clinicaltrials-gov-parser
Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov. Trigger: When user asks to track clinical trials, monitor trial status changes, get updates on specific trials, or analyze competitor trial activities. Use cases: Pharma competitive intelligence, trial monitoring, status tracking, recruitment updates, completion alerts.
Best use case
clinicaltrials-gov-parser is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov. Trigger: When user asks to track clinical trials, monitor trial status changes, get updates on specific trials, or analyze competitor trial activities. Use cases: Pharma competitive intelligence, trial monitoring, status tracking, recruitment updates, completion alerts.
Teams using clinicaltrials-gov-parser should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/clinicaltrials-gov-parser/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clinicaltrials-gov-parser Compares
| Feature / Agent | clinicaltrials-gov-parser | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov. Trigger: When user asks to track clinical trials, monitor trial status changes, get updates on specific trials, or analyze competitor trial activities. Use cases: Pharma competitive intelligence, trial monitoring, status tracking, recruitment updates, completion alerts.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# ClinicalTrials.gov Parser
Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov.
## 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
| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--sponsor` | string | - | No | Trial sponsor name |
| `--condition` | string | - | No | Medical condition/disease |
| `--status` | string | - | No | Trial status (Recruiting, Completed, etc.) |
| `--trials` | string | - | No | Comma-separated trial IDs (NCT numbers) |
| `--output` | string | json | No | Output format (json, csv) |
| `--days` | int | 30 | No | Number of days for monitoring |
## Usage
```python
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
```bash
# 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
| Method | Description |
|--------|-------------|
| `search_trials()` | Search trials with filters |
| `get_trial(nct_id)` | Get detailed trial information |
| `check_status_changes()` | Check for status updates |
| `get_recruitment_status()` | Get enrollment updates |
| `generate_summary()` | Generate competitor summary |
## 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
| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |
## 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
```bash
# 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
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