scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
npx skills add majiayu000/claude-skill-registry --skill scientific-critical-thinking-k-dense-ai-scientific-agent-ski --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Explains that the agent should evaluate scientific claims and evidence quality using frameworks like GRADE and Cochrane ROB, assess methodology, bias, confounding, and evidence strength, and teach critical analysis.
How it works
The skill provides concrete evaluation frameworks and checklists across multiple domains:
- Methodology critique: assess study design, validity (internal, external, construct, statistical conclusion), control and blinding, and measurement quality.
- Bias detection: identify cognitive, selection, measurement, analysis biases, and confounding, with guidance on where to look (e.g., preregistration, flow diagrams).
- Statistical analysis evaluation: review sample size and power, test choices, multiple comparisons, p-values interpretation, effect sizes with CIs, missing data handling, and modeling concerns.
- Evidence quality assessment: rank study designs, assess risk of bias, apply GRADE considerations, convergence of evidence, and contextual factors.
- Logical fallacy identification: name fallacies (causation, generalization, authority, statistical, structural, science-specific) and explain why reasoning is flawed.
- Research design guidance: assist in refining research questions, selecting designs, bias minimization, sample planning, measurement strategy, and analysis planning. The skill lists specific questions and criteria under each section to guide a rigorous critique, and references are cited for deeper principles.
When to use it
Use when evaluating research methodology and experimental design, assessing statistical validity and evidence quality, identifying biases and confounding, reviewing scientific claims, conducting systematic reviews or meta-analyses, applying GRADE or Cochrane risk-of-bias assessments, and providing critical analysis of research papers.
What it can touch
The skill relies on analytical guidance without network needs. It supports figures via the scientific-schematics skill when an API key and outbound access are available, and references are used to anchor evaluation criteria.
Caveats
No explicit licensing or usage caveats beyond MIT license in metadata; emphasizes that higher-level study designs are not always better quality and that certain features (like schematic generation) require OPENROUTER_API_KEY and outbound API access to OpenRouter.
# 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 Aids (Optional) Only add figures when the **user explicitly requests** a diagram (for example, a GRADE flowchart, bias decision tree, or evidence-quality framework). **When figures help:** - Critical thinking framework diagrams - Bias identification decision trees - Evidence quality assessment flowcharts - GRADE or risk-of-bias evaluation frameworks **How to create figures:** - **Preferred:** Use the **scientific-schematics** skill for AI-genera
- Overview
- When to Use This Skill
- Visual Aids (Optional)
- Core Capabilities
- 1. Methodology Critique
- 2. Bias Detection
- 3. Statistical Analysis Evaluation
- 4. Evidence Quality Assessment
- 5. Logical Fallacy Identification
- 6. Research Design Guidance
- 7. Claim Evaluation
- Application Guidelines
- General Approach
- When Providing Critique
python scripts/generate_schematic.py "GRADE evidence assessment flowchart with downgrade and upgrade factors" -o figures/grade_flowchart.png --doc-type report
What does the scientific-critical-thinking skill do?
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientific-critical-thinking-k-dense-ai-scientific-agent-ski --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.