Measure and compare web page, API, and build performance metrics against JSON baselines.
npx skills add https://github.com/Maelwalser/claude-config --skill benchmark-maelwalser
Please help me install this Agent Skill. Skill: benchmark Source: https://github.com/Maelwalser/claude-config/tree/main/skills/benchmark Command: npx skills add https://github.com/Maelwalser/claude-config --skill benchmark-maelwalser
Many changes introduce unnoticed performance regressions across pages, APIs, and build processes; this Skill provides repeatable baselining and deterministic comparisons to detect and quantify regressions before they reach users.
Run the benchmark baseline command to capture current page, API, and build metrics for later comparison.
High-intent search queries and answers about installing and using this skill.
Detect performance regressions in CI by capturing baseline metrics like Core Web Vitals and API latencies, storing them as JSON in your repo, and comparing before/after runs on pull requests to surface quantified deltas.
Benchmarking Core Web Vitals involves measuring LCP, CLS, INP, FCP, and TTFB for target URLs to capture resource sizes, count network requests, and identify render-blocking assets for repeatable performance auditing.
Measure API latency under load by hitting endpoints repeatedly to collect p50, p95, and p99 response times, status code distributions, response sizes, and running short concurrent-load checks to quantify performance.
Track build performance regressions by recording cold and incremental build times, hot reload and HMR latency, test suite duration, TypeScript check times, linting, and Docker build timings to monitor developer feedback loops.
Compare before and after performance metrics by capturing a baseline on your main branch, running the benchmark again on your feature branch in CI, and comparing the stored JSON deltas to decide whether to accept the change.
You do not need external dependencies to measure web vitals and API latencies locally; the Skill operates independently to collect metrics, store baselines as JSON files, and execute deterministic before/after comparisons.
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