sample-size-basic
Basic sample size calculator for clinical research planning with common statistical scenarios
Best use case
sample-size-basic is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Basic sample size calculator for clinical research planning with common statistical scenarios
Teams using sample-size-basic should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/sample-size-basic/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How sample-size-basic Compares
| Feature / Agent | sample-size-basic | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Basic sample size calculator for clinical research planning with common statistical scenarios
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Sample Size (Basic) Basic sample size estimation for clinical research planning. ## Use Cases - Quick sample size estimates for grant proposals - Preliminary study design calculations - Educational purposes for statistics training ## Parameters - `test_type`: Type of test (t_test, chi_square, proportion) - `alpha`: Significance level (default 0.05) - `power`: Statistical power (default 0.80) - `effect_size`: Expected effect size - `baseline_rate`: Baseline proportion (for proportion tests) ## Returns - Required sample size per group - Total sample size - Statistical assumptions summary ## Example Input: Two-sample t-test, alpha=0.05, power=0.80, effect_size=0.5 Output: n=64 per group, total=128 subjects ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites ```bash # Python dependencies pip install -r requirements.txt ``` ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support
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