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Scientific Critical Thinking

K-DenseCommunityCreated by K-Dense Inc.

  • Skill
  • Research and analysis
  • Science and research
  • MIT
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From the author

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.
— the description by K-Dense Inc.

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Scientific Critical Thinking provides a structured approach to assessing the validity of research claims. It enables users to perform rigorous critiques of methodology, statistical analysis, and experimental design. By leveraging established frameworks like GRADE and Cochrane Risk of Bias, the skill helps identify biases, logical fallacies, and confounding factors, ensuring that conclusions are supported by the evidence presented. It is designed to foster constructive, evidence-based analysis while maintaining transparency regarding uncertainty and limitations.

What it does

  • Critiques research methodology and experimental design for validity and rigor.
  • Identifies cognitive, selection, and measurement biases in scientific studies.
  • Evaluates statistical validity, including power, effect sizes, and p-value usage.
  • Applies formal evidence grading frameworks like GRADE and Cochrane ROB.
  • Detects logical fallacies and separates data from interpretation in scientific claims.
  • Provides actionable recommendations to strengthen research designs before data collection.

When to use it

  • When evaluating the methodology and conclusions of a research paper.
  • When designing a new experiment and seeking to minimize potential biases.
  • When conducting a systematic review or meta-analysis of existing literature.
  • When assessing the strength of evidence supporting a specific scientific claim.
  • When teaching or learning critical analysis techniques in a scientific context.

Advantages

  • Promotes consistent, objective evaluation standards across different studies.
  • Encourages constructive critique that identifies both strengths and weaknesses.
  • Provides a comprehensive taxonomy of biases and logical fallacies for easy reference.
  • Helps distinguish between major validity threats and minor limitations.
  • Supports transparent reporting of uncertainty and evidence strength.

Usage examples

“Evaluate the experimental design of this study for potential selection bias and confounding variables.”
The agent will analyze the study's participant selection process, identify potential systematic differences between groups, and assess whether the study design adequately controls for identified confounders.
“Apply the GRADE framework to assess the quality of evidence in this meta-analysis.”
The agent will evaluate the study based on risk of bias, inconsistency, indirectness, imprecision, and publication bias to determine the overall certainty of the evidence.
“Identify potential logical fallacies in the discussion section of this paper.”
The agent will scan the text for common errors in reasoning, such as correlation-causation confusion or overgeneralization, and explain why these arguments are logically flawed.
“Review my proposed study protocol and suggest improvements to strengthen internal validity.”
The agent will review the protocol, suggest specific adjustments to randomization, blinding, or control groups, and highlight potential threats to validity that should be addressed.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/k-dense-ai/scientific-critical-thinking-udjd/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip scientific-critical-thinking.zip -d .agents/skills

Documentation by 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.)

Run from the repository root, with OPENROUTER_API_KEY set:

python skills/scientific-schematics/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 (a third-party API). Do not include unpublished sensitive details unless that transmission is appropriate for your project.

Core Capabilities

Seven capability areas, each with the questions to ask and what the answers imply, are in references/core_capabilities.md:

  1. Methodology critique — design, controls, confounding, and whether the method can answer the question asked.

  2. Bias detection — selection, measurement, publication, and cognitive biases.

  3. Statistical analysis evaluation — power, multiplicity, p-value misuse, effect sizes.

  4. Evidence quality assessment — study hierarchy, replication, and strength of inference.

  5. Logical fallacy identification — the fallacies that recur in scientific argument.

  6. Research design guidance — how to strengthen a design before data collection.

  7. Claim evaluation — separating what was shown from what is being asserted.

Per-topic detail is in references/scientific_method.md, references/common_biases.md, references/statistical_pitfalls.md, references/evidence_hierarchy.md, references/logical_fallacies.md, and references/experimental_design.md.

Application Guidelines

General Approach

  1. Be Constructive

    • Identify strengths as well as weaknesses

    • Suggest improvements rather than just criticizing

    • Distinguish between fatal flaws and minor limitations

    • Recognize that all research has limitations

  2. Be Specific

    • Point to specific instances (e.g., "Table 2 shows..." or "In the Methods section...")

