Skill UI

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

Guide to instrument Azure-hosted ASP.NET Core or Node.js web apps with telemetry reporting to Azure App Insights for improved observability and health tracking.

This skill provides comprehensive command-line capabilities for interacting with Arize datasets. Use it to perform dataset CRUD operations (Create, Read, Update), list available datasets, retrieve detailed metadata, and export large volumes of example data. It is essential for managing the lifecycle of versioned datasets used in model evaluation and machine learning experimentation. Supports file formats like CSV, JSON, and Parquet.

Use production trace data and the ax CLI to locate LLM prompts, collect annotations/evals, and iterate on prompt templates for data-driven improvements.

This agent guides users in selecting the optimal Azure role assignment that strictly adheres to the principle of least privilege. Given a set of desired permissions, it utilizes specialized tools to either identify a suitable built-in role or generate a custom role definition. Finally, it provides actionable code snippets, including CLI commands and Bicep templates, ensuring proper and secure role assignment.

This toolkit provides comprehensive automation capabilities for Box cloud storage. It allows users to manage the entire document lifecycle, including file uploading and downloading, deep content searching using metadata filters, creating and modifying folders, and managing collaboration permissions. Ideal for integrating Box into automated business workflows, ensuring documents are always accessible and organized.

This toolkit automates core email marketing operations for Brevo (formerly Sendinblue) via Rube MCP. Users can manage the complete campaign lifecycle, including listing, updating, and configuring advanced A/B tests. It also provides comprehensive functionality to create, edit, and delete reusable email templates, ensuring streamlined and powerful communication workflows for professional marketing teams.

Provides Rube MCP-powered Calendly automation—including event listing, invitee management, scheduling link creation, availability checks, and organization administration—after retrieving the current tool schemas first.

This skill provides expert-level control over a live Chrome browser. It enables full web automation (clicking, form filling, navigation), deep debugging (analyzing console logs, network requests), visual inspection (screenshots, snapshots), and detailed performance profiling. Ideal for QA engineers, web scraping, and front-end development testing.

This toolkit provides comprehensive automation capabilities for Confluence platforms via Rube MCP. It allows users to automate the entire documentation lifecycle, including creating and updating pages, performing advanced content searches using CQL, managing spaces, and applying labels efficiently. Ideal for teams needing to streamline knowledge base maintenance and content governance.

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.