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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

skills/hypothesis-generation/assets/hypothesis_output_template.md

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+# Scientific Hypothesis Generation: [Phenomenon Name]
+
+## 1. Background & Context
+
+### Phenomenon Description
+[Clear description of the observation, pattern, or question that requires explanation. Include:
+- What was observed or what question needs answering
+- The specific context or system in which it occurs
+- Any relevant constraints or boundary conditions
+- Why this phenomenon is interesting or important]
+
+### Current Understanding
+[Synthesis of existing literature, including:
+- What is already known about this phenomenon
+- Established mechanisms or theories that may be relevant
+- Key findings from recent research
+- Gaps or limitations in current understanding
+- Conflicting findings or unresolved debates
+
+Include citations to key papers (Author et al., Year, Journal)]
+
+### Knowledge Gaps
+[Specific aspects that remain unexplained or poorly understood:
+- What aspects of the phenomenon lack clear explanation?
+- What contradictions exist in current understanding?
+- What questions remain unanswered?]
+
+---
+
+## 2. Competing Hypotheses
+
+### Hypothesis 1: [Concise Title]
+
+**Mechanistic Explanation:**
+[Detailed explanation of the proposed mechanism. This should explain HOW and WHY the phenomenon occurs, not just describe WHAT occurs. Include:
+- Specific molecular, cellular, physiological, or population-level mechanisms
+- Causal chain from initial trigger to observed outcome
+- Key components, pathways, or factors involved
+- Scale or level of explanation (molecular, cellular, organ, organism, population)]
+
+**Supporting Evidence:**
+[Evidence from literature that supports this hypothesis:
+- Analogous mechanisms in related systems
+- Direct evidence from relevant studies
+- Theoretical frameworks that align with this hypothesis
+- Include citations]
+
+**Key Assumptions:**
+[Explicit statement of assumptions underlying this hypothesis:
+- What must be true for this hypothesis to hold?
+- What conditions or contexts does it require?]
+
+---
+
+### Hypothesis 2: [Concise Title]
+
+**Mechanistic Explanation:**
+[Detailed mechanistic explanation distinct from Hypothesis 1]
+
+**Supporting Evidence:**
+[Evidence supporting this alternative explanation]
+
+**Key Assumptions:**
+[Assumptions underlying this hypothesis]
+
+---
+
+### Hypothesis 3: [Concise Title]
+
+**Mechanistic Explanation:**
+[Detailed mechanistic explanation distinct from previous hypotheses]
+
+**Supporting Evidence:**
+[Evidence supporting this explanation]
+
+**Key Assumptions:**
+[Assumptions underlying this hypothesis]
+
+---
+
+[Continue for Hypothesis 4, 5, etc. if applicable]
+
+---
+
+## 3. Quality Assessment
+
+### Evaluation Against Core Criteria
+
+| Criterion | Hypothesis 1 | Hypothesis 2 | Hypothesis 3 | [H4] | [H5] |
+|-----------|--------------|--------------|--------------|------|------|
+| **Testability** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+| **Falsifiability** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+| **Parsimony** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+| **Explanatory Power** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+| **Scope** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+| **Consistency** | [Rating & brief note] | [Rating & brief note] | [Rating & brief note] | | |
+
+**Rating scale:** Strong / Moderate / Weak
+
+### Detailed Evaluation
+
+#### Hypothesis 1
+**Strengths:**
+- [Specific strength 1]
+- [Specific strength 2]
+
+**Weaknesses:**
+- [Specific weakness 1]
+- [Specific weakness 2]
+
+**Overall Assessment:**
+[Brief summary of hypothesis quality and viability]
+
+#### Hypothesis 2
+[Similar structure]
+
+#### Hypothesis 3
+[Similar structure]
+
+---
+
+## 4. Experimental Designs
+
+### Testing Hypothesis 1: [Title]
+
+**Experiment 1A: [Brief title]**
+
+*Design Type:* [e.g., In vitro dose-response / In vivo knockout / Clinical RCT / Observational cohort / Computational model]
+
+*Objective:* [What specific aspect of the hypothesis does this test?]
+
+*Methods:*
+- **System/Model:** [What system, organism, or population?]
+- **Intervention/Manipulation:** [What is varied or manipulated?]
+- **Measurements:** [What outcomes are measured?]
+- **Controls:** [What control conditions?]
+- **Sample Size:** [Estimated n, with justification if possible]
+- **Analysis:** [Statistical or analytical approach]
+
+*Expected Timeline:* [Rough estimate]
+
+*Feasibility:* [High/Medium/Low, with brief justification]
+
+**Experiment 1B: [Brief title - alternative or complementary approach]**
+[Similar structure to 1A]
+
+---
+
+### Testing Hypothesis 2: [Title]
+
+**Experiment 2A: [Brief title]**
+[Structure as above]
+
+**Experiment 2B: [Brief title]**
+[Structure as above]
+
+---
+
+### Testing Hypothesis 3: [Title]
+
+**Experiment 3A: [Brief title]**
+[Structure as above]
+
+---
+
+## 5. Testable Predictions
+
+### Predictions from Hypothesis 1
+
+1. **Prediction 1.1:** [Specific, measurable prediction]
+   - **Conditions:** [Under what conditions should this be observed?]
+   - **Magnitude:** [Expected effect size or direction, if quantifiable]
+   - **Falsification:** [What observation would falsify this prediction?]
