ml-pipeline-guide
Build and deploy reproducible production ML pipelines for research
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-pipeline-guide --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.
# ML Pipeline Guide ## Overview Machine learning research increasingly demands reproducible, end-to-end pipelines that go beyond a single training script. A research ML pipeline encompasses data ingestion, feature engineering, model training, evaluation, experiment tracking, and artifact management. Without a structured pipeline, research results become difficult to reproduce, ablation studies bec
What does the ml-pipeline-guide skill do?
Build and deploy reproducible production ML pipelines for research
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-pipeline-guide --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.