tooluniverse-clinical-trial-matching▌
mims-harvard/tooluniverse · updated Apr 8, 2026
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Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.
Clinical Trial Matching for Precision Medicine
Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Patient-centric - Every recommendation considers the individual patient's profile
- Molecular-first matching - Prioritize trials targeting patient's specific biomarkers
Molecular Matching Priority
Match patients to trials by molecular profile FIRST (specific mutations), then by disease stage, then by prior treatments. A patient with EGFR L858R should match to EGFR-targeted trials regardless of other factors. 4. Evidence-graded - Every recommendation has an evidence tier (T1-T4) 5. Quantitative scoring - Trial Match Score (0-100) for every trial 6. Eligibility-aware - Parse and evaluate inclusion/exclusion criteria 7. Actionable output - Clear next steps, contact info, enrollment status 8. Source-referenced - Every statement cites the tool/database source 9. Completeness checklist - Mandatory section showing analysis coverage 10. English-first queries - Always use English terms in tool calls. Respond in user's language
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
When to Use
Apply when user asks:
- "What clinical trials are available for my NSCLC with EGFR L858R?"
- "Patient has BRAF V600E melanoma, failed ipilimumab - what trials?"
- "Find basket trials for NTRK fusion"
- "Breast cancer with HER2 amplification, post-CDK4/6 inhibitor trials"
- "KRAS G12C colorectal cancer clinical trials"
- "Immunotherapy trials for TMB-high solid tumors"
- "Clinical trials near Boston for lung cancer"
- "What are my options after failing osimertinib for EGFR+ NSCLC?"
NOT for (use other skills instead):
- Single variant interpretation without trial focus -> Use
tooluniverse-cancer-variant-interpretation - Drug safety profiling -> Use
tooluniverse-adverse-event-detection - Target validation -> Use
tooluniverse-drug-target-validation - General disease research -> Use
tooluniverse-disease-research
Input Parsing
Required Input
- Disease/cancer type: Free-text disease name (e.g., "non-small cell lung cancer", "melanoma")
Strongly Recommended
- Molecular alterations: One or more biomarkers (e.g., "EGFR L858R", "KRAS G12C", "PD-L1 50%", "TMB-high")
- Stage/grade: Disease stage (e.g., "Stage IV", "metastatic", "locally advanced")
- Prior treatments: Previous therapies and outcomes (e.g., "failed platinum chemotherapy", "progressed on osimertinib")
Optional
- Performance status: ECOG or Karnofsky score
- Geographic location: City/state for proximity filtering
- Trial phase preference: I, II, III, IV, or "any"
- Intervention type: drug, biological, device, etc.
- Recruiting status preference: recruiting, not yet recruiting, active
For biomarker parsing rules and gene symbol normalization, see MATCHING_ALGORITHMS.md.
