A professional toolset for transforming experimental AI prototypes into structured, distributable, and maintainable Python packages.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mlops-industrialization-cn
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mlops-industrialization-cn using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MLOps Industrialization?
MLOps Industrialization is a specialized skill designed to bridge the gap between data science experimentation and software engineering excellence. As part of the ecosystem of Openclaw Skills, it provides developers with the automation necessary to turn messy notebooks into clean, version-controlled Python packages. By enforcing a strict layout and architectural separation, it ensures that machine learning projects are not just functional, but scalable and ready for production environments.
This skill focuses on the structural integrity of AI projects, moving beyond scripts to create distributable assets. It is particularly valuable for teams looking to standardize their deployment pipelines and ensure that their Openclaw Skills contribute to high-quality, reproducible software development life cycles.
MLOps Industrialization Use Cases
- Transitioning from research-focused Jupyter Notebooks to production-grade Python libraries.
- Standardizing internal ML project structures across large engineering teams.
- Preparing machine learning models for seamless integration into CI/CD pipelines.
- Decoupling complex business logic from volatile I/O operations for better testability.
How MLOps Industrialization Works
- The user initiates the package generator script to create a standardized src-based directory layout.
- The skill organizes the codebase into a Three-Layer Architecture: Domain, I/O, and Application.
- Pure business logic and feature transformations are isolated in the Domain layer to prevent side effects.
- External interactions like data loading and model persistence are encapsulated within the I/O layer.
- The Application layer wires these components together to manage high-level tasks like training and inference.
- Finally, the skill helps define CLI entrypoints in the project configuration for easy execution.
MLOps Industrialization Setup
To get started with this addition to your Openclaw Skills, execute the generation script to scaffold your new package:
./scripts/create-package.sh my_package_name
After the directory structure is created, register your CLI tools by adding the following to your pyproject.toml file:
[project.scripts]
train = "my_package_name.application.train:main"
MLOps Industrialization Data Schema & Taxonomy
The skill enforces a clear taxonomy for files and metadata to ensure organizational clarity. The generated structure follows this pattern:
| Component | Location | Responsibility |
|---|---|---|
| Domain | src/package/domain/ |
Pure logic, feature engineering, and state-independent code. |
| I/O | src/package/io/ |
Data fetching, database interactions, and model saving/loading. |
| Application | src/package/application/ |
Orchestration of Domain and I/O, training loops, and main entrypoints. |
This data organization is essential for developers utilizing Openclaw Skills to maintain long-term project health.
MLOps Industrialization Advanced Features
- Automated Three-Layer Architecture scaffolding for immediate project startup.
- Strict separation of concern patterns to minimize technical debt in ML codebases.
- Built-in support for standardized src-layout distribution.
- Seamless CLI entrypoint integration for running training or inference pipelines via the terminal.
- Optimized for modularity, allowing different Openclaw Skills to interact with specific package layers independently.
SKILL.md
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