devops
The DevOps skill enables deployment and management of cloud infrastructure across multiple platforms including Cloudflare Workers, R2, D1, Docker, Google Cloud Platform services, and Kubernetes clusters. Use it for serverless applications, containerized workloads, CI/CD pipelines, GitOps workflows, multi-region deployments, and security auditing across enterprise infrastructure.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/devops && cp -r /tmp/devops/.claude/skills/devops ~/.claude/skills/devopsSKILL.md
# DevOps Skill Deploy and manage cloud infrastructure across Cloudflare, Docker, Google Cloud, and Kubernetes. ## When to Use - Deploy serverless apps to Cloudflare Workers/Pages - Containerize apps with Docker, Docker Compose - Manage GCP with gcloud CLI (Cloud Run, GKE, Cloud SQL) - Kubernetes cluster management (kubectl, Helm) - GitOps workflows (Argo CD, Flux) - CI/CD pipelines, multi-region deployments - Security audits, RBAC, network policies ## Platform Selection | Need | Choose | |------|--------| | Sub-50ms latency globally | Cloudflare Workers | | Large file storage (zero egress) | Cloudflare R2 | | SQL database (global reads) | Cloudflare D1 | | Containerized workloads | Docker + Cloud Run/GKE | | Enterprise Kubernetes | GKE | | Managed relational DB | Cloud SQL | | Static site + API | Cloudflare Pages | | Container orchestration | Kubernetes | | Package management for K8s | Helm | ## Quick Start ```bash # Cloudflare Worker wrangler init my-worker && cd my-worker && wrangler deploy # Docker docker build -t myapp . && docker run -p 3000:3000 myapp # GCP Cloud Run gcloud run deploy my-service --image gcr.io/project/image --region us-central1 # Kubernetes kubectl apply -f manifests/ && kubectl get pods ``` ## Reference Navigation ### Cloudflare Platform - `cloudflare-platform.md` - Edge computing overview - `cloudflare-workers-basics.md` - Handler types, patterns - `cloudflare-workers-advanced.md` - Performance, optimization - `cloudflare-workers-apis.md` - Runtime APIs, bindings - `cloudflare-r2-storage.md` - Object storage, S3 compatibility - `cloudflare-d1-kv.md` - D1 SQLite, KV store - `browser-rendering.md` - Puppeteer automation ### Docker - `docker-basics.md` - Dockerfile, images, containers - `docker-compose.md` - Multi-container apps ### Google Cloud - `gcloud-platform.md` - gcloud CLI, authentication - `gcloud-services.md` - Compute Engine, GKE, Cloud Run ### Kubernetes - `kubernetes-basics.md` - Core concepts, architecture, workloads - `kubernetes-kubectl.md` - Essential commands, debugging workflow - `kubernetes-helm.md` / `kubernetes-helm-advanced.md` - Helm charts, templates - `kubernetes-security.md` / `kubernetes-security-advanced.md` - RBAC, secrets - `kubernetes-workflows.md` / `kubernetes-workflows-advanced.md` - GitOps, CI/CD - `kubernetes-troubleshooting.md` / `kubernetes-troubleshooting-advanced.md` - Debug ### Scripts - `scripts/cloudflare-deploy.py` - Automate Worker deployments - `scripts/docker-optimize.py` - Analyze Dockerfiles ## Best Practices **Security:** Non-root containers, RBAC, secrets in env vars, image scanning **Performance:** Multi-stage builds, edge caching, resource limits **Cost:** R2 for large egress, caching, right-size resources **Development:** Docker Compose local dev, wrangler dev, version control IaC ## Resources - Cloudflare: https://developers.cloudflare.com - Docker: https://docs.docker.com - GCP: https://cloud.google.com/docs - Kubernetes: https://kubernetes.io/docs - Helm: https://helm.sh/docs
Manage MCP (Model Context Protocol) server integrations - discover tools/prompts/resources, analyze relevance for tasks, and execute MCP capabilities. Use when need to work with MCP servers, discover available MCP tools, filter MCP capabilities for specific tasks, execute MCP tools programmatically, or implement MCP client functionality. Keeps main context clean by handling MCP discovery in subagent context.
Stage all files and create a commit.
Stage, commit and push all code in the current branch
Create a pull request
Create a new agent skill
Utilize tools of Model Context Protocol (MCP) servers
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.