Claude-skill-registry docker-rocker

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/docker-rocker" ~/.claude/skills/majiayu000-claude-skill-registry-docker-rocker && rm -rf "$T"
manifest: skills/data/docker-rocker/SKILL.md
source content

Docker Rocker

Production-grade Docker automation for Python FastAPI web API projects using multi-stage builds, UV package manager, and performance optimization strategies.

What This Skill Does

  • Creates optimized multi-stage Dockerfiles (Builder → Runtime → Testing stages)
  • Configures UV package manager for 50-70% faster dependency installation
  • Implements layer caching strategies for rapid rebuilds
  • Sets up non-root user security configurations
  • Generates docker-compose files for development and production
  • Optimizes for SQLModel/Neon database connections in containers
  • Configures Pydantic settings for container environments

What This Skill Does NOT Do

  • Manage Kubernetes deployments (use dedicated k8s skills)
  • Handle cloud-specific container registries (AWS ECR, GCR, ACR)
  • Configure reverse proxies (Nginx, Traefik)
  • Manage container orchestration at scale
  • Handle secrets management beyond environment variables

Before Implementation

Gather context to ensure successful implementation:

SourceGather
CodebaseProject structure, existing Dockerfile, pyproject.toml/requirements.txt, app entry point
ConversationTarget deployment (dev/staging/prod), database connection requirements, special dependencies
Skill ReferencesMulti-stage patterns from
references/
, optimization strategies, security practices
User GuidelinesTeam Docker conventions, registry requirements, CI/CD integration needs

Ensure all required context is gathered before implementing. Only ask user for THEIR specific requirements (domain expertise is in this skill).


Required Clarifications

Ask about USER's context before generating Dockerfiles:

QuestionPurpose
Deployment target?Dev (hot-reload) vs Production (optimized) vs CI/CD (testing)
Package manager?UV (recommended) vs pip vs Poetry
Database type?Neon PostgreSQL, local PostgreSQL, SQLite, other
Special system deps?Libraries requiring apt packages (Pillow, psycopg2, etc.)
Port configuration?Default 80 or custom port

Docker Deployment Stages

Stage Overview

StagePurposeBase ImageFinal Size
BuilderInstall dependencies, compile packagespython:3.12-slim~400MB (discarded)
RuntimeProduction applicationpython:3.12-slim~150MB
TestingCI/CD test executionextends Builder~450MB
DevelopmentHot-reload for local devpython:3.12-slim~300MB

Stage Selection Logic

What is the deployment target?

Production deployment?
  → Use Builder + Runtime stages
  → Minimal image, no dev dependencies

CI/CD pipeline?
  → Use Builder + Testing stage
  → Include pytest, dev dependencies

Local development?
  → Use Development stage
  → Volume mounts, hot-reload enabled

All of the above?
  → Generate complete multi-stage Dockerfile with all targets

Workflow

1. Analyze Project Structure

# Check for existing configurations
ls -la pyproject.toml uv.lock requirements*.txt Dockerfile docker-compose.yml

Identify:

  • Package manager (UV if uv.lock exists, Poetry if poetry.lock, pip otherwise)
  • Entry point (app/main.py, src/main.py, main.py)
  • Dependencies requiring system packages

2. Select Dockerfile Template

Based on user's deployment target, use appropriate template from

assets/templates/
:

TargetTemplate
Production only
Dockerfile.production
With testing
Dockerfile.complete
Development
Dockerfile.dev
Full multi-stage
Dockerfile.multistage

3. Configure Environment Variables

# Performance optimizations
ENV UV_LINK_MODE=copy \
    UV_COMPILE_BYTECODE=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1

# Application settings
ENV APP_MODULE=app.main:app \
    PORT=80

4. Optimize Layer Caching

Order Dockerfile instructions by change frequency:

  1. Base image (rarely changes)
  2. System dependencies (rarely changes)
  3. Python dependencies (changes occasionally)
  4. Application code (changes frequently)

5. Configure Database Connections

For Neon/PostgreSQL in containers:

# Use NullPool - let external pooler handle connections
from sqlalchemy.pool import NullPool

engine = create_async_engine(
    DATABASE_URL,
    poolclass=NullPool,
    pool_pre_ping=True,
)

6. Generate docker-compose

Create appropriate compose file for target environment.

7. Validate Build

# Build and verify
docker build --target runtime -t app:latest .
docker run --rm app:latest python -c "import app; print('OK')"

Quick Commands

TaskCommand
Build production image
docker build --target runtime -t app:prod .
Build test image
docker build --target testing -t app:test .
Run tests in container
docker run --rm app:test pytest -v
Build with no cache
docker build --no-cache -t app:latest .
Check image size
docker images app:latest
Scan for vulnerabilities
docker scout cves app:latest

Error Handling

ErrorCauseSolution
uv: command not found
UV not installed in imageAdd
COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
ModuleNotFoundError
Dependencies not in runtimeVerify COPY of .venv from builder
Permission denied
Non-root user can't writeUse
--chown=app:app
on COPY
Connection refused
to DB
Network isolationUse docker-compose networks or host.docker.internal
OOM killed
Container memory limitIncrease memory limit or optimize workers
Build cache not workingLayer order wrongReorder: deps before code

Output Checklist

Before delivering Dockerfile, verify:

  • Multi-stage build separates builder and runtime
  • UV cache mounts configured for fast rebuilds
  • UV_COMPILE_BYTECODE=1
    set for startup speed
  • Non-root user created and used
  • .dockerignore
    includes .venv, pycache, .git
  • Layer order optimized (deps before code)
  • Health check configured
  • --proxy-headers
    flag for FastAPI behind load balancer
  • Database connection uses NullPool for external pooling
  • Secrets not hardcoded (use environment variables)

Reference Files

FileWhen to Read
references/multi-stage-builds.md
Understanding stage architecture and patterns
references/optimization-strategies.md
Reducing image size and build time
references/fastapi-patterns.md
FastAPI-specific Docker configurations
references/security-best-practices.md
Container security hardening
references/database-connections.md
SQLModel/Neon connection patterns in containers

Asset Templates

TemplateUse Case
assets/templates/Dockerfile.multistage
Complete multi-stage (Builder + Runtime + Testing)
assets/templates/Dockerfile.production
Production-only optimized build
assets/templates/Dockerfile.dev
Development with hot-reload
assets/templates/docker-compose.yml
Full development stack
assets/templates/docker-compose.prod.yml
Production compose
assets/templates/.dockerignore
Standard ignore patterns