    • Quote problematic statements

    • Provide concrete examples of issues

    • Reference specific principles or standards violated

  3. Be Proportionate

    • Match criticism severity to issue importance

    • Distinguish between major threats to validity and minor concerns

    • Consider whether issues affect primary conclusions

    • Acknowledge uncertainty in your own assessments

  4. Apply Consistent Standards

    • Use same criteria across all studies

    • Don't apply stricter standards to findings you dislike

    • Acknowledge your own potential biases

    • Base judgments on methodology, not results

  5. Consider Context

    • Acknowledge practical and ethical constraints

    • Consider field-specific norms for effect sizes and methods

    • Recognize exploratory vs. confirmatory contexts

    • Account for resource limitations in evaluating studies

When Providing Critique

Structure feedback as:

  1. Summary: Brief overview of what was evaluated

  2. Strengths: What was done well (important for credibility and learning)

  3. Concerns: Issues organized by severity

    • Critical issues (threaten validity of main conclusions)

    • Important issues (affect interpretation but not fatally)

    • Minor issues (worth noting but don't change conclusions)

  4. Specific Recommendations: Actionable suggestions for improvement

  5. Overall Assessment: Balanced conclusion about evidence quality and what can be concluded

Use precise terminology:

  • Name specific biases, fallacies, and methodological issues

  • Reference established standards and guidelines

  • Cite principles from scientific methodology

  • Use technical terms accurately

When Uncertain

  • Acknowledge uncertainty: "This could be X or Y; additional information needed is Z"

  • Ask clarifying questions: "Was [methodological detail] done? This affects interpretation."

  • Provide conditional assessments: "If X was done, then Y follows; if not, then Z is concern"

  • Note what additional information would resolve uncertainty

Reference Materials

This skill includes comprehensive reference materials that provide detailed frameworks for critical evaluation:

  • references/scientific_method.md - Core principles of scientific methodology, the scientific process, critical evaluation criteria, red flags in scientific claims, causal inference standards, peer review, and open science principles

  • references/common_biases.md - Comprehensive taxonomy of cognitive, experimental, methodological, statistical, and analysis biases with detection and mitigation strategies

  • references/statistical_pitfalls.md - Common statistical errors and misinterpretations including p-value misunderstandings, multiple comparisons problems, sample size issues, effect size mistakes, correlation/causation confusion, regression pitfalls, and meta-analysis issues

  • references/evidence_hierarchy.md - Traditional evidence hierarchy, GRADE system, study quality assessment criteria, domain-specific considerations, evidence synthesis principles, and practical decision frameworks

  • references/logical_fallacies.md - Logical fallacies common in scientific discourse organized by type (causation, generalization, authority, relevance, structure, statistical) with examples and detection strategies

  • references/experimental_design.md - Comprehensive experimental design checklist covering research questions, hypotheses, study design selection, variables, sampling, blinding, randomization, control groups, procedures, measurement, bias minimization, data management, statistical planning, ethical considerations, validity threats, and reporting standards

When to consult references:

  • Load references into context when detailed frameworks are needed

  • Use grep to search references for specific topics: grep -r "pattern" references/

  • References provide depth; SKILL.md provides procedural guidance

  • Consult references for comprehensive lists, detailed criteria, and specific examples

Remember

Scientific critical thinking is about:

  • Systematic evaluation using established principles

  • Constructive critique that improves science

  • Proportional confidence to evidence strength

  • Transparency about uncertainty and limitations

  • Consistent application of standards

  • Recognition that all research has limitations

  • Balance between skepticism and openness to evidence

Always distinguish between:

  • Data (what was observed) and interpretation (what it means)

  • Correlation and causation

  • Statistical significance and practical importance

  • Exploratory and confirmatory findings

  • What is known and what is uncertain

  • Evidence against a claim and evidence for the null

Goals of critical thinking:

  1. Identify strengths and weaknesses accurately

  2. Determine what conclusions are supported

  3. Recognize limitations and uncertainties

  4. Suggest improvements for future work

  5. Advance scientific understanding

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Files

references/logical_fallacies.md

Logical Fallacies in Scientific Discourse

Fallacies of Causation

1. Post Hoc Ergo Propter Hoc (After This, Therefore Because of This)

Description: Assuming that because B happened after A, A caused B.

