Skill UI

This guide details the process of building Model Context Protocol (MCP) servers. These servers are crucial for enabling Large Language Models (LLMs) to interact with external APIs and services, facilitating complex, real-world task completion. It provides comprehensive coverage on architectural planning, choosing appropriate languages (Python, TypeScript, C#), and selecting transport mechanisms (HTTP, stdio) for robust integration within enterprise ecosystems like Azure and Foundry.

Used for building, debugging, and extending MCP servers and clients. This skill implements robust connections between AI systems and external tools/data sources using structured protocols like JSON-RPC 2.0. Expertise includes defining tool schemas (Zod/Pydantic), configuring transport layers (HTTP/SSE/stdio), and scaffolding full-stack projects in TypeScript or Python.

Defines MCP server tooling with file search, query execution, resource endpoints, prompt templates, and transport configurations alongside a client setup example for streamlined project automation.

Codified best practices for senior transportation managers to negotiate freight components, keep scorecards, steward carrier portfolios, and balance cost, service, and compliance when running RFPs and managing mixed contract/spot freight programs.

Comprehensive Python toolkit for building, analyzing, and visualizing complex networks; supports algorithms, I/O, generation, and community detection across social, biological, transportation, and citation data.

A comprehensive technical reference for implementing advanced Lightning Network channel factories. It covers complex topics such as multi-party channels, Layer 2 scaling on Bitcoin, and advanced protocols including Decker-Wattenhofer invalidation trees, MuSig2 key aggregation, and encrypted Noise NK transport. Essential for developers building highly scalable and secure Bitcoin infrastructure.

A comprehensive skill set for senior transportation managers overseeing a large, multi-modal carrier portfolio. It covers the full lifecycle of carrier engagement, including sourcing, compliance checks (FMCSA SAFER), executing complex RFPs, negotiating granular rate components (linehaul, FSC, accessorials), and establishing performance scorecards. Use this skill to optimize logistics costs, mitigate supply chain risks, and ensure consistent service quality across varied freight types.

TLS 1.3 is the latest Transport Layer Security protocol, offering significant security and performance improvements over TLS 1.2. This guide details how to configure and validate TLS 1.3 on various environments, including Nginx, Apache, and Python applications. It covers advanced topics such as Perfect Forward Secrecy (PFS), HSTS implementation, modern cipher suites, and best practices for securing modern web services.

This guide provides a comprehensive methodology for assessing the security posture of Kubernetes etcd clusters. It covers critical areas including encryption at rest (for secrets), TLS transport security, access control validation, secure backup procedures, and network isolation checks, ensuring cluster data integrity and confidentiality.

This tool scaffolds a complete, production-ready Model Context Protocol (MCP) server project written in Rust. It utilizes the official `rmcp` SDK to ensure full compatibility. The generated structure includes modules for handling logic, defining tools, managing prompts, and providing resources. Users can configure different transports (stdio, SSE, HTTP) and immediately run the server for integration testing or production use.

A comprehensive scaffold generator for building Model Context Protocol (MCP) servers using TypeScript and Node.js. This tool provides a complete, production-ready boilerplate, including proper project structure, dependency management (e.g., @modelcontextprotocol/sdk, zod), and implementation details for tools and resources. It supports both HTTP and stdio transport mechanisms, ensuring type safety, robust error handling, and best practices for modern backend development.

NetworkX is a powerful Python library used for creating, manipulating, and analyzing complex networks and graphs. It supports graph structures (directed/undirected, multi-edges) and provides extensive algorithms for tasks such as finding shortest paths, calculating centrality measures (e.g., PageRank), detecting communities, and simulating various network types (e.g., scale-free, small-world). Ideal for social science, biology, and transportation network modeling.