Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问clear审计提醒

hypothesis-generation假设生成

Agent Skill

hypothesis-generation 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,160

周安装

90

GitHub Stars

19,732

下载量

720
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:hypothesis-generation(假设生成)
来源仓库:https://github.com/k-dense-ai/claude-scientific-skills
仓库路径:skills/hypothesis-generation
安装命令:
npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill hypothesis-generation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill hypothesis-generation

简介

hypothesis-generation 用于查找、检索和筛选相关信息,支持假设生成任务。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。
  • 可结合来源仓库和原始 README 核验具体用法与参数。
  • 安装前建议确认权限范围、维护状态及是否触发联网或文件读写。
  • 安装方式:github,命令:npx skills add https://github.com/k-dense-ai/claude-scientific-skills --skill hypothesis-generation

SKILL.md

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.


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: What range of observations does it cover? Consistency: Does it align with established principles? Novelty: Does it offer new insights beyond existing explanations?

Explicitly note the strengths and weaknesses of each hypothesis.

6. Design Experimental Tests

For each viable hypothesis, propose specific experiments or studies to test it. Consult references/experimental_design_patterns.md for common approaches:

Experimental design elements:

  • What would be measured or observed?
  • What comparisons or controls are needed?
  • What methods or techniques would be used?
  • What sample sizes or statistical approaches are appropriate?
  • What are potential confounds and how to address them?

Consider multiple approaches:

  • Laboratory experiments (in vitro, in vivo, computational)
  • Observational studies (cross-sectional, longitudinal, case-control)
  • Clinical trials (if applicable)
  • Natural experiments or quasi-experimental designs

7. Formulate Testable Predictions

For each hypothesis, generate specific, quantitative predictions:

  • State what should be observed if the hypothesis is correct
  • Specify expected direction and magnitude of effects when possible
  • Identify conditions under which predictions should hold
  • Distinguish predictions between competing hypotheses
  • Note predictions that would falsify the hypothesis

8. Present Structured Output

Generate a professional LaTeX document using the template in assets/hypothesis_report_template.tex. The report should be well-formatted with colored boxes for visual organization and divided into a concise main text with comprehensive appendices.

Document Structure:

Main Text (Maximum 4 pages):

  1. Executive Summary - Brief overview in summary box (0.5-1 page)
  2. Competing Hypotheses - Each hypothesis in its own colored box with brief mechanistic explanation and key evidence (2-2.5 pages for 3-5 hypotheses)

- IMPORTANT: Use \newpage before each hypothesis box to prevent content overflow - Each box should be ≤0.6 pages maximum

  1. Testable Predictions - Key predictions in amber boxes (0.5-1 page)
  2. Critical Comparisons - Priority comparison boxes (0.5-1 page)

Keep main text highly concise - only the most essential information. All details go to appendices.

Page Break Strategy:

  • Always use \newpage before hypothesis boxes to ensure they start on fresh pages
  • This prevents content from overflowing off page boundaries
  • LaTeX boxes (tcolorbox) do not automatically break across pages

Appendices (Comprehensive, Detailed):

  • Appendix A: Comprehensive literature review with extensive citations
  • Appendix B: Detailed experimental designs with full protocols
  • Appendix C: Quality assessment tables and detailed evaluations
  • Appendix D: Supplementary evidence and analogous systems

Colored Box Usage:

Use the custom box environments from hypothesis_generation.sty:

  • hypothesisbox1 through hypothesisbox5 - For each competing hypothesis (blue, green, purple, teal, orange)
  • predictionbox - For testable predictions (amber)
  • comparisonbox - For critical comparisons (steel gray)
  • evidencebox - For supporting evidence highlights (light blue)
  • summarybox - For executive summary (blue)

Each hypothesis box should contain (keep concise for 4-page limit):

  • Mechanistic Explanation: 1-2 brief paragraphs (6-10 sentences max) explaining HOW and WHY
  • Key Supporting Evidence: 2-3 bullet points with citations (most important evidence only)
  • Core Assumptions: 1-2 critical assumptions

All detailed explanations, additional evidence, and comprehensive discussions belong in the appendices.

