jorgealves/agent_skills

clinical-trial-schema-designer

Analyzes clinical trial protocols and generates CDISC-compliant (SDTM/ADaM) data schemas. Use when designing data ingestion pipelines for clinical research or preparing regulatory submissions.

First seen Jan 30, 2026

Installation

$ npx skills add jorgealves/agent_skills --skill clinical-trial-schema-designer

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 2
License license-compliance-auditor
Default branch main
Open issues 1
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,802 B
  • docs SUMMARY.md 230 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 180 installs

SKILL.md

Clinical Trial Schema Designer

Purpose and Intent

The clinical-trial-schema-designer bridges the gap between clinical research and data engineering. It helps automate the creation of standardized data structures (CDISC) based on clinical protocols, reducing the manual effort required for data ingestion and submission preparation.

When to Use

  • Study Setup: Use during the "Start-up" phase of a clinical trial to design the Electronic Data Capture (EDC) schemas.
  • Data Integration: When merging data from multiple sources into a single study standard.
  • Submission Prep: To ensure the data structure matches FDA/PMDA requirements for SDTM/ADaM.

When NOT to Use

  • Unvalidated Systems: Clinical data must be handled in GxP-validated environments. This tool generates the design, but the implementation must follow strict validation protocols.
  • Medical Decision Making: This is a data structuring tool, not a clinical diagnostic or treatment tool.

Error Conditions and Edge Cases

  • Ambiguous Protocols: If the input text is vague about how a variable is measured, the generated schema may be incomplete.
  • Non-Standard Studies: Phase 1 or highly experimental studies may use variables that don't fit existing CDISC domains perfectly.

Security and Data-Handling Considerations

  • IP Protection: Clinical protocols are intellectual property. Ensure the environment running this skill is secure.
  • No Patient Data: This tool works on protocols (the plan), not the actual results (the data).