+
+2. **Prediction 1.2:** [Specific, measurable prediction]
+   - **Conditions:** [Conditions]
+   - **Magnitude:** [Expected effect]
+   - **Falsification:** [Falsifying observation]
+
+3. **Prediction 1.3:** [Additional prediction]
+
+---
+
+### Predictions from Hypothesis 2
+
+1. **Prediction 2.1:** [Specific, measurable prediction]
+   - **Conditions:** [Conditions]
+   - **Magnitude:** [Expected effect]
+   - **Falsification:** [Falsifying observation]
+
+2. **Prediction 2.2:** [Additional prediction]
+
+---
+
+### Predictions from Hypothesis 3
+
+1. **Prediction 3.1:** [Specific, measurable prediction]
+   - **Conditions:** [Conditions]
+   - **Magnitude:** [Expected effect]
+   - **Falsification:** [Falsifying observation]
+
+---
+
+## 6. Critical Comparisons
+
+### Distinguishing Between Hypotheses
+
+**Comparison: Hypothesis 1 vs. Hypothesis 2**
+
+*Key Distinguishing Feature:*
+[What is the fundamental difference in mechanism or prediction?]
+
+*Discriminating Experiment:*
+[What experiment or observation would clearly favor one over the other?]
+
+*Outcome Interpretation:*
+- If [Result A], then Hypothesis 1 is supported
+- If [Result B], then Hypothesis 2 is supported
+- If [Result C], then both/neither are supported
+
+---
+
+**Comparison: Hypothesis 1 vs. Hypothesis 3**
+[Similar structure]
+
+---
+
+**Comparison: Hypothesis 2 vs. Hypothesis 3**
+[Similar structure]
+
+---
+
+### Priority Experiments
+
+**Highest Priority Test:**
+[Which experiment would most efficiently distinguish between hypotheses or most definitively test a hypothesis?]
+
+**Justification:**
+[Why is this the highest priority? Consider informativeness, feasibility, and cost]
+
+**Secondary Priority Tests:**
+1. [Second most important experiment]
+2. [Third most important]
+
+---
+
+## 7. Summary & Recommendations
+
+### Summary of Hypotheses
+
+[Brief paragraph summarizing the competing hypotheses and their relationships]
+
+### Recommended Testing Sequence
+
+**Phase 1 (Initial Tests):**
+[Which experiments should be done first? Why?]
+
+**Phase 2 (Contingent on Phase 1 results):**
+[What follow-up experiments depend on initial results?]
+
+**Phase 3 (Validation and Extension):**
+[How to validate findings and extend to broader contexts?]
+
+### Expected Outcomes and Implications
+
+**If Hypothesis 1 is supported:**
+[What would this mean for the field? What new questions arise?]
+
+**If Hypothesis 2 is supported:**
+[Implications and new questions]
+
+**If Hypothesis 3 is supported:**
+[Implications and new questions]
+
+**If multiple hypotheses are partially supported:**
+[How might mechanisms combine or interact?]
+
+### Open Questions
+
+[What questions remain even after these hypotheses are tested?]
+
+---
+
+## References
+
+[List key papers cited in the document, formatted consistently]
+
+1. Author1, A.B., & Author2, C.D. (Year). Title of paper. *Journal Name*, Volume(Issue), pages. DOI or URL
+
+2. [Continue for all citations]
+
+---
+
+## Notes on Using This Template
+
+- Replace all bracketed instructions with actual content
+- Not all sections are mandatory - adapt to your specific hypothesis generation task
+- For simpler phenomena, 3 hypotheses may be sufficient; complex phenomena may warrant 4-5
+- Experimental designs should be detailed enough to be actionable but can be refined later
+- Predictions should be as specific and quantitative as possible
+- The template emphasizes both generating hypotheses and planning how to test them
+- Citation format can be adjusted to field-specific standards

skills/hypothesis-generation/references/experimental_design_patterns.md

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+# Experimental Design Patterns
+
+## Common Approaches to Testing Scientific Hypotheses
+
+This reference provides patterns and frameworks for designing experiments across scientific domains. Use these patterns to develop rigorous tests for generated hypotheses.
+
+## Design Selection Framework
+
+Choose experimental approaches based on:
+- **Nature of hypothesis:** Mechanistic, causal, correlational, descriptive
+- **System studied:** In vitro, in vivo, computational, observational
+- **Feasibility:** Time, cost, ethics, technical capabilities
+- **Evidence needed:** Proof-of-concept, causal demonstration, quantitative relationship
+
+## Laboratory Experimental Designs
+
+### In Vitro Experiments
+
+**When to use:** Testing molecular, cellular, or biochemical mechanisms in controlled systems.
+
+**Common patterns:**
+
+#### 1. Dose-Response Studies
+- **Purpose:** Establish quantitative relationship between input and effect
+- **Design:** Test multiple concentrations/doses of intervention
+- **Key elements:**
+  - Negative control (no treatment)
+  - Positive control (known effective treatment)
+  - Multiple dose levels (typically 5-8 points)
+  - Technical replicates (≥3 per condition)
+  - Appropriate statistical analysis (curve fitting, IC50/EC50 determination)
+
+**Example application:**
+"To test if compound X inhibits enzyme Y, measure enzyme activity at 0, 1, 10, 100, 1000 nM compound X concentrations with n=3 replicates per dose."
+
+#### 2. Gain/Loss of Function Studies
+- **Purpose:** Establish causal role of specific component
+- **Design:** Add (overexpression) or remove (knockout/knockdown) component
+- **Key elements:**
+  - Wild-type control
+  - Gain-of-function condition (overexpression, constitutive activation)
+  - Loss-of-function condition (knockout, knockdown, inhibition)
+  - Rescue experiment (restore function to loss-of-function)
+  - Measure downstream effects
+
+**Example application:**
+"Test if protein X causes phenotype Y by: (1) knocking out X and observing phenotype loss, (2) overexpressing X and observing phenotype enhancement, (3) rescuing knockout with X re-expression."
