Skills kol-profiler
Analyze physician academic influence and collaboration networks
install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/kol-profiler" ~/.claude/skills/openclaw-skills-kol-profiler && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/aipoch-ai/kol-profiler" ~/.openclaw/skills/openclaw-skills-kol-profiler && rm -rf "$T"
manifest:
skills/aipoch-ai/kol-profiler/SKILL.mdsource content
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 |
|---|---|---|---|---|
| string | - | Yes | Disease field or therapeutic area |
| string | global | No | Regional scope (global, US, EU, Asia) |
| string | h-index | No | Metrics to analyze (h-index, citations, centrality, all) |
| string | stdout | No | Output file path |
| 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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