Workflow Overview
Input: Patient profile (disease + biomarkers + stage + prior treatments)
Phase 1: Patient Profile Standardization
- Resolve disease to EFO/ontology IDs (OpenTargets, OLS)
- Parse molecular alterations to gene + variant
- Resolve gene symbols to Ensembl/Entrez IDs (MyGene)
- Classify biomarker actionability (FDA-approved vs investigational)
Phase 2: Broad Trial Discovery
- Disease-based trial search (ClinicalTrials.gov)
- Biomarker-specific trial search
- Intervention-based search (for known drugs targeting patient's biomarkers)
- Deduplicate and collect NCT IDs
Phase 3: Trial Characterization (batch, groups of 10)
- Eligibility criteria, conditions/interventions, locations, status, descriptions
Phase 4: Molecular Eligibility Matching
- Parse eligibility text for biomarker requirements
- Match patient's molecular profile to trial requirements
- Score molecular eligibility (0-40 points)
Phase 5: Drug-Biomarker Alignment
- Identify trial intervention drugs and mechanisms (OpenTargets, ChEMBL)
- FDA approval status for biomarker-drug combinations
- Classify drugs (targeted therapy, immunotherapy, chemotherapy)
Phase 6: Evidence Assessment
- FDA-approved biomarker-drug combinations
- Clinical trial results (PubMed), CIViC evidence, PharmGKB
- Evidence tier classification (T1-T4)
Phase 7: Geographic & Feasibility Analysis
- Trial site locations, enrollment status, proximity scoring
Phase 8: Alternative Options
- Basket trials, expanded access, related studies
Phase 9: Scoring & Ranking (0-100 composite score)
- Tier classification: Optimal (80-100) / Good (60-79) / Possible (40-59) / Exploratory (0-39)
Phase 10: Report Synthesis
- Executive summary, ranked trial list, evidence grading, completeness checklist
Critical Tool Parameters
Clinical Trial Search Tools
| Tool | Key Parameters | Notes |
|---|---|---|
search_clinical_trials |
query_term (REQ), condition, intervention, pageSize |
Main search |
search_clinical_trials |
action="search_studies" (REQ), condition, intervention, limit |
Alternative search |
get_clinical_trial_descriptions |
action="get_study_details" (REQ), nct_id (REQ) |
Full trial details |
Batch Trial Detail Tools (all take nct_ids array)
| Tool | Second Required Param | Returns |
|---|---|---|
get_clinical_trial_eligibility_criteria |
eligibility_criteria="all" |
Eligibility text |
get_clinical_trial_locations |
location="all" |
Site locations |
get_clinical_trial_conditions_and_interventions |
condition_and_intervention="all" |
Arms/interventions |
get_clinical_trial_status_and_dates |
status_and_date="all" |
Status/dates |
get_clinical_trial_descriptions |
description_type="brief" or "full" |
Titles/summaries |
get_clinical_trial_outcome_measures |
outcome_measures="all" |
Outcomes |
Gene/Disease Resolution
| Tool | Key Parameters |
|---|---|
MyGene_query_genes |
query, species |
OpenTargets_get_disease_id_description_by_name |
diseaseName |
OpenTargets_get_target_id_description_by_name |
targetName |
ols_search_efo_terms |
query, limit |
Drug Information
| Tool | Key Parameters | Notes |
|---|---|---|
OpenTargets_get_drug_id_description_by_name |
drugName |
Resolve drug to ChEMBL ID |
OpenTargets_get_drug_mechanisms_of_action_by_chemblId |
chemblId |
Drug MoA and targets |
OpenTargets_get_associated_drugs_by_target_ensemblID |
ensemblId, size |
Drugs for a target |
drugbank_get_targets_by_drug_name_or_drugbank_id |
query, case_sensitive, exact_match, limit (ALL REQ) |
Drug targets |
fda_pharmacogenomic_biomarkers |
(none) | FDA biomarker-drug list |
FDA_get_indications_by_drug_name |
drug_name, limit |
FDA indications |
Evidence Tools
| Tool | Key Parameters |
|---|---|
PubMed_search_articles |
query, max_results |
civic_get_variants_by_gene |
gene_id (CIViC int ID), limit |
PharmGKB_search_genes |
query |
Known CIViC Gene IDs
EGFR=19, BRAF=5, ALK=1, ABL1=4, KRAS=30, TP53=45, ERBB2=20, NTRK1=197, NTRK2=560, NTRK3=561, PIK3CA=37, MET=52, ROS1=118, RET=122, BRCA1=2370, BRCA2=2371
Critical Parameter Notes
- DrugBank tools: ALL 4 parameters (
query,case_sensitive,exact_match,limit) are REQUIRED search_clinical_trials:query_termis REQUIRED even for disease-only searchessearch_clinical_trials:actionmust be exactly"search_studies"- CIViC
civic_search_variants: Does NOT filter by query - returns alphabetically - CIViC
civic_get_variants_by_gene: Takes CIViC gene ID (integer), NOT gene symbol - Batch clinical trial tools: Accept arrays of NCT IDs, process in batches of 10
Scoring Summary
Trial Match Score (0-100):
- Molecular Match: 0-40 pts (exact variant=40, gene-level=30, pathway=20, none=10, excluded=0)
- Clinical Eligibility: 0-25 pts (all met=25, most=18, some=10, ineligible=0)
- Evidence Strength: 0-20 pts (FDA-approved=20, Phase III=15, Phase II=10, Phase I=5)
- Trial Phase: 0-10 pts (III=10, II=8, I/II=6, I=4)
- Geographic: 0-5 pts (local=5, same country=3, international=1)
Recommendation Tiers: Optimal (80-100), Good (60-79), Possible (40-59), Exploratory (0-39)
Evidence Tiers: T1 (FDA/guideline), T2 (Phase III), T3 (Phase I/II), T4 (computational)
For detailed scoring logic, see SCORING_CRITERIA.md.
