Search and analyze research literature across local and scholarly sources.
npx skills add https://github.com/Shallow-W/llm-wiki --skill research-lit-shallow-w
Please help me install this Agent Skill. Skill: research-lit Source: https://github.com/Shallow-W/llm-wiki/tree/main/.claude/skills/research-lit Command: npx skills add https://github.com/Shallow-W/llm-wiki --skill research-lit-shallow-w
This Skill helps you locate relevant research papers and produce an organized literature review instead of manually searching across fragmented sources.
Ask the system to run a literature search for diffusion models and include both local and web sources.
High-intent search queries and answers about installing and using this skill.
To find relevant research papers, this Skill searches local PDFs, Zotero, Obsidian notes, and external scholarly sources like arXiv and Semantic Scholar simultaneously. It ranks candidates by relevance and removes duplicates to produce a coherent set of related work.
Yes, you can generate a literature review using both local libraries and web sources. The Skill requires YAML-configured source selection to query local PDFs or external academic APIs, ensuring your review covers all available materials.
Citation verification is integrated directly into the paper discovery workflow. The Skill filters out fabricated or incorrect bibliographic identifiers by validating candidate papers before summarizing them, ensuring only accurate academic literature is included.
When MCP integrations or helper tools are unavailable, the paper search employs robust fallbacks to maintain functionality. This ensures you can still perform topic-focused relevance ranking and academic summarization without interruption.
To summarize arXiv papers, the Skill retrieves arXiv PDF metadata and structured paper information. It then analyzes the content to provide academic summarization, helping you quickly digest related-work discovery results.
The best way to deduplicate search results across arXiv and Semantic Scholar is through the Skill's topic-focused de-duplication feature. It automatically removes duplicate papers found across multiple indexes during the literature search process.
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