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AI Paper Mill & Research Integrity Screener
First-pass screener that flags AI-generated, paper-mill, and fabricated scholarship using tortured-phrase, citation, statistical, and template detectors.
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You say
Screen this abstract for research-integrity red flags before I send it out for peer review: "In this work we apply counterfeit consciousness and profound learning to bosom malignancy classification. Across 10 patients, accuracy was 33.3% (p = 0.000). Our irregular woodland model bolsters the discoveries." Report any concerns with evidence and a concern level.
Your agent does
Overall concern level: HIGH (triage signal, not a determination).
A. Tortured phrases (STRONG): "counterfeit consciousness" (artificial intelligence), "profound learning" (deep learning), "bosom malignancy" (breast cancer), "irregular woodland" (random forest). Four synonym-swapped technical terms; no innocent stylistic origin.
C. Statistical impossibility (STRONG): "33.3% of 10 patients" is not achievable — 3/10 = 30%, 4/10 = 40%; no integer count yields 33.3% at N=10. "p = 0.000" reported without a bound. Innocent explanation: transcription/rounding error, but combination is implausible.
Convergence: two STRONG, independent signal classes (A + C) → HIGH concern.
Recommended next steps for a human reviewer: request the raw data and exact counts; confirm sample size; verify any cited references online; route to the editor/research-integrity office per venue policy.
Disclaimer: This is an automated first-pass triage. It identifies signals, not misconduct, and may produce false positives. No statement asserts that any person engaged in fraud.
What you get
About this skill
Journals, universities, and research-integrity offices are being flooded with AI-generated and paper-mill submissions, and the available screening tools are still immature. This skill encodes the red flags that experienced integrity investigators look for and turns any compatible agent into a structured, evidence-cited first-pass screener.
It runs six detector passes over a manuscript, abstract, or review report:
A. Tortured phrases & lexical fingerprints — established technical terms mangled by synonym-swapping paraphrase tools (e.g., "counterfeit consciousness" for artificial intelligence). B. Citation fabrication & manipulation — hallucinated references, DOI/title mismatches, citation-content mismatches, and coercive self-citation patterns. C. Statistical & numerical impossibilities — GRIM/GRIMMER inconsistencies, impossible percentages, p-value vs test-statistic conflicts, and out-of-range effect sizes. D. Template & paper-mill fingerprints — shared skeletons with swapped topic nouns, placeholder residue, and recycled figures. E. AI-generation stylistic markers — filler scaffolding and hedged uniformity (weighted low; only counts in clusters). F. Metadata & provenance anomalies — affiliation/email mismatches, brokered-authorship language, and version churn.
Every flag is reported with the exact quoted span, its location, a confidence level, and at least one innocent explanation, then aggregated into a calibrated concern level (LOW / ELEVATED / HIGH) using a convergence-based scoring rubric. The skill is deliberately built as triage, not verdict: it never asserts misconduct, never screens on author identity or nationality, and always ends with recommended next steps for a human reviewer.
What's included: a lean SKILL.md orchestrator plus a bundled REFERENCE.md containing the detailed detector playbooks, the convergence-based scoring rubric, and a ready-to-fill report template. Designed for editors, peer reviewers, and research-integrity officers who need a fast, defensible, human-in-the-loop first pass before deeper investigation.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
PubsProToolkit builds rigor-first skills for AI agents — they write your docs and content properly, then adversarially review them to catch what's wrong before it ships. The result: cleaner output and a hard quality gate in one toolkit. Built by a CMPP-certified, PhD medical writer who brings regulated-industry standards to developer docs, content, compliance, and research integrity.
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