# Hypothesis Generation

Structured hypothesis formulation from observations.

- Repository: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills (4869 stars)
- Category: Design
- License: MIT license
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill hypothesis-generation -g -a claude-code -y`
Codex: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill hypothesis-generation -g -a codex -y`
Cursor: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill hypothesis-generation -g -a cursor -y`
Gemini CLI: `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill hypothesis-generation -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

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  ║  本文件为开源 Skill 原始文档，收录仅供学习与研究参考        ║
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  来源仓库: https://github.com/K-Dense-AI/claude-scientific-skills
  项目名称: claude-scientific-skills
  开源协议: MIT License
  收录日期: 2026-04-02

  声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者
  提供 AI Agent Skills 的集中参考。如有侵权，请联系删除。
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# 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

The rest: https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/blob/HEAD/skills/03-K-Dense-AI-claude-scientific-skills/hypothesis-generation/SKILL.md