docker-deployment
Docker container deployment with Nginx HTTPS configuration and Cloudflare Tunnel integration. Use when deploying web applications with Docker, configuring SSL/TLS certificates, setting up Nginx reverse proxy, or integrating with Cloudflare Tunnel for secure external access.
Works with
--- name: docker-deployment description: Docker container deployment with Nginx HTTPS configuration and Cloudflare Tunnel integration. Use when deploying web applications with Docker, configuring SSL/TLS certificates, setting up Nginx reverse proxy, or integrating with Cloudflare Tunnel for secure external access. license: MIT --- # Docker Deployment with Nginx HTTPS ## Quick Start For Docker web application deployment with HTTPS support: 1. **Configure Nginx** with SSL certificates (see [nginx-https.md](references/nginx-https.md)) 2. **Set up docker-compose.yml** with certificate volume mounting 3. **Configure Cloudflare Tunnel** to connect external domain to local container ## Common Tasks | Task | Reference | |------|-----------| | Nginx HTTPS configuration | [nginx-https.md](references/nginx-https.md) | | Cloudflare Origin Certificate | [cf-origin-cert.md](references/cf-origin-cert.md) | | Docker data persistence | [data-persistence.md](references/data-persistence.md) | | Cloudflare Tunnel setup | [cf-tunnel.md](references/cf-tunnel.md) | ## Architecture Overview ``` Internet → Cloudflare Edge (HTTPS) → Cloudflare Tunnel → Ubuntu/Docker (Nginx) ``` ## Key Principles - **Always use named Docker volumes** for persistent data - **Nginx should redirect HTTP (80) to HTTPS (443)** in production - **Cloudflare Origin Certificates** are for CF-to-origin encryption only - **Tunnel connects to HTTP or HTTPS** - configure based on nginx setup ## Troubleshooting **HTTPS not working after enabling Cloudflare force HTTPS?** - Check if nginx listens on port 443 - Verify SSL certificates are mounted correctly - Ensure Cloudflare Tunnel service URL matches (http:// or https://) **Data lost after container restart?** - Check docker-compose.yml uses named volumes, not bind mounts for critical data - Verify database path points to mounted volume directory See individual reference files for detailed solutions.
More Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