Examples:

  • "I took this supplement and my cold went away, so the supplement cured my cold."

  • "Autism diagnoses increased after vaccine schedules changed, so vaccines cause autism."

  • "I wore my lucky socks and won the game, so the socks caused the win."

Why fallacious: Temporal sequence is necessary but not sufficient for causation. Correlation ≠ causation.

Related: Cum hoc ergo propter hoc (with this, therefore because of this) - correlation mistaken for causation even without temporal order.

2. Confusing Correlation with Causation

Description: Assuming correlation implies direct causal relationship.

Examples:

  • "Countries that eat more chocolate have more Nobel Prize winners, so chocolate makes you smarter."

  • "Ice cream sales correlate with drowning deaths, so ice cream causes drowning."

Reality: Often due to confounding variables (hot weather causes both ice cream sales and swimming).

3. Reverse Causation

Description: Confusing cause and effect direction.

Examples:

  • "Depression is associated with inflammation, so inflammation causes depression." (Could be: depression causes inflammation)

  • "Wealthy people are healthier, so wealth causes health." (Could be: health enables wealth accumulation)

Solution: Longitudinal studies and experimental designs to establish temporal order.

4. Single Cause Fallacy

Description: Attributing complex phenomena to one cause when multiple factors contribute.

Examples:

  • "Crime is caused by poverty." (Ignores many other contributing factors)

  • "Heart disease is caused by fat intake." (Oversimplifies multifactorial disease)

Reality: Most outcomes have multiple contributing causes.

Fallacies of Generalization

5. Hasty Generalization

Description: Drawing broad conclusions from insufficient evidence.

Examples:

  • "My uncle smoked and lived to 90, so smoking isn't dangerous."

  • "This drug worked in 5 patients, so it's effective for everyone."

  • "I saw three black swans, so all swans are black."

Why fallacious: Small, unrepresentative samples don't support universal claims.

6. Anecdotal Fallacy

Description: Using personal experience or isolated examples as proof.

Examples:

  • "I know someone who survived cancer using alternative medicine, so it works."

  • "My grandmother never exercised and lived to 100, so exercise is unnecessary."

Why fallacious: Anecdotes are unreliable due to selection bias, memory bias, and confounding. Plural of anecdote ≠ data.

7. Cherry Picking (Suppressing Evidence)

Description: Selecting only evidence that supports your position while ignoring contradictory evidence.

Examples:

  • Citing only studies showing supplement benefits while ignoring null findings

  • Highlighting successful predictions while ignoring failed ones

  • Showing graphs that start at convenient points

Detection: Look for systematic reviews, not individual studies.

8. Ecological Fallacy

Description: Inferring individual characteristics from group statistics.

Example:

  • "Average income in this neighborhood is high, so this person must be wealthy."

  • "This country has low disease rates, so any individual from there is unlikely to have disease."

Why fallacious: Group-level patterns don't necessarily apply to individuals.

Fallacies of Authority and Tradition

9. Appeal to Authority (Argumentum ad Verecundiam)

Description: Accepting claims because an authority figure said them, without evidence.

Examples:

  • "Dr. X says this treatment works, so it must." (If Dr. X provides no data)

  • "Einstein believed in God, so God exists." (Einstein's physics expertise doesn't transfer)

  • "99% of doctors recommend..." (Appeal to majority + authority without evidence)

Valid use of authority: Experts providing evidence-based consensus in their domain.

Invalid: Authority opinions without evidence, or outside their expertise.

10. Appeal to Antiquity/Tradition

Description: Assuming something is true or good because it's old or traditional.

Examples:

  • "Traditional medicine has been used for thousands of years, so it must work."

  • "This theory has been accepted for decades, so it must be correct."

Why fallacious: Age doesn't determine validity. Many old beliefs have been disproven.

11. Appeal to Novelty

Description: Assuming something is better because it's new.

Examples:

  • "This is the latest treatment, so it must be superior."

  • "New research overturns everything we knew." (Often overstated)

Why fallacious: New ≠ better. Established treatments often outperform novel ones.

