Can this skill generate diagrams for my research?

Yes, it integrates with scientific-schematics to generate publication-quality flowcharts, bias decision trees, and evidence assessment diagrams via simple natural language descriptions.

Is this tool limited to clinical trials?

No, while it uses medical evidence frameworks, its core logic for statistical validity, logical fallacies, and experimental design applies to all empirical scientific disciplines.

What frameworks does this skill support?

It specifically supports the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) system and the Cochrane Risk of Bias (ROB) framework.

How does this skill help with peer review?

It provides a systematic framework to identify flaws in methodology, statistical errors, and potential biases in research manuscripts, though specialized peer-review skills should be used for formal writing.

How does it detect bias in research?

The skill performs a systematic review for cognitive biases, selection biases, measurement inaccuracies, and analysis errors like p-hacking or outcome switching.

Scientific Critical Thinking

Name: Scientific Critical Thinking
Author: henriquescastilho

byhenriquescastilho

•

データサイエンスとML

Evaluates scientific claims and evidence quality using rigorous frameworks like GRADE and Cochrane Risk of Bias.

Scientific Critical Thinking is a specialized skill designed to perform deep, systematic evaluations of research rigor and evidence quality. It empowers users to critically assess experimental designs, identify various forms of bias, and verify statistical validity through established scientific frameworks. Whether you are conducting a systematic review, reviewing a peer-reviewed manuscript, or assessing the strength of a scientific claim, this tool provides the structured methodology needed to separate high-quality evidence from flawed research. Additionally, it features integrated support for generating publication-quality scientific schematics to visualize complex frameworks and study workflows.

主な機能

01Automated generation of publication-quality scientific schematics

02Rigorous methodology and experimental design critique

03Evidence quality grading using GRADE and Cochrane ROB frameworks

041 GitHub stars

05Statistical validity assessment including p-value and power analysis

06Systematic detection of cognitive, selection, and analysis biases

ユースケース

01Evaluating the methodology and validity of claims in a published research paper

02Reviewing study protocols and experimental designs for potential flaws and confounders

03Assessing the risk of bias in studies during a systematic review or meta-analysis

主な機能

01Automated generation of publication-quality scientific schematics

02Rigorous methodology and experimental design critique

03Evidence quality grading using GRADE and Cochrane ROB frameworks

041 GitHub stars

05Statistical validity assessment including p-value and power analysis

06Systematic detection of cognitive, selection, and analysis biases

ユースケース

01Evaluating the methodology and validity of claims in a published research paper

02Reviewing study protocols and experimental designs for potential flaws and confounders

03Assessing the risk of bias in studies during a systematic review or meta-analysis