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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-clinical-trial-matchingExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-clinical-trial-matching from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tooluniverse-clinical-trial-matching. Access via /tooluniverse-clinical-trial-matching in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
KEY PRINCIPLES:
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
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.
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.
Apply when user asks:
NOT for (use other skills instead):
tooluniverse-cancer-variant-interpretationtooluniverse-adverse-event-detectiontooluniverse-drug-target-validationtooluniverse-disease-researchFor biomarker parsing rules and gene symbol normalization, see MATCHING_ALGORITHMS.md.
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
| 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 |
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 |
| 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 |
| 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 |
| Tool | Key Parameters |
|---|---|
PubMed_search_articles |
query, max_results |
civic_get_variants_by_gene |
gene_id (CIViC int ID), limit |
PharmGKB_search_genes |
query |
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
query, case_sensitive, exact_match, limit) are REQUIREDsearch_clinical_trials: query_term is REQUIRED even for disease-only searchessearch_clinical_trials: action must be exactly "search_studies"civic_search_variants: Does NOT filter by query - returns alphabeticallycivic_get_variants_by_gene: Takes CIViC gene ID (integer), NOT gene symbolTrial Match Score (0-100):
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.
Group 1 (Phase 1 - simultaneous):
MyGene_query_genes per gene, OpenTargets disease search, ols_search_efo_terms, fda_pharmacogenomic_biomarkersGroup 2 (Phase 2 - simultaneous):
search_clinical_trials by disease, biomarker, and intervention; search_clinical_trials alternativeGroup 3 (Phase 3 - simultaneous):
Group 4 (Phases 5-6 - per drug):
| 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 |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
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mattpocock/skills
tooluniverse-clinical-trial-matching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
tooluniverse-clinical-trial-matching fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: tooluniverse-clinical-trial-matching is the kind of skill you can hand to a new teammate without a long onboarding doc.
tooluniverse-clinical-trial-matching has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend tooluniverse-clinical-trial-matching for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: tooluniverse-clinical-trial-matching is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in tooluniverse-clinical-trial-matching — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tooluniverse-clinical-trial-matching is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: tooluniverse-clinical-trial-matching is focused, and the summary matches what you get after install.
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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