+
+#### 3. Time-Course Studies
+- **Purpose:** Understand temporal dynamics and sequence of events
+- **Design:** Measure outcomes at multiple time points
+- **Key elements:**
+  - Time 0 baseline
+  - Early time points (capture rapid changes)
+  - Intermediate time points
+  - Late time points (steady state)
+  - Sufficient replication at each time point
+
+**Example application:**
+"Measure protein phosphorylation at 0, 5, 15, 30, 60, 120 minutes after stimulus to determine peak activation timing."
+
+### In Vivo Experiments
+
+**When to use:** Testing hypotheses in whole organisms to assess systemic, physiological, or behavioral effects.
+
+**Common patterns:**
+
+#### 4. Between-Subjects Designs
+- **Purpose:** Compare different groups receiving different treatments
+- **Design:** Randomly assign subjects to treatment groups
+- **Key elements:**
+  - Random assignment to groups
+  - Appropriate sample size (power analysis)
+  - Control group (vehicle, sham, or standard treatment)
+  - Blinding (single or double-blind)
+  - Standardized conditions across groups
+
+**Example application:**
+"Randomly assign 20 mice each to vehicle control or drug treatment groups, measure tumor size weekly for 8 weeks, with experimenters blinded to group assignment."
+
+#### 5. Within-Subjects (Repeated Measures) Designs
+- **Purpose:** Each subject serves as own control, reducing inter-subject variability
+- **Design:** Same subjects measured across multiple conditions/time points
+- **Key elements:**
+  - Baseline measurements
+  - Counterbalancing (if order effects possible)
+  - Washout periods (for sequential treatments)
+  - Appropriate repeated-measures statistics
+
+**Example application:**
+"Measure cognitive performance in same participants at baseline, after training intervention, and at 3-month follow-up."
+
+#### 6. Factorial Designs
+- **Purpose:** Test multiple factors and their interactions simultaneously
+- **Design:** Cross all levels of multiple independent variables
+- **Key elements:**
+  - Clear main effects and interactions
+  - Sufficient power for interaction tests
+  - Full factorial or fractional factorial as appropriate
+
+**Example application:**
+"2×2 design crossing genotype (WT vs. mutant) × treatment (vehicle vs. drug) to test whether drug effect depends on genotype."
+
+### Computational/Modeling Experiments
+
+**When to use:** Testing hypotheses about complex systems, making predictions, or when physical experiments are infeasible.
+
+#### 7. In Silico Simulations
+- **Purpose:** Model complex systems, test theoretical predictions
+- **Design:** Implement computational model and vary parameters
+- **Key elements:**
+  - Well-defined model with explicit assumptions
+  - Parameter sensitivity analysis
+  - Validation against known data
+  - Prediction generation for experimental testing
+
+**Example application:**
+"Build agent-based model of disease spread, vary transmission rate and intervention timing, compare predictions to empirical epidemic data."
+
+#### 8. Bioinformatics/Meta-Analysis
+- **Purpose:** Test hypotheses using existing datasets
+- **Design:** Analyze large-scale data or aggregate multiple studies
+- **Key elements:**
+  - Appropriate statistical corrections (multiple testing)
+  - Validation in independent datasets
+  - Control for confounds and batch effects
+  - Clear inclusion/exclusion criteria
+
+**Example application:**
+"Test if gene X expression correlates with survival across 15 cancer datasets (n>5000 patients total), using Cox regression with clinical covariates."
+
+## Observational Study Designs
+
+### When Physical Manipulation is Impossible or Unethical
+
+#### 9. Cross-Sectional Studies
+- **Purpose:** Examine associations at a single time point
+- **Design:** Measure variables of interest in population at one time
+- **Strengths:** Fast, inexpensive, can establish prevalence
+- **Limitations:** Cannot establish temporality or causation
+- **Key elements:**
+  - Representative sampling
+  - Standardized measurements
+  - Control for confounding variables
+  - Appropriate statistical analysis
+
+**Example application:**
+"Survey 1000 adults to test association between diet pattern and biomarker X, controlling for age, sex, BMI, and physical activity."
+
+#### 10. Cohort Studies (Prospective/Longitudinal)
+- **Purpose:** Establish temporal relationships and potentially causal associations
+- **Design:** Follow group over time, measuring exposures and outcomes
+- **Strengths:** Can establish temporality, calculate incidence
+- **Limitations:** Time-consuming, expensive, subject attrition
+- **Key elements:**
+  - Baseline exposure assessment
+  - Follow-up at defined intervals
+  - Minimize loss to follow-up
+  - Account for time-varying confounders
+
+**Example application:**
+"Follow 5000 initially healthy individuals for 10 years, testing if baseline vitamin D levels predict cardiovascular disease incidence."
+
+#### 11. Case-Control Studies
+- **Purpose:** Efficiently study rare outcomes by comparing cases to controls
+- **Design:** Identify cases with outcome, select matched controls, compare exposures
+- **Strengths:** Efficient for rare diseases, relatively quick
+- **Limitations:** Recall bias, selection bias, cannot calculate incidence
+- **Key elements:**
+  - Clear case definition
+  - Appropriate control selection (matching or statistical adjustment)
+  - Retrospective exposure assessment
+  - Control for confounding
+
+**Example application:**
+"Compare 200 patients with rare disease X to 400 matched controls without X, testing if early-life exposure Y differs between groups."
+
+## Clinical Trial Designs
+
+#### 12. Randomized Controlled Trials (RCTs)
+- **Purpose:** Gold standard for testing interventions in humans
+- **Design:** Randomly assign participants to treatment or control
+- **Key elements:**
+  - Randomization (simple, block, or stratified)
+  - Concealment of allocation
+  - Blinding (participants, providers, assessors)
+  - Intention-to-treat analysis
+  - Pre-registered protocol and analysis plan
+
+**Example application:**
+"Double-blind RCT: randomly assign 300 patients to receive drug X or placebo for 12 weeks, measure primary outcome of symptom improvement."
