Anti-Autoresearch
Detects fabricated or manipulated research papers through 46 structural integrity patterns, reducing risk of publishing or citing fraudulent work.
Don't trust an autoresearch paper at face value. Reviewer-side integrity forensics (self-consistency + fabrication), deterministic verdict. 61 signals: 46 integrity hack-patterns (families A–H, verdict-bearing) + 13 zero-weight AI writing-style impressions (AIS) + 2 advisory. Not an opaque AI-text classifier. The dual of ARIS.
- Detect fabricated citations and inconsistencies in submitted research papers before peer review.
- Identify common integrity hack-patterns across academic submissions using 46 forensic signals.
- Verify research paper authenticity by checking self-consistency across claims and methodology sections.
Detects fabricated or manipulated research papers through 46 structural integrity patterns, reducing risk of publishing or citing fraudulent work. Provides deterministic, explainable verdicts rather than black-box classifiers.
Research review committees and institutional quality gatekeepers validating paper submissions before acceptance or citation.
https://github.com/wanshuiyin/Anti-Autoresearch
By wanshuiyin
How to Get It
claude plugins install wanshuiyin/Anti-Autoresearch
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me detect fabricated citations and inconsistencies in submitted research papers before peer review
Trust Signals Auto-scanned
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Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 129 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
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Evaluation
Scored from trust signals (evidence-eval-v1): 129 GitHub stars; contributors unknown; last commit 0d ago; license MIT.