clinical-trials

$npx mdskill add mkurman/zorai/clinical-trials

Query and analyze clinical trial data from ClinicalTrials.gov API.

  • Retrieves specific trial records matching research conditions.
  • Depends on ClinicalTrials.gov API v2 for data access.
  • Filters results by study phase, intervention, and status.
  • Outputs structured JSON containing study designs and results.

SKILL.md

.github/skills/clinical-trialsView on GitHub ↗
---
name: clinical-trials
description: "ClinicalTrials.gov API client and analysis toolkit. Search, filter, and download trial records. Analyze trial designs, endpoints, enrollment, sponsors, and results. Automate systematic trial discovery."
tags: [clinical-trials, research, api, systematic-review, healthcare, zorai]
---
## Overview

Search, filter, and download clinical trial records from ClinicalTrials.gov. Analyze trial designs, endpoints, enrollment, sponsors, and results. Automate systematic trial discovery.

## Installation

```bash
uv pip install requests
```

## Search Trials

```python
import requests

params = {
    "query.term": "diabetes AND metformin AND phase 3",
    "pageSize": 25,
    "format": "json",
    "sort": "LastUpdateDate",
}

resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params)
data = resp.json()

for study in data.get("studies", []):
    p = study["protocolSection"]
    nct = p["identificationModule"]["nctId"]
    title = p["identificationModule"]["briefTitle"]
    status = p["statusModule"].get("overallStatus", "Unknown")
    print(f"{nct}: {title[:60]} [{status}]")
```

## Study Details

```python
resp = requests.get("https://clinicaltrials.gov/api/v2/studies/NCT04251195")
study = resp.json()
design = study["protocolSection"]["designModule"]
print(f"Purpose: {design.get('primaryPurpose')}")
```

## Workflow

1. Search trials via ClinicalTrials.gov API v2
2. Filter by condition, intervention, phase, status
3. Download structured trial data (JSON)
4. Extract PICO: Population, Intervention, Comparison, Outcome
5. Analyze trial designs, enrollment, and results

More from mkurman/zorai

SkillDescription
account-management>
agile-scrum>
albumentationsFast image augmentation library (Albumentations). 70+ transforms for classification, segmentation, object detection, keypoints, and pose estimation. Optimized OpenCV-based pipeline with unified API across all CV tasks. Supports images, masks, bounding boxes, and keypoints simultaneously. Note: classic Albumentations (MIT) is no longer maintained; successor AlbumentationsX uses AGPL-3.0. For torchvision-native augmentations, use torchvision.transforms.v2.
aml-complianceAnti-Money Laundering (AML) and Know Your Customer (KYC) compliance workflow. Sanctions screening, PEP detection, transaction monitoring, suspicious activity reporting (SAR), and OFAC compliance.
anki-connectThis skill is for interacting with Anki through AnkiConnect, and should be used whenever a user asks to interact with Anki, including to read or modify decks, notes, cards, models, media, or sync operations.
approval-checkpoint-long-taskCanonical long-task pack for daemon-managed work with deliberate approval checkpoints, status summaries, rollback notes, and mobile-safe governance-aware updates.
auditing-goal-artifactsUse when reviewing recent zorai goal run outputs, closure markers, ledgers, or evidence bundles to judge whether completion is credible or to identify remaining uncertainty.
autogenAutoGen (Microsoft) — multi-agent conversation framework. Agent-to-agent chat, code generation & execution, tool use, group chat, and human-in-the-loop. Build collaborative AI systems with specialized agents.
backtraderPython backtesting framework for trading strategies. Data feeds, brokers, analyzers, and live trading support. Strategy development with commission models, slippage, and signal-based execution.
beautiful-mermaidRender Mermaid diagrams as SVG and PNG using the Beautiful Mermaid library. Use when the user asks to render a Mermaid diagram.