseline: Profile before changing anything — measure CPU, memory, and latency.
  2. Identify: Find top bottleneck (N+1 query, hot loop, memory leak).
  3. Fix: Apply targeted optimization from sections below.
  4. 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:
    Set
    for lookups,
    List
    for iteration.
  • 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