scientific-summarization
Summarize and simplify scientific literature, educational content, and research papers
Best use case
scientific-summarization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Summarize and simplify scientific literature, educational content, and research papers
Teams using scientific-summarization 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/scientific-summarization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How scientific-summarization Compares
| Feature / Agent | scientific-summarization | 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?
Summarize and simplify scientific literature, educational content, and research papers
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 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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