power-analysis
Use when the user is planning a two-arm comparison (an A/B test, a simple RCT, a behavioral study, or a two-model/two-config evaluation) and needs to size it and preregister it before collecting data — finding the per-group sample size that hits target statistical power for the smallest effect worth detecting, auditing the design against a validity checklist, and locking it in a preregistration. Only for a single two-arm comparison with one primary outcome. Not for factorial, repeated-measures, clustered/multilevel, time-series, adaptive, or survival designs; not for analyzing data already col
npx skills add gaasher/Agent-Loop-Skills --skill power-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## Scope & limitations This loop does exactly three things, in a loop: **(1)** computes power and required sample size for a **two-group comparison** by simulation, **(2)** runs a fixed **validity checklist** over the design, and **(3)** writes a **preregistration**. The vendored power model (`tools/power_sim.py`) covers **two-sample mean** (continuous outcome) and **two-proportion** (binary outcome) tests only. It is **not** a general experiment designer. It does **not** handle factorial, repeated-measures, clustered/multilevel, time-s
- Scope & limitations
- When to use
- Setup
- The loop
- Ledger
- Constraints
What does the power-analysis skill do?
Use when the user is planning a two-arm comparison (an A/B test, a simple RCT, a behavioral study, or a two-model/two-config evaluation) and needs to size it and preregister it before collecting data — finding the per-group sample size that hits target statistical power for the smallest effect worth detecting, auditing the design against a validity checklist, and locking it in a preregistration. Only for a single two-arm comparison with one primary outcome. Not for factorial, repeated-measures, clustered/multilevel, time-series, adaptive, or survival designs; not for analyzing data already col
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill power-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.