Skill UI

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

This skill wraps the `getCompactContext` functionality to efficiently retrieve a compacted ReasoningBank context, specifically optimized for cost-analysis queries. It generates a detailed report summarizing critical metrics such as tokens saved, memories retrieved, and agentic-flow availability. It is essential for monitoring and optimizing resource usage in complex AI workflows, providing deep visibility into the efficiency and cost of the context retrieval mechanism.

This tool enables the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static agents, DAA agents possess the ability to learn, adapt their internal behavior based on real-time performance metrics, and continuously evolve through interactions. It facilitates knowledge sharing among multiple agents, ensuring systemic, continuous improvement in complex AI systems. Ideal for scenarios requiring high adaptability and evolving intelligence.

A comprehensive, human-guided onboarding skill designed for users new to AI coding agents and Power BI agentic development. It provides structured setup help, deep conceptual explanations, and conversational guidance, mimicking a mentor's session. Modes include concise setup assistance and deep conceptual learning. Use cases: Setting up a new agent environment, understanding core AI concepts (models, prompts, tools), or auditing existing agent configurations.

This skill performs a comprehensive, setup-wide health check for agentic developers. It audits various components—including skills, memory, tools, configuration, and workflow habits—to identify inefficiencies and potential improvements. It provides a detailed report and guides the user through applying targeted fixes, ensuring the agent environment remains lean and optimized.

A comprehensive skill for integrating agentic operational flows into the RuView sensing ecosystem (RVF). It allows external agents to read, reason about, and respond to real-time cross-component sensing data, including BFLD events, vitals, and pose estimation. Use cases include automated caregiving responses, complex event negotiation, and maintaining auditable agent state within the sensing data stream.

A comprehensive guide for enhancing existing agentic web applications built on the CopilotKit frontend and Microsoft Agent Framework running on Azure AI Foundry. Covers advanced topics such as adding and gating agent tools, implementing human-in-the-loop (HITL) approvals, managing shared state, debugging event streams, and safely deploying agent updates. This skill is for maintenance and extension, not for initial project scaffolding.

A specialized downstream skill for analyzing security vulnerabilities in complex agentic systems. This tool is designed for red teaming and penetration testing, focusing on detecting prompt injection, retrieval poisoning, memory contamination, planner drift, and tool boundary abuse within multi-step LLM chains. Use when proving how untrusted inputs compromise system integrity.

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.

Audit and fix a site's readiness for AI browsing agents: the Lighthouse Agentic Browsing fraction, accessibility tree, robots.txt and Content-Signal, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known files and WebMCP tools.