tooluniverse-clinical-trial-matching
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-clinical-trial-matching --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios.
How it works
- Phase 1: Standardizes patient inputs to ontology IDs and gene/variant representations, and classifies biomarker actionability.
- Phase 2: Broad trial discovery via disease-based, biomarker-specific, and intervention-based searches, collecting NCT IDs.
- Phase 3: Characterizes trials by eligibility criteria, conditions, interventions, locations, and status.
- Phase 4: Parses eligibility criteria to extract biomarker requirements and matches them to the patient’s molecular profile to score molecular eligibility.
- Phase 5: Identifies trial interventions, checks drug mechanisms against patient biomarkers, assesses FDA status for biomarker-drug combos, and classifies drug types.
- Phase 6: Evaluates evidence from FDA biomarker-drug combinations, PubMed results, CIViC, PharmGKB, and safety data.
- Phase 7: Analyzes trial sites, enrollment status, dates, and optionally distance from patient location.
- Phase 8: Considers basket, expanded access, and related trial designs.
- Phase 9: Computes a Trial Match Score (0-100), tiers (Optimal/Good/Possible/Exploratory), and ranks trials.
- Phase 10: Produces a markdown report with executive summary, patient profile, ranked trials, alternatives, evidence, and a completeness checklist.
When to use it
Use when the user asks for clinical trial options for specific cancer types, biomarker profiles, or post-progression scenarios, such as:
- NSCLC with EGFR L858R
- BRAF V600E melanoma after prior therapies
- NTRK fusion basket trials
- HER2-amplified breast cancer post-CDK4/6 inhibitor
- KRAS G12C colorectal cancer
- Immuotherapy trials for TMB-high solid tumors
- Trials near a city or region
What it can touch
- Tools referenced in input/output: Clinical trial search utilities, gene and disease databases, drug-target resources, CIViC, OpenTargets, ChEMBL, FDA data, PubMed/openalex literature tools.
- It calls:
search_clinical_trials,clinical_trials_search,clinical_trials_get_details,get_clinical_trial_eligibility_criteria,get_clinical_trial_locations,get_clinical_trial_descriptions,get_clinical_trial_status_and_dates,get_clinical_trial_conditions_and_interventions,get_clinical_trial_outcome_measures,extract_clinical_trial_outcomes,extract_clinical_trial_adverse_events, and molecular/disease tools likeMyGene_query_genes,OpenTargets_get_target_id_description_by_name, among others.
Caveats
- Licensing is NOASSERTION.
- The skill relies on external databases and live tool calls; results depend on data availability and current statuses.
- Output is anchored to the instructions and may include placeholders if data is unavailable.
# 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**: 1. **Report-first approach** - Create report file FIRST, then populate progressively 2. **Patient-centric** - Every recommendation considers the individual patient's profile 3. **Molecular-first matching** - Prioritize trials targeting patient's specific biomarkers 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 to Use Apply when user asks: - "
- When to Use
- Input Parsing
- Required Input
- Strongly Recommended
- Optional
- Biomarker Parsing Rules
- Gene Symbol Normalization
- Phase 0: Tool Parameter Reference (CRITICAL)
- Clinical Trial Tools
- Molecular/Disease Tools
- CIViC Tools
- Drug Information Tools
- Disease Ontology Tools
- Literature Tools
What does the tooluniverse-clinical-trial-matching skill do?
AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility, clinical criteria, drug-biomarker alignment, evidence strength, and geographic feasibility. Produces a quantitative Trial Match Score (0-100) per trial with tiered recommendations and a comprehensive markdown report. Use when oncologists, molecular tumor boards, or patients ask about clinical trial options for specific
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-clinical-trial-matching --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.