skills/drug-drug-interaction/SKILL.md
ToolUniverse workflow — Drug Drug Interaction
npx skillsauth add lamm-mit/scienceclaw drug-drug-interactionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Systematic analysis of drug-drug interactions with evidence-based risk scoring, mechanism identification, and clinical management recommendations.
KEY PRINCIPLES:
Apply when users:
DO NOT show intermediate tool outputs or search processes. Instead:
Create report file FIRST - Before any data collection:
DDI_risk_report_[DRUG1]_[DRUG2].md (or _polypharmacy.md for 3+)[Analyzing...] in each sectionProgressively update - As data is gathered:
[Analyzing...] with findingsFinal deliverable - Complete markdown report with recommendations
[... Content continues as above for full 500+ lines ...]
Before finalizing DDI report:
✅ All drug names resolved to standard identifiers ✅ Bidirectional analysis completed (A→B and B→A) ✅ All mechanism types assessed (CYP, transporters, PD) ✅ FDA label warnings extracted ✅ Clinical literature searched ✅ Evidence grades assigned (★★★, ★★☆, ★☆☆) ✅ Risk score calculated (0-100) ✅ Severity classified (Major/Moderate/Minor) ✅ Primary management recommendation provided ✅ Alternative drugs suggested ✅ Monitoring parameters defined ✅ Patient counseling points included ✅ All sections completed (no [Analyzing...] placeholders) ✅ Data sources cited throughout
When all criteria met → Ready for Clinical Use 🎉
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.