rdahis

rdahis / research-unit-tests

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Structured quality checks for academic research papers — analogous to unit tests in software engineering

11
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100% credibility
Found Apr 17, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A library of declarative checklists for evaluating academic research papers by methodology, severity, and clarity, intended for use by AI agents to produce structured quality reports.

How It Works

1
🔍 Discover the checklists

You hear about a handy set of checklists that help spot quality issues in research papers, just like quick checks in everyday work.

2
đź“– Explore the tests

You browse simple lists of checks grouped by research style, like experiments or surveys, each with clear pass or fail rules.

3
🤝 Link to your AI helper

You easily connect these checklists to your friendly AI assistant by adding a note to your project folder.

4
đź“„ Review a paper

You share the paper's details or description with your AI and ask it to run the right checks for that type of study.

5
⏳ Wait for the analysis

Your AI reads through the paper using the checklists and decides pass or fail for each one, explaining why.

âś… Get your quality report

You receive a neat summary of strengths and fixes needed, making your paper review faster and more reliable.

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AI-Generated Review

What is research-unit-tests?

This Python project delivers a registry of structured quality checks for academic research papers, analogous to unit tests in software engineering. Feed a paper PDF or description to an LLM like Claude, specify the methodology (DiD, IV, RDD, etc.), and it runs relevant tests—checking replication reproducibility, parallel trends plots, first-stage F-stats—outputting a structured pass/fail report with severity levels (blocker, warning, info). It solves inconsistent peer review by providing declarative, agent-readable checklists that flag poor quality structured data in regressions, experiments, and theory.

Why is it gaining traction?

Unlike vague review guidelines, these tests are taxonomy-organized by methodology and clarity (deterministic to judgment-based), enabling github llm structured output for consistent evaluations without custom code. The hook is drop-in integration with Claude projects for instant structured output github-style reports, plus community contributions for expanding tests—think structured rag github meets academic rigor. It stands out by making quality checks actionable and reproducible, cutting through subjective noise.

Who should use this?

Econ PhDs and social science researchers reviewing DiD/IV papers or proposals, tired of endless replication fails and weak instrument debates. Journal editors or grant panels needing a structured effectiveness quality evaluation scale for submissions. Anyone building LLM agents for academic peer review workflows.

Verdict

Promising early experiment (11 stars, 1.0% credibility score) with solid docs and validation, but too nascent for production—test it on your next seminar paper first. Worth starring if you're in academia pushing structured quality improvement.

(178 words)

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