mlops-courses/mlops-coding-skills
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Agent skills based on the MLOps Coding Course
概要
This project contains a collection of designed to accompany the MLOps Coding Course. These skills are tailored to help AI coding assistants (like Antigravity, Gemini CLI, GitHub Copilot, and Claude Code) understand and guide you through the best practices of MLOps, from prototyping to production. Agent Skills are specialized context or instructions that you can load into your AI assistant. Each skill is a folder containing a SKILL.md file (YAML frontmatter + Markdown instructions) that provides the "Know-How" for a specific task, ensuring the AI follows the course's methodology, standards, and patterns. Each skill derives from one chapter of the MLOps Coding Course: Skills follow the open SKILL.md standard, so most agents discover them from a well-known folder. The workspace-standard location is .agents/skills/; each tool then reads from its own path (see the table below). 1. to a location on your machine (e.g. ~/mlops-coding-skills). 1.
README
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MLOps Coding Skills
Welcome to the MLOps Coding Skills repository! 🚀
This project contains a collection of Agent Skills designed to accompany the MLOps Coding Course. These skills are tailored to help AI coding assistants (like Antigravity, Gemini CLI, GitHub Copilot, and Claude Code) understand and guide you through the best practices of MLOps, from prototyping to production.
🧠 What are Agent Skills?
Agent Skills are specialized context or instructions that you can load into your AI assistant. Each skill is a folder containing a SKILL.md file (YAML frontmatter + Markdown instructions) that provides the “Know-How” for a specific task, ensuring the AI follows the course’s methodology, standards, and patterns.
📚 Skills in this repository
Each skill derives from one chapter of the MLOps Coding Course:
| Skill | Course chapter | Focus |
|---|---|---|
mlops-initialization |
1. Initializing | Set up a robust project structure with uv, git, and VS Code. |
mlops-prototyping |
2. Prototyping | Write clean, reproducible Jupyter notebooks and prevent data leakage. |
mlops-industrialization |
3. Productionizing | Turn prototypes into a distributable package (src layout, hybrid paradigm). |
mlops-validation |
4. Validating | Add typing (ty), linting (ruff), testing (pytest), logging, scans. |
mlops-automation |
5. Refining | Automate tasks, containers, CI/CD, and experiment tracking (MLflow). |
mlops-collaboration |
6. Sharing | Prepare the project for sharing: license, docs, templates, releases. |
mlops-observability |
7. Observability | Add reproducibility, lineage, monitoring, alerting, and explainability. |
🛠️ Installation & Usage
Skills follow the open SKILL.md standard, so most agents discover them from a well-known folder. The workspace-standard location is .agents/skills/; each tool then reads from its own path (see the table below).
Recommended setup (symlinks)
-
Clone this repository to a location on your machine (e.g.
~/mlops-coding-skills). -
Create the skills directory in your target project or workspace, then symlink the skills you need:
mkdir -p .agents/skills # Symlink every MLOps skill (or pick individual folders): ln -s ~/mlops-coding-skills/mlops-* .agents/skills/ -
Expose them to your agent using the per-tool path below.
Per-tool install paths
推奨ツール
別のキーワードを試すか、フィルタを外してください。
インストール
npx skillfish add mlops-courses/mlops-coding-skills