# Scientific Critical Thinking > Evalúa afirmaciones científicas y la calidad de la evidencia: validez del diseño experimental, sesgos, factores de confusión y marcos GRADE o Cochrane Risk of Bias. Para revisión por pares formal, usa peer-review. Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-critical-thinking Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-critical-thinking.md Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills Autor: K-Dense-AI Licencia: MIT license Actualizado: el mes pasado Coste de contexto: 85 tok instalada, 2.3k tok al activarse, 26.5k tok con todos los archivos del bundle Bundle: 8 archivos, 103 KB Permisos que pide: read write edit ## Instalación Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina. ```bash # Claude Code npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent claude-code # Cursor npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent cursor # Codex npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent codex # Gemini CLI npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent gemini # Windsurf npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent windsurf # Cline npx -y skills add K-Dense-AI/scientific-agent-skills --skill scientific-critical-thinking --agent cline ``` ## Qué hace - Evalúa metodología, diseño experimental, validez estadística y sesgos de un estudio o afirmación - Aplica los marcos GRADE y Cochrane Risk of Bias para calificar la calidad de la evidencia - Identifica falacias lógicas y errores estadísticos comunes en el discurso científico - Estructura la crítica en resumen, fortalezas, problemas por severidad y recomendaciones concretas - Genera diagramas opcionales (árbol de sesgos, flujo GRADE) con scientific-schematics, solo si el usuario los pide ## Cuándo usarla - Se evalúa la metodología o el diseño experimental de una investigación - Se valora la validez estadística o la calidad de la evidencia de un estudio - Se identifican sesgos o factores de confusión en una investigación - Se aplican marcos GRADE o Cochrane risk of bias, o se enseña análisis crítico ## Cuándo no - Se necesita redactar una revisión por pares formal (usar la skill peer-review) ## Qué la activa - "Evalúa el diseño experimental de este estudio y sus posibles sesgos" - "Aplica GRADE a la evidencia de estos tres ensayos clínicos" - "Identifica falacias lógicas en las conclusiones de este paper" - "Haz una crítica constructiva de la validez estadística de este análisis" ## Antes de instalar - Las figuras opcionales vía la skill scientific-schematics requieren OPENROUTER_API_KEY y acceso saliente a esa API. - Necesita en el PATH: python ## Archivos - SKILL.md — 9 KB - references/common_biases.md — 11 KB - references/core_capabilities.md — 16 KB - references/evidence_hierarchy.md — 13 KB - references/experimental_design.md — 16 KB - references/logical_fallacies.md — 18 KB - references/scientific_method.md — 6 KB - references/statistical_pitfalls.md — 15 KB ## SKILL.md Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT license. Esta sección es el documento original y está en inglés. # 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: ```bash 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. --- ## Core Capabilities Seven capability areas, each with the questions to ask and what the answers imply, are in [references/core_capabilities.md](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/scientific_method.md), [references/common_biases.md](references/common_biases.md), [references/statistical_pitfalls.md](references/statistical_pitfalls.md), [references/evidence_hierarchy.md](references/evidence_hierarchy.md), [references/logical_fallacies.md](references/logical_fallacies.md), and [references/experimental_design.md](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 ## Dónde encaja - Categoría: [Investigación](https://skillsagentes.com/categorias/investigacion.md) — Investigación estructurada, búsqueda de fuentes y síntesis. - Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio - [Todas las skills](https://skillsagentes.com/skills.md) - [Ranking de instalaciones](https://skillsagentes.com/ranking.md) ## Otras skills del mismo repositorio - [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas. - [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual. - [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver). - [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo. - [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras. --- Skills Agentes · [Índice de páginas en markdown](https://skillsagentes.com/sitemap.md) · [Inicio](https://skillsagentes.com/index.md)