+
+#### 13. Crossover Trials
+- **Purpose:** Each participant receives all treatments in sequence
+- **Design:** Participants crossed over between treatments with washout
+- **Strengths:** Reduces inter-subject variability, requires fewer participants
+- **Limitations:** Order effects, requires reversible conditions, longer duration
+- **Key elements:**
+  - Adequate washout period
+  - Randomized treatment order
+  - Carryover effect assessment
+
+**Example application:**
+"Crossover trial: participants receive treatment A for 4 weeks, 2-week washout, then treatment B for 4 weeks (randomized order)."
+
+## Advanced Design Considerations
+
+### Sample Size and Statistical Power
+
+**Key questions:**
+- What effect size is meaningful to detect?
+- What statistical test will be used?
+- What alpha (significance level) and beta (power) are appropriate?
+- What is expected variability in the measurement?
+
+**General guidelines:**
+- Conduct formal power analysis before experiment
+- For pilot studies, n≥10 per group minimum
+- For definitive studies, aim for ≥80% power
+- Account for potential attrition in longitudinal studies
+
+### Controls
+
+**Types of controls:**
+- **Negative control:** No intervention (baseline)
+- **Positive control:** Known effective intervention (validates system)
+- **Vehicle control:** Delivery method without active ingredient
+- **Sham control:** Mimics intervention without active component (surgery, etc.)
+- **Historical control:** Prior data (weakest, avoid if possible)
+
+### Blinding
+
+**Levels:**
+- **Open-label:** No blinding (acceptable for objective measures)
+- **Single-blind:** Participants blinded (reduces placebo effects)
+- **Double-blind:** Participants and experimenters blinded (reduces bias in assessment)
+- **Triple-blind:** Participants, experimenters, and analysts blinded (strongest)
+
+### Replication
+
+**Technical replicates:** Repeated measurements on same sample
+- Reduce measurement error
+- Typically 2-3 replicates sufficient
+
+**Biological replicates:** Independent samples/subjects
+- Address biological variability
+- Critical for generalization
+- Minimum: n≥3, preferably n≥5-10 per group
+
+**Experimental replicates:** Repeat entire experiment
+- Validate findings across time, equipment, operators
+- Gold standard for confirming results
+
+### Confound Control
+
+**Strategies:**
+- **Randomization:** Distribute confounds evenly across groups
+- **Matching:** Pair similar subjects across conditions
+- **Blocking:** Group by confound, then randomize within blocks
+- **Statistical adjustment:** Measure confounds and adjust in analysis
+- **Standardization:** Keep conditions constant across groups
+
+## Selecting Appropriate Design
+
+**Decision tree:**
+
+1. **Can variables be manipulated?**
+   - Yes → Experimental design (RCT, lab experiment)
+   - No → Observational design (cohort, case-control, cross-sectional)
+
+2. **What is the system?**
+   - Cells/molecules → In vitro experiments
+   - Whole organisms → In vivo experiments
+   - Humans → Clinical trials or observational studies
+   - Complex systems → Computational modeling
+
+3. **What is the primary goal?**
+   - Mechanism → Gain/loss of function, dose-response
+   - Causation → RCT, cohort study with good controls
+   - Association → Cross-sectional, case-control
+   - Prediction → Modeling, machine learning
+   - Temporal dynamics → Time-course, longitudinal
+
+4. **What are the constraints?**
+   - Time limited → Cross-sectional, in vitro
+   - Budget limited → Computational, observational
+   - Ethical concerns → Observational, in vitro
+   - Rare outcome → Case-control, meta-analysis
+
+## Integrating Multiple Approaches
+
+Strong hypothesis testing often combines multiple designs:
+
+**Example: Testing if microbiome affects cognitive function**
+1. **Observational:** Cohort study showing association between microbiome composition and cognition
+2. **Animal model:** Germ-free mice receiving microbiome transplants show cognitive changes
+3. **Mechanism:** In vitro studies showing microbial metabolites affect neuronal function
+4. **Clinical trial:** RCT of probiotic intervention improving cognitive scores
+5. **Computational:** Model predicting which microbiome profiles should affect cognition
+
+**Triangulation approach:**
+- Each design addresses different aspects/limitations
+- Convergent evidence from multiple approaches strengthens causal claims
+- Start with observational/in vitro, then move to definitive causal tests
+
+## Common Pitfalls
+
+- Insufficient sample size (underpowered)
+- Lack of appropriate controls
+- Confounding variables not accounted for
+- Inappropriate statistical tests
+- P-hacking or multiple testing without correction
+- Lack of blinding when subjective assessments involved
+- Failure to replicate findings
+- Not pre-registering analysis plans (clinical trials)
+
+## Practical Application for Hypothesis Testing
+
+When designing experiments to test hypotheses:
+
+1. **Match design to hypothesis specifics:** Causal claims require experimental manipulation; associations can use observational designs
+2. **Start simple, then elaborate:** Pilot with simple design, then add complexity
+3. **Plan controls carefully:** Controls validate the system and isolate the specific effect
+4. **Consider feasibility:** Balance ideal design with practical constraints
+5. **Plan for multiple experiments:** Rarely does one experiment definitively test a hypothesis
+6. **Pre-specify analysis:** Decide statistical tests before data collection
+7. **Build in validation:** Independent replication, orthogonal methods, convergent evidence

skills/hypothesis-generation/references/hypothesis_quality_criteria.md

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+# Hypothesis Quality Criteria
+
+## Framework for Evaluating Scientific Hypotheses
+
+Use these criteria to assess the quality and rigor of generated hypotheses. A robust hypothesis should score well across multiple dimensions.
+
+## Core Criteria
+
+### 1. Testability
+
+**Definition:** The hypothesis can be empirically tested through observation or experimentation.
