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BytesAgain β€Ί Skills β€Ί KOL Profiler
πŸ¦€ ClawHub

KOL Profiler

by @aipoch-ai

Analyze physician academic influence and collaboration networks

Versionv1.0.0
Downloads681
#research#legal
TERMINAL
clawhub install kol-profiler

πŸ“– About This Skill

name: kol-profiler description: Analyze physician academic influence and collaboration networks version: 1.0.0 category: Pharma tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

KOL Profiler

Key Opinion Leader analysis tool.

Use Cases

  • KOL identification
  • Collaboration mapping
  • Speaker bureau selection
  • Advisory board planning

    • Parameters

    | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --therapeutic-area | string | - | Yes | Disease field or therapeutic area | | --geography | string | global | No | Regional scope (global, US, EU, Asia) | | --metrics | string | h-index | No | Metrics to analyze (h-index, citations, centrality, all) | | --output, -o | string | stdout | No | Output file path | | --format | string | json | No | Output format (json, csv, html)

    Returns

  • Ranked KOL list
  • Network visualization data
  • Publication timeline
  • Collaboration clusters

    • Example

    Oncology KOLs in East Asia with high trial participation

    Risk Assessment

    | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

    Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

    • 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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

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

    ⚑ When to Use

    TriggerAction
    - Collaboration mapping
    - Speaker bureau selection
    - Advisory board planning

    πŸ’‘ Examples

    Oncology KOLs in East Asia with high trial participation

    βš™οΈ Configuration

    No additional Python packages required.