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Skills ecrf-designer

Design clinical trial CRFs with proper validation rules

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 "$T/skills/aipoch-ai/ecrf-designer" ~/.claude/skills/openclaw-skills-ecrf-designer && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/aipoch-ai/ecrf-designer" ~/.openclaw/skills/openclaw-skills-ecrf-designer && rm -rf "$T"
manifest: skills/aipoch-ai/ecrf-designer/SKILL.md
tags
#clinical-trials#data-validation#cdisc#crf-design#data-modeling
source content

eCRF Designer

Clinical data collection form design.

Use Cases

  • Case report form creation
  • CDISC SDTM compliance
  • EDC system setup
  • Data validation rules

Parameters

  • visit_schedule
    : Time points
  • data_elements
    : Variables to collect
  • cdisc_domain
    : SDTM domain

Returns

  • CRF specifications
  • Field validation rules
  • Logic skip patterns
  • Data dictionary

Example

Demographics form with edit checks for age range

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