What is the Performance Theory Tester?

It is a specialized Claude Code skill designed to automate and standardize the process of performance benchmarking and scientific hypothesis testing within your codebase.

How does it validate performance changes?

The skill follows a strict protocol: it confirms a clean baseline, applies exactly one change, runs at least two validation passes, and records the resulting metric delta.

Why does it require a hypothesis-driven approach?

A hypothesis-driven approach prevents 'shotgun debugging' and ensures that any performance gains or losses are directly and scientifically attributable to specific code changes.

Can I run multiple benchmarks in parallel?

No, the skill explicitly constrains experiments to single changes and non-parallel runs to ensure environmental consistency and accurate metric reporting.

What kind of output does the theory tester provide?

It generates a structured report containing the hypothesis ID, a summary of the change, metric deltas, a verdict (accept, reject, or inconclusive), and the evidence used.

Performance Theory Tester

Name: Performance Theory Tester
Author: avifenesh

byavifenesh

•

311

セキュリティとテスト

Executes controlled performance experiments to validate hypotheses with scientific rigor and repeatable metrics.

概要

The Performance Theory Tester skill enables Claude to conduct systematic performance benchmarking and optimization experiments using a scientific approach. By enforcing a strict protocol—confirming clean baselines, applying isolated single-variable changes, and performing multiple validation passes—it ensures that performance improvements are statistically valid and reproducible. This tool is essential for developers who need to move beyond anecdotal speed observations to data-driven performance tuning, providing structured evidence for every code optimization decision.

主な機能

Standardized experiment reporting with evidence logging
Hypothesis-driven performance experimentation framework
Automated baseline verification and delta tracking
Multi-pass validation for statistical significance
Strict single-change enforcement for variable isolation
311 GitHub stars

ユースケース

Validating if a specific refactor genuinely reduces execution time
Comparing performance deltas between different algorithms or library implementations
Identifying and documenting performance regressions during the development cycle

概要

主な機能

Standardized experiment reporting with evidence logging
Hypothesis-driven performance experimentation framework
Automated baseline verification and delta tracking
Multi-pass validation for statistical significance
Strict single-change enforcement for variable isolation
311 GitHub stars

ユースケース

Validating if a specific refactor genuinely reduces execution time
Comparing performance deltas between different algorithms or library implementations
Identifying and documenting performance regressions during the development cycle