Skill UI

This skill provides foundational security patterns and implementation guides for building AI voice agents and automating phone call workflows using Retell AI. It covers essential SDK usage (TypeScript), agent listing, and critical error handling (e.g., 401 Unauthorized, 429 Rate Limit) when integrating Retell into voice or telephony applications.

This skill provides comprehensive implementation patterns and detailed steps for migrating and upgrading Retell AI integrations. It is designed for developers building advanced AI voice agents, phone call automation, and complex telephony workflows using the Retell SDK. It covers setup procedures, best practices, and standard error handling for robust deployment.

This skill provides a comprehensive guide for setting up and managing webhook endpoints for Retell AI. It allows developers to capture and process crucial call lifecycle events—such as call start, end, and analysis—as well as handle function calls triggered during live conversations. Use this when building sophisticated voice agents or call automation systems that require real-time backend interaction with Retell AI.

Runs AgentShield to audit Claude Code configuration files and MCP components for secrets, injection risks, and overly permissive settings, offering CLI, JSON/Markdown/HTML reports, auto-fix, Opus deep analysis, and CI-ready GitHub Action hooks to harden new or existing projects.

This skill implements a robust, multi-layered security hardening process based on the Defense-in-Depth strategy and DevSecOps principles. It coordinates specialized agents to perform comprehensive security assessments (SAST, DAST, SBOM, Threat Modeling), identify critical vulnerabilities, and execute layered remediation. The process covers application code, backend APIs, frontend components, and mobile platforms, ensuring a resilient security posture across the entire development lifecycle.

Runs Semgrep static analysis with auto language detection, parallel Task subagents, Semgrep Pro checks, and configurable “run all” or “important only” modes to audit codebases for vulnerabilities and bug patterns.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

This skill simulates a complete, multi-agent software development lifecycle following the principles of Agile and Scrum. It guides the project through six distinct, iterative phases: Architectural Planning, Parallel Implementation, Comprehensive Testing (Unit/E2E), Review, and Finalization. The process uses a convergent diffusion model, refining specifications and resolving conflicts across multiple specialized agents until the project converges and is deemed complete.

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This skill provides opinionated and evolving constraints designed to guide AI agents effectively when building user interfaces. It offers structured patterns and guidelines, ensuring that the generated interfaces adhere to specific best practices and development rules. Use this skill when your task requires highly structured, guided output for UI development, moving beyond simple generation to guided design.

Scaffolds production-ready generative AI agents on Vertex AI with Gemini models, multi-modal tools, RAG, and Cloud Run/Endpoint deployment, including observability and evaluation for enterprise scenarios.