Agent skill · Backend & API

tooluniverse-clinical-trial-design

Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence grading, enrollment projections, and trial design recommendations. Use when planning Phase 1/2 trials, assessing trial feasibility, or designing biomarker-driven studies.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-clinical-trial-design --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 9
SKILL.md size: 42 KB
Bundled scripts: yes
Path: skills/openclaw/tooluniverse-clinical-trial-design/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Clinical Trial Design Feasibility Assessment Systematically assess clinical trial feasibility by analyzing 6 research dimensions. Produces comprehensive feasibility reports with quantitative enrollment projections, endpoint recommendations, and regulatory pathway analysis. **IMPORTANT**: Always use English terms in tool calls (drug names, disease names, biomarker names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language. ## Core Principles ### 1. Report-First Approach (MANDATORY) **DO NOT** show tool outputs to user. Instead: 1. Create `[INDICATION]_trial_feasibility_report.md` FIRST 2. Initialize with all section headers 3. Progressively update as data arrives 4. Present only the final report ### 2. Evidence Grading System | Grade | Symbol | Criteria | Examples | |-------|--------|----------|----------| | **A** | ★★★ | Regulatory acceptance, multiple precedents | FDA-approved endpoint in same indication | | **B** | ★★☆ | Clinical validation, single precedent | Phase 3 trial in related indication | | **C** | ★☆☆ | Preclinical or exploratory | Phase 1 use, biomarker validation o

What's inside
Steps it walks through
  1. Core Principles
  2. 1. Report-First Approach (MANDATORY)
  3. 2. Evidence Grading System
  4. 3. Feasibility Score (0-100)
  5. When to Use This Skill
  6. Quick Start
  7. Core Strategy: 6 Research Paths
  8. Report Structure (14 Sections)
  9. 1. Executive Summary
  10. 2. Disease Background
  11. 3. Patient Population Analysis
  12. 4. Biomarker Strategy
  13. 5. Endpoint Selection & Justification
  14. 6. Comparator Analysis
Ships with 8 files
  • .env.template
  • EXAMPLES.md
  • QUICK_START.md
  • README.md
  • Trial_Feasibility_osimertinib.md
  • UPDATE_SUMMARY.md
  • python_implementation.py
  • trial_pipeline.py
More from awesome-bio-agent-skills
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About this skill
What does the tooluniverse-clinical-trial-design skill do?

Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence grading, enrollment projections, and trial design recommendations. Use when planning Phase 1/2 trials, assessing trial feasibility, or designing biomarker-driven studies.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-clinical-trial-design --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going