Critical Overflow Prevention:

  • Insert \newpage before each hypothesis box to start it on a fresh page
  • Keep each complete hypothesis box to ≤0.6 pages (approximately 15-20 lines of content)
  • If content exceeds this, move additional details to Appendix A
  • Never let boxes overflow off page boundaries - this creates unreadable PDFs

Citation Requirements:

Aim for extensive citation to support all claims:

  • Main text: 10-15 key citations for most important evidence only (keep concise for 4-page limit)
  • Appendix A: 40-70+ comprehensive citations covering all relevant literature
  • Total target: 50+ references in bibliography

Main text citations should be selective - cite only the most critical papers. All comprehensive citation and detailed literature discussion belongs in the appendices. Use \citep{author2023} for parenthetical citations.

LaTeX Compilation:

The template requires XeLaTeX or LuaLaTeX for proper rendering:

xelatex hypothesis_report.tex
bibtex hypothesis_report
xelatex hypothesis_report.tex
xelatex hypothesis_report.tex

Required packages: The hypothesis_generation.sty style package must be in the same directory or LaTeX path. It requires: tcolorbox, xcolor, fontspec, fancyhdr, titlesec, enumitem, booktabs, natbib.

Page Overflow Prevention:

To prevent content from overflowing on pages, follow these critical guidelines:

  1. Monitor Box Content Length: Each hypothesis box should fit comfortably on a single page. If content exceeds ~0.7 pages, it will likely overflow.
  2. Use Strategic Page Breaks: Insert \newpage before boxes that contain substantial content: \newpage \begin{hypothesisbox1}[Hypothesis 1: Title] % Long content here \end{hypothesisbox1}
  3. Keep Main Text Boxes Concise: For the 4-page main text limit:

- Each hypothesis box: Maximum 0.5-0.6 pages - Mechanistic explanation: 1-2 brief paragraphs only (6-10 sentences max) - Key evidence: 2-3 bullet points only - Core assumptions: 1-2 items only - If content is longer, move details to appendices

  1. Break Long Content: If a hypothesis requires extensive explanation, split across main text and appendix:

- Main text box: Brief mechanistic overview + 2-3 key evidence points - Appendix A: Detailed mechanism explanation, comprehensive evidence, extended discussion

  1. Test Page Boundaries: Before each new box, consider if remaining page space is sufficient. If less than 0.6 pages remain, use \newpage to start the box on a fresh page.
  2. Appendix Page Management: In appendices, use \newpage between major sections to avoid overflow in detailed content areas.

Quick Reference: See assets/FORMATTING_GUIDE.md for detailed examples of all box types, color schemes, and common formatting patterns.

Quality Standards

Ensure all generated hypotheses meet these standards:

  • Evidence-based: Grounded in existing literature with citations
  • Testable: Include specific, measurable predictions
  • Mechanistic: Explain how/why, not just what
  • Comprehensive: Consider alternative explanations
  • Rigorous: Include experimental designs to test predictions

Resources

references/

  • hypothesis_quality_criteria.md - Framework for evaluating hypothesis quality (testability, falsifiability, parsimony, explanatory power, scope, consistency)
  • experimental_design_patterns.md - Common experimental approaches across domains (RCTs, observational studies, lab experiments, computational models)
  • literature_search_strategies.md - Effective search techniques for PubMed and general scientific sources

assets/

  • hypothesis_generation.sty - LaTeX style package providing colored boxes, professional formatting, and custom environments for hypothesis reports
  • hypothesis_report_template.tex - Complete LaTeX template with main text structure and comprehensive appendix sections
  • FORMATTING_GUIDE.md - Quick reference guide with examples of all box types, color schemes, citation practices, and troubleshooting tips

Related Skills

When preparing hypothesis-driven research for publication, consult the venue-templates skill for writing style guidance:

  • 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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

24.84%
按下载量换算179

OpenCode

23.24%
按下载量换算167

Gemini CLI

18.19%
按下载量换算131

Codex

11.12%
按下载量换算80

Antigravity

7.96%
按下载量换算57

Cursor

3.29%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

继续浏览同类 Skills