Which benchmarking tools are supported by this skill?

It integrates with popular tools including k6, Artillery, wrk, Apache Bench (ab), and Hyperfine for both web API and CLI-based benchmarking.

Does it provide support for memory leak identification?

Yes, it compares heap snapshots and monitors memory growth rates over time to flag potential leaks and identify suspect object types.

How does this skill detect performance regressions?

The skill identifies a baseline version via git tags, executes automated load tests, and applies statistical tests like the t-test to determine if performance changes are statistically significant.

Can I set custom thresholds for my performance metrics?

Yes, you can configure specific warning and critical thresholds for latency (p50-p99), throughput (RPS), and memory usage in a dedicated YAML configuration file.

Regression Performance Monitor

Name: Regression Performance Monitor
Author: jmagly

byjmagly

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102

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Analíticas y Monitorización

Detects and analyzes performance regressions across software versions using statistical significance testing and automated benchmarking.

This skill enables Claude to automatically identify performance degradation by comparing current metrics against historical baselines. It integrates with industry-standard tools like k6 and Artillery to measure latency percentiles (p50-p99), throughput, and memory utilization. By applying rigorous statistical tests like Welch's t-test and Mann-Whitney U, the skill filters out environmental noise to provide high-confidence reports on regressions, helping developers catch bottlenecks and memory leaks early in the SDLC.

Características Principales

01Automated baseline identification from git tags and historical monitoring data

02Deep memory leak detection via heap snapshot and growth rate analysis

03Advanced statistical significance testing to eliminate benchmark noise

04102 GitHub stars

05Comprehensive reporting with latency distributions and resource utilization trends

06Multi-tool integration for load testing including k6, Artillery, and wrk

Casos de Uso

01Detecting subtle memory leaks and heap growth in high-traffic applications

02Comparing API latency distributions (p95, p99) between production releases

03Validating performance stability before merging major architectural refactors

Características Principales

01Automated baseline identification from git tags and historical monitoring data

02Deep memory leak detection via heap snapshot and growth rate analysis

03Advanced statistical significance testing to eliminate benchmark noise

04102 GitHub stars

05Comprehensive reporting with latency distributions and resource utilization trends

06Multi-tool integration for load testing including k6, Artillery, and wrk

Casos de Uso

01Detecting subtle memory leaks and heap growth in high-traffic applications

02Comparing API latency distributions (p95, p99) between production releases

03Validating performance stability before merging major architectural refactors