The academic-skills repository is a collection of prompt engineering assets designed to assist with academic research workflows when used with large language models like ChatGPT. It provides eight distinct skills, each encapsulated in a self-contained directory containing a skill description (SKILL.md), platform-specific metadata (agents/openai.yaml), and optional reference templates or scripts. The skills cover tasks such as reading and noting papers, writing literature surveys, monitoring arXiv for new publications, analyzing reviewer comments, summarizing experiment logs, extracting benchmark information, identifying research gaps, and generating weekly lab updates. The repository emphasizes reliability and reproducibility by constraining LLM outputs to avoid hallucination, providing clear input processing rules, and offering structured templates for consistent results. One skill (paper-feishu-digest) includes a deterministic Python script for fetching and filtering arXiv papers, demonstrating a hybrid approach that combines LLM assistance with traditional data processing. Additional tools for validating the repository structure and packaging skills for distribution are included, all written using only the Python standard library to minimize dependencies. The codebase exhibits strong modularity, with each skill following a uniform structure that facilitates maintenance and extension. The arxiv_digest.py script stands out as a reusable component for arXiv data processing, featuring robust error handling, keyword-based ranking, and conservative inference of paper contributions and limitations from abstracts. Overall, the repository represents a well-crafted toolkit for enhancing academic productivity through LLM augmentation, balancing prompt engineering with selective use of deterministic scripts where appropriate.