hypothesis-generation▌
K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026
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### 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 expe..."
- ›allowed-tools: "Read Write Edit Bash"
| 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 |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
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:
- Generate at minimum ONE schematic or diagram (e.g., hypothesis framework showing competing explanations)
- 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.mdfor 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):
- Executive Summary - Brief overview in summary box (0.5-1 page)
- 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
\newpagebefore each hypothesis box to prevent content overflow - Each box should be ≤0.6 pages maximum
- IMPORTANT: Use
- Testable Predictions - Key predictions in amber boxes (0.5-1 page)
- 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
\newpagebefore 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:
hypothesisbox1throughhypothesisbox5- 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
\newpagebefore 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:
-
Monitor Box Content Length: Each hypothesis box should fit comfortably on a single page. If content exceeds ~0.7 pages, it will likely overflow.
-
Use Strategic Page Breaks: Insert
\newpagebefore boxes that contain substantial content:\newpage \begin{hypothesisbox1}[Hypothesis 1: Title] % Long content here \end{hypothesisbox1} -
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
-
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
-
Test Page Boundaries: Before each new box, consider if remaining page space is sufficient. If less than 0.6 pages remain, use
\newpageto start the box on a fresh page. -
Appendix Page Management: In appendices, use
\newpagebetween 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 reportshypothesis_report_template.tex- Complete LaTeX template with main text structure and comprehensive appendix sectionsFORMATTING_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
How to use hypothesis-generation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add hypothesis-generation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches hypothesis-generation from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate hypothesis-generation. Access the skill through slash commands (e.g., /hypothesis-generation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★44 reviews- ★★★★★Anika Bhatia· Dec 8, 2024
Registry listing for hypothesis-generation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 4, 2024
Registry listing for hypothesis-generation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Xiao Iyer· Dec 4, 2024
Keeps context tight: hypothesis-generation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Anaya Gonzalez· Nov 27, 2024
hypothesis-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Sakshi Patil· Nov 23, 2024
hypothesis-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Jin Martinez· Nov 23, 2024
I recommend hypothesis-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Xiao Gupta· Oct 18, 2024
I recommend hypothesis-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Oct 14, 2024
I recommend hypothesis-generation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Alexander Liu· Oct 14, 2024
hypothesis-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Oshnikdeep· Sep 25, 2024
hypothesis-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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