Automate AI/ML research workflows from paper discovery to BibTeX generation.
npx skills add https://github.com/arsity/scholar-tools --skill research-arsity
Please help me install this Agent Skill. Skill: research Source: https://github.com/arsity/scholar-tools/tree/main/skills/research Command: npx skills add https://github.com/arsity/scholar-tools --skill research-arsity
Academic AI/ML research often requires coordinating discovery, reading, brainstorming, citation, and writing across multiple tools. This Skill provides an integrated workflow that channels external content and domain knowledge into a coherent research process.
Ask the skill to surface and organize AI/ML research papers to accelerate discovery and writing.
High-intent search queries and answers about installing and using this skill.
Automating AI/ML research paper discovery and reading involves routing queries through a domain-skill router to find papers, perform deep reading, and generate discussions. This workflow integrates sources like OpenReview and Semantic Scholar to streamline the entire research lifecycle.
Yes, you can generate BibTeX citations automatically for NeurIPS, ICLR, CVF, OpenReview, DBLP, and CrossRef sources. The workflow fetches and caches provenance data to ensure your citation generation is accurate and reproducible for your academic writing.
The best way to draft research sections is using a unified workflow that channels external content and domain knowledge into coherent writing. It transitions directly from brainstorming and deep reading analysis into drafting sections, keeping the entire research process integrated.
Yes, the research workflow integrates directly with Semantic Scholar and AlphaXiv, alongside DBLP, CrossRef, and OpenReview. These integrations handle finding papers, content caching, and provenance tracking to ensure reliable academic research discovery.
Brainstorming research ideas based on existing literature works by injecting expert domain guidance from a wide catalog of AI/ML skills into the workflow. This routes your queries to improve analysis quality and channel external content into coherent idea generation.
Cached workflows for academic research are designed to ensure reproducible results when finding papers and generating BibTeX citations. However, the current implementation focuses specifically on the AI/ML domain, meaning its domain-skill routing may not suit other academic disciplines.
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