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.
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node --versionclinicaltrials-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches clinicaltrials-database from davila7/claude-code-templates and configures it for Cursor.
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Restart Cursor to activate clinicaltrials-database. Access via /clinicaltrials-database in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
This skill should be used when working with clinical trial data in scenarios such as:
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")
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']
Find trials studying specific medical conditions or diseases using the query.cond parameter.
Example: Find recruiting diabetes trials
from scripts.query_clinicaltrials import search_studies
results = search_studies(
condition="type 2 diabetes",
status="RECRUITING",
page_size=20,
sort="LastUpdatePostDate:desc"
)
print(f"Found {results['totalCount']} recruiting diabetes trials")
for study in results['studies']:
protocol = study['protocolSection']
nct_id = protocol['identificationModule']['nctId']
title = protocol['identificationModule']['briefTitle']
print(f"{nct_id}: {title}")
Common use cases:
Search for trials testing specific interventions, drugs, devices, or procedures using the query.intr parameter.
Example: Find Phase 3 trials testing Pembrolizumab
from scripts.query_clinicaltrials import search_studies
results = search_studies(
intervention="Pembrolizumab",
status=["RECRUITING", "ACTIVE_NOT_RECRUITING"],
page_size=50
)
# Filter by phase in results
phase3_trials = [
study for study in results['studies']
if 'PHASE3' in study['protocolSection'].get('designModule', {}).get('phases', [])
]
Common use cases:
Find trials in specific locations using the query.locn parameter.
Example: Find cancer trials in New York
from scripts.query_clinicaltrials import search_studies
results = search_studies(
condition="cancer",
location="New York",
status="RECRUITING",
page_size=100
)
# Extract location details
for study in results['studies']:
locations_module = study['protocolSection'].get('contactsLocationsModule', {})
locations = locations_module.get('locations', [])
for loc in locations:
if 'New York' in loc.get('city', ''):
print(f"{loc['facility']}: {loc['city']}, {loc.get('state', '')}")
Common use cases:
Find trials conducted by specific organizations using the query.spons parameter.
Example: Find trials sponsored by NCI
from scripts.query_clinicaltrials import search_studies
results = search_studies(
sponsor="National Cancer Institute",
page_size=100
)
# Extract sponsor information
for study in results['studies']:
sponsor_module = study['protocolSection']['sponsorCollaboratorsModule']
lead_sponsor = sponsor_module['leadSponsor']['name']
collaborators = sponsor_module.get('collaborators', [])
print(f"Lead: {lead_sponsor}")
if collaborators:
print(f" Collaborators: {', '.join([c['name'] for c in collaborators])}")
Common use cases:
Filter trials by recruitment or completion status using the filter.overallStatus parameter.
Valid status values:
RECRUITING - Currently recruiting participantsNOT_YET_RECRUITING - Not yet open for recruitmentENROLLING_BY_INVITATION - Only enrolling by invitationACTIVE_NOT_RECRUITING - Active but no longer recruitingSUSPENDED - Temporarily haltedTERMINATED - Stopped prematurelyCOMPLETED - Study has concludedWITHDRAWN - Withdrawn prior to enrollmentExample: Find recently completed trials with results
from scripts.query_clinicaltrials import search_studies
results = search_studies(
condition="alzheimer disease",
status="COMPLETED",
sort="LastUpdatePostDate:desc"✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.8★★★★★53 reviews- YYuki Haddad★★★★★Dec 24, 2024
clinicaltrials-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- IIshan Farah★★★★★Dec 20, 2024
clinicaltrials-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMin Tandon★★★★★Dec 20, 2024
Keeps context tight: clinicaltrials-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
- IIsabella Harris★★★★★Dec 16, 2024
Registry listing for clinicaltrials-database matched our evaluation — installs cleanly and behaves as described in the markdown.
- MMaya Patel★★★★★Nov 15, 2024
clinicaltrials-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
- MMia Ndlovu★★★★★Nov 11, 2024
clinicaltrials-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKabir Gonzalez★★★★★Nov 11, 2024
clinicaltrials-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- MMaya Chen★★★★★Oct 6, 2024
We added clinicaltrials-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- XXiao Martin★★★★★Oct 2, 2024
clinicaltrials-database fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- XXiao Khanna★★★★★Oct 2, 2024
Solid pick for teams standardizing on skills: clinicaltrials-database is focused, and the summary matches what you get after install.
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