Skill UI

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

This skill provides comprehensive management for multiple local Command Line Interface (CLI) agents. It allows users to start, stop, monitor, and assign tasks to these agents, running them in separate tmux sessions for parallel execution. It is ideal for complex, scheduled workflows requiring multi-agent coordination, task monitoring, and log tailing, making it invaluable for advanced automation setups.

This skill is designed to configure a Rails project workspace to function correctly within Conductor, a platform for parallel coding agents. It sets up the necessary scaffolding, including `conductor.json`, executable setup scripts, and server scripts. Additionally, it updates critical Rails configuration files (such as Sidekiq, Cable, and development environment settings) to ensure proper environment variable handling, Redis isolation, and port management for reliable parallel agent execution.

This comprehensive guide details the end-to-end setup process for integrating Lindy AI into external applications. Learn how to obtain and manage API keys, install and initialize SDKs (Python/Node.js), and securely configure webhook authentication endpoints to enable programmatic access and trigger AI agents.

This guide details the best practices for configuring and isolating AI agents across development, staging, and production environments. It covers the setup of separate workspaces, secure secret management using tools like Vault/AWS/GCP, and integrating environment-aware logic into CI/CD pipelines to ensure data integrity and smooth promotion from dev to prod.

This comprehensive checklist guides the deployment of AI agents into a live production environment. It covers critical areas including security best practices (API key management, SSO), robust error handling, monitoring setup, operational runbooks, and compliance adherence (HIPAA, GDPR). Use this when validating an agent for go-live to ensure maximum reliability and stability.

This comprehensive guide details how to optimize the Replit development environment. It covers setting up run commands, implementing hot reloading for various languages (Node.js, Python), configuring ports, managing development vs. production databases, and leveraging the Replit Agent for powerful AI-assisted coding. Use this guide when setting up a new project or refining your entire dev workflow.

This skill provides implementation patterns for setting up and managing a multi-environment connection with Retell AI. It is essential for developers building advanced AI voice agents, integrating complex phone call automation, or utilizing robust telephony workflows. It guides users through SDK usage and handles common API error scenarios.

This skill facilitates the rapid and standardized onboarding of new projects onto the Windsurf platform. It optimizes the development environment by configuring the Cascade AI agent, establishing necessary project rules (.windsurfrules), setting team context defaults, and ensuring best practices for AI-assisted coding. Use this when initiating a new project or integrating a team to maximize AI efficiency.

Windsurf is a powerful, AI-powered code editor built on VS Code. This guide covers the complete setup process, including initial installation for macOS/Linux/Windows, crucial Codeium authentication using Google/GitHub, and advanced configuration for enterprise or team deployments. Learn to use Supercomplete and interact with the Cascade AI agent to boost your development workflow.

This comprehensive guide details how to set up and configure AWS Machine Capability Platform (MCP) servers for advanced AI agent use. It provides two options: a Full AWS MCP Server (for API calls and documentation search) and a basic AWS Documentation MCP (for documentation search only). Instructions cover prerequisites, configuration methods (via JSON settings), necessary IAM permissions, and troubleshooting tips for seamless integration of AWS services into AI workflows.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.