Fallacies of Relevance

12. Ad Hominem (Attack the Person)

Description: Attacking the person making the argument rather than the argument itself.

Types:

  • Abusive: "He's an idiot, so his theory is wrong."

  • Circumstantial: "She's funded by industry, so her findings are false."

  • Tu Quoque: "You smoke, so your anti-smoking argument is invalid."

Why fallacious: Personal characteristics don't determine argument validity.

Note: Conflicts of interest are worth noting but don't invalidate evidence.

13. Genetic Fallacy

Description: Judging something based on its origin rather than its merits.

Examples:

  • "This idea came from a drug company, so it's wrong."

  • "Ancient Greeks believed this, so it's outdated."

Better approach: Evaluate evidence regardless of source.

14. Appeal to Emotion

Description: Manipulating emotions instead of presenting evidence.

Types:

  • Appeal to fear: "If you don't vaccinate, your child will die."

  • Appeal to pity: "Think of the suffering patients who need this unproven treatment."

  • Appeal to flattery: "Smart people like you know that..."

Why fallacious: Emotional reactions don't determine truth.

15. Appeal to Consequences (Argumentum ad Consequentiam)

Description: Arguing something is true/false based on whether consequences are desirable.

Examples:

  • "Climate change can't be real because the solutions would hurt the economy."

  • "Free will must exist because without it, morality is impossible."

Why fallacious: Reality is independent of what we wish were true.

16. Appeal to Nature (Naturalistic Fallacy)

Description: Assuming "natural" means good, safe, or effective.

Examples:

  • "This treatment is natural, so it's safe."

  • "Organic food is natural, so it's healthier."

  • "Vaccines are unnatural, so they're harmful."

Why fallacious:

  • Many natural things are deadly (arsenic, snake venom, hurricanes)

  • Many synthetic things are beneficial (antibiotics, vaccines)

  • "Natural" is often poorly defined

17. Moralistic Fallacy

Description: Assuming what ought to be true is true.

Examples:

  • "There shouldn't be sex differences in ability, so they don't exist."

  • "People should be rational, so they are."

Why fallacious: Desires about reality don't change reality.

Fallacies of Structure

18. False Dichotomy (False Dilemma)

Description: Presenting only two options when more exist.

Examples:

  • "Either you're with us or against us."

  • "It's either genetic or environmental." (Usually both)

  • "Either the treatment works or it doesn't." (Ignores partial effects)

Reality: Most issues have multiple options and shades of gray.

19. Begging the Question (Circular Reasoning)

Description: Assuming what you're trying to prove.

Examples:

  • "This medicine works because it has healing properties." (What are healing properties? That it works!)

  • "God exists because the Bible says so, and the Bible is true because it's God's word."

Detection: Check if the conclusion is hidden in the premises.

20. Moving the Goalposts

Description: Changing standards of evidence after initial standards are met.

Example:

  • Skeptic: "Show me one study."

  • [Shows study]

  • Skeptic: "That's just one study; show me a meta-analysis."

  • [Shows meta-analysis]

  • Skeptic: "But meta-analyses have limitations..."

Why problematic: No amount of evidence will ever be sufficient.

21. Slippery Slope

Description: Arguing that one step will inevitably lead to extreme outcomes without justification.

Example:

  • "If we allow gene editing for disease, we'll end up with designer babies and eugenics."

When valid: If intermediate steps are actually likely.

When fallacious: If chain of events is speculative without evidence.

22. Straw Man

Description: Misrepresenting an argument to make it easier to attack.

Example:

  • Position: "We should teach evolution in schools."

  • Straw man: "So you think we should tell kids they're just monkeys?"

Detection: Ask: Is this really what they're claiming?

Fallacies of Statistical and Scientific Reasoning

23. Texas Sharpshooter Fallacy

Description: Cherry-picking data clusters to fit a pattern, like shooting arrows then drawing targets around them.

Examples:

  • Finding cancer clusters and claiming environmental causes (without accounting for random clustering)

  • Data mining until finding significant correlations

Why fallacious: Patterns in random data are inevitable; finding them doesn't prove causation.