+
+**Evaluation questions:**
+- Can specific experiments or observations test this hypothesis?
+- Are the predicted outcomes measurable?
+- Can the hypothesis be tested with current or near-future methods?
+- Are there multiple independent ways to test it?
+
+**Strong testability examples:**
+- "Increased expression of protein X will reduce cell proliferation rate by >30%"
+- "Patients receiving treatment Y will show 50% reduction in symptom Z within 4 weeks"
+
+**Weak testability examples:**
+- "This process is influenced by complex interactions" (vague, no specific prediction)
+- "The mechanism involves quantum effects" (if no method to test quantum effects exists)
+
+### 2. Falsifiability
+
+**Definition:** Clear conditions or observations would disprove the hypothesis (Popperian criterion).
+
+**Evaluation questions:**
+- What specific observations would prove this hypothesis wrong?
+- Are the falsifying conditions realistic to observe?
+- Is the hypothesis stated clearly enough to be disproven?
+- Can null results meaningfully falsify the hypothesis?
+
+**Strong falsifiability examples:**
+- "If we knock out gene X, phenotype Y will disappear" (can be falsified if phenotype persists)
+- "Drug A will outperform placebo in 80% of patients" (clear falsification threshold)
+
+**Weak falsifiability examples:**
+- "Multiple factors contribute to the outcome" (too vague to falsify)
+- "The effect may vary depending on context" (built-in escape clauses)
+
+### 3. Parsimony (Occam's Razor)
+
+**Definition:** Among competing hypotheses with equal explanatory power, prefer the simpler explanation.
+
+**Evaluation questions:**
+- Does the hypothesis invoke the minimum number of entities/mechanisms needed?
+- Are all proposed elements necessary to explain the phenomenon?
+- Could a simpler mechanism account for the observations?
+- Does it avoid unnecessary assumptions?
+
+**Parsimony considerations:**
+- Simple ≠ simplistic; complexity is justified when evidence demands it
+- Established mechanisms are "simpler" than novel, unproven ones
+- Direct mechanisms are simpler than elaborate multi-step pathways
+- One well-supported mechanism beats multiple speculative ones
+
+### 4. Explanatory Power
+
+**Definition:** The hypothesis accounts for a substantial portion of the observed phenomenon.
+
+**Evaluation questions:**
+- How much of the observed data does this hypothesis explain?
+- Does it account for both typical and atypical observations?
+- Can it explain related phenomena beyond the immediate observation?
+- Does it resolve apparent contradictions in existing data?
+
+**Strong explanatory power indicators:**
+- Explains multiple independent observations
+- Accounts for quantitative relationships, not just qualitative patterns
+- Resolves previously puzzling findings
+- Makes sense of seemingly contradictory results
+
+**Limited explanatory power indicators:**
+- Only explains part of the phenomenon
+- Requires additional hypotheses for complete explanation
+- Leaves major observations unexplained
+
+### 5. Scope
+
+**Definition:** The range of phenomena and contexts the hypothesis can address.
+
+**Evaluation questions:**
+- Does it apply only to the specific case or to broader situations?
+- Can it generalize across conditions, species, or systems?
+- Does it connect to larger theoretical frameworks?
+- What are its boundaries and limitations?
+
+**Broader scope (generally preferable):**
+- Applies across multiple experimental conditions
+- Generalizes to related systems or species
+- Connects phenomenon to established principles
+
+**Narrower scope (acceptable if explicitly defined):**
+- Limited to specific conditions or contexts
+- Requires different mechanisms in different settings
+- Context-dependent with clear boundaries
+
+### 6. Consistency with Established Knowledge
+
+**Definition:** Alignment with well-supported theories, principles, and empirical findings.
+
+**Evaluation questions:**
+- Is it consistent with established physical, chemical, or biological principles?
+- Does it align with or reasonably extend current theories?
+- If contradicting established knowledge, is there strong justification?
+- Does it require violating well-supported laws or findings?
+
+**Levels of consistency:**
+- **Fully consistent:** Applies established mechanisms in new context
+- **Mostly consistent:** Extends current understanding in plausible ways
+- **Partially inconsistent:** Contradicts some findings but has explanatory value
+- **Highly inconsistent:** Requires rejecting well-established principles (requires exceptional evidence)
+
+### 7. Novelty and Insight
+
+**Definition:** The hypothesis offers new understanding beyond merely restating known facts.
+
+**Evaluation questions:**
+- Does it provide new mechanistic insight?
+- Does it challenge assumptions or conventional wisdom?
+- Does it suggest unexpected connections or relationships?
+- Does it open new research directions?
+
+**Novel contributions:**
+- Proposes previously unconsidered mechanisms
+- Reframes the problem in a productive way
+- Connects disparate observations
+- Suggests non-obvious testable predictions
+
+**Note:** Novelty alone doesn't make a hypothesis valuable; it must also be testable, parsimonious, and explanatory.
+
+## Comparative Evaluation
+
+When evaluating multiple competing hypotheses:
+
+### Trade-offs and Balancing
+
+Hypotheses often involve trade-offs:
+- More parsimonious but less explanatory power
+- Broader scope but less testable with current methods
+- Novel insights but less consistent with current knowledge
+
+**Evaluation approach:**
+- No hypothesis needs to be perfect on all dimensions
+- Identify each hypothesis's strengths and weaknesses
+- Consider which criteria are most important for the specific phenomenon
+- Note which hypotheses are most immediately testable
+- Identify which would be most informative if supported
+
+### Distinguishability
+
+**Key question:** Can experiments distinguish between competing hypotheses?
