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Activates the DevOps-Commander agent for infrastructure, CI/CD, and cloud operations. Use when you need GitHub Actions or GitLab CI pipeline design, Dockerfile and docker-compose configuration, Kubernetes deployment manifests, Terraform/Pulumi infrastructure as code, Prometheus + Grafana monitoring setup, or incident response runbooks. Outputs complete, production-ready configuration files.

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

Generate Terraform infrastructure-as-code for AWS, GCP, or Azure. Creates modular VPC, compute, database, cache, CDN, and monitoring configs with per-environment sizing, remote state, cost estimates, and security best practices. Use when you need to set up cloud infrastructure, provision servers, create IaC, deploy to AWS/GCP/Azure, or scaffold a Terraform project.

Audit deployment readiness across CI/CD, containers, monitoring, IaC, secrets, CDN, and DNS — score each area 0-100, identify critical gaps, and optionally auto-fix by chaining deploy sub-skills

Audit an emergency resource management system for crisis readiness. Evaluates inventory tracking accuracy, deployment request-to-arrival pipeline, logistics and route optimization, supply chain resilience, inter-agency resource sharing (NIMS/EDXL), staging area management, and real-time capacity dashboards. Use when building or reviewing FEMA-style resource platforms, disaster logistics systems, o

Orienta Claude a aconselhar profissionais de marketing sobre como selecionar, priorizar e implementar recursos visuais em páginas de produtos e de destino para apoiar a mensagem e impulsionar a conversão.

Generate production-grade Kubernetes manifests — Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, Secrets — with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps

Coordinate multi-system deployment steps as a review-first runbook with explicit approval before any external write or promotion action.

Run a full pre-launch quality gate before shipping — chains product review, growth audit, UX audit, security scan, and deployment preflight into a go/no-go launch decision. Stops on critical blockers. Use before v1 launch, beta release, major version rollout, or feature launch.

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

Emergency fix-and-deploy pipeline — diagnose a bug, apply a minimal fix, verify safety, deploy to production, and confirm the service is healthy. Supports Vercel, AWS, Railway, Fly.io, Heroku, and Kubernetes. Tracks incident timeline and MTTR. Triggered by 'fix and deploy', 'hotfix and ship', 'emergency fix', 'fix this bug and deploy', 'patch and push', 'fix and ship'.

Full-stack app pipeline — build, test, review, and deploy across one or ALL repos. Scans CLAUDE.md, MEMORY.md, TODOs, open PRs, and issues for shippable work. Supports 'ship all' for multi-repo. Use when: 'build and ship', 'ship it', 'ship all', 'deploy everything', 'full pipeline', 'what needs shipping', 'end to end'.