Deployment AI Agent Skills

End-to-end GenAIOps template for building LLM-infused apps with Prompt Flow, including CI/CD pipelines, A/B deployment, and lifecycle management.

Docker containerization expert with deep knowledge of multi-stage builds, image optimization, container security, Docker Compose orchestration, and production deployment patterns. Use PROACTIVELY for Dockerfile optimization, container issues, image size problems, security hardening, networking, and orchestration challenges.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Build comprehensive ML pipelines, experiment tracking, and model

Master Flutter development with Dart 3, advanced widgets, and

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence.

Build Unity games with optimized C# scripts, efficient rendering,

Master Temporal workflow orchestration with Python SDK. Implements

Deploy containerized applications to Azure Container Apps using Azure Developer CLI (azd). Use when setting up azd projects, writing azure.yaml configuration, creating Bicep infrastructure for Container Apps, configuring remote builds with ACR, implementing idempotent deployments, managing environment variables across local/.azure/Bicep, or troubleshooting azd up failures. Triggers on requests for azd configuration, Container Apps deployment, multi-service deployments, and infrastructure-as-code

Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.

Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

A Model Context Protocol (MCP) server that provides AWS EC2 instance pricing data. This project includes both a traditional server implementation and a serverless Lambda function. The Lambda function provides the same functionality as the server but with serverless benefits: For detailed Lambda documentation, see LAMBDA.md. - EC2 Pricing Data: Find the cheapest EC2 instances based on specification

Chat with your Kubernetes cluster using natural language! This project connects OpenWebUI to Kubernetes, letting you manage your cluster through conversational AI. Ask questions like "What pods are running?" or "Scale my deployment to 5 replicas" and get instant results. - Quick Start - Configure Open-WebUI - Step 1: Access Open-WebUI - Step 2: Add the MCP Bridge Tool Server - Step 3: Add Connecti

AI-powered debugging and inspection for Kubernetes clusters using Inspektor Gadget. - AI-powered interface for Kubernetes troubleshooting and monitoring - One-click Inspektor Gadget deployment and removal - Intelligent output summarization and analysis - Automatic gadget discovery from Artifact Hub 1. Ensure you have Docker and a valid kubeconfig file 2. Configure the MCP server in VS Code (see IN

kubectl-mcp-server Control your entire Kubernetes infrastructure through natural language conversations with AI. Talk to your clusters like you talk to a DevOps expert. Debug crashed pods, optimize costs, deploy applications, audit security, manage Helm charts, and visualize dashboards—all through natural language. - What Can You Do? - Why kubectl-mcp-server? - Live Demos - Installation - Quick St

A powerful Model Context Protocol (MCP) server for Docker operations, enabling seamless container and compose stack management through Claude AI. - 🚀 Container creation and instantiation - 📦 Docker Compose stack deployment - 🔍 Container logs retrieval - 📊 Container listing and status monitoring To try this in Claude Desktop app, add this to your claude config files: To install Docker MCP for C

Batch process books through the complete pipeline - generate cropped images for split pages, OCR all pages, then translate with context. Use when asked to process, OCR, translate, or batch process one or more books.

Archive session learnings, debugging solutions, and deployment logs to .archive/yyyy-mm-dd/ as indexed markdown with searchable tags. Use when completing a significant task, resolving a tricky bug, deploying, or when the user says \"archive this\". Maintains .archive/MEMORY.md index for cross-session knowledge reuse.

� Migrate to MCP Platform • 💬 Discord Community • � Legacy Docs Zero-configuration deployment of production-ready MCP servers with Docker containers, comprehensive CLI tools, and intelligent caching. Focus on AI integration, not infrastructure setup. That's it! Your MCP server is running at http://localhost:8080 Perfect for: AI developers, data scientists, DevOps teams building with MCP. Deploy M

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

A Model Context Protocol (MCP) server implementation for 4EVERLAND Hosting enabling instant deployment of AI-generated code to decentralized storage networks like Greenfield, IPFS, and Arweave. The 4EVERLAND Hosting MCP Server enables users to leverage AI-driven workflows to deploy code instantly to decentralized storage networks such as Greenfield, IPFS, and Arweave. Upon deployment, it provides

A Model Context Protocol (MCP) server that allows interacting with the CipherTrust RestFul Data Protection (CRDP) service. This MCP server enables AI applications and LLMs to securely protect and reveal sensitive data through the CipherTrust CRDP service. It supports both individual and bulk protect and reveal operations with versioning support. - Video 1: [https://youtu.be/O2pQRoykaaU] - Deployme

A remote Model Context Protocol (MCP) server that connects AI assistants to your crash reporting and real user monitoring data in Raygun through natural language conversations. 📚 Tool Reference • 🚀 Quick Start • 🔑 Get API Token - 🐛 Error Management - Investigate, resolve, and track application errors and crashes with full stack traces and context - 🚀 Deployment Tracking - Monitor releases and