What types of performance tests can this skill perform?

The skill supports microbenchmarks for individual functions, macrobenchmarks for end-to-end workflows, and various load tests including stress, soak, spike, and breakpoint testing.

How does it ensure benchmark accuracy?

It follows best practices such as JIT/cache warmup, isolation from other processes, and statistical rigor using iterations (n > 100) and percentile reporting (P95, P99) rather than simple averages.

What metrics are included in the performance reports?

Reports include execution timing (Mean, Median, P95, P99), memory allocation patterns, garbage collection statistics, and comparative analysis against previous commits.

Can this skill help prevent performance regressions?

Yes, it provides CI/CD integration patterns for tools like GitHub Actions to automatically compare current performance against a baseline and alert developers to regressions.

Performance Benchmarking & Optimization

Name: Performance Benchmarking & Optimization
Author: marcel-Ngan

bymarcel-Ngan

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分析と監視

Measures, analyzes, and tracks code performance through systematic benchmarking and automated regression detection.

This skill empowers Claude to establish performance baselines and validate optimizations across microbenchmarks, macrobenchmarks, and load tests. By implementing statistical rigor and standardized reporting templates, it ensures that code changes are evaluated for their impact on latency, throughput, and resource consumption. Whether used during development to compare algorithm alternatives or integrated into CI/CD pipelines for automated regression detection, this skill provides the structured methodology needed to maintain high-performance software standards and verify SLAs.

主な機能

01Comprehensive load testing configurations for stress, soak, and spike scenarios

02CI/CD integration patterns for automated regression detection

03Automated performance reporting with statistical analysis (P95, P99, Std Dev)

041 GitHub stars

05Microbenchmark generation for granular algorithm and operation comparison

06Standardized templates for benchmark design and environment documentation

ユースケース

01Capacity planning through systematic stress and load testing to find system breakpoints

02Detecting performance regressions during pull request reviews in CI/CD pipelines

03Validating the effectiveness of code optimizations against established baselines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add marcel-ngan/ai-dev-team performance

For use in Claude.ai and ChatGPT

主な機能

01Comprehensive load testing configurations for stress, soak, and spike scenarios

02CI/CD integration patterns for automated regression detection

03Automated performance reporting with statistical analysis (P95, P99, Std Dev)

041 GitHub stars

05Microbenchmark generation for granular algorithm and operation comparison

06Standardized templates for benchmark design and environment documentation

ユースケース

01Capacity planning through systematic stress and load testing to find system breakpoints

02Detecting performance regressions during pull request reviews in CI/CD pipelines

03Validating the effectiveness of code optimizations against established baselines