Retraction Watcher

Source: https://github.com/aipoch/medical-research-skills

Retraction Watcher

A specialized skill for identifying retracted, corrected, or questionable papers in academic reference lists before they compromise research integrity.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

When to Use

• Use this skill when the task needs Automatically scan document reference lists and check against Retraction.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Purpose

Academic misconduct and errors can lead to paper retractions. Citing retracted work undermines research credibility. This skill:

• Scans reference lists from manuscripts, papers, or bibliographies
• Cross-checks citations against Retraction Watch and other retraction databases
• Identifies papers with retraction notices, expressions of concern, or corrections
• Provides detailed reports with retraction reasons and dates

Trigger Conditions

Activate this skill when:

1. User provides a document with references and asks to check for retractions
2. User explicitly requests "check my references" or "scan for retracted papers"
3. User submits a bibliography or reference list for verification
4. Pre-submission manuscript review is requested
5. User wants to verify citation integrity

Input Format

Accepted inputs:

• PDF files (manuscripts, papers, theses)
• Plain text files (.txt, .bib, .ris)
• Raw text containing reference lists
• URLs to papers or reference lists
• Clipboard content with citations

Output Format

Report Header

🔍 RETRACTION WATCH REPORT
Documents Scanned: [N]
References Found: [N]
Check Date: [YYYY-MM-DD]

Status Categories

🔴 RETRACTED - Paper has been officially retracted

• Reason for retraction
• Retraction date
• Original DOI/PMID
• Recommended action: Remove citation

🟡 EXPRESSION OF CONCERN - Journal has raised concerns

• Nature of concern
• Date issued
• Recommended action: Verify current status, consider alternative sources

🟠 CORRECTED - Paper has published corrections/errata

• Correction details
• Date of correction
• Recommended action: Check if correction affects cited claims

🟢 CLEAR - No retraction issues found

Technical Approach

Citation Parsing Strategy

1. Format Detection: Identify citation style (APA, MLA, Vancouver, Chicago, etc.)
2. Field Extraction: Parse DOI, PMID, title, authors, journal, year
3. Identifier Resolution: Normalize DOIs (remove prefixes, validate format)
4. Title Matching: Extract article titles for fuzzy matching

Database Checking

1. Retraction Watch Database - Primary source for retraction data
2. Crossref API - Retraction metadata via "update-type: retraction"
3. PubMed API - Retraction notices via publication type filters
4. Open Retractions - Aggregated retraction data

Matching Algorithm

• Exact Match: DOI/PMID exact match (highest confidence)
• Title Match: Normalized title comparison (90%+ similarity threshold)
• Author + Year: Secondary verification for ambiguous matches
• Fuzzy Matching: Handle minor title variations and typos

Difficulty Level

Medium-High - Requires:

• Robust citation parsing across multiple formats
• API integration with retraction databases
• Handling of partial/incomplete citation data
• Fuzzy matching for title-based lookups
• Rate limiting and caching for API calls

Quality Criteria

A successful scan must:

• Parse >90% of citations correctly from standard formats
• Achieve <1% false positive rate on retraction detection
• Provide actionable recommendations for each flagged citation
• Handle missing DOIs/PMIDs via title matching fallback
• Complete checks within reasonable time (<30s for 50 references)
• Preserve reference numbering for easy identification

Limitations

• Requires internet connection for database lookups
• Rate limits may apply to free API tiers
• Very recent retractions (<48 hours) may not be indexed
• Title-only matching may produce false positives with similar titles
• Non-English papers may have limited coverage
• Preprint citations (arXiv, bioRxiv) typically not tracked for retractions

Example Usage

# Check a PDF manuscript
python scripts/main.py --input manuscript.pdf --format detailed

# Check a BibTeX file
python scripts/main.py --input references.bib --output report.txt

# Check raw text
python scripts/main.py --text "[paste references here]"

# Quick check with summary only
python scripts/main.py --input paper.pdf --format summary

Data Sources

• Retraction Watch Database: https://retractionwatch.com/
• Crossref API: https://api.crossref.org/
• PubMed E-utilities: https://www.ncbi.nlm.nih.gov/home/develop/api/
• Open Retractions: https://openretractions.com/

References

See references/ for:

• citation-formats.md: Supported citation format specifications
• api-documentation.md: Database API reference and rate limits
• example-reports/: Sample output reports for testing

Author: AI Assistant
Version: 1.0
Last Updated: 2026-02-06
Status: Ready for use
Requires: Internet connection for database lookups

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• API requests use HTTPS only
• Input validated against allowed patterns
• API timeout and retry mechanisms implemented
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no internal paths exposed)
• Dependencies audited
• No exposure of internal service architecture

Prerequisites

# Python dependencies
pip install -r requirements.txt

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