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Skills conflict-of-interest-checker

Check for co-authorship conflicts between authors and suggested reviewers

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 "$T/skills/aipoch-ai/conflict-of-interest-checker" ~/.claude/skills/openclaw-skills-conflict-of-interest-checker && rm -rf "$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 "$T/skills/aipoch-ai/conflict-of-interest-checker" ~/.openclaw/skills/openclaw-skills-conflict-of-interest-checker && rm -rf "$T"
manifest: skills/aipoch-ai/conflict-of-interest-checker/SKILL.md
tags
#conflict-checking#peer-review#data-analysis#compliance#writing
source content

Conflict of Interest Checker

Reviewer conflict detection tool.

Use Cases

  • Journal submission prep
  • Editorial decisions
  • Peer review integrity
  • Compliance verification

Parameters

ParameterTypeDefaultRequiredDescription
--authors
, 
-a
string-YesComma-separated author names
--reviewers
, 
-r
string-YesComma-separated reviewer names
--publications
, 
-p
string-NoCSV 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 IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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