$ show chtc66/academic-skills
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.
The LLM identified 3 files worth highlighting for reuse:
A self-contained script that fetches recent arXiv papers, filters and ranks them by keyword relevance, and generates a Chinese digest in Markdown and JSON formats. It demonstrates non-obvious skill in its heuristic-based scoring and inference of paper contributions and limitations from abstracts, using only the Python standard library. The script is isolated, has no internal dependencies, and could be extracted for use in other projects requiring arXiv data processing.
A CLI tool that packages all skills in the repository into zip archives for distribution. It is self-contained, uses only the standard library, and follows a clear, configurable structure. While tailored to this repository's skill layout, its core logic for iterating directories and creating archives is generic and could be adapted for packaging any set of files with minimal modification.
A CLI tool that validates the repository structure against a predefined layout. It is self-contained, uses only the standard library, and provides clear error reporting. The validation logic is separated from the specific paths (which are configured in a data structure), making it reusable for other projects by adjusting the required paths and skill definitions.