Skill: aipoch-ai/conflict-of-interest-checker (version 0.1.4)

--- name: conflict-of-interest-checker description: Check for co-authorship conflicts between authors and suggested reviewers version: 1.0.0 category: Writing tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Conflict 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 ```csv author,reviewer,paper_id Smith,Brown,paper1 Smith,Jones,paper2 ``` ## Usage ```bash # 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 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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)