conflict-of-interest-checker
Check for co-authorship conflicts between authors and suggested reviewers
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/conflict-of-interest-checkerConflict of Interest Checker
Reviewer conflict detection tool.
Use Cases
- Journal submission prep
- Editorial decisions
- Peer review integrity
- Compliance verification
Parameters
| Parameter | Type | Default | Required | Description |
|---|---|---|---|---|
--authors, -a | string | - | Yes | Comma-separated author names |
--reviewers, -r | string | - | Yes | Comma-separated reviewer names |
--publications, -p | string | - | No | CSV file with publication records |
CSV Format
author,reviewer,paper_id
Smith,Brown,paper1
Smith,Jones,paper2
Usage
# Check with demo data
python scripts/main.py --authors "Smith,Jones,Lee" --reviewers "Brown,Davis,Wilson"
# Check with publication records
python scripts/main.py --authors "Smith,Jones" --reviewers "Brown,Davis" --publications pubs.csv
Returns
- Conflict flagging (coauthorship, institutional)
- Shared publication list
- Recommendation: Accept/Recuse
- Alternative reviewer suggestions
Example Output
⚠ Found 2 potential conflict(s):
1. COAUTHORSHIP CONFLICT
Reviewer: Brown
Author: Smith
Shared papers: paper1
2. COAUTHORSHIP CONFLICT
Reviewer: Wilson
Author: Smith
Shared papers: paper2
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
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aipoch-ai-conflict-of-interest-checker": {
"enabled": true,
"auto_update": true
}
}
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