Skill UI

This skill provides advanced integration for deploying applications on Fly.io, focusing on edge computing architectures. It covers building production-ready Docker images, configuring `fly.toml` for services and health checks, and executing sophisticated deployment strategies like blue-green and canary releases. It ensures reliable, scalable deployment across multiple regions, including automated rollbacks and micro-VM optimization.

This guide demonstrates how to deploy a minimal "Hello World" application to the Fly.io platform. It covers two primary deployment methods: using the simple `flyctl` Command Line Interface (CLI) for quick setup, and utilizing the advanced Machines API for programmatic deployment. This skill is ideal for beginners starting a new Fly.io project, or for implementing robust, globally distributed edge compute infrastructure.

This comprehensive checklist guides developers through hardening their application for production deployment on Fly.io. It covers critical aspects such as multi-region redundancy, secure secret management, robust health checks, graceful shutdown procedures, and automated rollback mechanisms. Use it to ensure your edge-deployed application is highly available, reliable, and resilient against common operational failures.

A comprehensive TypeScript SDK providing production-ready patterns for interacting with the Fly.io Machines REST API. It includes a typed client for managing the entire machine lifecycle—creation, state polling, and deletion—across multiple global regions. Features like robust error handling, rate-limit retries, and deployment builders ensure reliable multi-region orchestration for edge computing applications.

This guide helps determine the optimal deployment strategy for Qdrant, a vector database. It compares four main options—Local Mode, Self-Hosted (Docker), Qdrant Cloud (Zero-Ops), and Qdrant EDGE—based on project requirements such as control needs, operational overhead, data residency, and required latency. Use it to decide whether to prototype, manage full infrastructure, or prioritize minimal latency.

This skill provides high-performance, real-time object detection utilizing the Google Coral Edge TPU accelerator. It processes live camera frames to identify up to 80 COCO classes (e.g., person, car, dog). Optimized for native macOS and Linux deployment, it achieves extremely low latency (e.g., ~4ms at 320x320), making it ideal for embedded vision systems, surveillance, and low-latency monitoring applications.

Build and operate Python Azure IoT Edge modules for telemetry processing, local inference, and protocol translation. Includes deployment, observability, and production readiness checks.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

A meta-skill designed to capture hard-won, non-obvious knowledge—such as complex debugging steps, multi-step operational workflows (e.g., deployment, migrations), or project-specific facts—and convert them into reusable Agent Skills. It ensures that future sessions, regardless of the user or team member, start with proven procedures, preventing the need to rediscover solutions from scratch.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This MCP server provides a persistent and searchable memory bank for AI agents. It enables reading, writing, and searching long-term knowledge regarding architecture, patterns, and decisions. It automatically syncs with project documentation and includes a standalone dashboard for visualization. Requires Node.js setup for local deployment.

This skill provides comprehensive guidance for developing AWS infrastructure using the Cloud Development Kit (CDK). It supports TypeScript and Python, helping users create new stacks, refactor existing infrastructure, and implement Lambda functions. It includes best practices for resource naming, pre-deployment validation using cdk-nag, and integration with MCP servers for AWS knowledge access. Ideal for cloud architects and developers building scalable serverless applications.