/hypothesis-generation

name: hypothesis-generation
description: Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
allowed-tools: Read Write Edit Bash
license: MIT license
required_environment_variables: [{"name": "OPENROUTER_API_KEY", "prompt": "OpenRouter API key for the skill's LLM-powered steps.", "required_for": "optional features"}]
metadata: {"version": "1.1", "skill-author": "K-Dense Inc.", "openclaw": {"primaryEnv": "OPENROUTER_API_KEY", "envVars": [{"name": "OPENROUTER_API_KEY", "required": false, "description": "OpenRouter API key for the skill's LLM-powered steps."}]}}

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

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:**

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

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.

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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:** Wh