Scienceclaw Generation
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scienceclaw-generation is a skill that generates scientific hypotheses, experimental designs, and scientific writing across disciplines.
About
Scienceclaw-generation is a skill that produces scientific hypotheses, experimental designs, and paper drafts. It formulates testable hypotheses, plans experiments with sample-size and analysis details, and drafts IMRaD sections. A researcher uses it to propose hypotheses, design studies, or write scientific content grounded in existing knowledge.
- Generates testable hypotheses in H0/H1 form with variables and predictions
- Designs experiments with sample-size, controls, analysis plan, and ethics
- Drafts IMRaD scientific writing sections
Scienceclaw Generation by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,318 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
scienceclaw-generation capabilities & compatibility
- Capabilities
- research
- Use cases
- research
What scienceclaw-generation says it does
Generate hypotheses, experimental designs, and scientific writing across all disciplines.
Format: "If [independent variable] then [predicted effect on dependent variable] because [mechanism/rationale]"
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Propose testable hypotheses, design experiments, and draft IMRaD scientific writing for a research question.
Who is it for?
Proposing hypotheses, designing experiments, and drafting IMRaD scientific writing.
Skip if: Running data analysis, literature searching, verifying claims, or pure information extraction.
When should I use this skill?
You need to propose hypotheses, design an experiment, or draft a methods or abstract section.
What you get
Testable hypotheses, a reproducible experimental design, and IMRaD draft sections.
- Testable hypotheses
- Experimental design with analysis plan
- IMRaD draft sections
By the numbers
- 4 generation types: hypothesis, experimental design, writing, research questions
Files
Scientific Generation Skill
Generate hypotheses, experimental designs, and scientific writing across all disciplines.
When to Use
- "Propose hypotheses for this research question"
- "Design an experiment to test..."
- "Draft a methods section for..."
- "Generate research questions for this topic"
- "Write an abstract for these findings"
- Planning new research directions
When NOT to Use
- Running data analysis (use code-execution + scipy-analysis)
- Literature searching (use literature-search)
- Verifying claims (use scienceclaw-verification)
- Pure information extraction (use scienceclaw-ie)
Generation Types
1. Hypothesis Generation
Follow the structured workflow: 1. Observation: State the observed phenomenon or gap 2. Literature Context: Reference existing knowledge and gaps 3. Hypothesis Statement: Formulate as testable H0/H1 4. Variables: Identify independent, dependent, and control variables 5. Predictions: State specific, measurable predictions 6. Falsifiability: Explain what would disprove the hypothesis 7. Novelty Assessment: Rate novelty (incremental/moderate/transformative)
Format: "If [independent variable] then [predicted effect on dependent variable] because [mechanism/rationale]"
2. Experimental Design
Include all components:
- Objective: Clear research question
- Design Type: RCT, factorial, quasi-experimental, etc.
- Sample: Size calculation (power analysis), selection criteria, randomization
- Variables: IV, DV, controls, confounds
- Protocol: Step-by-step procedure
- Analysis Plan: Statistical tests, significance thresholds
- Ethics: IRB/IACUC considerations
- Reproducibility Checklist: Materials, data sharing, pre-registration
3. Scientific Writing
Support all IMRaD sections:
- Introduction: Background, gap, objective, significance
- Methods: Detailed, reproducible protocol
- Results: Findings with statistical reporting
- Discussion: Interpretation, limitations, implications
- Abstract: Structured summary (Background, Methods, Results, Conclusions)
4. Research Question Generation
From a broad topic, generate:
- Descriptive questions (What/How/When)
- Comparative questions (differences between groups)
- Correlational questions (relationships between variables)
- Causal questions (cause-effect with mechanisms)
Quality Criteria
All generated content must: 1. Be grounded in existing scientific knowledge 2. Use discipline-appropriate terminology 3. Be specific and testable (for hypotheses) 4. Include feasibility assessment 5. Consider ethical implications 6. Acknowledge limitations and assumptions 7. Cite relevant foundational work when possible
Citation Format
When referencing prior work in generated content, use:
- Inline: (Author et al., Year) or [DOI]
- Note which citations need verification
- Distinguish confirmed vs. suggested references
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What can it generate?
Hypotheses in H0/H1 form, experimental designs with power analysis and ethics, IMRaD writing, and research questions.
How are hypotheses formatted?
As 'If [independent variable] then [predicted effect] because [mechanism/rationale]', with a falsifiability statement.