Retraction Watcher

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

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.

Key Features

• - Scope-focused workflow aligned to: Automatically scan document reference lists and check against Retraction.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• - Python: 3.10+. Repository baseline for current packaged skills.
• INLINECODE5: unspecified. Declared in requirements.txt.
• INLINECODE8: unspecified. Declared in requirements.txt.

Example Usage

CODEBLOCK0bash
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.

## 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

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
• INLINECODE13: Database API reference and rate limits
• INLINECODE14: Sample output reports for testing

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

CODEBLOCK4

Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 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:

Output Requirements

Every final response should make these items explicit when they are relevant:

• - Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• - If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of retraction-watcher and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

INLINECODE17 only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• - references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. 1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.