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Found 14 Skills
Query DeepWiki for repository documentation and structure. Use to understand open source projects, find API docs, and explore codebases.
Generate DeepWiki-style source code documentation for local codebases, transforming engineering experience into reusable cognitive structures
Ask questions and read documentation about any GitHub repository using DeepWiki MCP. Use when you need to understand a codebase, find specific APIs, or get context about a repository.
Retrieve and explore DeepWiki-generated documentation for public GitHub repositories. Use when listing repository documentation topics, reading DeepWiki pages, or asking focused questions about a codebase that needs current repository structure, architecture notes, or component explanations.
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking questions about how a specific open-source project works. Provides AI-powered repo analysis and RAG-based Q&A about source code. NOT for fetching library API docs (use fetching-library-docs instead) or local files.
Query any public GitHub repo's documentation via DeepWiki. Use when needing to understand a library, framework, or dependency. Triggers on "look up docs", "how does X work", "deepwiki", "deepwiki".
Query Light Protocol and related repositories via DeepWiki MCP. Use when answering questions about compressed accounts, Light SDK, Solana development, Claude Code features, or agent skills. Triggers on technical questions requiring repository context.
Skills for accessing and searching docs in DeepWiki/GitHub’s public code repositories can help users understand open-source project source codes, and users can also ask questions directly about the code docs.
Access AI-generated documentation and insights for GitHub repositories via DeepWiki. This skill should be used when exploring unfamiliar codebases, understanding repository architecture, finding implementation patterns, or asking questions about how a GitHub project works. Supports any public GitHub repository.
Deep research on any topic using Perplexity, DeepWiki, and Context7. Use for comprehensive investigation of technologies, libraries, patterns, or domain questions.
Generate DeepWiki-style repository analysis reports. Deeply analyze codebase architecture, module dependencies, and core systems, outputting structured documentation with Mermaid diagrams, source file references, and tables.