building-automl-pipelines
Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline". '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill building-automl-pipelines --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Building Automl Pipelines ## Overview Build an end-to-end AutoML pipeline: data checks, feature preprocessing, model search/tuning, evaluation, and exportable deployment artifacts. Use this when you want repeatable training runs with a clear budget (time/compute) and a structured output (configs, reports, and a runnable pipeline). ## Prerequisites Before using this skill, ensure you have: - Pyth
What does the building-automl-pipelines skill do?
Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline". '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill building-automl-pipelines --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.