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perf-theory-tester

Validate performance hypotheses through controlled experiments with structured output.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to rigorously validate performance hypotheses through controlled experiments, ensuring results are reliable, reproducible, and actionable.

Core Features & Use Cases

  • Baseline verification: ensures a clean starting point before applying any changes.
  • Single-change experiments: isolates the impact of a hypothesis-driven modification.
  • Multi-pass validation: requires 2+ passes to confirm stability of results.
  • Structured reporting: outputs a clear record with hypothesis id, change summary, metric deltas, verdict, and supporting evidence.
  • Use Case: optimizing a service's latency where you test a candidate change against a baseline across repeated runs.

Quick Start

Confirm the baseline, apply the hypothesis-related change, run 2+ validation passes, and revert to baseline when preparing for the next experiment.

Frequently Asked Questions about perf-theory-tester

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

FAQPage Schema
How do I validate a performance hypothesis with controlled experiments?▼

Controlled experiments for performance validation isolate a single change against a clean baseline, run 2+ validation passes to confirm stability, and output a structured record with hypothesis, change, delta, verdict, and evidence.

Why do I need a clean baseline before benchmarking software changes?▼

A clean baseline ensures measured metric deltas are solely attributable to the single change applied. Without baseline verification, software benchmarking results lack reproducibility and reliable attribution.

What is the best way to isolate the impact of a software optimization change?▼

Enforcing a single change per experiment against a verified baseline isolates the impact of a software optimization change. Requiring 2+ validation passes ensures the metric delta is stable and reproducible.

How do I ensure reproducibility when profiling service latency?▼

Running 2+ validation passes for each single-change experiment and reverting to the baseline before the next one ensures reproducibility when profiling service latency. This structured approach guarantees stable and repeatable metric measurements.

Does performance testing work if I apply multiple changes in one experiment?▼

No, this approach enforces a single change per experiment to accurately attribute metric deltas. Applying multiple changes simultaneously invalidates baseline isolation and compromises the reliability of the verdict.

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