scienceclaw-generation
Generate scientific hypotheses, experimental designs, and paper drafts. Use when: user asks to propose hypotheses, design experiments, or write scientific content. NOT for: data analysis or literature search.
Best use case
scienceclaw-generation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate scientific hypotheses, experimental designs, and paper drafts. Use when: user asks to propose hypotheses, design experiments, or write scientific content. NOT for: data analysis or literature search.
Teams using scienceclaw-generation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/scienceclaw-generation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scienceclaw-generation Compares
| Feature / Agent | scienceclaw-generation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate scientific hypotheses, experimental designs, and paper drafts. Use when: user asks to propose hypotheses, design experiments, or write scientific content. NOT for: data analysis or literature search.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
Related Skills
scientific-generation
Generate scientific code, protocols, and domain-specific text with quality control
scientific-diagram-generation
No description provided.
scienceclaw-verification
Verify scientific claims, check calculations, validate experimental designs, and fact-check citations. Use when: (1) checking a claim against evidence, (2) validating statistical analyses, (3) verifying experimental reproducibility claims, (4) fact-checking references, (5) adversarial review of research. NOT for: generating new content (use scienceclaw-generation), simple QA (use scienceclaw-qa).
scienceclaw-summarization
Summarize scientific papers, datasets, experimental results, and literature reviews. Use when: (1) condensing research papers, (2) creating literature reviews, (3) summarizing experimental findings, (4) meta-analysis synthesis, (5) creating executive summaries of research. NOT for: information extraction (use scienceclaw-ie), full paper retrieval (use scienceclaw-retrieval), or writing new content (use scienceclaw-generation).
scienceclaw-retrieval
Retrieve scientific information from databases, literature, and knowledge bases. Use when: (1) finding relevant papers, (2) querying scientific databases, (3) cross-referencing findings, (4) building bibliographies, (5) systematic literature search. NOT for: answering questions (use scienceclaw-qa), summarizing (use scienceclaw-summarization), or data analysis (use code-execution skill).
scienceclaw-reasoning
Perform multi-step scientific reasoning, proof construction, causal inference, and logical argumentation. Use when: (1) deriving conclusions from premises, (2) causal analysis, (3) mathematical proofs, (4) hypothesis evaluation, (5) counterfactual reasoning. NOT for: simple factual questions (use scienceclaw-qa), data analysis (use code-execution), or literature search (use scienceclaw-retrieval).
scienceclaw-qa
Answer scientific questions across all disciplines with evidence-based responses and citations. Use when: (1) user asks factual science questions, (2) needs explanation of concepts/theories/methods, (3) multi-step scientific reasoning needed. Covers natural sciences (physics, chemistry, biology, medicine, materials, astronomy, earth science, math, CS) and social sciences (economics, sociology, psychology, political science, linguistics, history, law, philosophy, education). NOT for: opinion-based questions, non-scientific queries, or when code execution is needed (use code-execution skill).
scienceclaw-prediction
Predict scientific properties, trends, and outcomes. Use when: user asks for property prediction, trend forecasting, or model-based estimation. NOT for: historical data lookup or real-time monitoring.
scienceclaw-ie
Extract structured information from scientific texts: entities, relations, data tables, methods, results. Use when: (1) parsing papers for key data, (2) extracting experimental parameters, (3) building knowledge graphs from literature, (4) NER on scientific documents, (5) extracting methods/results sections. NOT for: summarization (use scienceclaw-summarization), full text retrieval (use scienceclaw-retrieval).
scienceclaw-discovery
Identify research gaps, synthesize cross-disciplinary insights, and generate novel hypotheses. Use when: user asks about unexplored areas, cross-field connections, or new research directions. NOT for: routine literature review or data analysis.
scienceclaw-classification
Classify scientific content by discipline, methodology, topic, and quality. Use when: user asks to categorize papers, methods, or research outputs. NOT for: simple keyword tagging or non-scientific content.
pptx-generation
No description provided.