Can it help me identify flaws in my own research design?

Yes, it can evaluate study protocols and experimental designs for internal and external validity, helping you identify potential confounding variables and sample size issues before you begin.

What frameworks does this skill use to evaluate evidence?

This skill utilizes standardized scientific frameworks including GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) and the Cochrane Risk of Bias (ROB) assessment.

Is this tool intended for peer review?

While it provides a structured and systematic approach to critiquing papers, it is designed to assist researchers and should be used as a supplement to expert human judgment.

How does it handle statistical analysis review?

The skill evaluates statistical test appropriateness, p-value interpretations, confidence intervals, and checks for common errors like p-hacking or ignoring regression to the mean.

Does this skill support visual data representation?

Yes, it integrates with the scientific-schematics skill to automatically generate publication-quality diagrams, flowcharts, and bias assessment visualizations.

Scientific Critical Thinking

Name: Scientific Critical Thinking
Author: K-Dense-AI

byK-Dense-AI

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3,719

Data Science & ML

Evaluates the rigor of scientific research by analyzing methodology, statistical validity, and potential biases using standardized frameworks like GRADE and Cochrane.

About

The scientific-critical-thinking skill provides a systematic framework for evaluating the quality and validity of scientific claims within Claude. It enables deep analysis of research methodology, detection of cognitive and statistical biases, and assessment of evidence quality using industry-standard tools like the GRADE and Cochrane Risk of Bias frameworks. Designed for researchers, data scientists, and technical writers, this skill ensures that conclusions are based on robust experimental design and sound statistical interpretation while offering integrated support for generating publication-quality scientific schematics.

Key Features

3,719 GitHub stars
Rigorous statistical evaluation of p-values, effect sizes, power analysis, and common pitfalls.
Integrated schematic generation for visualizing complex research workflows and bias decision trees.
Comprehensive methodology critique including internal, external, and construct validity analysis.
Systematic bias detection across researcher, selection, measurement, and analysis categories.
Evidence quality assessment based on established hierarchies and GRADE considerations.

Use Cases

Vetting the experimental design and statistical soundness of research papers or technical proposals.
Conducting systematic reviews or meta-analyses by assessing the risk of bias in multiple studies.
Identifying logical fallacies and data-driven misinterpretations in scientific communication and claims.

About

Key Features

3,719 GitHub stars
Rigorous statistical evaluation of p-values, effect sizes, power analysis, and common pitfalls.
Integrated schematic generation for visualizing complex research workflows and bias decision trees.
Comprehensive methodology critique including internal, external, and construct validity analysis.
Systematic bias detection across researcher, selection, measurement, and analysis categories.
Evidence quality assessment based on established hierarchies and GRADE considerations.

Use Cases

Vetting the experimental design and statistical soundness of research papers or technical proposals.
Conducting systematic reviews or meta-analyses by assessing the risk of bias in multiple studies.
Identifying logical fallacies and data-driven misinterpretations in scientific communication and claims.