Skill UI

Deploy and govern MCP declarative agents within the Microsoft 365 admin center, managing their lifecycle, assignments, access controls, and compliance so Copilot delivers organizational capabilities securely.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.

This tool acts as an expert prompt engineer, guiding users through a systematic discovery process to create highly structured, production-ready prompts for AI assistants like GitHub Copilot. It covers defining persona, specifying complex tasks, managing context variables, integrating tools, and enforcing strict output standards, ensuring the resulting prompt is comprehensive and actionable for advanced AI agents.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

This guide maps user questions and topics (e.g., configuration, memory, skills, CLI) to specific, relevant documentation paths and source code entry points within the QwenPaw ecosystem. It is designed for built-in QA Agents to eliminate aimless searching, ensuring the agent reads the most accurate files to provide comprehensive and precise answers about the system's features.

A powerful command-line interface for managing and synchronizing AI skills and agents across dozens of AI tools (e.g., Claude, Cursor). It provides a unified source of truth for skill deployment, handling global and project-specific configurations. Use it to install, sync, update, discover, and audit complex AI workflows, including managing structured extras (rules, commands) and custom content transformations via extensions.

Scans repository context and chat history to recommend awesome-copilot Custom Agent files that are missing or outdated locally, outlines relevance/rationale, and waits for user approval before installing or updating.

Automatically update implementation plan files to reflect new requirements, ensuring structured, machine-readable phases with explicit tasks and validation criteria for AI agents to execute deterministically.

A comprehensive automation toolkit for managing Zendesk operations via Rube MCP. It provides workflows to list, search, create, update, and delete support tickets, manage user accounts (agents and end-users), and handle organization details. Ideal for integrating support workflows and automating customer service processes.

This skill guides the parallel assignment of specialized agents when debugging multiple, independent failure domains. Instead of using one agent for all issues, isolate each problem (e.g., a specific test file or subsystem) to its own dedicated agent. This allows agents to work concurrently without sharing context or interfering with each other, significantly accelerating the debugging process and maintaining context purity.

Acontext serves as a robust memory layer for production-grade AI agents. It provides persistent sessions, disk-based storage, and sophisticated learning spaces that distill complex conversations into reusable, structured skills. Users can manage the entire system—from project setup and login to skill uploading—via a powerful Command Line Interface (CLI) and API, significantly enhancing agents' contextual understanding and long-term knowledge retention.