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SKILL verified Apache-2.0 Self-run

Common Performance Engineering

skill-hoangnguyen0403-agent-skills-standard-common-performance-engineering · by HoangNguyen0403

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.

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Install

$ agentstack add skill-hoangnguyen0403-agent-skills-standard-common-performance-engineering

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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About

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](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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.