Skill UI

This guide details how to build an MCP app, which is an MCP server enhanced to serve interactive UI resources. It allows developers to render complex widgets—such as forms, pickers, dashboards, and confirmation dialogs—directly inline within a chat conversation. Learn about the architecture, host specifics, and when to use a widget versus standard text output.

This guide assists developers in designing and architecting Model Context Protocol (MCP) servers that integrate deeply with LLMs like Claude. It is not a coding skill, but a comprehensive consultation process that guides users through use case interrogation, determines the optimal deployment model (e.g., Remote HTTP, MCPB), and selects the appropriate tool-design pattern for seamless, robust integration.

MCPB is a specialized local server bundle that packages the entire runtime environment (Node.js or Python) alongside the server code. It allows developers to distribute and run complex MCP services directly on a user's machine without requiring the user to pre-install specific runtimes or toolchains. Use this when the server must interact with the local filesystem, desktop applications, or OS-level APIs.

This agent facilitates comprehensive debugging and testing of iOS applications running on a simulated environment. It leverages XcodeBuildMCP to manage the entire lifecycle, including building, running, interacting with the UI (tap, type, gesture), taking screenshots, and capturing detailed runtime logs. Use it whenever you need to simulate real-world app testing or diagnose issues on a booted simulator.

Use Clarvia AEO (Agent Experience Optimization) to score any MCP server, API, or CLI tool for agent-readiness. It evaluates 15,400+ AI tools across four dimensions—API accessibility, data structuring, agent compatibility, and trust signals—ensuring the components are robust enough for advanced AI agent workflows and dynamic selection processes.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

A comprehensive skill for integrating development workflows directly with Jira. Users can retrieve, analyze, and update Jira tickets, including extracting testable acceptance criteria, transitioning status, adding comments, and searching issues using JQL. Supports both MCP-based and direct REST API interactions.

Automates updating the Context Mode plugin: fetches the latest GitHub version, rebuilds and installs it, updates the global npm config, and ensures hooks are set, reporting progress through a checklist; falls back to a CLI upgrade command if ctx_upgrade MCP fails.

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

A governance skill designed to secure AI agent tool calls using advanced authorization policies. It implements Cedar policies to control which tools agents can access and under what conditions. Key features include running in 'Shadow mode' for safe observation, 'Enforce mode' for strict blocking, and generating cryptographically signed Ed25519 receipts to provide an immutable, tamper-evident audit trail for every policy decision.

This tool enables deep inspection and validation of web applications by connecting to the browser's DevTools Protocol (MCP). It allows agents to perform tasks like DOM inspection, capturing console logs, monitoring network requests, profiling performance, and taking screenshots in a live environment. Essential for debugging UIs, validating API integrations, and performing end-to-end automated testing.

Guides agents through researching Microsoft technologies via Learn MCP docs, then scaffolds hybrid skills that store essential knowledge locally while enabling dynamic lookups for depth.