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hypothesis-gen

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Structured hypothesis generation workflow. Use when: user needs to formulate testable scientific hypotheses from observations, gaps, or preliminary data. NOT for: testing hypotheses or running experiments.

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Source SKILL.md: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/hypothesis-gen/SKILL.md

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Hypothesis Generation Skill

Structured workflow for generating testable scientific hypotheses.

When to Use

  • "Generate hypotheses for this research question"
  • "What could explain this observation?"
  • "Propose testable ideas based on this data"
  • "Help me formulate H0 and H1"
  • "What hypotheses does this gap suggest?"

When NOT to Use

  • Testing/verifying hypotheses (use scienceclaw-verification)
  • Designing experiments (use experimental-design)
  • Literature searching (use literature-search)
  • Writing full papers (use paper-writing)

Generation Workflow

Step 1: Observe

Identify the phenomenon, anomaly, or gap:

  • What was observed?
  • What is unexpected or unexplained?
  • What contradicts existing theory?
  • What data pattern needs explanation?

Step 2: Contextualize

Ground in existing literature:

  • What do current theories predict?
  • What related findings exist?
  • Where are the knowledge gaps?
  • What alternative explanations exist?

Step 3: Formulate

State the hypothesis formally:

Template: "If [independent variable/condition], then [predicted effect on dependent variable], because [proposed mechanism]."

Null Hypothesis (H0): No effect / no difference / no relationship Alternative Hypothesis (H1): The predicted effect exists Directional: Specify direction (increase/decrease) when justified

Step 4: Evaluate

Score each hypothesis on:

CriterionScore (1-5)Description
Testability_Can be experimentally tested?
Falsifiability_Can be proven wrong?
Novelty_How new is this idea?
Mechanism_Is the proposed mechanism plausible?
Feasibility_Can current methods test it?
Impact_How significant if confirmed?

Step 5: Prioritize

Rank hypotheses by:

  • Total evaluation score
  • Risk-reward ratio (impact / feasibility)
  • Alignment with available resources
  • Potential for publication

Output Format

## Hypothesis [N]: [Short title]

**Statement**: If [condition], then [prediction], because [mechanism].
**H0**: [Null hypothesis]
**H1**: [Alternative hypothesis]

**Variables**:
- Independent: [variable]
- Dependent: [variable]
- Controls: [variables to hold constant]

**Evaluation**: Testability=[X] Falsifiability=[X] Novelty=[X] Mechanism=[X] Feasibility=[X] Impact=[X]
**Priority Score**: [Total/30]

**Key References**: [relevant citations]
**Suggested Test**: [brief experimental approach]

Quality Checklist

  • Hypothesis is specific and testable
  • Variables are clearly identified
  • Mechanism is plausible given known science
  • Null hypothesis is properly stated
  • Predictions are measurable
  • At least one path to falsification exists
  • Novel relative to existing literature
  • Ethical considerations addressed