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skills/hypothesis-generation/SKILL.md

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+---
+name: hypothesis-generation
+description: "Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains."
+---
+
+# Scientific Hypothesis Generation
+
+## Overview
+
+Hypothesis generation is a systematic process for developing testable explanations. Formulate evidence-based hypotheses from observations, design experiments, explore competing explanations, and develop predictions. Apply this skill for scientific inquiry across domains.
+
+## When to Use This Skill
+
+This skill should be used when:
+- Developing hypotheses from observations or preliminary data
+- Designing experiments to test scientific questions
+- Exploring competing explanations for phenomena
+- Formulating testable predictions for research
+- Conducting literature-based hypothesis generation
+- Planning mechanistic studies across scientific domains
+
+## Workflow
+
+Follow this systematic process to generate robust scientific hypotheses:
+
+### 1. Understand the Phenomenon
+
+Start by clarifying the observation, question, or phenomenon that requires explanation:
+
+- Identify the core observation or pattern that needs explanation
+- Define the scope and boundaries of the phenomenon
+- Note any constraints or specific contexts
+- Clarify what is already known vs. what is uncertain
+- Identify the relevant scientific domain(s)
+
+### 2. Conduct Comprehensive Literature Search
+
+Search existing scientific literature to ground hypotheses in current evidence. Use both PubMed (for biomedical topics) and general web search (for broader scientific domains):
+
+**For biomedical topics:**
+- Use WebFetch with PubMed URLs to access relevant literature
+- Search for recent reviews, meta-analyses, and primary research
+- Look for similar phenomena, related mechanisms, or analogous systems
+
+**For all scientific domains:**
+- Use WebSearch to find recent papers, preprints, and reviews
+- Search for established theories, mechanisms, or frameworks
+- Identify gaps in current understanding
+
+**Search strategy:**
+- Begin with broad searches to understand the landscape
+- Narrow to specific mechanisms, pathways, or theories
+- Look for contradictory findings or unresolved debates
+- Consult `references/literature_search_strategies.md` for detailed search techniques
+
+### 3. Synthesize Existing Evidence
+
+Analyze and integrate findings from literature search:
+
+- Summarize current understanding of the phenomenon
+- Identify established mechanisms or theories that may apply
+- Note conflicting evidence or alternative viewpoints
+- Recognize gaps, limitations, or unanswered questions
+- Identify analogies from related systems or domains
+
+### 4. Generate Competing Hypotheses
+
+Develop 3-5 distinct hypotheses that could explain the phenomenon. Each hypothesis should:
+
+- Provide a mechanistic explanation (not just description)
+- Be distinguishable from other hypotheses
+- Draw on evidence from the literature synthesis
+- Consider different levels of explanation (molecular, cellular, systemic, population, etc.)
+
+**Strategies for generating hypotheses:**
+- Apply known mechanisms from analogous systems
+- Consider multiple causative pathways
+- Explore different scales of explanation
+- Question assumptions in existing explanations
+- Combine mechanisms in novel ways
+
+### 5. Evaluate Hypothesis Quality
+
+Assess each hypothesis against established quality criteria from `references/hypothesis_quality_criteria.md`:
+
+**Testability:** Can the hypothesis be empirically tested?
+**Falsifiability:** What observations would disprove it?
+**Parsimony:** Is it the simplest explanation that fits the evidence?
+**Explanatory Power:** How much of the phenomenon does it explain?
+**Scope:** What range of observations does it cover?
+**Consistency:** Does it align with established principles?
+**Novelty:** Does it offer new insights beyond existing explanations?
+
+Explicitly note the strengths and weaknesses of each hypothesis.
+
+### 6. Design Experimental Tests
+
+For each viable hypothesis, propose specific experiments or studies to test it. Consult `references/experimental_design_patterns.md` for common approaches:
+
+**Experimental design elements:**
+- What would be measured or observed?
+- What comparisons or controls are needed?
+- What methods or techniques would be used?
+- What sample sizes or statistical approaches are appropriate?
+- What are potential confounds and how to address them?
+
+**Consider multiple approaches:**
+- Laboratory experiments (in vitro, in vivo, computational)
+- Observational studies (cross-sectional, longitudinal, case-control)
+- Clinical trials (if applicable)
+- Natural experiments or quasi-experimental designs
+
+### 7. Formulate Testable Predictions
+
+For each hypothesis, generate specific, quantitative predictions:
+
+- State what should be observed if the hypothesis is correct
+- Specify expected direction and magnitude of effects when possible
+- Identify conditions under which predictions should hold
+- Distinguish predictions between competing hypotheses
+- Note predictions that would falsify the hypothesis
+
+### 8. Present Structured Output
+
+Use the template in `assets/hypothesis_output_template.md` to present hypotheses in a clear, consistent format:
+
+**Standard structure:**
+1. **Background & Context** - Phenomenon and literature summary
+2. **Competing Hypotheses** - Enumerated hypotheses with mechanistic explanations
+3. **Quality Assessment** - Evaluation of each hypothesis
+4. **Experimental Designs** - Proposed tests for each hypothesis
+5. **Testable Predictions** - Specific, measurable predictions
+6. **Critical Comparisons** - How to distinguish between hypotheses
+
+## Quality Standards
+
+Ensure all generated hypotheses meet these standards:
+
+- **Evidence-based:** Grounded in existing literature with citations
+- **Testable:** Include specific, measurable predictions
+- **Mechanistic:** Explain how/why, not just what
+- **Comprehensive:** Consider alternative explanations
+- **Rigorous:** Include experimental designs to test predictions
+
+## Resources
+
+### references/
+
+- `hypothesis_quality_criteria.md` - Framework for evaluating hypothesis quality (testability, falsifiability, parsimony, explanatory power, scope, consistency)
+- `experimental_design_patterns.md` - Common experimental approaches across domains (RCTs, observational studies, lab experiments, computational models)
+- `literature_search_strategies.md` - Effective search techniques for PubMed and general scientific sources
+
+### assets/
+
+- `hypothesis_output_template.md` - Structured format for presenting hypotheses consistently with all required sections