24. Base Rate Fallacy

Description: Ignoring prior probability when evaluating evidence.

Example:

  • Disease affects 0.1% of population; test is 99% accurate

  • Positive test ≠ 99% probability of disease

  • Actually ~9% probability (due to false positives exceeding true positives)

Solution: Use Bayesian reasoning; consider base rates.

25. Prosecutor's Fallacy

Description: Confusing P(Evidence|Innocent) with P(Innocent|Evidence).

Example:

  • "The probability of this DNA match occurring by chance is 1 in 1 million, so there's only a 1 in 1 million chance the defendant is innocent."

Why fallacious: Ignores base rates and prior probability.

26. McNamara Fallacy (Quantitative Fallacy)

Description: Focusing only on what can be easily measured while ignoring important unmeasured factors.

Example:

  • Judging school quality only by test scores (ignoring creativity, social skills, ethics)

  • Measuring healthcare only by quantifiable outcomes (ignoring quality of life)

Quote: "Not everything that counts can be counted, and not everything that can be counted counts."

27. Multiple Comparisons Fallacy

Description: Not accounting for increased false positive rate when testing many hypotheses.

Example:

  • Testing 20 hypotheses at p < .05 gives ~65% chance of at least one false positive

  • Claiming jellybean color X causes acne after testing 20 colors

Solution: Correct for multiple comparisons (Bonferroni, FDR).

28. Reification (Hypostatization)

Description: Treating abstract concepts as if they were concrete things.

Examples:

  • "Evolution wants organisms to survive." (Evolution doesn't "want")

  • "The gene for intelligence" (Intelligence isn't one gene)

  • "Nature selects..." (Nature doesn't consciously select)

Why problematic: Can lead to confused thinking about mechanisms.

Fallacies of Scope and Definition

29. No True Scotsman

Description: Retroactively excluding counterexamples by redefining criteria.

Example:

  • "No natural remedy has side effects."

  • "But poison ivy is natural and causes reactions."

  • "Well, no true natural remedy has side effects."

Why fallacious: Moves goalposts to protect claim from falsification.

30. Equivocation

Description: Using a word with multiple meanings inconsistently.

Example:

  • "Evolution is just a theory. Theories are guesses. So evolution is just a guess."

  • (Conflates colloquial "theory" with scientific "theory")

Detection: Check if key terms are used consistently.

31. Ambiguity

Description: Using vague language that can be interpreted multiple ways.

Example:

  • "Quantum healing" (What does "quantum" mean here?)

  • "Natural" (Animals? Not synthetic? Organic? Common?)

Why problematic: Claims become unfalsifiable when terms are undefined.

32. Mind Projection Fallacy

Description: Projecting mental constructs onto reality.

Example:

  • Assuming categories that exist in language exist in nature

  • "Which chromosome is the gene for X on?" when X is polygenic and partially environmental

Better: Recognize human categories may not carve nature at the joints.

Fallacies Specific to Science

33. Galileo Gambit

Description: "They laughed at Galileo, and he was right, so if they're laughing at me, I must be right too."

Why fallacious:

  • They laughed at Galileo, and he was right

  • They also laughed at countless crackpots who were wrong

  • Being an outsider doesn't make you right

Reality: Revolutionary ideas are usually well-supported by evidence.

34. Argument from Ignorance (Ad Ignorantiam)

Description: Assuming something is true because it hasn't been proven false (or vice versa).

Examples:

  • "No one has proven homeopathy doesn't work, so it works."

  • "We haven't found evidence of harm, so it must be safe."

Why fallacious: Absence of evidence ≠ evidence of absence (though it can be, depending on how hard we've looked).

Burden of proof: Falls on the claimant, not the skeptic.

35. God of the Gaps

Description: Explaining gaps in knowledge by invoking supernatural or unfalsifiable causes.

Examples:

  • "We don't fully understand consciousness, so it must be spiritual."

  • "This complexity couldn't arise naturally, so it must be designed."

Why problematic:

  • Fills gaps with non-explanations

  • Discourages genuine investigation

  • History shows gaps get filled by natural explanations

36. Nirvana Fallacy (Perfect Solution Fallacy)

Description: Rejecting solutions because they're imperfect.

