/scientific-critical-thinking

name: scientific-critical-thinking
description: 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.
allowed-tools: Read Write Edit
license: MIT license
compatibility: Analytical guidance needs no network. Optional figures via the scientific-schematics skill require OPENROUTER_API_KEY and outbound API access to OpenRouter.
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}

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-generated diagrams from a natural-language description
• **Alternative:** Build figures in your usual tools (draw.io, PowerPoint, matplotlib, etc.)

From the `scientific-schematics` skill directory, with `OPENROUTER_API_KEY` set:

python scripts/generate_schematic.py "GRADE evidence assessment flowchart with downgrade and upgrade factors" -o figures/grade_flowchart.png --doc-type report

**Disclosure:** AI schematic generation sends your prompt to [OpenRouter](https://openrouter.ai/) (a third-party API). Do not include unpublished sensitive details unless that transmission is appropriate for your project.

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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:** Are only "survivors" visible in sample?
• Examine participant flow diagrams and compare baseline characteristics

3. **Measurement Biases**

• **Observer bias:** Could expectations influence observations?
• **Recall bias:** Are retrospective reports systematically inaccurate?
• **Social desirability:** Are responses biased toward acceptability?
• **Instrument bias:** Do measurement tools systematically err?
• Evalua