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1anj / academic-integrity-skill

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耿同学 Skill ｜ Academic-Integrity Self-Audit Skill

16
1
85% credibility
Found Jun 01, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is a self-check tool for academic researchers to review their papers before submitting to journals. It helps detect potential issues like reused images, statistical anomalies, citation problems, and missing materials. The tool analyzes your manuscript, figures, and data files, then produces a detailed report that flags areas needing attention. It emphasizes self-correction rather than accusation, and clearly states it cannot replace institutional investigations or professional forensics. The goal is to help researchers catch and fix problems early, before editors or reviewers find them.

How It Works

1
💬 A colleague mentions the tool

Someone in your research group tells you about a self-check tool for academic papers before journal submission.

2
📥 You download and set up the tool

You get a copy of the tool and place it alongside your paper materials in your computer folder.

3
📁 You gather your paper materials

You collect your manuscript, figures, source data, and any reviewer comments into one organized folder.

4
🔍 The tool checks everything automatically

The tool scans your images for duplicates, your numbers for suspicious patterns, and your citations for problems—all at once.

5
📋 You receive a detailed report

You get a clear report showing any areas that need attention, organized by risk level from low to serious.

6
✏️ You create a self-correction plan

For any flagged items, the report suggests specific actions: revise figure legends, request original images, or clarify methods.

✅ You submit with confidence

Your paper is now thoroughly checked, and you have documented evidence of your integrity review to share with editors if needed.

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AI-Generated Review

What is academic-integrity-skill?

This is a Python toolkit that helps researchers catch potential problems in their manuscripts before submission. Think of it as a pre-flight checklist for academic integrity - it screens images for duplication, checks that statistics add up correctly, and flags citation issues. It runs as a collection of command-line tools that plug into AI coding assistants like Claude Code and Antigravity, so you can audit a paper by pointing it at your files and asking for a risk assessment. The output is a structured report with evidence logs and severity ratings from Green through Black.

Why is it gaining traction?

The scientific publishing world is under increasing scrutiny around image fraud and statistical manipulation. Researchers submitting to high-profile journals like Nature need to proactively demonstrate integrity, not just when accused. This tool automates the tedious parts of self-audit - comparing microscopy fields, checking Western blot lanes, verifying reported means against source data - and packages everything into actionable reports with citation checks and method gaps. The modular design lets you run individual checks or chain them together.

Who should use this?

Graduate students preparing first submissions, PIs responding to reviewer concerns or PubPeer questions, and lab managers running pre-publication audits. Anyone submitting to Nature Portfolio will find the built-in policy anchors useful. The numerical screening is particularly concrete for researchers with extracted data tables who want quick statistical sanity checks before submission.

Verdict

This fills a real need and the credibility score of 0.8500000238418579% reflects thoughtful design. However, with only 16 stars and limited adoption, consider it a well-structured foundation rather than a battle-tested solution. The documentation is comprehensive and the offline-first approach is practical. Use it as a starting checkpoint, but don't skip human review of flagged items.

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