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KOL Profiler

面向医药领域的 KOL 分析命令行工具:依据治疗领域、地区和指标(h-index、引用、中心性)参数,输出排序后的 KOL 列表、网络可视化数据、发表时间线和协作聚类,支持 json、csv、html 格式;本地执行 Python 脚本,无外部 API 调用。

aipoch-ai
aipoch-ai
热度 37.37
最近更新 2026-05-11 17:36:23
分类:数据依赖:代码执行 / 读写文件应用场景:市场分析 / 研究 / 报告撰写 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add kol-profiler
Metadata
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 → 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