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HY

Hypothesis Generation

Maintainer K-Dense Inc. · Last updated April 1, 2026

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.

Claude CodeOpenClawNanoClawTrainingEvaluationhypothesis-generationanalysisanalysis & methodologyhypothesis generation

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/hypothesis-generation

Maintainer
K-Dense Inc.
License
MIT license
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • 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.
  • Developing hypotheses from observations or preliminary data.
  • Designing experiments to test scientific questions.
  • Exploring competing explanations for phenomena.
  • Formulating testable predictions for research.

Source Doc

Excerpt From SKILL.md

Visual Enhancement with Scientific Schematics

⚠️ MANDATORY: Every hypothesis generation report MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.

This is not optional. Hypothesis reports without visual elements are incomplete. Before finalizing any document:

  1. Generate at minimum ONE schematic or diagram (e.g., hypothesis framework showing competing explanations)
  2. Prefer 2-3 figures for comprehensive reports (mechanistic pathway, experimental design flowchart, prediction decision tree)

How to generate figures:

  • Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
  • Simply describe your desired diagram in natural language
  • Nano Banana Pro will automatically generate, review, and refine the schematic

How to generate schematics:

The AI will automatically:

  • Create publication-quality images with proper formatting
  • Review and refine through multiple iterations
  • Ensure accessibility (colorblind-friendly, high contrast)
  • Save outputs in the figures/ directory

When to add schematics:

  • Hypothesis framework diagrams showing competing explanations
  • Experimental design flowcharts
  • Mechanistic pathway diagrams
  • Prediction decision trees
  • Causal relationship diagrams
  • Theoretical model visualizations
  • Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.


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)

Use cases

  • Developing hypotheses from observations or preliminary data.
  • Designing experiments to test scientific questions.
  • Exploring competing explanations for phenomena.
  • Formulating testable predictions for research.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.
  • Do not treat this catalog entry as a substitute for the full upstream workflow.

Upstream Related Skills

  • venue_writing_styles.md - Master guide comparing styles across venues
  • Venue-specific guides for Nature/Science, Cell Press, medical journals, and ML/CS conferences
  • reviewer_expectations.md - What reviewers look for when evaluating research hypotheses

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