Clinicaltrials Gov Parser

Source: https://github.com/aipoch/medical-research-skills

ClinicalTrials.gov Parser

Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

• Use this skill when the task needs Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Use Cases

• Trial Monitoring: Track status changes of specific clinical trials
• Competitive Intelligence: Monitor competitor trial activities and milestones
• Recruitment Tracking: Get updates on enrollment status
• Completion Alerts: Monitor trial completion and results posting

Parameters

Usage

from scripts.main import ClinicalTrialsMonitor

# Initialize monitor
monitor = ClinicalTrialsMonitor()

# Search for trials
trials = monitor.search_trials(
    sponsor="Pfizer",
    condition="Diabetes",
    status="Recruiting"
)

# Get trial details
trial = monitor.get_trial("NCT05108922")

# Check for status changes
changes = monitor.check_status_changes(trial_ids=["NCT05108922"])

CLI Usage

# Search trials
python scripts/main.py search --sponsor "Pfizer" --condition "Diabetes"

# Get trial details
python scripts/main.py get NCT05108922

# Monitor status changes
python scripts/main.py monitor --trials NCT05108922,NCT05108923 --output json

# Generate summary report
python scripts/main.py report --sponsor "Pfizer" --days 30

API Methods

Technical Details

• API: ClinicalTrials.gov API v2
• Rate Limit: 10 requests/second
• Data Format: JSON
• Difficulty: Medium

References

• See references/api-docs.md for API documentation
• See references/status-codes.md for trial status definitions
• See references/examples.md for usage examples

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.