浏览 maneeshanif 维护的已发布 AI 技能，比较用途与说明，并通过来源仓库了解安装方式。
已收录技能
53收录技能的仓库 Stars 合计
0仓库 Forks
0平均每个技能的仓库 Stars
0同一仓库中的多个技能可能共享 Stars，合计包含重复仓库数据。
Build FastAPI backend for OpenAI ChatKit with SSE streaming, conversation persistence, and AI agent integration. Handles /chatkit endpoint, ChatKit-compatible SSE format, conversation models, and message storag...
DEPRECATED - Use chatkit-backend skill instead. Chat API functionality is now part of the chatkit-backend skill.
Implement WebSocket service for real-time task synchronization across clients. Use when building real-time updates for Phase 5. (project)
Initialize and configure OpenAI Agents SDK for building AI-powered chatbots. Uses NATIVE MCP integration via MCPServerStreamableHttp - no @function_tool wrappers needed! Use when setting up AI agents for Phase...
Set up GitHub Actions CI/CD pipelines for testing, building, and deploying to Kubernetes. Use when implementing continuous integration and deployment for Phase 5. (project)
DEPRECATED - Use chatkit-frontend skill instead. This skill has been consolidated into the chatkit-frontend skill for better organization.
Setup and use Docker AI (Gordon) for intelligent container operations
Implement advanced task features - Priorities, Tags, Due Dates, Reminders, Recurring Tasks, Search, Filter, and Sort. Use when adding Phase 5 advanced functionality. (project)
Build conversation history UI with sidebar, thread switching, and conversation CRUD operations. Implements conversation list, rename, delete, and persistence. Use when adding conversation management features fo...
Create and manage Helm charts for Todo application deployment
Ensure textbook content is accessible, readable, and understandable for learners of all skill levels. Use when reviewing content for clarity, adding explanations for beginners, or improving content accessibilit...
Create and manage diagrams, illustrations, tables, and visual components for the textbook. Use when adding architecture diagrams, flowcharts, hardware schematics, or data tables.
Add Better Auth signup/signin, personalize chapter content, and persist user preferences. Use when implementing authentication, user sessions, or personalized content delivery.
Enforce consistent chapter formatting, numbering conventions, and structural alignment across the entire textbook. Use when creating new chapters, reviewing existing content for consistency, or establishing for...
Scaffold, structure, and deploy the Physical AI textbook in Docusaurus with book-aware content and RAG-ready exports. Use when creating or updating the Docusaurus site, adding chapters, configuring sidebar, or...
Build the FastAPI-based RAG service using OpenAI Agents/ChatKit, Neon Postgres, and Qdrant for the textbook. Use when creating ingestion pipelines, query endpoints, or configuring vector storage for book conten...
Embed the chatbot UI inside Docusaurus and connect it to the FastAPI RAG backend. Use when building chat components, handling streaming responses, or integrating chat widgets into MDX pages.
Write technical content for the Physical AI textbook including chapters, examples, code snippets, and exercises. Use when creating new chapters, writing module content, adding code examples, or creating assessm...
Provide per-chapter Urdu translation toggle and manage translated content delivery. Use when translating chapters, caching translations, or implementing language toggles.
Filter tasks by priority, status, tags, due dates, and other criteria. Use when implementing filtering functionality.
Initialize Python project structure with proper directory layout, configuration files, and best practices. Use when creating new projects or restructuring existing ones.
Create beautiful Rich terminal output with tables, panels, progress bars, and styled text. Use when building terminal UI components.
Implement search functionality for finding tasks by keywords in title and description. Use when building search features.
Create interactive terminal prompts using questionary for user input. Use when building forms, selection menus, and confirmation dialogs.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。