hypothesis-generation
Structured hypothesis formulation from observations.
Skill by brycewang-stanford
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/brycewang-stanford-hypothesis-generation
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, testing, data, ml
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/hypothesis-generation
Tools it pre-approves 4
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
ReadWriteEditBash
What's inside
- Skill 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.
- License
- MIT
- Allowed tools
- Read, Write, Edit, Bash
- Metadata
- skill-author: K-Dense Inc.
- Instructions
- 231 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves any shell command
Agent Skills spec issues
- files: referenced but missing: assets/hypothesis_report_template.tex
Files (4)
- SKILL.md
- experimental_design_patterns.md · references/
- hypothesis_quality_criteria.md · references/
- literature_search_strategies.md · references/
SKILL.md (first lines)
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档,收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝ 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。 --> # 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:** ```bash 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. --- ## 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
From brycewang-stanford/Auto-Empirical-Research-Skills, MIT.
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Signals
- Stars
- 4,757
- Forks
- 556
- Open issues
- 1
- Last commit (this folder)
- Jul 15, 2026 (3 months ago)
- Last release
- Jul 2, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Website
- copaper.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
hypothesis-generation skill. Agent Mart. https://agentmart.dev/skills/brycewang-stanford-hypothesis-generation (last updated 2026-10-24)