perf-benchmarker

Automate sequential performance benchmarks with fixed durations and warmups, outputting JSON metrics.

1.9k|545|Updated Oct 22, 2025
One-click install
npx skills add https://github.com/ComposioHQ/awesome-claude-plugins --skill perf-benchmarker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: perf-benchmarker
Source: https://github.com/ComposioHQ/awesome-claude-plugins/tree/main/perf/skills/benchmark
Command: npx skills add https://github.com/ComposioHQ/awesome-claude-plugins --skill perf-benchmarker

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates performance benchmarking by running workloads sequentially to establish baselines and detect regressions.

Core Features & Use Cases

  • Sequential benchmarks: Enforce non-parallel executions to ensure consistent results.
  • Minimum duration & warmup: Guarantee a 60-second minimum run with a 10-second warmup and 30 seconds for binary search scenarios.
  • Anomaly handling: Re-run anomalous results and emit a structured metrics block for comparison and regression detection.

Quick Start

Run the perf-benchmarker against your benchmark script to establish a baseline and validate regressions.

Frequently Asked Questions about perf-benchmarker

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I establish performance baselines to detect regressions in my code?▼

Sequential benchmarking prevents parallel executions to ensure consistent performance results. By enforcing a 60-second minimum run with a 10-second warmup, it eliminates background noise and resource contention, providing reliable baselines for regression detection.

How do I automate benchmarking scripts with fixed durations and warmups?▼

Automate benchmarking by enforcing sequential executions with a 60-second minimum run and 10-second warmup. The process outputs a JSON metrics block bounded by PERF_METRICS_START and PERF_METRICS_END markers for direct regression analysis.

What is the minimum run duration required for binary search benchmarking?▼

Binary search benchmarking requires a 30-second minimum run duration alongside a 10-second warmup. This timing ensures stable metrics collection before outputting the final JSON performance block.

How are performance metrics outputted for automated regression tracking?▼

Performance metrics are outputted as a structured JSON block placed between PERF_METRICS_START and PERF_METRICS_END markers. This format allows automated systems to parse baseline data and validate regressions accurately.

Can I run parallel workloads to speed up performance baseline collection?▼

No, parallel workloads are not supported for baseline collection. The benchmarking process enforces strict sequential execution to eliminate resource contention and ensure deterministic, consistent performance results across runs.