/scientific-critical-thinking

name: scientific-critical-thinking
description: "Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims."
allowed-tools: [Read, Write, Edit, Bash]

<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档，收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝

来源仓库: https://github.com/K-Dense-AI/claude-scientific-writer 项目名称: claude-scientific-writer 开源协议: MIT License 收录日期: 2026-04-02

声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。 -->

Scientific Critical Thinking

Overview

Critical thinking is a systematic process for evaluating scientific rigor. Assess methodology, experimental design, statistical validity, biases, confounding, and evidence quality using GRADE and Cochrane ROB frameworks. Apply this skill for critical analysis of scientific claims.

When to Use This Skill

This skill should be used when:

• Evaluating research methodology and experimental design
• Assessing statistical validity and evidence quality
• Identifying biases and confounding in studies
• Reviewing scientific claims and conclusions
• Conducting systematic reviews or meta-analyses
• Applying GRADE or Cochrane risk of bias assessments
• Providing critical analysis of research papers

Visual Enhancement with Scientific Schematics

**When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.**

If your document does not already contain schematics or diagrams:

• Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
• Simply describe your desired diagram in natural language
• Nano Banana Pro will automatically generate, review, and refine the schematic

**For new documents:** Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.

**How to generate schematics:**

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

• Create publication-quality images with proper formatting
• Review and refine through multiple iterations
• Ensure accessibility (colorblind-friendly, high contrast)
• Save outputs in the figures/ directory

**When to add schematics:**

• Critical thinking framework diagrams
• Bias identification decision trees
• Evidence quality assessment flowcharts
• GRADE assessment methodology diagrams
• Risk of bias evaluation frameworks
• Validity assessment visualizations
• Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

Core Capabilities

1. Methodology Critique

Evaluate research methodology for rigor, validity, and potential flaws.

**Apply when:**

• Reviewing research papers
• Assessing experimental designs
• Evaluating study protocols
• Planning new research

**Evaluation framework:**

1. **Study Design Assessment**

• Is the design appropriate for the research question?
• Can the design support causal claims being made?
• Are comparison groups appropriate and adequate?
• Consider whether experimental, quasi-experimental, or observational design is justified

2. **Validity Analysis**

• **Internal validity:** Can we trust the causal inference?
• Check randomization quality
• Evaluate confounding control
• Assess selection bias
• Review attrition/dropout patterns
• **External validity:** Do results generalize?
• Evaluate sample representativeness
• Consider ecological validity of setting
• Assess whether conditions match target application
• **Construct validity:** Do measures capture intended constructs?
• Review measurement validation
• Check operational definitions
• Assess whether measures are direct or proxy
• **Statistical conclusion validity:** Are statistical inferences sound?
• Verify adequate power/sample size
• Check assumption compliance
• Evaluate test appropriateness

3. **Control and Blinding**

• Was randomization properly implemented (sequence generation, allocation concealment)?
• Was blinding feasible and implemented (participants, providers, assessors)?
• Are control conditions appropriate (placebo, active control, no treatment)?
• Could performance or detection bias affect results?

4. **Measurement Quality**

• Are instruments validated and reliable?
• Are measures objective when possible, or subjective with acknowledged limitations?
• Is outcome assessment standardized?
• Are multiple measures used to triangulate findings?

**Reference:** See `references/scientific_method.md` for detailed principles and `references/experimental_design.md` for comprehensive design checklist.

2. Bias Detection

Identify and evaluate potential sources of bias that could distort findings.

**Apply when:**

• Reviewing published research
• Designing new studies
• Interpreting conflicting evidence
• Assessing research quality

**Systematic bias review:**

1. **Cognitive Biases (Researcher)**

• **Confirmation bias:** Are only supporting findings highlighted?
• **HARKing:** Were hypotheses stated a priori or formed after seeing results?
• **Publication bias:** Are negative results missing from literature?
• **Cherry-picking:** Is evidence selectively reported?
• Check for preregistration and analysis plan transparency

2. **Selection Biases**

• **Sampling bias:** Is sample representative of target population?
• **Volunteer bias:** Do participants self-select in systematic ways?
• **Attrition bias:** Is dropout differential between groups?
• **Survivorship bias: