power-analysis-calculator
Skill for statistical power analysis and sample size calculation
Profile →npx skills add a5c-ai/babysitter --skill power-analysis-calculator --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 Calculator Skill ## Purpose Calculate statistical power and required sample sizes for experimental designs to ensure adequate study power. ## Capabilities - Calculate required sample size - Compute statistical power - Estimate effect sizes - Handle multiple comparisons - Support various designs - Generate power curves ## Usage Guidelines 1. Specify design type 2. Define effect size 3. Set alpha and power 4. Calculate sample size 5. Generate power curves 6. Document analysis ## Process Integration Works within scientific discovery workflows for: - Study planning - Grant proposals - Protocol development - Design optimization ## Configuration - Statistical test selection - Effect size specifications - Power thresholds - Output formatting ## Output Artifacts - Sample size calculations - Power analyses - Effect size estimates - Power curves
- Purpose
- Capabilities
- Usage Guidelines
- Process Integration
- Configuration
- Output Artifacts
What does the power-analysis-calculator skill do?
Skill for statistical power analysis and sample size calculation
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill power-analysis-calculator --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 a5c-ai/babysitter, a repository with 1,642 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.