Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/x-cmd/skill/clinicaltrials-databasenpx skills add x-cmd/skill --skill clinicaltrials-databasegit clone --depth 1 https://github.com/x-cmd/skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/x-cmd/skill/clinicaltrials-database)<a href="https://agentmods.dev/skills/x-cmd/skill/clinicaltrials-database"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/clinicaltrials-database.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00047 | $0.03610 |
| Opus 5 | $0.00023 | $0.01805 |
| Sonnet 5 | $0.00009 | $0.00722 |
| Haiku 4.5 | $0.00005 | $0.00361 |
Grade A, and why
clinicaltrials-database scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url, params=params) This is a copy
83% identical to clinicaltrials-database — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ClinicalTrials.gov Database
Overview
ClinicalTrials.gov is a comprehensive registry of clinical studies conducted worldwide, maintained by the U.S. National Library of Medicine. Access API v2 to search for trials, retrieve detailed study information, filter by various criteria, and export data for analysis. The API is public (no authentication required) with rate limits of ~50 requests per minute, supporting JSON and CSV formats.
When to Use This Skill
This skill should be used when working with clinical trial data in scenarios such as:
- Patient matching - Finding recruiting trials for specific conditions or patient populations
- Research analysis - Analyzing clinical trial trends, outcomes, or study designs
- Drug/intervention research - Identifying trials testing specific drugs or interventions
- Geographic searches - Locating trials in specific locations or regions
- Sponsor/organization tracking - Finding trials conducted by specific institutions
- Data export - Extracting clinical trial data for further analysis or reporting
- Trial monitoring - Tracking status updates or results for specific trials
- Eligibility screening - Reviewing inclusion/exclusion criteria for trials
Quick Start
Basic Search Query
Search for clinical trials using the helper script:
cd scientific-databases/clinicaltrials-database/scripts
python3 query_clinicaltrials.py
Or use Python directly with the requests library:
import requests
url = "https://clinicaltrials.gov/api/v2/studies"
params = {
"query.cond": "breast cancer",
"filter.overallStatus": "RECRUITING",
"pageSize": 10
}
response = requests.get(url, params=params)
data = response.json()
print(f"Found {data['totalCount']} trials")
Retrieve Specific Trial
Get detailed information about a trial using its NCT ID:
import requests
nct_id = "NCT04852770"
url = f"https://clinicaltrials.gov/api/v2/studies/{nct_id}"
response = requests.get(url)
study = response.json()
# Access specific modules
title = study['protocolSection']['identificationModule']['briefTitle']
status = study['protocolSection']['statusModule']['overallStatus']
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 507 lines · 47 tokens per session scan A 4ec0470f79e2
clinicaltrials-database is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 3,610 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 83% identical to clinicaltrials-database, differing in 6 lines, and is treated as a copy.
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