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Find clinical trials for a gene, variant, or condition from ClinicalTrials.gov + EUCTR, with FHIR R4 output
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/stanfish06/skillquarium --skill clinical-trial-finderコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
build a repo-local skill and install a matching iterated coding-agent GitHub Actions workflow, prompt, memory file, and reference templates
interview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflow
narrow React component prop types to match live code paths
SOC 職業分類に基づく
SKILL.md を表示中
| name | clinical-trial-finder |
| description | Find clinical trials for a gene, variant, or condition from ClinicalTrials.gov + EUCTR, with FHIR R4 output |
| license | MIT |
| metadata | {"version":"0.1.0","author":"Duvet05 <gonzalo.galvezc@pucp.edu.pe>","domain":"clinical","tags":["clinical-trials","genomics","drug-discovery","clinical","precision-medicine"],"inputs":[{"name":"input_file","type":"file","format":"[Truncated]","description":"Text file with one search term per line (gene name, condition, or drug)","required":false},{"name":"query","type":"string","description":"Direct search query string (alternative to input_file)","required":false},{"name":"gene","type":"string","description":"Gene symbol (e.g. BRCA1) — enriched via OpenTargets gene-to-disease mapping","required":false},{"name":"rsid","type":"string","description":"dbSNP rsID (e.g. rs3798220) — resolved via GWAS Catalog to disease traits, then queried against CT.gov","required":false},{"name":"demo","type":"flag","description":"Run with built-in demo data (BRCA1 breast cancer)","required":false}],"outputs":[{"name":"report","type":"file","format":"md","description":"Markdown report with matched trials, status, phase, and links to ClinicalTrials.gov"},{"name":"summary","type":"file","format":"json","description":"Machine-readable trial data for programmatic use"},{"name":"fhir_bundle","type":"file","format":"json","description":"FHIR R4 Bundle of ResearchStudy resources with MeSH-coded conditions (written with --fhir)"},{"name":"phase_distribution","type":"file","format":"png","description":"Stacked bar chart of trial counts by phase, coloured by recruitment status (figures/phase_distribution.png)"},{"name":"commands","type":"file","format":"sh","description":"Exact CLI invocation to reproduce this run (commands.sh)"},{"name":"checksums","type":"file","format":"txt","description":"SHA-256 digests of all generated outputs (checksums.sha256)"},{"name":"html_report","type":"file","format":"html","description":"Interactive HTML report with JS filters (status, phase, text search) and coloured trial cards (report.html)"},{"name":"csv_table","type":"file","format":"csv","description":"Trial data as CSV for import into Excel, R, or pandas (tables/trials.csv)"}],"dependencies":{"python":">=3.11"},"demo_data":[{"path":"demo_input.txt","description":"Synthetic query for BRCA1 breast cancer trials — exercises recruiting and completed status paths"}],"endpoints":{"cli_gene":"python skills/clinical-trial-finder/clinical_trial_finder.py --gene {gene} --output {output_dir}","cli_rsid":"python skills/clinical-trial-finder/clinical_trial_finder.py --rsid {rsid} --output {output_dir}","cli_query":"python skills/clinical-trial-finder/clinical_trial_finder.py --query \"{query}\" --output {output_dir}","cli_file":"python skills/clinical-trial-finder/clinical_trial_finder.py --input {input_file} --output {output_dir}","cli_demo":"python skills/clinical-trial-finder/clinical_trial_finder.py --demo --output {output_dir}"},"openclaw":{"requires":{"bins":"[Truncated]"},"always":false,"emoji":"🏥","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"trigger_keywords":["clinical trial","clinical trials","trial finder","FHIR","ResearchStudy","NCT","ClinicalTrials.gov","GWAS","rsID","variant"]}} |
[!note] Running these commands in this vault The
cli_*examples below are written for the upstream ClawBio repo layout, where skills live underskills/. This flat skills vault has noskills/prefix: run the script directly — e.g.python clinical-trial-finder/clinical_trial_finder.py --gene BRCA1 --output <dir>.
Source database: ClinicalTrials.gov API v2 (https://clinicaltrials.gov/api/v2) — the authoritative US registry mandated by FDAAA 801 (2007) and mirrored by WHO ICTRP. Chosen over EudraCT/EUCTR because it covers the largest global trial volume (>500 000 studies), provides a stable versioned REST API, and is the primary registry for FDA-regulated interventions. Reference: Zarin et al., NEJM 2011; 364:852–860.
Query field: query.cond (condition/disease field), not query.term (free-text across all fields). query.cond is indexed against MeSH descriptors by the NLM indexing pipeline, giving substantially better recall for condition queries than unstructured text search. Reference: ClinicalTrials.gov API v2 specification, https://clinicaltrials.gov/data-api/api.
MeSH condition coding: MeSH IDs are read from derivedSection.conditionBrowseModule.meshes — the NLM-curated MeSH mapping that ClinicalTrials.gov computes internally during study indexing. This avoids a separate NLM API call and uses the same vocabulary that query.cond is indexed against, ensuring query/result consistency. Reference: NLM Medical Subject Headings, https://www.nlm.nih.gov/mesh/.
Max results: 20 trials per query by default (configurable with --max-results). Clinical actionability does not scale with result volume — a clinician reviewing >20 trials without eligibility pre-screening is unlikely to act on any. The default balances coverage with usability.
