conflict-of-interest-checker

Check for co-authorship conflicts between authors and suggested reviewers

1.94x

Quality

52%

Does it follow best practices?

Impact

99%

1.94x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

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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

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

Repository: aipoch/medical-research-skills
Commit: 4a48721

Last updated: about 12 hours ago
Created: about 12 hours ago

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