+
+- Identify predictions that differ between hypotheses
+- Prioritize hypotheses that make distinct predictions
+- Note which experiments would most efficiently narrow the field
+- Consider whether hypotheses could all be partially correct
+
+## Common Pitfalls
+
+### Untestable Hypotheses
+- Too vague to generate specific predictions
+- Invoke unobservable or unmeasurable entities
+- Require technology that doesn't exist
+
+### Unfalsifiable Hypotheses
+- Built-in escape clauses ("may or may not occur")
+- Post-hoc explanations that fit any outcome
+- No specification of what would disprove them
+
+### Overly Complex Hypotheses
+- Invoke multiple unproven mechanisms
+- Add unnecessary steps or entities
+- Complexity not justified by explanatory gains
+
+### Just-So Stories
+- Plausible narratives without testable predictions
+- Explain observations but don't predict new ones
+- Impossible to distinguish from alternative stories
+
+## Practical Application
+
+When generating hypotheses:
+
+1. **Draft initial hypotheses** focusing on mechanistic explanations
+2. **Apply quality criteria** to identify weaknesses
+3. **Refine hypotheses** to improve testability and clarity
+4. **Develop specific predictions** to enhance testability and falsifiability
+5. **Compare systematically** across all criteria
+6. **Prioritize for testing** based on distinguishability and feasibility
+
+Remember: The goal is not a perfect hypothesis, but a set of testable, falsifiable, informative hypotheses that advance understanding of the phenomenon.

skills/hypothesis-generation/references/literature_search_strategies.md

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+# Literature Search Strategies
+
+## Effective Techniques for Finding Scientific Evidence
+
+Comprehensive literature search is essential for grounding hypotheses in existing evidence. This reference provides strategies for both PubMed (biomedical literature) and general scientific search.
+
+## Search Strategy Framework
+
+### Three-Phase Approach
+
+1. **Broad exploration:** Understand the landscape and identify key concepts
+2. **Focused searching:** Target specific mechanisms, theories, or findings
+3. **Citation mining:** Follow references and related articles from key papers
+
+### Before You Search
+
+**Clarify search goals:**
+- What aspects of the phenomenon need evidence?
+- What types of studies are most relevant (reviews, primary research, methods)?
+- What time frame is relevant (recent only, or historical context)?
+- What level of evidence is needed (mechanistic, correlational, causal)?
+
+## PubMed Search Strategies
+
+### When to Use PubMed
+
+Use WebFetch with PubMed URLs for:
+- Biomedical and life sciences research
+- Clinical studies and medical literature
+- Molecular, cellular, and physiological mechanisms
+- Disease etiology and pathology
+- Drug and therapeutic research
+
+### Effective PubMed Search Techniques
+
+#### 1. Start with Review Articles
+
+**Why:** Reviews synthesize literature, identify key concepts, and provide comprehensive reference lists.
+
+**Search strategy:**
+- Add "review" to search terms
+- Use PubMed filters: Article Type → Review, Systematic Review, Meta-Analysis
+- Look for recent reviews (last 2-5 years)
+
+**Example searches:**
+- `https://pubmed.ncbi.nlm.nih.gov/?term=wound+healing+diabetes+review`
+- `https://pubmed.ncbi.nlm.nih.gov/?term=gut+microbiome+cognition+systematic+review`
+
+#### 2. Use MeSH Terms (Medical Subject Headings)
+
+**Why:** MeSH terms are standardized vocabulary that captures concept variations.
+
+**Strategy:**
+- PubMed auto-suggests MeSH terms
+- Helps find papers using different terminology for same concept
+- More comprehensive than keyword-only searches
+
+**Example:**
+- Instead of just "heart attack," use MeSH term "Myocardial Infarction"
+- Captures papers using "MI," "heart attack," "cardiac infarction," etc.
+
+#### 3. Boolean Operators and Advanced Syntax
+
+**AND:** Narrow search (all terms must be present)
+- `diabetes AND wound healing AND inflammation`
+
+**OR:** Broaden search (any term can be present)
+- `(Alzheimer OR dementia) AND gut microbiome`
+
+**NOT:** Exclude terms
+- `cancer treatment NOT surgery`
+
+**Quotes:** Exact phrases
+- `"oxidative stress"`
+
+**Wildcards:** Variations
+- `gene*` finds gene, genes, genetic, genetics
+
+#### 4. Filter by Publication Type and Date
+
+**Publication types:**
+- Clinical Trial
+- Meta-Analysis
+- Systematic Review
+- Research Support, NIH
+- Randomized Controlled Trial
+
+**Date filters:**
+- Recent work (last 2-5 years): Cutting-edge findings
+- Historical work: Foundational studies
+- Specific time periods: Track development of understanding
+
+#### 5. Use "Similar Articles" and "Cited By"
+
+**Strategy:**
+- Find one highly relevant paper
+- Click "Similar articles" for related work
+- Use cited by tools to find newer work building on it
+
+### PubMed Search Examples by Hypothesis Goal
+
+**Mechanistic understanding:**
+```
+https://pubmed.ncbi.nlm.nih.gov/?term=(mechanism+OR+pathway)+AND+[phenomenon]+AND+(molecular+OR+cellular)
+```
+
+**Causal relationships:**
+```
+https://pubmed.ncbi.nlm.nih.gov/?term=[exposure]+AND+[outcome]+AND+(randomized+controlled+trial+OR+cohort+study)
+```
+
+**Biomarkers and associations:**
+```
+https://pubmed.ncbi.nlm.nih.gov/?term=[biomarker]+AND+[disease]+AND+(association+OR+correlation+OR+prediction)
+```
+
+**Treatment effectiveness:**
+```
+https://pubmed.ncbi.nlm.nih.gov/?term=[intervention]+AND+[condition]+AND+(efficacy+OR+effectiveness+OR+clinical+trial)
+```
+
+## General Scientific Web Search Strategies
+
+### When to Use Web Search
+
+Use WebSearch for:
+- Non-biomedical sciences (physics, chemistry, materials, earth sciences)
+- Interdisciplinary topics
+- Recent preprints and unpublished work
+- Grey literature (technical reports, conference proceedings)
+- Broader context and cross-domain analogies
+
+### Effective Web Search Techniques
+
+#### 1. Use Domain-Specific Search Terms
+
+**Include field-specific terminology:**
+- Chemistry: "mechanism," "reaction pathway," "synthesis"
+- Physics: "model," "theory," "experimental validation"
+- Materials science: "properties," "characterization," "synthesis"
+- Ecology: "population dynamics," "community structure"
+
+#### 2. Target Academic Sources
+
+**Search operators:**
