Skill UI

This is the foundational plumbing layer connecting AI agents to the Power Automate platform via FlowStudio MCP. It provides essential capabilities such as secure authentication setup, robust tool discovery (`list_skills`, `tool_search`), and handling complex, oversized API responses. It serves as the prerequisite layer for specialized workflow skills (e.g., building, debugging, monitoring).

Guidance for positioning autonomous AI agents, addressing accountability objections, choosing between copilot/agent/teammate framing, and pricing variable-cost AI when selling to enterprises that fear operational breakages.

A comprehensive guide for developers to build advanced, declarative agents for Microsoft 365 Copilot. This skill details the integration process using a Model Context Protocol (MCP) server, allowing the agent to connect with external systems, select tools, and handle complex authentication (OAuth 2.0/SSO). You will learn to generate key JSON manifests required for deployment.

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 comprehensive CLI tool designed to manage, synchronize, and audit AI-specific skills and agents across multiple AI platforms and tools (e.g., Claude, Cursor, etc.). It provides a single source of truth for AI configuration, supporting installation, updating, syncing, and managing complex 'extras' like rules and commands. Useful for setting up CI/CD pipelines and maintaining consistent AI development environments.

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 technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.