Explorar skills

Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use when architecting deployment workflows, setting up continuous delivery, or implementing GitOps practices.

Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.

Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Create, manage, and optimize LXC containers in Proxmox. Control container lifecycle, manage resources, and coordinate container deployments across nodes.

Analyze codebases and provide technical overviews covering core components, interactions, deployment architecture, and runtime behavior. Use when the user asks for codebase analysis, project overview, technical architecture explanation, or system documentation.

Execute verificações de qualidade abrangentes, incluindo linting, verificação de tipo, testes e auditorias de segurança, antes de commits ou implantações.

Use DigitalOcean MCP and related tools to check deployment health, crash logs, environment consistency, and runtime issues for Unite-Hub / Synthex. Use when diagnosing deployment failures or verifying readiness.

Optional base URL for self-hosted Firecrawl deployments.

Orchestrate end-to-end backend feature development from requirements to deployment. Use when coordinating multi-phase feature delivery across teams and services.

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.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.