running-benchmarks

Automates deterministic benchmark suites to detect performance regressions in backpressured loops.

62|2|Updated May 29, 2026

Search Tags:#automation #performance #regression #latency #benchmark #baseline #measurement

Ready to run

npx skills add https://github.com/lucasfcosta/backpressured --skill running-benchmarks

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: running-benchmarks
Source: https://github.com/lucasfcosta/backpressured/tree/main/skills/running-benchmarks
Command: npx skills add https://github.com/lucasfcosta/backpressured --skill running-benchmarks

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automate targeted performance checks to prevent regressions in backpressured loops by validating changes against a fast, repeatable benchmark before broader validation.

Core Features & Use Cases

Targeted benchmarking: run fast, focused suites to validate the most impacted paths after each patch.
Baseline comparison: measure patched results against a stable baseline to determine regression, improvement, or wash.
Evidence-driven decisions: collect medians, counts, and noise metrics with documented run counts to support release decisions.
Use Case: after modifying latency-sensitive code, run the fast benchmark to decide if a patch is safe to merge.

Quick Start

Run the fast benchmark suite after each patch to surface regressions before deeper validation.

Frequently Asked Questions about running-benchmarks

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

FAQPage Schema

How do I catch performance regressions in backpressured loops before merging code?▼

To catch performance regressions in backpressured loops, you can automate targeted benchmark suites that validate patches against a stable baseline. This approach compares medians and noise metrics to determine if a change is a regression, improvement, or wash.

How does baseline comparison work for latency benchmarking?▼

Baseline comparison for latency benchmarking works by measuring patched results against a stable baseline using multiple runs. It collects medians, counts, and noise metrics to produce a clear verdict on whether the performance change is safe.

What is the best way to automate performance checks for latency-sensitive code?▼

The best way to automate performance checks for latency-sensitive code is running fast, focused benchmark suites after each patch. This surfaces regressions early by enforcing multiple runs and comparing medians before broader validation.

Can I run targeted benchmarks iteratively during development without slowing down my workflow?▼

Yes, you can run targeted benchmarks iteratively during development without major slowdowns. The process applies to iterative workflows by validating changes with fast, focused suites before deeper testing, ensuring repeatability without excessive overhead.

How do I handle noise and ensure repeatability when measuring performance regressions?▼

To handle noise and ensure repeatability when measuring performance regressions, the benchmarking process performs multiple runs and records documented run counts. It uses medians and noise metrics to support reliable release decisions.

When should I not use automated benchmark suites for regression detection?▼

You should not use automated benchmark suites for regression detection when you need full testing instead of fast, targeted validation. This approach is designed for early patch validation in iterative workflows, not for replacing comprehensive performance testing.