Generate structured analysis reports for AI/ML codebases.
npx skills add https://github.com/skywalkjian/skywalkjian-skills --skill code-dissect
Please help me install this Agent Skill. Skill: code-dissect Source: https://github.com/skywalkjian/skywalkjian-skills/tree/main/ai-code-dissect Command: npx skills add https://github.com/skywalkjian/skywalkjian-skills --skill code-dissect
本技能提供对 AI/ML 代码库的结构化、全面分析，帮助研究人员在不逐行阅读代码的情况下快速理解项目架构、数据流和训练/推理流程。
将一个代码仓库输入给模型，即可生成完整的项目分析报告。
High-intent search queries and answers about installing and using this skill.
To analyze an unfamiliar AI repo structure without reading code line by line, you can generate a comprehensive analysis report that maps module boundaries, entry points, and inter-module relationships. This provides a complete overview of the project architecture.
Tracing data flow and training pipelines in an ML codebase is best achieved by automating the extraction of data-to-output flow and entry points. This approach clearly visualizes the complete training and inference pipelines without manual code tracing.
Yes, you can automatically generate a visual report of inter-module relationships for an AI project. The analysis interprets repository metadata and module connections to outline a complete data-to-output flow, which can be shared for team communication and review.
Yes, this codebase analysis approach works specifically for complex training and inference pipelines. It automatically locates key dependencies and data flow paths within AI/ML repositories to outline the complete pipeline structure.
To quickly grasp project structure and module boundaries in a new repository, generate a structured analysis report. This report dissects repository metadata and inter-module relationships to provide a complete visualization of the project architecture.
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