# ml-pipeline-automation

A skill for building ML pipelines with Airflow, Kubeflow and MLflow: scheduled retraining, experiment tracking, failure alerts.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 389 installs · ★ 236
- Author: secondsky
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/secondsky/claude-skills/tree/HEAD/plugins/ml-pipeline-automation/skills/ml-pipeline-automation/SKILL.md
- Updated: 2026-10-20
- HTML: https://affpapa.org/en/nevibecode/skills/ml-pipeline-automation

## What it does

The skill hands your agent working patterns for ML orchestration, from data ingestion to model deployment. It ships Airflow DAG examples, an MLflow tracking setup for parameters and metrics, and a walkthrough of common breakages such as a task failing with nobody notified.

Quick start installs `apache-airflow` and `mlflow` with pip, initialises the Airflow database, then writes a DAG to `dags/ml_training_pipeline.py`. You start the scheduler and webserver, trigger the pipeline, and open the UI on port 8080. Package versions in the example are pinned as of publication, so check PyPI before you install.

This is for teams already running Airflow who want retries, task dependencies and experiment tracking under control. If your project has no orchestrator yet, the examples assume one — that is the first thing to sort out.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add secondsky/claude-skills --skill ml-pipeline-automation -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ml-pipeline-automation/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ml-pipeline-automation/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add secondsky/claude-skills --skill ml-pipeline-automation -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ml-pipeline-automation/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add secondsky/claude-skills --skill ml-pipeline-automation -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/ml-pipeline-automation/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install secondsky/claude-skills ml-pipeline-automation
```

2. **Manually: put the files in place**
- File: `.github/skills/ml-pipeline-automation/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add secondsky/claude-skills --skill ml-pipeline-automation -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/secondsky/claude-skills --path plugins/ml-pipeline-automation/skills/ml-pipeline-automation
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ml-pipeline-automation/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add secondsky/claude-skills --skill ml-pipeline-automation -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/secondsky/claude-skills/tree/HEAD/plugins/ml-pipeline-automation/skills/ml-pipeline-automation/SKILL.md

## When to use it
- wire a DAG that validates data then trains a model
- send a Slack alert when a task fails
- log parameters and metrics to MLflow per run
- set retries and a delay between attempts
- pass a data path between tasks with XCom

## Example prompts
- Build me an Airflow DAG that retrains the model daily and logs runs to MLflow
- Set up an on_failure_callback so a failed task posts to Slack
- Review my `dags/ml_training_pipeline.py` for task dependency mistakes

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 23a3ffc94bda51e8.

BEGIN THIRD-PARTY FILE 23a3ffc94bda51e8
````text
---
name: ml-pipeline-automation
description: Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
license: MIT
metadata:
  keywords: "ML pipeline, Airflow, Kubeflow, MLflow, MLOps, workflow orchestration, data pipeline, model training automation, experiment tracking, model registry, Airflow DAG, task dependencies, pipeline monitoring, data quality, drift detection, hyperparameter tuning, model versioning, artifact management, Kubeflow Pipelines, pipeline automation, retries, sensors"
---

# ML Pipeline Automation

Orchestrate end-to-end machine learning workflows from data ingestion to production deployment with production-tested Airflow, Kubeflow, and MLflow patterns.

## When to Use This Skill

Load this skill when:
- **Building ML Pipelines**: Orchestrating data → train → deploy workflows
- **Scheduling Retraining**: Setting up automated model retraining schedules
- **Experiment Tracking**: Tracking experiments, parameters, metrics across runs
- **MLOps Implementation**: Building reproducible, monitored ML infrastructure
- **Workflow Orchestration**: Managing complex multi-step ML workflows
- **Model Registry**: Managing model versions and deployment lifecycle

## Quick Start: ML Pipeline in 5 Steps

```bash
# 1. Install Airflow and MLflow (check for latest versions at time of use)
pip install apache-airflow==3.1.5 mlflow==3.7.0

# Note: These versions are current as of December 2025
# Check PyPI for latest stable releases: https://pypi.org/project/apache-airflow/

# 2. Initialize Airflow database
airflow db init

# 3. Create DAG file: dags/ml_training_pipeline.py
cat > dags/ml_training_pipeline.py << 'EOF'
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'ml-team',
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG(
    'ml_training_pipeline',
    default_args=default_args,
    schedule_interval='@daily',
    start_date=datetime(2025, 1, 1)
)

def train_model(**context):
    import mlflow
    import mlflow.sklearn
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

    mlflow.set_tracking_uri('http://localhost:5000')
    mlflow.set_experiment('iris-training')

    with mlflow.start_run():
        model = RandomForestClassifier(n_estimators=100)
        model.fit(X_train, y_train)
````
END THIRD-PARTY FILE 23a3ffc94bda51e8

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