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