Examples:

  • "Vaccines aren't 100% effective, so they're worthless."

  • "This diet doesn't work for everyone, so it doesn't work."

Reality: Most interventions are partial; perfection is rare.

Better: Compare to alternatives, not to perfection.

37. Special Pleading

Description: Applying standards to others but not to oneself.

Examples:

  • "My anecdotes count as evidence, but yours don't."

  • "Mainstream medicine needs RCTs, but my alternative doesn't."

  • "Correlation doesn't imply causation—except when it supports my view."

Why fallacious: Evidence standards should apply consistently.

38. Unfalsifiability

Description: Formulating claims in ways that cannot be tested or disproven.

Examples:

  • "This energy can't be detected by any instrument."

  • "It works, but only if you truly believe."

  • "Failures prove the conspiracy is even deeper."

Why problematic: Unfalsifiable claims aren't scientific; they can't be tested.

Good science: Makes specific, testable predictions.

39. Affirming the Consequent

Description: If A, then B. B is true. Therefore, A is true.

Example:

  • "If the drug works, symptoms improve. Symptoms improved. Therefore, the drug worked."

  • (Could be placebo, natural history, regression to mean)

Why fallacious: Other causes could produce the same outcome.

Valid form: Modus ponens: If A, then B. A is true. Therefore, B is true.

40. Denying the Antecedent

Description: If A, then B. A is false. Therefore, B is false.

Example:

  • "If you have fever, you have infection. You don't have fever. Therefore, you don't have infection."

Why fallacious: B can be true even when A is false.

Avoiding Logical Fallacies

Practical Steps

  1. Identify the claim - What exactly is being argued?

  2. Identify the evidence - What supports the claim?

  3. Check the logic - Does the evidence actually support the claim?

  4. Look for hidden assumptions - What unstated beliefs does the argument rely on?

  5. Consider alternatives - What other explanations fit the evidence?

  6. Check for emotional manipulation - Is the argument relying on feelings rather than facts?

  7. Evaluate the source - Are there conflicts of interest? Is this within their expertise?

  8. Look for balance - Are counterarguments addressed fairly?

  9. Assess the evidence - Is it anecdotal, observational, or experimental? How strong?

  10. Be charitable - Interpret arguments in their strongest form (steel man, not straw man).

Questions to Ask

  • Is the conclusion supported by the premises?

  • Are there unstated assumptions?

  • Is the evidence relevant to the conclusion?

  • Are counterarguments acknowledged?

  • Could alternative explanations account for the evidence?

  • Is the reasoning consistent?

  • Are terms defined clearly?

  • Is evidence being cherry-picked?

  • Are emotions being manipulated?

  • Would this reasoning apply consistently to other cases?

Common Patterns

Good Arguments:

  • Clearly defined terms

  • Relevant, sufficient evidence

  • Valid logical structure

  • Acknowledges limitations and alternatives

  • Proportional conclusions

  • Transparent about uncertainty

  • Applies consistent standards

Poor Arguments:

  • Vague or shifting definitions

  • Irrelevant or insufficient evidence

  • Logical leaps

  • Ignores counterevidence

  • Overclaimed conclusions

  • False certainty

  • Double standards

Remember

  • Fallacious reasoning doesn't mean the conclusion is false - just that this argument doesn't support it.

  • Identifying fallacies isn't about winning - it's about better understanding reality.

  • We all commit fallacies - recognizing them in ourselves is as important as in others.

  • Charity principle - Interpret arguments generously; don't assume bad faith.

  • Focus on claims, not people - Ad hominem goes both ways.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs an API key, Needs Python

The author says: Analytical guidance needs no network. Optional figures via the scientific-schematics skill require OPENROUTER_API_KEY and outbound API access to OpenRouter.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
K-Dense Inc.
Publisher
K-Dense (Community)
Source
K-Dense-AI/claude-scientific-writer · skills/scientific-critical-thinking · commit 529b9f7
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Also distributed in
K-Dense-AI/claude-scientific-writer

AutoGineer is not affiliated with or endorsed by K-Dense Inc. or K-Dense.

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