skills/30-zirui-song-claude-skills/lit-review/SKILL.md
Summarize academic papers, extract key findings, and identify research gaps
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research lit-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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When the user provides a paper (PDF, URL, or description), help them systematically analyze and document it for their research.
When summarizing a paper, extract and organize the following:
When the user says:
When comparing multiple papers on a topic:
| Aspect | Paper 1 | Paper 2 | Paper 3 | |--------|---------|---------|---------| | Research Question | | | | | Sample/Period | | | | | Identification | | | | | Main Finding | | | | | Limitation | | | |
When asked to identify gaps, consider:
When summarizing, also provide a BibTeX entry:
@article{AuthorYear,
author = {Last1, First1 and Last2, First2},
title = {Paper Title},
journal = {Journal Name},
year = {YYYY},
volume = {XX},
number = {X},
pages = {XXX--XXX},
doi = {10.xxxx/xxxxx}
}
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".