# Smoke Test Ml Pipeline

> Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real data/ source and predicts on a disjoint portion that deliberately carries no pre-history buffer. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from build-ml-pipeline. TRIGGER when: test-ml-pipeline has dispatched here to write the smoke test for an approved experiment; pytest tests/smoke/ is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in build-ml-pipeline needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to iterate-ml-experiment); the user is asking about a regression test or schema invariant (route to regression-test-ml-pipeline / distribution-test-ml-pipeline once those exist); the question is the interpretation of CV metrics, not predict-time correctness (route to evaluate-ml-pipeline). HOW TO USE: read the matching experiment's journal/NN.md and experiments/NN.py first to understand the pipeline's source binding (what env-dict keys does buildlearner expect?). Then construct two env-dicts from the real data/ source — a train env and a predict env — such that the predict env carries only the rows we want predictions for and no pre-history buffer. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within 3 × CVmean (or the task-appropriate analogue). Do not write the design note or run CV — that's other skills' job.

## Facts
- Page: https://tashan.sh/capability/skill-probabl-ai-smoke-test-ml-pipeline
- tashan id: skill:probabl-ai/smoke-test-ml-pipeline
- Source: https://github.com/probabl-ai/skills
- Type: skill
- Category: other
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 97.0
- Freshness: 94.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: BSD-3-Clause
- Official: no

## Install

```sh
cp -r smoke-test-ml-pipeline ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-14 by tashan (https://tashan.sh) from public evidence. Scorer s5.