perf-theory-tester

Validate performance hypotheses through controlled experiments with baseline restoration.

951|110|Updated Jan 15, 2026

Search Tags:#validation #benchmark #baseline #experiments #hypotheses #perf

Ready to run

npx skills add https://github.com/avifenesh/awesome-slash --skill perf-theory-tester-avifenesh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: perf-theory-tester
Source: https://github.com/avifenesh/awesome-slash/tree/main/plugins/perf/skills/perf-theory-tester
Command: npx skills add https://github.com/avifenesh/awesome-slash --skill perf-theory-tester-avifenesh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validate performance claims with repeatable, controlled experiments to ensure changes behave as expected under real workloads.

Core Features & Use Cases

Structured experiment workflow: baseline confirmation, single-change application, 2+ validation passes, and clean restoration.
Standardized Output Format to capture hypothesis, change, delta, verdict, and evidence.
Use cases include evaluating code changes, configuration tweaks, and runtime parameter experiments across benchmarks.

Quick Start

Run a controlled perf experiment by applying one change, performing at least two validation passes, and recording the baseline and results.

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 performance hypotheses through controlled experiments?▼

Validate performance hypotheses by applying a single change to a baseline, running at least two validation passes, and recording the delta. This structured workflow ensures results are repeatable and not skewed by workloads.

What is the single-change rule in performance benchmarking?▼

The single-change rule in performance benchmarking isolates variables by applying only one modification per experiment. This prevents overlapping effects, ensuring the measured delta accurately reflects the impact of the specific code or configuration tweak.

How do I run baseline comparisons for code changes and configuration tweaks?▼

Run baseline comparisons by confirming the initial state, applying a single modification, and executing two or more validation passes. You then restore the baseline and record the hypothesis, change, delta, and verdict in a structured output.

Why do I need multiple validation passes for regression checks?▼

Multiple validation passes are required for regression checks to eliminate noise and confirm repeatability. Running at least two passes ensures the measured performance delta is consistent and reliable under real workloads rather than an anomaly.

Can I test feature-flag experiments across different benchmarks?▼

Feature-flag experiments can be tested across benchmarks by applying the flag as the single change. The workflow requires baseline confirmation, two or more validation passes, and a structured output capturing the hypothesis, delta, and final verdict.

What is the best way to document performance experiment results?▼

The best way to document performance experiment results is using a standardized Output Format that captures the hypothesis, applied change, measured delta, final verdict, and supporting evidence. This ensures claims are repeatable and validated.