Status display: All statuses returned are shown — no pre-filtering. Recruiting trials are highlighted; TERMINATED and WITHDRAWN trials are flagged with a distinct visual indicator and never omitted. Selective display of only active trials would introduce reporting bias and obscure negative evidence. Reference: Chan et al., PLoS Med 2004; 1:e62 (trial publication bias).
Phase reporting: Phases are reported verbatim from the API and mapped to HL7 FHIR R4 codes. No lay-term substitution is made to preserve accuracy and avoid misrepresentation.
ResearchStudy.phaseFHIR version: FHIR R4, not R5. The ONC 21st Century Cures Act Final Rule (2020) mandates FHIR R4 for certified EHR systems in the US, making R4 the de facto standard for EHR interoperability with Epic, Cerner, and Oracle Health. R5 is in early adoption as of 2026 — using R5 would reduce compatibility with deployed infrastructure. Reference: 45 CFR Part 170, https://www.healthit.gov/cures/sites/default/files/cures/2020-03/ONCCuresActFinalRule.pdf.
FHIR resource type: ResearchStudy — the canonical HL7 FHIR R4 resource for clinical trials. Status and phase codes map verbatim from the published R4 value sets: research-study-status (http://hl7.org/fhir/research-study-status) and research-study-phase (http://terminology.hl7.org/CodeSystem/research-study-phase).
Gene enrichment source: OpenTargets Platform (--gene mode), not DisGeNET. DisGeNET requires a commercial API key as of 2026. OpenTargets is public, freely accessible, and aggregates evidence across GWAS, somatic mutation, differential expression, and literature sources into a single harmonised score. Reference: Ochoa et al., Nucleic Acids Research 2023; 51:D1353–D1359.
Association score threshold: ≥ 0.6 (--ot-min-score). The OpenTargets overall association score is a harmonic sum across evidence types, normalised to [0, 1]. Scores < 0.5 typically reflect single-source, indirect, or low-confidence associations. The 0.6 threshold retains multi-evidence, replicated associations while excluding speculative links. Reference: Ochoa et al. 2023 (above); OpenTargets Platform scoring documentation, https://platform-docs.opentargets.org/associations.
Max diseases per gene: 5 (--ot-max-diseases). Querying more diseases per gene produces diminishing returns on trial relevance and increases API load. The top-5 by association score covers the primary phenotypic spectrum of most disease genes without introducing noise from peripheral associations.
Status filter (--status): Optional post-fetch filter to a single recruitment status (e.g. RECRUITING). Applied client-side after the API call so the chart and summary always reflect unfiltered counts first — filtered output is a view, not a re-query.
Reproducibility outputs: Every run writes commands.sh (exact CLI to reproduce) and checksums.sha256 (SHA-256 of all outputs). This ensures results are auditable and re-runnable without ambiguity.
Eligibility criteria: Not parsed. The API returns eligibility as unstructured free text. Automated parsing would require NLP and introduces a high error rate for clinical use — users must review the full trial record on ClinicalTrials.gov before making any enrollment decisions.
Retry with exponential backoff: Transient failures (HTTP 429, 5xx, network timeouts) are retried up to 3 times with exponential backoff (1s, 2s, 4s). Non-retryable errors (4xx except 429) raise immediately. This follows the retry pattern recommended by CT.gov API documentation for rate-limited endpoints.
Multi-page pagination: CT.gov API v2 caps pageSize at 1000. For queries requesting more, the skill paginates via nextPageToken and accumulates results until max_results is reached. This ensures correct behaviour for large result sets without hitting API limits.
Country filter (--country): Uses CT.gov query.locn parameter to restrict results to trials in a specific country. Accepts ISO 3166-1 country names or codes. Applied at the API level (not post-fetch) to reduce bandwidth and improve relevance.
EU Clinical Trials Register (--euctr): Secondary European source queried as a best-effort complement. The EUCTR API returns XML with no versioning guarantees and may be unavailable. Results are normalised to the same schema as CT.gov trials and merged with deduplication. All EUCTR failures degrade gracefully to an empty list — the skill never fails due to EUCTR unavailability.
Variant-to-trial pipeline (--rsid): Queries the EBI GWAS Catalog REST API (/singleNucleotidePolymorphisms/{rsid}/associations?projection=associationBySnp) to resolve a dbSNP rsID to genome-wide significant disease traits (p < 5 x 10^-8), then searches CT.gov for each trait. Disease traits are ranked above biomarker measurements to maximise trial relevance. Gene symbols are extracted from authorReportedGenes in the association loci. Reference: Buniello et al., Nucleic Acids Research 2019; 47:D1005--D1012 (GWAS Catalog).
HTML report: Self-contained HTML with inline CSS and JavaScript, no external dependencies. Trial cards are colour-coded by recruitment status. Interactive client-side filters (status, phase, free-text search) with a live counter allow users to narrow results without re-querying. Opens correctly from any file manager or browser without a web server.
CSV output: Always generated at tables/trials.csv. List fields (conditions, interventions) are pipe-delimited to survive CSV parsing. Designed for direct import into Excel, R, or pandas.
FHIR inline validation: When --fhir is used, the generated Bundle is validated against basic structural rules: required fields, status/phase value set membership, entry count consistency. This catches authoring errors before an external validator (e.g., HAPI) is needed.
The agent (LLM) dispatches the skill and explains results in plain language. The skill (Python) queries ClinicalTrials.gov and formats the output.
The agent must NOT: