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.
Best use case
scienceclaw-classification is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using scienceclaw-classification 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-classification/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scienceclaw-classification Compares
| Feature / Agent | scienceclaw-classification | 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?
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.
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.
Related Guides
SKILL.md Source
# Scientific Classification Skill Classify and categorize scientific content across all disciplines. ## When to Use - "Classify this paper by discipline" - "What research methodology does this use?" - "Categorize these results by topic" - "Assess the quality tier of this journal/paper" - Sorting literature by approach or field - Identifying study design type (RCT, cohort, case-control, etc.) ## When NOT to Use - Simple keyword extraction (use scienceclaw-ie) - Full paper summarization (use scienceclaw-summarization) - Fact verification (use scienceclaw-verification) ## Classification Dimensions ### 1. Discipline Classification Classify into one of 17+ primary disciplines and subdisciplines: - **Natural Sciences**: Physics, Chemistry, Biology, Medicine, Materials Science, Astronomy, Earth Science, Environmental Science, Agricultural Science - **Formal Sciences**: Mathematics, Computer Science - **Social Sciences**: Economics, Sociology, Psychology, Political Science, Linguistics - **Humanities**: Philosophy, History, Law ### 2. Methodology Classification - **Empirical**: experimental, observational, survey, case study - **Theoretical**: mathematical modeling, simulation, analytical - **Computational**: data-driven, machine learning, numerical methods - **Review**: systematic review, meta-analysis, scoping review, narrative review - **Mixed Methods**: combining qualitative and quantitative ### 3. Study Design Classification - Randomized controlled trial (RCT) - Cohort study (prospective/retrospective) - Case-control study - Cross-sectional study - Longitudinal study - Qualitative study (ethnography, phenomenology, grounded theory) ### 4. Quality Assessment - **Tier 1**: High-quality evidence (large RCTs, systematic reviews with meta-analysis) - **Tier 2**: Moderate evidence (cohort studies, well-designed experiments) - **Tier 3**: Low evidence (case reports, expert opinion, preliminary studies) - **Tier 4**: Pre-print or non-peer-reviewed ## Output Format Always structure classification output as: ``` **Discipline**: [Primary] > [Subdiscipline] **Methodology**: [Type] **Study Design**: [Design type] **Quality Tier**: [1-4] — [Justification] **Key Topics**: [topic1, topic2, ...] **Confidence**: [High/Medium/Low] ``` ## Guidelines 1. Always provide confidence level for classifications 2. Note when content spans multiple disciplines (interdisciplinary) 3. Distinguish between primary and secondary methodologies 4. Consider journal impact factor and peer-review status for quality assessment 5. Flag potential misclassifications or ambiguous cases 6. Use standardized vocabulary (MeSH terms for biomedical, ACM CCS for CS, etc.)
Related Skills
scientific-classification
Classify scientific objects, detect patterns, and categorize data across astronomy, biology, and social sciences
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-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.
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.
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