Dockerize Any Project Automatically

    1

    Automatically containerize your project with Docker.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    See it in action

    You say

    I have a Python FastAPI app with a requirements.txt file. Can you dockerize this so I can run it easily?

    Your agent does

    Project Detected: Python (FastAPI)

    Docker Setup Created:

    • Multi-stage Dockerfile
    • .dockerignore
    • docker-compose.yml

    Files Modified:

    • Dockerfile
    • .dockerignore

    Commands Executed:

    • docker build -t fastapi-app .

    Build Result: Successfully built fastapi-app:latest.

    What you get

    Generate optimized multi-stage Dockerfiles based on project source code.Create .dockerignore files to prevent sensitive data leaks and reduce bloat.Orchestrate multi-service environments with docker-compose.yml.Debug and resolve existing Docker build errors or image size issues.

    About this skill

    The problem

    Manually writing Dockerfiles and Compose configurations is repetitive and error-prone. Developers often struggle with optimizing build contexts, managing dependency caching, or selecting the correct base images for unfamiliar stacks.

    What it does

    • Analyzes project roots to detect languages, frameworks, and runtimes automatically.
    • Generates optimized Dockerfiles, .dockerignore files, and docker-compose.yml configurations.
    • Implements multi-stage builds and slim/alpine base images to minimize image size.
    • Configures dependency caching patterns to speed up subsequent builds.
    • Validates configurations by running local builds and reporting exact results.

    Frameworks & tools

    Docker, Docker Compose, Node.js, Python, Go, Rust, and standard web frameworks.

    Why this beats prompting it yourself

    Generic prompts often produce bloated images or skip critical .dockerignore patterns that leak local secrets. This skill follows a strict multi-step execution process to inspect your specific dependency files before writing a single line of configuration, ensuring the build actually works on the first try.

    Use cases

    • Containerize a legacy Node.js or Python application for cloud migration.
    • Add a docker-compose setup to a project requiring multiple services.
    • Fix broken Docker builds by auditing existing configurations and logs.
    • Standardize Docker patterns across a fragmented microservices repository.

    Known limitations

    Cannot deploy containers to external registries or production environments. Requires local Docker installation to perform build validation; otherwise, it only generates the configuration files.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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