Skill UI

Creates an Agent Authority Charter to define permissions, delegation, approvals, escalation, and audit requirements for AI agents before enterprise or regulated deployment. Helps legal, compliance, risk, and product teams document what an agent may and may not do, prepare for human review, and ensure auditable governance across the agent lifecycle.

Build a production-ready behavior tree runtime with Blackboard, action/condition leaves, and composites. Implement Utility AI using response curves and action evaluators. Create hybrid agents combining both for complex NPC decision-making beyond simple FSMs. Suitable for game development and AI system architecture.

Establishes project quality standards as a written contract to prevent agents from lowering them. Interviews users on key dimensions, sets sane defaults, and records constraints in CONSTRAINTS.md. Monitors for weakened bars like suppressed checks or skipped tests. Use when defining quality gates, setting coverage thresholds, or ensuring autonomous agents adhere to standards.

Runs a multi-persona dev team session where PM, Architect, Developer, and QA each respond to a topic from their own role. Use for feature design, proposal review, or initiative onboarding when you want multi-role perspectives without switching agents.

This guide outlines the full workflow for integrating Microsoft Copilot Studio agents into a Power Apps code application. It covers checking the memory bank, adding the connector via CLI, configuring the agent setup, and executing the agent asynchronously using the reliable API endpoint.

Orchestrates building, launching, grading, and scheduling Claude Managed Agents by routing session goals to phase sub-skills and compiling execution loops or workflows. Emits BYOK curl launches without making API calls.

This skill creates, updates, and deploys generative pages for Microsoft Power Apps model-driven apps using React, TypeScript, and Fluent UI. It orchestrates specialist agents for planning, entity creation, and code generation. Use it when building pages within existing Power Apps environments, supporting connector discovery and custom API bindings. Not for standalone web apps or canvas apps.

This workflow implements a bounded, iterative refinement loop for AI agents. It utilizes a required markdown rubric to grade the agent’s output in an isolated context. Based on the grader's verdict, it recommends the next move (sharpen, re-run, or promote). Once the agent passes the rubric, held-back evaluation cases are run in parallel to ensure generalization before final deployment.

This tool initializes a dedicated AgentHub session for multi-agent collaborations. It requires defining the core task, specifying the number of parallel agents, and configuring detailed evaluation criteria (including the evaluation command, target metric, and measurement direction). Upon execution, it generates a unique session ID and prepares the entire environment for subsequent agent spawning and competitive evaluation, optionally capturing a performance baseline.

A comprehensive guide for creating and maintaining an atomic, index-driven knowledge base (KB) using Markdown. This method enables AI agents to bypass traditional vector search limitations by reading a central index file (INDEX.md) and opening only the most relevant, highly signaled notes, drastically improving context efficiency.

This skill provides a comprehensive audit framework for evaluating AI agents used in legal, compliance, or business operations. It assesses the risks associated with delegated authority—such as sending messages, filing documents, or making payments—by checking for adequate controls, logging, revocation mechanisms, and clear accountability chains. It is essential for firms connecting agents to sensitive systems like CRM, court portals, or financial accounts to ensure safe and compliant deployment.

This skill manages the Ouroboros AI framework configuration. It allows users to open the settings GUI via browser or TUI, or configure settings conversationally. Users can edit runtime choices, model selections, and LLM backends. It supports local and remote server modes, enabling customization of agents and execution profiles through ouroboros config commands.