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)
Best use case
common-performance-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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)
Teams using common-performance-engineering should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/common-performance-engineering/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How common-performance-engineering Compares
| Feature / Agent | common-performance-engineering | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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)
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Performance Engineering Standards ## **Priority: P0 (CRITICAL)** ## Workflow 1. **Baseline**: Profile before changing anything — measure CPU, memory, and latency. 2. **Identify**: Find the top bottleneck (N+1 query, hot loop, memory leak). 3. **Fix**: Apply the targeted optimization from the 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](references/implementation.md) 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](references/implementation.md) — profiling patterns, benchmark setup
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