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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
A comprehensive, long-horizon skill for designing, coding, debugging, and scaling AI-integrated systems — including Python backends, React frontends, RAG pipelines, vector databases, and tool orchestration. Ensures production-grade quality and consistency across every code and architecture decision.
python-telegram-bot library for building Telegram bots. Use for handlers, callbacks, inline keyboards, conversations, and bot commands.
Build AI agents using Google's Agent Development Kit (ADK) for Python. Use this skill when the user wants to create ADK agents, multi-agent systems, agents with tools, workflow agents (Sequential, Parallel, Loop), or deploy agents to Google Cloud. Triggers include mentions of "ADK", "Agent Development Kit", "google-adk", "adk agent", "multi-agent system", or requests to build AI agents with Google's framework.
Neuro-symbolic architecture patterns. Use when building the bridge between LLM (Neural) and Python rules (Symbolic).
Expert in building MCP servers using the Official Python MCP SDK. Use this when defining tools, resources, and prompts that allow AI agents to manage application state.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
OpenAI official SDK usage (Python, Node.js). Use when: writing code that calls OpenAI API, implementing chat/embeddings/images/audio features, handling streaming responses, async patterns, error handling with SDK. For raw HTTP/REST calls, see `openai-api` skill.
Expert Python developer specializing in modern Python 3.11+ with deep expertise in type safety, async programming, testing, and production-grade code. Invoke for Pythonic patterns, type hints, pytest, async/await, dataclasses.
Expert assistance for building SignalWire AI Agents in Python. Automatically activates when working with AgentBase, SWAIG functions, skills, SWML, voice configuration, DataMap, or any signalwire_agents code. Provides patterns, best practices, and complete working examples.
Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).
Comprehensive FastAPI knowledge for building production-ready APIs from basic to planet-scale. Use when building FastAPI applications, implementing REST APIs, setting up database operations with SQLModel, implementing authentication (OAuth2/JWT), deploying to Docker/Kubernetes, or needing guidance on middleware, WebSockets, background tasks, dependency injection, security, scalability, or performance optimization. Triggers include "FastAPI", "build API", "REST endpoint", "SQLModel", "OAuth2", "JWT", "deploy FastAPI", "Docker FastAPI", "Kubernetes", "API security", or questions about Python web frameworks.
FastAPI development assistant for building modern Python web APIs. Provides guidance on routing, request/response handling, dependency injection, authentication, middleware, WebSockets, testing, and Pydantic models. Use when: (1) Creating FastAPI applications or endpoints, (2) Implementing CRUD operations, (3) Setting up authentication/authorization, (4) Working with request parameters (path, query, body, headers, cookies, forms, files), (5) Configuring middleware or CORS, (6) Implementing WebSocket connections, (7) Writing tests for FastAPI apps, (8) Defining Pydantic models for validation.
Context management tools for Claude Code - provides intelligent codebase mapping with Python, Rust, and C++ parsing, duplicate detection, and MCP-powered symbol queries. Use this skill when working with large codebases that need automated indexing and context management.
Comprehensive computational mathematics toolkit for Claude Code agents. Provides tools for five core domains from MIT's Mathematics for Computer Science curriculum - Proofs (propositions, predicates, induction, verification), Structures (number theory, graphs, RSA, partial orders), Counting (asymptotics, combinatorics, generating functions), Probability (distributions, Bayes, random walks, bounds), and Recurrences (divide-and-conquer, Master theorem, Akra-Bazzi). Use when solving algorithm analysis, proving correctness, computing probabilities, analyzing complexity, or working with discrete mathematical structures. Implemented in Python with TypeScript tool definitions for Claude Code integration.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Comprehensive Python AI/ML development expert with 10+ years experience using UV package manager. Covers PyTorch, TensorFlow, scikit-learn, transformers, langchain, pandas, numpy, OpenCV, and all major AI/ML libraries. Automatically audits projects, generates production-ready code with type hints, optimizes performance, sets up RAG pipelines, manages dependencies with UV, and ensures best practices. Use for AI/ML project setup, model training, data processing, LLM applications, computer vision, code generation, dependency management, performance optimization, and comprehensive project auditing. Excludes Tkinter and desktop UI libraries.
Expert guidance for Python programming covering modern best practices, standard library usage, popular frameworks (Flask, Django, FastAPI), data science libraries (pandas, numpy), async programming, testing, type hints, packaging, debugging, and performance optimization.
Knowledge management for AI-assisted development. Use this skill when: - Starting a new session or resuming work - User asks about tasks, bugs, notes, or project status - User mentions: "what's next", "where did we leave off", "TODO", "track this" - Creating, updating, or closing tasks - Capturing notes, ideas, or research findings - Checking project vision or plans - User asks about project context or goals - Coordinating with other AI agents on the same codebase - User mentions: "save this", "remember", "note", "task", "bug", "idea" - User wants to explore or document a Python API - User mentions: "docs", "documentation", "API", "explore package"
