Skill UI

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.

Automated Linux Commands Guide skill offers step-by-step help with foundational DevOps tasks such as version control, containerization, CI/CD, and infrastructure routines whenever you mention linux commands guide.

Auto-activating DevOps skill that walks through makefile generator requests, offering best-practice guidance, production-ready snippets, and standard validations for foundational version control, containerization, and CI/CD workflows.

Helps manage container registries like AWS ECR, Google GCR, and Harbor by generating configuration code, guiding access controls, and advising best practices for secure, production-ready image storage and deployment automation.

Enables Claude to orchestrate isolated test environments using Docker Compose, Testcontainers, and environment variables, ensuring consistent setups for database, cache, and message-queue based testing across software projects.

This comprehensive guide details the deployment process for Mistral AI-powered applications across various cloud and container platforms, including Vercel, Docker, Google Cloud Run, and self-hosted vLLM setups. It covers secure API key management, platform-specific secret configuration, and the full steps required to establish robust, scalable production pipelines for your AI model.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

This skill provides a complete guide to deploying a containerized OpenEvidence clinical evidence integration service using Docker. It establishes a HIPAA-conscious production environment, enabling secure connections to the OpenEvidence API. Users can query clinical evidence, retrieve summarized medical literature, and validate treatment recommendations, all while maintaining audit logging and data-at-rest encryption compliance. Includes setup for health checks and rolling updates.

Orchestrate PhantomBuster cloud agents through the Composio MCP integration, automating lead generation, web scraping, usage monitoring, container tracking, CSV exports, and hCaptcha solving in a single workflow.

Auto Readme Generator offers guided documentation workflows for DevOps basics, triggering whenever "readme generator" is mentioned, and delivering best-practice code, configurations, and validations to support version control, containerization, and CI/CD.

Provides systematic methods for diagnosing complex, intermittent errors in the Replit environment. This guide covers advanced topics like container lifecycle issues, Nix build failures, memory leaks, and deployment crash loops using sophisticated shell scripting and runtime monitoring techniques. Use when standard troubleshooting steps fail to identify the root cause.