hypothesis-generation
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.
npx skills add majiayu000/claude-skill-registry --skill hypothesis-generation-k-dense-ai-scientific-agent-ski-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 flo
- Overview
- When to Use This Skill
- Visual Enhancement with Scientific Schematics
- Workflow
- 1. Understand the Phenomenon
- 2. Conduct Comprehensive Literature Search
- 3. Synthesize Existing Evidence
- 4. Generate Competing Hypotheses
- 5. Evaluate Hypothesis Quality
- 6. Design Experimental Tests
- 7. Formulate Testable Predictions
- 8. Present Structured Output
- Quality Standards
- Resources
python scripts/generate_schematic.py "your diagram description" -o figures/output.png xelatex hypothesis_report.tex bibtex hypothesis_report
What does the hypothesis-generation skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill hypothesis-generation-k-dense-ai-scientific-agent-ski-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.