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scientific-summarization

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Summarize and simplify scientific literature, educational content, and research papers

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How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/beita6969/ScienceClaw/blob/HEAD/skills/scientific-summarization/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/scientific-summarization/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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