Agent skill

tooluniverse-drug-repurposing

Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-drug-repurposing --agent claude-code

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

Facts
Files in the skill folder: 4
SKILL.md size: 17 KB
Bundled scripts: none
Path: skills/openclaw/tooluniverse-drug-repurposing/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

# Drug Repurposing with ToolUniverse Systematically identify and evaluate drug repurposing candidates using multiple computational strategies. **IMPORTANT**: Always use English terms in tool calls (drug names, disease names, target 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 Strategies ### 1. Target-Based Repurposing Start with disease targets → Find drugs that modulate those targets ### 2. Compound-Based Repurposing Start with approved drugs → Find new disease indications ### 3. Disease-Driven Repurposing Start with disease → Find targets → Match to existing drugs ## Quick Start ```python from tooluniverse import ToolUniverse tu = ToolUniverse(use_cache=True) tu.load_tools() # Example: Find repurposing candidates for a disease disease_name = "rheumatoid arthritis" # Step 1: Get disease information disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name( diseaseName=disease_name ) # Step 2: Get associated targets disease_id = disease_info['data']['id'] targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId( efoId=disease_id, l

What's inside
Steps it walks through
  1. Core Strategies
  2. 1. Target-Based Repurposing
  3. 2. Compound-Based Repurposing
  4. 3. Disease-Driven Repurposing
  5. Quick Start
  6. Complete Workflow
  7. Phase 1: Disease & Target Analysis
  8. Phase 2: Drug Discovery
  9. Phase 3: Safety & Feasibility Assessment
  10. Phase 4: Literature Evidence
  11. Phase 5: Scoring & Ranking
  12. Alternative Strategies
  13. Strategy A: Mechanism-Based Repurposing
  14. Strategy B: Network-Based Repurposing
Ships with 3 files
  • EXAMPLES.md
  • README.md
  • REFERENCE.md
More from awesome-bio-agent-skills
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About this skill
What does the tooluniverse-drug-repurposing skill do?

Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-drug-repurposing --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.

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