This repository is a curated knowledge base and skill package for academic paper writing, specifically targeting ML/CV/NLP-style research papers. It is not a software application or data pipeline; rather, it provides structured writing guides, templates, and examples organized as a reusable 'skill' for AI coding agents (Codex, Claude Code, Gemini). The core content includes section-specific writing guides (Abstract, Introduction, Method, Experiments, Conclusion), paragraph clarity checks, adversarial self-review checklists, and example banks with reusable sentence skeletons. The architecture is a static file collection with a main entry point (SKILL.md) that defines the workflow and references to sub-guides. There are no executable scripts, API endpoints, or data processing components. The repository is well-maintained with high community engagement (6549 stars) and demonstrates strong craftsmanship in organizing and presenting writing methodology, though it is not a software engineering artifact.
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.