+- `site:arxiv.org` - Preprints (physics, CS, math, quantitative biology)
+- `site:biorxiv.org` - Biology preprints
+- `site:edu` - Academic institutions
+- `filetype:pdf` - Academic papers (often)
+
+**Example searches:**
+- `superconductivity high temperature mechanism site:arxiv.org`
+- `CRISPR off-target effects site:biorxiv.org`
+
+#### 3. Search for Authors and Labs
+
+**When you find a relevant paper:**
+- Search for the authors' other work
+- Find their lab website for unpublished work
+- Identify key research groups in the field
+
+#### 4. Use Google Scholar Approaches
+
+**Strategies:**
+- Use "Cited by" to find newer related work
+- Use "Related articles" to expand search
+- Set date ranges to focus on recent work
+- Use author: operator to find specific researchers
+
+#### 5. Combine General and Specific Terms
+
+**Structure:**
+- Specific phenomenon + general concept
+- "tomato plant growth" + "bacterial promotion"
+- "cognitive decline" + "gut microbiome"
+
+**Boolean logic:**
+- Use quotes for exact phrases: `"spike protein mutation"`
+- Use OR for alternatives: `(transmissibility OR transmission rate)`
+- Combine: `"spike protein" AND (transmissibility OR virulence) AND mutation`
+
+## Cross-Database Search Strategies
+
+### Comprehensive Literature Search Workflow
+
+1. **Start with reviews (PubMed or Web Search):**
+   - Identify key concepts and terminology
+   - Note influential papers and researchers
+   - Understand current state of field
+
+2. **Focused primary research (PubMed):**
+   - Search for specific mechanisms
+   - Find experimental evidence
+   - Identify methodologies
+
+3. **Broaden with web search:**
+   - Find related work in other fields
+   - Locate recent preprints
+   - Identify analogous systems
+
+4. **Citation mining:**
+   - Follow references from key papers
+   - Use "cited by" to find recent work
+   - Track influential studies
+
+5. **Iterative refinement:**
+   - Add new terms discovered in papers
+   - Narrow if too many results
+   - Broaden if too few relevant results
+
+## Topic-Specific Search Strategies
+
+### Mechanisms and Pathways
+
+**Goal:** Understand how something works
+
+**Search components:**
+- Phenomenon + "mechanism"
+- Phenomenon + "pathway"
+- Phenomenon + specific molecules/pathways suspected
+
+**Examples:**
+- `diabetic wound healing mechanism inflammation`
+- `autophagy pathway cancer`
+
+### Associations and Correlations
+
+**Goal:** Find what factors are related
+
+**Search components:**
+- Variable A + Variable B + "association"
+- Variable A + Variable B + "correlation"
+- Variable A + "predicts" + Variable B
+
+**Examples:**
+- `vitamin D cardiovascular disease association`
+- `gut microbiome diversity predicts cognitive function`
+
+### Interventions and Treatments
+
+**Goal:** Evidence for what works
+
+**Search components:**
+- Intervention + condition + "efficacy"
+- Intervention + condition + "randomized controlled trial"
+- Intervention + condition + "treatment outcome"
+
+**Examples:**
+- `probiotic intervention depression randomized controlled trial`
+- `exercise intervention cognitive decline efficacy`
+
+### Methods and Techniques
+
+**Goal:** How to test hypothesis
+
+**Search components:**
+- Method name + application area
+- "How to measure" + phenomenon
+- Technique + validation
+
+**Examples:**
+- `CRISPR screen cancer drug resistance`
+- `measure protein-protein interaction methods`
+
+### Analogous Systems
+
+**Goal:** Find insights from related phenomena
+
+**Search components:**
+- Mechanism + different system
+- Similar phenomenon + different organism/condition
+
+**Examples:**
+- If studying plant-microbe symbiosis: search `nitrogen fixation rhizobia legumes`
+- If studying drug resistance: search `antibiotic resistance evolution mechanisms`
+
+## Evaluating Source Quality
+
+### Primary Research Quality Indicators
+
+**Strong quality signals:**
+- Published in reputable journals
+- Large sample sizes (for statistical power)
+- Pre-registered studies (reduces bias)
+- Appropriate controls and methods
+- Consistent with other findings
+- Transparent data and methods
+
+**Red flags:**
+- No peer review (use cautiously)
+- Conflicts of interest not disclosed
+- Methods not clearly described
+- Extraordinary claims without extraordinary evidence
+- Contradicts large body of evidence without explanation
+
+### Review Quality Indicators
+
+**Systematic reviews (highest quality):**
+- Pre-defined search strategy
+- Explicit inclusion/exclusion criteria
+- Quality assessment of included studies
+- Quantitative synthesis (meta-analysis)
+
+**Narrative reviews (variable quality):**
+- Expert synthesis of field
+- May have selection bias
+- Useful for context and framing
+- Check author expertise and citations
+
+## Time Management in Literature Search
+
+### Allocate Search Time Appropriately
+
+**For straightforward hypotheses (30-60 min):**
+- 1-2 broad review articles
+- 3-5 targeted primary research papers
+- Quick web search for recent developments
+
+**For complex hypotheses (1-3 hours):**
+- Multiple reviews for different aspects
+- 10-15 primary research papers
+- Systematic search across databases
+- Citation mining from key papers
+
+**For contentious topics (3+ hours):**
+- Systematic review approach
+- Identify competing perspectives
+- Track historical development
+- Cross-reference findings
+
+### Diminishing Returns
+
+**Signs you've searched enough:**
+- Finding the same papers repeatedly
+- New searches yield mostly irrelevant papers
+- Sufficient evidence to support/contextualize hypotheses
+- Multiple independent lines of evidence converge
+
+**When to search more:**
+- Major gaps in understanding remain
+- Conflicting evidence needs resolution
+- Hypothesis seems inconsistent with literature
+- Need specific methodological information
+
+## Documenting Search Results
+
+### Information to Capture
+
+**For each relevant paper:**
+- Full citation (authors, year, journal, title)
+- Key findings relevant to hypothesis
+- Study design and methods
+- Limitations noted by authors
+- How it relates to hypothesis
+
+### Organizing Findings
+
+**Group by:**
+- Supporting evidence for hypothesis A, B, C
+- Methodological approaches
+- Conflicting findings requiring explanation
+- Gaps in current knowledge
+
+**Synthesis notes:**
+- What is well-established?
