Skills sample-size-basic

Basic sample size calculator for clinical research planning with common

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/sample-size-basic" ~/.claude/skills/clawdbot-skills-sample-size-basic && rm -rf "$T"

manifest: skills/aipoch-ai/sample-size-basic/SKILL.md

source content

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

# 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

Basic Functionality: Standard input → Expected output
Edge Case: Invalid input → Graceful error handling
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