Scientific Summarization
- 16 installs
- 869 repo stars
- Updated June 8, 2026
- beita6969/scienceclaw
scientific-summarization is a Claude skill that summarizes and simplifies scientific papers and educational content into structured, lay, technical, or educational summaries while preserving numerical results.
About
This skill summarizes and simplifies scientific papers, educational materials, and technical documents. A developer or researcher uses it to produce structured abstracts, plain-language lay summaries, technical briefs, or educational summaries calibrated to a target audience. It enforces a fidelity check so numerical results like p-values and confidence intervals are preserved exactly and no claims are introduced beyond the source.
- Generates structured, lay, technical, and educational summaries of scientific papers
- Enforces fidelity: preserves p-values and effect sizes exactly, flags author-stated limitations
- Five-step protocol from document analysis to hallucination check
Scientific Summarization by the numbers
- 16 all-time installs (skills.sh)
- Ranked #1,049 of 1,879 Documentation skills by installs in the Skillselion catalog
- Data as of Aug 2, 2026 (Skillselion catalog sync)
scientific-summarization capabilities & compatibility
- Capabilities
- research · documentation
- Use cases
- research · documentation
What scientific-summarization says it does
Generate concise, accurate summaries of scientific papers, educational materials, and complex technical documents.
Preserve numerical results exactly (p-values, effect sizes, confidence intervals)
Verify no hallucinated claims; all statements traceable to source
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| Installs | 16 |
|---|---|
| repo stars | ★ 869 |
| Last updated | June 8, 2026 |
| Repository | beita6969/scienceclaw ↗ |
What it does
Summarize a research paper into an audience-appropriate structured, lay, or technical brief without hallucinating claims.
Who is it for?
Condensing scientific papers and educational materials into faithful summaries for experts, students, or the public.
Skip if: Summarizing content where invented or extrapolated claims are acceptable.
When should I use this skill?
You need a faithful summary of a research paper or educational document calibrated to a specific audience.
What you get
A structured summary where every claim is traceable to the source and numerical results are preserved exactly.
- structured abstract
- lay summary
- technical brief
By the numbers
- 5-step summarization protocol
- 4 summary types
Files
Scientific Summarization & Simplification
Purpose
Generate concise, accurate summaries of scientific papers, educational materials, and complex technical documents.
Key Datasets
- PubMed Summarization (ccdv/pubmed-summarization): Article-abstract pairs for biomedical summarization
- LearningQ (AngusGLChen/LearningQ): TED-Ed (7K) + Khan Academy (223K) educational QA for learning-oriented summarization
Protocol
1. Document analysis — Identify paper structure (IMRaD, review, case report) 2. Key claim extraction — Extract main findings, methods, and conclusions 3. Audience calibration — Adjust complexity to target audience (expert, student, public) 4. Summary generation — Structured summary with key takeaways 5. Fidelity check — Verify no hallucinated claims; all statements traceable to source
Summary Types
- Structured abstract: Background, Methods, Results, Conclusions
- Lay summary: Plain-language explanation for non-experts
- Technical brief: Key findings and implications for domain experts
- Educational summary: Concept-first explanation with learning objectives
Rules
- Never introduce claims not present in the source material
- Preserve numerical results exactly (p-values, effect sizes, confidence intervals)
- Flag study limitations mentioned by authors
- Distinguish between authors' conclusions and your interpretation
- For educational content, maintain pedagogical structure
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What summary types does it produce?
Structured abstract, lay summary, technical brief, and educational summary.
How does it avoid hallucination?
A fidelity check verifies no claims are added and every statement is traceable to the source, preserving p-values, effect sizes, and confidence intervals exactly.