Deployment AI Agent Skills

� 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

Universal Deployment Tracker for AI Assistants Track deployments across all platforms directly in your AI conversation. No more context-switching. No more dashboard hunting. Watch Full Demo · Live Badge Demo ───────────────────────── Get started in under 30 seconds: That's it! The server is now running and ready to be configured in your AI assistant. ───────────────────────── deploy-mcp supports m

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.

Backend: Node.js with Express.js

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

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

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.

Claude Code plugins for AWS development with specialized knowledge and MCP server integrations, including CDK, serverless architecture, cost optimization, and Bedrock AgentCore for AI agent deployment. Shared AWS agent skills including AWS Documentation MCP configuration for querying up-to-date AWS knowledge. - AWS MCP server configuration guide - Documentation MCP setup for querying AWS knowledge

A comprehensive security operations platform that integrates multiple security tools into a unified interface. This platform provides a centralized way to run various security scanning and testing tools. - Unified Interface: Single entry point for multiple security tools - Docker Support: Easy deployment using Docker - JSON Output: Consistent JSON output format across all tools - Error Handling: R

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

Master Temporal workflow orchestration with Python SDK. Implements

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 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

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

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

Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.

Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern g

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

You are a configuration management expert specializing in validating, testing, and ensuring the correctness of application configurations. Create comprehensive validation schemas, implement configurat

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.

Expert deployment engineer specializing in modern CI/CD pipelines,

mcp-k8s-eye is a tool that can manage kubernetes cluster and analyze workload status. - [x] Connect to a Kubernetes cluster - [x] Generic Kubernetes Resources management capabilities - Support all navtie resources: Pod, Deployment, Service, StatefulSet, Ingress... - Support CustomResourceDefinition resources - Operations include: list, get, create, update, delete - [x] Pod management capabilities

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

Deploy Expo apps to production