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claude-codePharma

Skills randomization-gen

Generate block randomization lists for RCTs

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 "$T/skills/aipoch-ai/randomization-gen" ~/.claude/skills/clawdbot-skills-randomization-gen && rm -rf "$T"
manifest: skills/aipoch-ai/randomization-gen/SKILL.md
source content

Randomization Gen

RCT randomization table generator.

Use Cases

  • Clinical trial design
  • Animal study randomization
  • Blocked randomization
  • Stratified allocation

Parameters

ParameterTypeRequiredDescription
n_subjects
intYesTotal sample size
n_groups
intYesNumber of arms/groups
block_size
intYesBlock size (must be multiple of n_groups)
--output
stringNoOutput file path (default: randomization.txt)

Returns

  • Randomization sequence
  • Block assignments
  • Allocation concealment ready

Example

Input: n=120, 3 groups, block=6 Output: Sealed randomization list

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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

No additional Python packages required.

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