Agent skill

competitor-trial-monitor

Monitor competitor clinical trial progress and alert on market risks

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/competitor-trial-monitor

SKILL.md

Competitor Trial Monitor (ID: 178)

Monitor competitor clinical trial progress and alert on market risks.

Features

  • Monitor changes in clinical trial status for specified competitors
  • Track key milestones: enrollment completion, data unblinding, final results publication
  • Alert on potential market competition risks

Data Sources

  • ClinicalTrials.gov - US Clinical Trials Registry
  • EU Clinical Trials Register - EU Clinical Trials Registry
  • WHO ICTRP - International Clinical Trials Registry Platform

Parameters

Commands

Command Description Parameters
add Add trial to watchlist --nct (required), --company, --drug, --indication
list List all monitored trials None
remove Remove trial from watchlist --nct (required)
scan Scan for updates None
report Generate risk report --days (default: 30)

Command Parameters

add command:

Parameter Type Default Required Description
--nct string - Yes ClinicalTrials.gov NCT ID
--company string Unknown No Competitor company name
--drug string Unknown No Drug name
--indication string Unknown No Indication/disease

remove command:

Parameter Type Default Required Description
--nct string - Yes NCT ID to remove

report command:

Parameter Type Default Required Description
--days int 30 No Report time range in days

Usage

Add Monitoring Target

bash
python scripts/main.py add --nct NCT05108922 --company "Pfizer" --drug "PF-07321332" --indication "COVID-19"

Scan for Updates

bash
python scripts/main.py scan

View Monitoring List

bash
python scripts/main.py list

Remove Monitoring Target

bash
python scripts/main.py remove --nct NCT05108922

Generate Risk Report

bash
python scripts/main.py report --days 30

Data Storage

Monitoring configuration and data stored in ~/.openclaw/competitor-trial-monitor/:

  • watchlist.json - Monitoring list
  • history/ - Historical snapshots
  • alerts/ - Alert records

Alert Rules

Event Risk Level Description
Enrollment Completion 🟡 Medium Competitor enters next phase
Data Unblinding 🔴 High Results about to be announced
Results Publication 🔴 High Direct impact on market competition
Regulatory Submission 🔴 High Marketing application in progress
Approval Granted 🔴 Critical Direct competition begins

Dependencies

bash
pip install requests python-dateutil

Configuration File

~/.openclaw/competitor-trial-monitor/config.json:

json
{
  "alert_channels": ["feishu"],
  "scan_interval_hours": 24,
  "risk_threshold": "medium"
}

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

No additional Python packages required.

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

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