Scienceclaw Classification
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scienceclaw-classification is a skill that classifies scientific content by discipline, methodology, study design, and quality tier.
About
Scienceclaw-classification is a skill that categorizes scientific content by discipline, methodology, study design, and quality. It sorts papers and research outputs into 17+ disciplines and rates evidence on a four-tier quality scale. A developer or researcher uses it to organize literature by field or approach and to assess study rigor.
- Classifies scientific content by discipline, methodology, study design, and quality
- Covers 17+ primary disciplines from physics to law
- Assigns a 4-tier evidence-quality rating
Scienceclaw Classification 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-classification capabilities & compatibility
- Capabilities
- research
- Use cases
- research
What scienceclaw-classification says it does
Classify and categorize scientific content across all disciplines.
Classify into one of 17+ primary disciplines and subdisciplines:
npx skills add https://github.com/beita6969/scienceclaw --skill scienceclaw-classificationAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Classify scientific papers by discipline, methodology, study design, and evidence-quality tier.
Who is it for?
Categorizing papers by discipline, identifying study design, and assessing evidence-quality tier.
Skip if: Simple keyword extraction (use scienceclaw-ie), full paper summarization, or fact verification.
When should I use this skill?
You want to categorize a paper by discipline, methodology, study design, or quality tier.
By the numbers
- 17+ primary disciplines
- 4 evidence-quality tiers
Files
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.)
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How many disciplines does it cover?
It classifies into 17+ primary disciplines across natural, formal, and social sciences plus humanities.
How does it rate quality?
It uses four tiers from Tier 1 (high-quality RCTs/meta-analyses) to Tier 4 (pre-print/non-peer-reviewed).