kol-profiler

What This Skill Does

The kol-profiler is a specialized OpenClaw AI agent skill designed for medical affairs professionals, researchers, and pharmaceutical strategy teams. Its primary function is to analyze the academic influence, publication impact, and professional collaboration networks of Key Opinion Leaders (KOLs). By processing large datasets of academic output, the skill maps out who is currently leading specific therapeutic areas, identifies emerging experts, and visualizes complex networks of researchers working together across global institutions. This allows organizations to make data-driven decisions regarding speaker selection, advisory board formation, and strategic clinical trial partnerships.

Installation

To integrate this tool into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/aipoch-ai/kol-profiler

Ensure your workspace has appropriate read/write permissions, as the skill may generate output files for complex network visualizations.

Use Cases

• KOL Identification: Discover top-tier experts in niche medical fields to serve as primary advisors.
• Collaboration Mapping: Understand the interconnectedness of research groups to identify hubs of innovation.
• Speaker Bureau Selection: Identify physicians who balance high clinical expertise with strong academic publication records.
• Advisory Board Planning: Curate a diverse group of experts by evaluating both established thought leaders and rising stars.

Example Prompts

1. "Analyze oncology KOLs in the US with at least 50 citations and recent trial participation."
2. "Generate a network visualization of European experts in rare autoimmune diseases using the h-index metric."
3. "Identify the top 10 most influential researchers in Asian neurology and export the findings to a CSV file named neurology_experts.csv."

Tips & Limitations

• Metrics Selection: For broader insights, use the 'all' option for the --metrics parameter to get a comprehensive view including centrality and citation counts.
• Output Formats: Use 'html' when you need an interactive map, or 'json' if you intend to pipe the data into another internal tool or database.
• Limitations: The skill currently does not perform live internet scraping; it relies on the pre-processed data available in the target workspace. Always ensure your input files are formatted correctly before running analysis to avoid parsing errors. For performance, larger datasets should be segmented by geography or therapeutic area to maintain quick processing times.