ML Fail-Fast Validation Skill for Missing Columns
Check data and feature schemas at pipeline boundaries so a missing column fails before training starts.
中文Install in Claude Code
Use Python, pandas, and a synthetic feature pipeline with required fields defined for each stage.
- Get the complete `plugins/devops-tools/skills/ml-failfast-validation` directory from the pinned GitHub commit.
- Copy it into the target project's `.claude/skills/`. Check `.claude/skills/ml-failfast-validation/SKILL.md`, start Claude Code at the project root, and call `/ml-failfast-validation`.
Example task
Require weight, width, and label. Run a ten-row CSV missing width, verify the raw_data failure, then add width and rerun.What to check
The missing fixture stops before training and names width. The repaired fixture proceeds to feature calculation.
Original SKILL.md on GitHub · Updated
Frequently asked question
Should schema checks run only immediately before training?
Validate at the data and feature boundaries so failures identify which stage changed the structure.