What frameworks does this skill use for evaluation?

The skill utilizes globally recognized scientific frameworks including GRADE for evidence quality profiling and Cochrane Risk of Bias (ROB) for evaluating methodology.

How does it handle complex scientific concepts visually?

It integrates with the scientific-schematics tool to automatically generate publication-quality diagrams, such as evidence flowcharts and bias identification decision trees.

Can it help identify p-hacking or reporting bias?

Yes, it specifically scans for common statistical pitfalls such as p-hacking, outcome switching, selective reporting, and suspicious clustering of p-values.

Is it suitable for peer review assistance?

Absolutely. It provides a structured checklist for critiquing study design, internal validity, and external generalizability, making it an ideal assistant for academic reviews.

Scientific Critical Thinking

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

byK-Dense-AI

•

2,188

데이터 과학 및 ML

Evaluates the rigor, methodology, and statistical validity of scientific research using established frameworks like GRADE and Cochrane.

소개

Scientific Critical Thinking is a specialized Claude Code skill designed to systematically analyze research quality and identify potential flaws in scientific claims. It provides a structured framework for evaluating experimental designs, detecting cognitive and sampling biases, assessing statistical conclusion validity, and weighing evidence according to international standards. Whether you are reviewing a peer-reviewed paper, designing a new study, or conducting a meta-analysis, this skill ensures that logical fallacies and confounding variables are identified, helping researchers and developers maintain the highest standards of scientific integrity through rigorous analysis.

주요 기능

Comprehensive bias detection including cognitive, selection, and measurement biases
Systematic methodology and experimental design critique
Integration with scientific-schematics for visual representation of research frameworks
Advanced statistical analysis evaluation and identification of common pitfalls
2,188 GitHub stars
Evidence quality assessment using GRADE and Cochrane Risk of Bias frameworks

사용 사례

Identifying confounding variables and systemic biases in complex datasets
Conducting systematic reviews or meta-analyses using standardized quality assessments
Reviewing and critiquing academic research papers or study protocols for peer review

소개

주요 기능

Comprehensive bias detection including cognitive, selection, and measurement biases
Systematic methodology and experimental design critique
Integration with scientific-schematics for visual representation of research frameworks
Advanced statistical analysis evaluation and identification of common pitfalls
2,188 GitHub stars
Evidence quality assessment using GRADE and Cochrane Risk of Bias frameworks

사용 사례

Identifying confounding variables and systemic biases in complex datasets
Conducting systematic reviews or meta-analyses using standardized quality assessments
Reviewing and critiquing academic research papers or study protocols for peer review