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Found 33 Skills
Build Model Context Protocol (MCP) servers and tools from scratch. Full-stack MCP development with TypeScript/Python, testing, deployment, and registry publishing.
Generate Makefiles with testing, linting, formatting, and automation targets for new projects.
Write, fix, and standardize Python docstrings in Google style. Use whenever the user asks to add or improve docstrings, convert mixed docstrings to Google format, add missing Args/Returns/Raises/Attributes/Example sections, or make docstrings concise and API-focused.
Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.
Expert-level maritime systems, vessel tracking, port operations, cargo management, and maritime logistics
Create and manage Infrahub Generators. Use when building design-driven automation that creates infrastructure objects from templates, topology definitions, or any design-to-implementation workflow in Infrahub.
Guide for building high-quality MCP (Model Context Protocol) servers in Python or Node/TypeScript to integrate external APIs/services.
Create, update, refactor, explain, or review Microsoft Agent Framework solutions using shared guidance plus language-specific references for .NET and Python.
runpod-flash — code-first serverless: write Python locally, run it on remote Runpod GPUs/CPUs with `flash dev` (hot-reload + live worker logs), then `flash deploy`. Use for @Endpoint/@remote functions, resource config, and debugging flash deployments. For CLI-only infra management use runpodctl or runpod-mcp.
Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.
Python skill router. Use when planning, implementing, or reviewing Python changes and you need to select focused skills for workflow, design, typing/contracts, reliability, testing, data/state, concurrency, integrations, runtime operations, or notebook async behavior.
Automatically add or improve type annotations in legacy Python code. Use to improve code readability, IDE support, and catch type errors early.