home / skills / autumnsgrove / groveengine / docker-workflows
This skill helps you containerize Python projects with UV by generating Dockerfiles, docker-compose setups, and secure multi-stage workflows.
npx playbooks add skill autumnsgrove/groveengine --skill docker-workflowsReview the files below or copy the command above to add this skill to your agents.
---
name: docker-workflows
description: Create Dockerfiles, docker-compose configurations, and container workflows for Python projects with UV. Use when containerizing applications, setting up development environments, or deploying with Docker.
---
# Docker Workflows Skill
## When to Activate
Activate this skill when:
- Creating Dockerfiles for applications
- Setting up docker-compose environments
- Containerizing Python/UV projects
- Configuring multi-stage builds
- Managing container secrets
## Quick Commands
```bash
# Build image
docker build -t my-app:latest .
# Run container
docker run -d -p 8000:8000 --name my-app my-app:latest
# View logs
docker logs -f my-app
# Execute in container
docker exec -it my-app bash
# Stop and remove
docker stop my-app && docker rm my-app
# Clean up
docker system prune -a
```
## Basic Dockerfile (Python/UV)
```dockerfile
FROM python:3.11-slim
WORKDIR /app
# Install UV
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
# Copy dependency files (layer caching)
COPY pyproject.toml uv.lock ./
# Install dependencies
RUN uv sync --frozen --no-dev
# Copy application
COPY . .
EXPOSE 8000
CMD ["uv", "run", "python", "main.py"]
```
## Multi-Stage Build (Production)
```dockerfile
# Stage 1: Builder
FROM python:3.11-slim AS builder
WORKDIR /app
COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev
COPY . .
# Stage 2: Runtime
FROM python:3.11-slim
WORKDIR /app
# Create non-root user
RUN useradd -m -u 1000 appuser && chown appuser:appuser /app
# Copy from builder
COPY --from=builder /app/.venv /app/.venv
COPY --from=builder /app /app
USER appuser
ENV PATH="/app/.venv/bin:$PATH"
EXPOSE 8000
CMD ["python", "main.py"]
```
## .dockerignore
```
__pycache__/
*.pyc
.git/
.env
.venv/
venv/
*.log
.DS_Store
.pytest_cache/
tests/
docs/
*.md
```
## Docker Compose (App + Database)
```yaml
version: '3.8'
services:
app:
build: .
ports:
- "8000:8000"
environment:
- DATABASE_URL=postgresql://user:password@db:5432/myapp
depends_on:
- db
volumes:
- ./app:/app # Development: live reload
db:
image: postgres:15-alpine
environment:
- POSTGRES_USER=user
- POSTGRES_PASSWORD=password
- POSTGRES_DB=myapp
volumes:
- postgres_data:/var/lib/postgresql/data
ports:
- "5432:5432"
volumes:
postgres_data:
```
## Compose Commands
```bash
# Start all services
docker-compose up -d
# View logs
docker-compose logs -f app
# Stop services
docker-compose down
# Rebuild and restart
docker-compose up -d --build
# Run command in service
docker-compose exec app bash
# Remove volumes (deletes data!)
docker-compose down -v
```
## Layer Caching Best Practice
```dockerfile
# Good: Dependencies cached separately
COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev
COPY . .
# Bad: Cache invalidated on every code change
COPY . .
RUN uv sync --frozen --no-dev
```
## Security Essentials
```dockerfile
# Use official slim images
FROM python:3.11-slim
# Run as non-root
RUN useradd -m -u 1000 appuser
USER appuser
# Don't include secrets in images
# Use runtime environment variables instead
```
## Runtime Secrets
```bash
# Pass via environment
docker run -e API_KEY=secret my-app
# Use env file
docker run --env-file .env.production my-app
# With compose
services:
app:
env_file:
- .env.production
```
## Volume Types
```bash
# Named volume (data persistence)
docker run -v postgres_data:/var/lib/postgresql/data postgres
# Bind mount (development)
docker run -v $(pwd)/app:/app my-app
```
## Debugging
```bash
# Interactive shell
docker exec -it container_name bash
# Real-time logs
docker logs -f --tail 100 container_name
# Inspect configuration
docker inspect container_name
# Resource usage
docker stats container_name
# Copy files
docker cp container_name:/app/logs ./logs
```
## Common Issues
### Container exits immediately
```bash
docker logs container_name # Check for errors
docker run -it app:v1 bash # Debug interactively
```
### Can't connect to container
```bash
docker ps # Check port mapping
docker inspect container_name # Check network config
```
### Out of disk space
```bash
docker system df # Check usage
docker system prune -a # Clean everything
```
## Related Resources
See `AgentUsage/docker_guide.md` for complete documentation including:
- Advanced multi-stage patterns
- Docker Compose variations
- Production optimization
- CI/CD integration
This skill creates Dockerfiles, docker-compose configurations, and container workflows tailored for Python projects that use UV. It helps you containerize applications, set up development environments with live reload, and prepare multi-stage builds for production. The focus is practical: repeatable builds, secure images, and efficient layer caching. Use it to standardize container workflows for a multi-tenant blog platform or similar services.
The skill generates Dockerfiles optimized for Python/UV, including single-stage and multi-stage production patterns. It produces docker-compose files that wire the app to services like Postgres and supports development bind mounts and persistent volumes. It also recommends .dockerignore entries, runtime secret handling, and commands for building, running, debugging, and cleaning images and containers.
How should I pass secrets to containers?
Do not bake secrets into images. Use environment variables, --env-file, or compose env_file entries and consider a secrets manager for production.
When should I use multi-stage builds?
Use multi-stage builds when you need to exclude build-time tools and dependencies from the runtime image to reduce size and attack surface.