Skill UI

This skill provides step-by-step guidance for creating production-ready Kubernetes manifests. It covers essential resources including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complete, robust containerized application architectures, managing network connectivity, stateful workloads, and ensuring adherence to Kubernetes best practices like resource limits and security contexts.

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.

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.

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.

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.

This comprehensive guide serves as a quick reference for diagnosing and fixing the most common technical errors encountered in the Replit development environment. It covers critical infrastructure issues such as container lifecycle failures, port binding conflicts, Nix build failures, database limitations, and deployment timeouts, ensuring users can effectively troubleshoot and maintain continuous development workflows.

This guide details essential patterns for building fault-tolerant and highly available Replit applications. It covers handling cold starts, implementing graceful shutdowns (via SIGTERM), and crucially, migrating persistent application state from ephemeral local filesystems to robust external storage (like Replit KV or Object Storage). Essential for production-grade deployments.

Scans container images and running containers using Trivy and Snyk to identify vulnerabilities, rank them by severity, and return remediation guidance whenever users ask for a security assessment before or after deployment.

Comprehensive DevOps toolkit automating CI/CD pipelines, infrastructure-as-code scaffolding, and container deployments with monitoring and rollback support.

This guide outlines the end-to-end process for deploying Speak language learning features to modern cloud environments, including Vercel, Cloud Run, or containerized setups. It covers initial configuration, implementing the core logic using TypeScript, and performing production verification. Use this skill when setting up a new Speak integration in a production environment or managing the platform's deployment lifecycle.

This skill leverages AI to analyze your application and automatically generate highly optimized Dockerfiles. It incorporates industry best practices like multi-stage builds, minimal base images, and robust security configurations. Use it when containerizing applications or needing to optimize existing Docker configurations for production deployment and efficient orchestration.