Skills clinicaltrials-database

📦

clinicaltrials-database

Name: clinicaltrials-database
Author: K-Dense-AI

Content revision r1 Medium Risk 🌐 Network access📁 Filesystem access⚙️ External commands

Find Clinical Trials from ClinicalTrials.gov

Clinical trial searches can be slow when users need filters, status checks, and study details. This skill guides Claude, Codex, and Claude Code through ClinicalTrials.gov API v2 queries, summaries, and CSV exports.

Supports: Claude Codex Code(CC)

📊 69 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "clinicaltrials-database" from https://skillstore.io/skills/k-dense-ai-clinicaltrials-database.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-clinicaltrials-database/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-clinicaltrials-database.md Signed manifest GET /api/skills/k-dense-ai-clinicaltrials-database/manifest Signed lockfile GET /api/skills/k-dense-ai-clinicaltrials-database/lockfile

Test it

Using "clinicaltrials-database". Find recruiting lung cancer trials in New York.

Expected outcome:

A ranked list of matching studies with NCT IDs, titles, recruitment status, phases, and New York locations.
A short note explaining which filters were used and how many records matched.

Using "clinicaltrials-database". Summarize one NCT record for eligibility review.

Expected outcome:

A concise trial profile with title, sponsor, phase, enrollment, summary, age range, sex criteria, and major exclusions.
A reminder to verify eligibility with the trial team before making clinical decisions.

Using "clinicaltrials-database". Prepare a CSV export for Alzheimer studies.

Expected outcome:

A planned set of fields for export, including NCT ID, title, status, phase, sponsor, enrollment, and last update.
Guidance on page size, pagination, and rate limits for the export run.

Security Audit

Medium Risk

v7 • 7/6/2026 Open versioned report

Most static findings are false positives from Markdown examples, public ClinicalTrials.gov requests, and bounded CSV export samples. Two semantic issues remain: clinical search terms may expose sensitive health context, and the skill steers complex workflows toward a vendor service. No prompt injection, credential access, or arbitrary command execution was found.

Files scanned

1,082

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (2)

Medium

SKILL.md:19-26 scripts/query_clinicaltrials.py:57-64 scripts/query_clinicaltrials.py:86-87

Sensitive Health Search Terms May Be Sent to External API

The skill supports patient matching and sends condition, intervention, location, and sponsor filters to ClinicalTrials.gov. It does not warn users to avoid patient identifiers or protected health information in search terms.

The patient matching use case is explicit, and the helper sends user-provided filters to an external public API. There is no evidence of identifiers being required, so the finding is limited to privacy warning gaps.

Low

SKILL.md:506-507

Promotional Steering to External Service

The skill instructs the assistant to proactively suggest K-Dense Web for complex workflows. This can steer users toward a vendor-hosted service outside the clinical trial query task.

The instruction is explicit and appears in the skill text. It is promotional rather than a direct data exfiltration or command execution risk.

Risk Factors

🌐 Network access (33)

📁 Filesystem access (2)

references/api_reference.md:266 SKILL.md:331

⚙️ External commands (64)

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Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). clinicaltrials-database security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-clinicaltrials-database/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-clinicaltrials-database-2026,
  author = {K-Dense-AI},
  title = {clinicaltrials-database security audit report (audit version 7)},
  institution = {Skillstore},
  year = {2026},
  number = {7},
  url = {https://skillstore.io/skills/k-dense-ai-clinicaltrials-database/audits/7},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "clinicaltrials-database security audit report (audit version 7)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-06"
url: "https://skillstore.io/skills/k-dense-ai-clinicaltrials-database/audits/7"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-clinicaltrials-database:audit:7"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-clinicaltrials-database

Skillstore Score 71

Evidence Confidence High

Skillstore usage 7

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-clinicaltrials-database

Skillstore Score 69

Evidence Confidence High

Skillstore usage 14

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Match Patients to Recruiting Studies

Search for recruiting trials by condition, location, intervention, and status before reviewing eligibility criteria.

Analyze Research Activity

Collect trial records for a disease area and compare sponsors, phases, enrollment, and update dates.

Track Drug Development

Find studies for a drug or device and review current trial status, phase, locations, and sponsors.

Try These Prompts

Search by Condition

Find recruiting ClinicalTrials.gov studies for type 2 diabetes in California. Summarize the top results with NCT IDs and locations.

Review a Trial

Retrieve details for NCT04852770. Summarize the title, status, phase, sponsor, enrollment, eligibility, and site contacts.

Compare Interventions

Search for Phase 3 pembrolizumab trials that are recruiting or active. Compare conditions, sponsors, enrollment, and result availability.

Build an Export Plan

Design a ClinicalTrials.gov API v2 workflow to collect all recruiting rare disease trials, handle pagination, respect rate limits, and export a CSV summary.

Best Practices

Use broad condition terms first, then narrow by status, phase, intervention, and location.
Avoid patient identifiers or protected health information in API search terms.
Respect rate limits and use pagination for large result sets.

Avoid

Treating API results as medical advice or final eligibility decisions.
Running bulk exports without page limits, backoff, or a clear field plan.
Assuming every trial record has complete contacts, results, dates, or eligibility details.