+- What is controversial or uncertain?
+- What analogies exist in other systems?
+- What methods are commonly used?
+
+## Practical Search Workflow
+
+### Step-by-Step Process
+
+1. **Define search goals (5 min):**
+   - What aspects of phenomenon need evidence?
+   - What would support or refute hypotheses?
+
+2. **Broad review search (15-20 min):**
+   - Find 1-3 review articles
+   - Skim abstracts for relevance
+   - Note key concepts and terminology
+
+3. **Targeted primary research (30-45 min):**
+   - Search for specific mechanisms/evidence
+   - Read abstracts, scan figures and conclusions
+   - Follow most promising references
+
+4. **Cross-domain search (15-30 min):**
+   - Look for analogies in other systems
+   - Find recent preprints
+   - Identify emerging trends
+
+5. **Citation mining (15-30 min):**
+   - Follow references from key papers
+   - Use "cited by" for recent work
+   - Identify seminal studies
+
+6. **Synthesize findings (20-30 min):**
+   - Summarize evidence for each hypothesis
+   - Note patterns and contradictions
+   - Identify knowledge gaps
+
+### Iteration and Refinement
+
+**When initial search is insufficient:**
+- Broaden terms if too few results
+- Add specific mechanisms/pathways if too many results
+- Try alternative terminology
+- Search for related phenomena
+- Consult review articles for better search terms
+
+**Red flags requiring more search:**
+- Only finding weak or indirect evidence
+- All evidence comes from single lab or source
+- Evidence seems inconsistent with basic principles
+- Major aspects of phenomenon lack any relevant literature
+
+## Common Search Pitfalls
+
+### Pitfalls to Avoid
+
+1. **Confirmation bias:** Only seeking evidence supporting preferred hypothesis
+   - **Solution:** Actively search for contradicting evidence
+
+2. **Recency bias:** Only considering recent work, missing foundational studies
+   - **Solution:** Include historical searches, track development of ideas
+
+3. **Too narrow:** Missing relevant work due to restrictive terms
+   - **Solution:** Use OR operators, try alternative terminology
+
+4. **Too broad:** Overwhelmed by irrelevant results
+   - **Solution:** Add specific terms, use filters, combine concepts with AND
+
+5. **Single database:** Missing important work in other fields
+   - **Solution:** Search both PubMed and general web, try domain-specific databases
+
+6. **Stopping too soon:** Insufficient evidence to ground hypotheses
+   - **Solution:** Set minimum targets (e.g., 2 reviews + 5 primary papers per hypothesis aspect)
+
+7. **Cherry-picking:** Citing only supportive papers
+   - **Solution:** Represent full spectrum of evidence, acknowledge contradictions
+
+## Special Cases
+
+### Emerging Topics (Limited Literature)
+
+**When little published work exists:**
+- Search for analogous phenomena in related systems
+- Look for preprints (arXiv, bioRxiv)
+- Find conference abstracts and posters
+- Identify theoretical frameworks that may apply
+- Note the limited evidence in hypothesis generation
+
+### Controversial Topics (Conflicting Literature)
+
+**When evidence is contradictory:**
+- Systematically document both sides
+- Look for methodological differences explaining conflict
+- Check for temporal trends (has understanding shifted?)
+- Identify what would resolve the controversy
+- Generate hypotheses explaining the discrepancy
+
+### Interdisciplinary Topics
+
+**When spanning multiple fields:**
+- Search each field's primary databases
+- Use field-specific terminology for each domain
+- Look for bridging papers that cite across fields
+- Consider consulting domain experts
+- Translate concepts between disciplines carefully
+
+## Integration with Hypothesis Generation
+
+### Using Literature to Inform Hypotheses
+
+**Direct applications:**
+- Established mechanisms to apply to new contexts
+- Known pathways relevant to phenomenon
+- Similar phenomena in related systems
+- Validated methods for testing
+
+**Indirect applications:**
+- Analogies from different systems
+- Theoretical frameworks to apply
+- Gaps suggesting novel mechanisms
+- Contradictions requiring resolution
+
+### Balancing Literature Dependence
+
+**Too literature-dependent:**
+- Hypotheses merely restate known mechanisms
+- No novel insights or predictions
+- "Hypotheses" are actually established facts
+
+**Too literature-independent:**
+- Hypotheses ignore relevant evidence
+- Propose implausible mechanisms
+- Reinvent already-tested ideas
+- Inconsistent with established principles
+
+**Optimal balance:**
+- Grounded in existing evidence
+- Extend understanding in novel ways
+- Acknowledge both supporting and challenging evidence
+- Generate testable predictions beyond current knowledge