Skill UI

Scans repository context and chat history to recommend awesome-copilot Custom Agent files that are missing or outdated locally, outlines relevance/rationale, and waits for user approval before installing or updating.

This tool generates a complete, structured TypeSpec declarative agent definition tailored for Microsoft 365 Copilot. It systematically creates necessary components including detailed role instructions, conversation starters, and a comprehensive list of required capabilities (e.g., WebSearch, OneDrive, Teams, CodeInterpreter). This ensures the agent is fully functional and ready for deployment within the M365 ecosystem.

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 enables personal execution of implementation plans task-by-task in the current session without spawning subagents per task. It employs Test-Driven Development (TDD) for verification, tracks progress via a ledger, and resolves conflicts autonomously. Suitable for inline execution choices or when subagent tools are unavailable, it reduces context costs while ensuring quality through final review.

Dispatch dedicated agents with isolated contexts to handle multiple independent failures or tasks in parallel. Use when you have several unrelated issues that can be investigated separately without shared state, while preserving your own context for coordination and review.

Dispatch a code review sub-agent to validate work quality before merging or completing tasks. This skill ensures issues are caught early, preventing bugs from spreading. It involves preparing git SHAs, using specific templates, and handling feedback from the review agent to maintain code quality and architectural integrity.

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.

Consult another agent for help, plans, reviews, or decision support in multi-agent workflows. Explains using list_agents() and chat_with_agent(), background submission and status checks, session ID reuse, and loop-avoidance best practices.

This skill is designed for scenarios requiring complex, multi-step task decomposition. Instead of asking an Agent to perform the task directly, it guides the process of requesting a clear, actionable, sequential execution plan from a stronger Agent. The core function emphasizes that the user must execute the generated plan themselves, ensuring the output is a roadmap, not a final answer.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.