Parallelization Strategy
Group 1 (Phase 1 - simultaneous):
MyGene_query_genesper gene,OpenTargetsdisease search,ols_search_efo_terms,fda_pharmacogenomic_biomarkers
Group 2 (Phase 2 - simultaneous):
search_clinical_trialsby disease, biomarker, and intervention;search_clinical_trialsalternative
Group 3 (Phase 3 - simultaneous):
- All batch detail tools (eligibility, interventions, locations, status, descriptions)
Group 4 (Phases 5-6 - per drug):
- Drug resolution, MoA, FDA indications, PubMed evidence
Error Handling
- Wrap every tool call in try/except
- Check for empty results and string error responses
- Use fallback tools when primary fails (e.g., OLS if OpenTargets fails)
- Document failures in completeness checklist
- Never let one failure block the entire analysis
Reference Files
| File | Contents |
|---|---|
| TOOLS_REFERENCE.md | Full tool inventory with parameters and response structures |
| MATCHING_ALGORITHMS.md | Patient profile standardization, biomarker parsing, molecular eligibility matching, drug-biomarker alignment code |
| SCORING_CRITERIA.md | Detailed scoring tables, molecular match logic, drug-biomarker alignment scoring |
| REPORT_TEMPLATE.md | Full markdown report template with all sections |
| TRIAL_SEARCH_PATTERNS.md | Search functions, batch retrieval, parallelization, common use patterns, edge cases |
| EXAMPLES.md | Worked examples for different matching scenarios |
| QUICK_START.md | Quick-start guide for common workflows |
How to use tooluniverse-clinical-trial-matching on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add tooluniverse-clinical-trial-matching
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches tooluniverse-clinical-trial-matching from GitHub repository mims-harvard/tooluniverse and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate tooluniverse-clinical-trial-matching. Access the skill through slash commands (e.g., /tooluniverse-clinical-trial-matching) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★31 reviews- ★★★★★Kiara Gupta· Dec 24, 2024
tooluniverse-clinical-trial-matching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yash Thakker· Nov 15, 2024
tooluniverse-clinical-trial-matching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★William Liu· Nov 15, 2024
Keeps context tight: tooluniverse-clinical-trial-matching is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dhruvi Jain· Oct 6, 2024
tooluniverse-clinical-trial-matching has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Isabella Desai· Oct 6, 2024
I recommend tooluniverse-clinical-trial-matching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Piyush G· Sep 21, 2024
Keeps context tight: tooluniverse-clinical-trial-matching is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★William Garcia· Sep 13, 2024
Useful defaults in tooluniverse-clinical-trial-matching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Daniel Bhatia· Sep 1, 2024
tooluniverse-clinical-trial-matching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Amelia Rahman· Aug 20, 2024
Solid pick for teams standardizing on skills: tooluniverse-clinical-trial-matching is focused, and the summary matches what you get after install.
- ★★★★★Shikha Mishra· Aug 12, 2024
I recommend tooluniverse-clinical-trial-matching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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