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 plugin and skill set for Claude Code (and compatible CLI assistants like Codex CLI and OpenCode), designed to transform the AI into a semi-automated research assistant for academic and software development workflows. It is not a standalone application but a configuration package that installs skills, commands, agents, hooks, and rules into the user's Claude environment. The architecture follows a skill-based pattern: each skill is a directory with a SKILL.md entry point, references, examples, and optional scripts, all consumed by the AI at runtime. The repository covers the full research lifecycle—ideation, literature review (via Zotero MCP and arXiv APIs), experiment analysis, ML paper writing with conference-specific LaTeX templates (NeurIPS, ICLR, ICML, ACL, AAAI), reviewer response drafting, publication chart creation, and post-acceptance promotion. It also integrates deeply with Obsidian for project knowledge management and includes a meta-toolchain for developing and QA-ing skills themselves. While primarily an application configuration, it contains substantial research-oriented content (Kaggle competition analyses, citation verification workflows, writing pattern references), making it a hybrid tool that bridges software engineering and data science research. The codebase is actively maintained, well-documented, and demonstrates strong craftsmanship in its script utilities and hook infrastructure.