Power Analysis Loop

Power Analysis Loop is an AI Engineering skill for Claude Code, published by gaasher in Agent-Loop-Skills.

166 stars19 forkson gaasher/Agent-Loop-SkillsAdded 2026/09/08+2% in starsRepository updated 2026/06/30

agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents

Install in seconds

Install Power Analysis Loop

Copy Power Analysis Loop into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/power-analysis ~/.claude/skills/power-analysis

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/gaasher/Agent-Loop-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: loops/power-analysis/SKILL.md in gaasher/Agent-Loop-Skills
Installs to: ~/.claude/skills/power-analysis
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What Power Analysis Loop does

Power Analysis Loop sizes a single two-arm comparison by simulation, checks the design for validity flaws, and writes a preregistration before data collection. Use it for A/B tests, simple RCTs, or two-model evaluations with one primary outcome.

Power Analysis Loop is cataloged under AI Engineering on DirSkills. Power Analysis Loop comes from a repository tagged agent-skills, agentic-loops, agentic-workflows, ai-agents and anthropic.

Documentation

README

Excerpt

Power Analysis Loop

A power-analysis-and-preregister loop for a two-arm comparison. The artifact is the study's statistical plan; the feedback signal is two parts — statistical power (estimated by Monte-Carlo simulation of the planned test) and a count of validity flaws. Each iteration simulates power, solves for the sample size that reaches the target, audits the design for flaws, and revises — until power clears the target and the flaw list is empty. The deliverable is a sample-size justification plus a preregistration that pins the hypothesis, primary outcome, analysis, sample size, and stopping rule before any data is seen.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Power Analysis Loop

What else does gaasher publish alongside Power Analysis Loop?
Power Analysis Loop is one of 25 skills that DirSkills catalogs from gaasher/Agent-Loop-Skills, the repository it ships in. Its siblings there include Alpha Evolve, Anomaly Investigation and Blue Team. Each one is a separate skill with its own page in this directory, installs the same way Power Analysis Loop does, and is maintained by gaasher in that same repository. The rest of the collection is listed on the gaasher/Agent-Loop-Skills page.
How does Power Analysis Loop compare to other AI Engineering skills?
Power Analysis Loop ranks #3340 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Power Analysis Loop against them. Open each page to compare what they document and how they install.