Traction scoreGitHub stars can be faked, so popularity alone can be misleading. Traction Score looks for broader signs of recent attention, adoption, and active maintenance.
Paper Finder searches ML/AI/CV/NLP research papers across arxiv, Semantic Scholar, Google Scholar, and top conferences, but its real strength is the multi-angle strategy: it also surfaces papers by cross-domain synonyms, enabling mechanism terms, and motivating application framings. The skill maintains a per-topic memory bank and mind-graph that grows with every search, tracks BibTeX references, and invites deeper paper analysis via companion skills. It serves researchers, graduate students, and interdisciplinary scientists who need thorough coverage of a topic without manually repeating searches across venues and vocabularies.
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
Optional kebab-case topic folder name ([TOPIC-FOLDER]); otherwise derive one from the query
Web-search and web-fetch access
Check the repository for this skill’s setup.
Use the paper-finder skill to find relevant ML, AI, computer vision, or NLP research papers about [TOPIC]. Use web search and the documented multi-source search strategy. Create or use the kebab-case topic folder [TOPIC-FOLDER], then organize the initial findings in its memory bank, mind graph, and BibTeX reference file. Do not download PDFs unless I ask.
Claude