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literature-in-depth-reading
Deep structured analysis of academic papers using a first-principles, domain-expert framework.
README
Literature In-Depth Reading 
A reusable prompt that makes any LLM read academic papers deeply, using a first-principles, domain-expert framework. Works with ChatGPT, Claude, Gemini, local models, or Claude Code as a native plugin.
Use
Install once per platform. After that, just send the paper — no more copy-paste.
Claude Code
Inside Claude Code, run:
/plugin marketplace add XuananLu/literature-In-depth-reading-skill
/plugin install literature-in-depth-reading@xuananlu
The skill then auto-triggers whenever you hand Claude Code a paper.
Other platforms
Copy the body of skills/literature-in-depth-reading/SKILL.md (everything below the --- frontmatter) and paste it into the platform's persistent-instruction slot — once:
- Claude.ai — create a Project → paste into Project instructions.
- ChatGPT — create a Custom GPT → paste into Instructions.
- Gemini — create a Gem → paste into Gem instructions.
- Any API — put it in the
systemfield of your requests.
From then on, every chat / call inside that Project / GPT / Gem already knows the framework. Just send the paper.
One-off try without setup: paste the prompt body as the first message in a fresh chat, then send the paper.
Supported inputs
- Pasted text
- PDF file (upload or drag in, if your platform supports it)
- arXiv or other paper URL (if your platform can fetch URLs)
Example prompts
Any language, the output matches your language.
analyze this paperwhat's the novelty herebreak down the motivation分析这篇论文
What you get
A structured analysis in six sections:
- Task — the problem, stated formally.
- Challenge — why it is hard and where prior methods fail.
- Insight & Novelty — Inspiration → Insight → Novelty, with each novelty written as
[problem] → [insight] → [design]. - Potential Flaw — where the setting is too narrow, what data would break it, and which flaw is worth a follow-up paper.
- Motivation — the author's idea reconstructed as first-principles questions.
- Code — link to the official repo and a walkthrough of the code that implements the core novelty.
Ground rules
- Every claim about the paper is traceable to the paper.
- Outside knowledge is labeled: "Based on established knowledge…".
- If the paper does not cover something, the prompt says so instead of guessing.
- Code is only discussed when it is verifiable — from the repo or the paper itself.
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Version1.0.0
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MaintenanceGood
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