This repository is a collection of Claude Code skills and Python scripts that automate daily paper recommendation and note-taking for researchers. It fetches papers from HuggingFace Daily Papers, HuggingFace Trending, and arXiv API, scores them against user-defined keywords, enriches metadata (authors, affiliations, figures, method names), and generates a tiered daily recommendation markdown file (must-read, worth reading, skip). For must-read papers, it invokes a paper-reader skill that produces detailed structured notes with figures, formulas, tables, and concept wiki entries, all saved into an Obsidian vault. It also fetches GitHub Trending repositories and writes notes. The system includes a web viewer (FastAPI + vanilla JS) for browsing the vault content, with full-text search, wikilink resolution, and a Claude chat interface. The architecture is a three-step pipeline (fetch, review, notes) orchestrated by Claude Code skills, with Python scripts handling data fetching, scoring, enrichment, and file management. The codebase demonstrates solid engineering: async HTTP with fallback chains, robust HTML parsing, heuristic text extraction, and clean modular design. It is primarily an application (a set of tools) rather than a data research project, though it involves significant data processing.
This repository is a comprehensive data pipeline and curation system for academic papers in the fields of search, recommendation, and advertising (known as 搜广推 in Chinese). It consists of three primary automated workflows: daily arXiv paper fetching with LLM-based relevance scoring (rough and fine ranking) and output to HTML/Feishu notifications; conference paper aggregation where metadata from top venues (KDD, SIGIR, RecSys, etc.) is crawled from DBLP/DBLP and stored as markdown tables; and a curated industry practice article index with its own HTML page. All are driven by GitHub Actions and deploy to GitHub Pages. The codebase is functional but exhibits typical research-project messiness: legacy scripts coexist with a newer paperBotV2 refactor, many conference paper tables have incomplete fields (empty abstracts, zero recommendation scores), and the environment relies on unpinned Python package versions and external APIs (DeepSeek, arXiv, DBLP). The arXiv daily pipeline is the most polished component, featuring retry logic, status tracking, and two-stage LLM ranking. Overall, the repo is a pragmatic automation for researchers but not production-grade software.