<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> "chtc66/academic-skills" provides a standardized toolkit of ChatGPT skills designed to streamline academic research workflows for Chinese-speaking researchers. It addresses tasks from paper reading and survey writing to experiment summarization and rebuttal drafting, offering a unified structure for improved maintainability and collaboration.

How It Works

The project organizes specialized ChatGPT skills within a consistent directory structure and naming convention. Each skill targets a distinct research task, utilizing a unified metadata style. This standardization facilitates local management, batch packaging for ChatGPT, and collaborative development on GitHub, with an emphasis on using Python's standard library for core tooling.

Quick Start & Requirements

Python 3.10+ is recommended. Core tooling scripts (validate_repo_structure.py, package_all_skills.py) rely solely on Python's standard library, minimizing external dependencies. Detailed skill usage, like paper-feishu-digest/scripts/arxiv_digest.py --help, is available via script arguments. Refer to skills-overview.md for comprehensive skill positioning.

Highlighted Details

• Unified Skill Framework: Standardized directory layout, naming, and metadata across all academic workflow skills.
• Comprehensive Task Coverage: Includes deep paper notes, survey generation, arXiv monitoring with Feishu integration, review rebuttal drafting, experiment logging, benchmark extraction, research gap analysis, and weekly/meeting material preparation.
• Language Flexibility: Default output is Chinese, with explicit English modes available for certain skills.
• Minimal Dependencies: Repository scripts are built using only Python's standard library.

Maintenance & Community

Collaboration is encouraged via separate branches for skill updates, prioritizing documentation (references/, SKILL.md) before scripts. Specific community links (Discord, Slack) are not provided.

Licensing & Compatibility

A LICENSE file is present at the root. Specific license terms and compatibility for commercial use are not detailed in the provided README excerpt.

Limitations & Caveats

Skills must adhere to strict data integrity rules, avoiding fabrication of information or experimental details. The primary focus is on Chinese research workflows, and detailed skill templates reside in references/ rather than individual SKILL.md files.