performance-test-harness

Name: performance-test-harness
Availability: InStock
Author: nguyenpv1980-wq

Design performance measurement harnesses with baselines and variance-aware regression gates.

2|Updated Jul 6, 2026

Search Tags:#performance testing #regression detection #baselines #ci gates #noise bands #load measurement

Ready to run

npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill performance-test-harness

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: performance-test-harness
Source: https://github.com/nguyenpv1980-wq/Project-Aegis/tree/main/.claude/skills/performance-test-harness
Command: npx skills add https://github.com/nguyenpv1980-wq/Project-Aegis --skill performance-test-harness

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill designs the measurement harness that proves whether software got slower, replacing guesswork with reproducible performance evidence.

Core Features & Use Cases

Surface-specific measurement design: Defines what to measure for APIs, database queries, frontend pages, jobs, and edge functions.
Environment contracts and baselines: Requires pinned hardware, declared cache state, representative data volume, and versioned baselines so results are comparable.
Variance-aware regression gates: Uses repeated runs, noise bands, and CI tiers to detect real regressions without relying on single-run diffs.
Honest reporting: Stamps conditions on every result, reports UNRUN clearly, and keeps production load tests approval-gated.
Use case: A team needs to catch a slower release before it reaches customers and wants a reliable nightly and pre-release perf gate.

Quick Start

Ask for a performance test harness design for your product that names the measured surfaces, environment contract, baseline rules, variance-aware detection, and CI gating.

Frequently Asked Questions about performance-test-harness

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

FAQPage Schema

How do I detect performance regressions in a CI pipeline without false positives?▼

Detect performance regressions by using variance-aware CI gates with repeated runs, noise bands, and versioned baselines. This approach replaces single-run diffs with reproducible evidence, ensuring reliable regression detection for APIs, database queries, and frontend pages.

What is an environment contract for performance testing and why do I need one?▼

An environment contract for performance testing specifies pinned hardware, declared cache state, and representative data volume. You need it to establish comparable, reproducible baselines so that measured results accurately reflect software changes rather than environmental noise.

How do I set up a performance measurement harness for API endpoints and background jobs?▼

Set up a performance measurement harness by defining surface-specific metrics for API endpoints and background jobs, establishing versioned baselines, and applying variance-aware CI tiers. This design catches slower releases pre-deployment with reliable, cited thresholds.

Can I run load measurement tests on production-adjacent environments safely?▼

Yes, you can run load measurement tests on production-adjacent environments safely by using approval-gated handling. The harness requires explicit approval for production-adjacent load testing and clearly stamps conditions and reports UNRUN states on every result.

Why are single-run performance tests unreliable for regression detection?▼

Single-run performance tests are unreliable for regression detection because they ignore variance. Using repeated runs and noise bands creates variance-aware gates that filter out environmental fluctuations, providing honest reporting and reproducible performance evidence.

Does this performance testing approach work for edge functions and frontend pages?▼

Yes, this performance testing approach works for edge functions and frontend pages. The harness designs surface-specific measurement rules for these targets, applying environment contracts and variance-aware CI gates to validate pre-release performance.