hypothesis-generation▌
davila7/claude-code-templates · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
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.
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
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 davila7/claude-code-templates 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▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★71 reviews- ★★★★★Chaitanya Patil· Dec 28, 2024
Solid pick for teams standardizing on skills: hypothesis-generation is focused, and the summary matches what you get after install.
- ★★★★★Hassan Park· Dec 28, 2024
We added hypothesis-generation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aisha Flores· Dec 28, 2024
hypothesis-generation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Camila Choi· Dec 20, 2024
Solid pick for teams standardizing on skills: hypothesis-generation is focused, and the summary matches what you get after install.
- ★★★★★Olivia Smith· Dec 4, 2024
hypothesis-generation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Piyush G· Nov 19, 2024
We added hypothesis-generation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Daniel Bhatia· Nov 19, 2024
Solid pick for teams standardizing on skills: hypothesis-generation is focused, and the summary matches what you get after install.
- ★★★★★Soo Johnson· Nov 19, 2024
hypothesis-generation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Camila Jackson· Nov 11, 2024
We added hypothesis-generation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ira Ndlovu· Nov 11, 2024
Registry listing for hypothesis-generation matched our evaluation — installs cleanly and behaves as described in the markdown.
showing 1-10 of 71