Test Ml Pipeline

Test ML Pipeline (router)

Where tests for an ML workspace live, what gets paired with what, and which subskill owns the body of each test category.

First action (every turn)

Before answering anything else:

1. Confirm an approved design note exists for the test the user is asking about. The pairing rule is hard: tests/<category>/test_NN_<short_name>.py only exists if journal/NN_<short_name>.md is at least approved and experiments/NN_<short_name>.py is the matching script. If the design note doesn't exist, hand back to iterate-ml-experiment.
2. Emit the Pre-flight checklist (below) as visible text in your response, with each box marked.
3. Use the Dispatch table to pick the subskill that owns the test category, then hand off.

Pre-flight — emit this checklist as visible text before any test work

Pre-flight (test-ml-pipeline):
- [ ] `journal/NN_<short_name>.md` exists and is at least `approved`
      (or confirmed n/a — about to hand off to `iterate-ml-experiment`)
- [ ] `experiments/NN_<short_name>.py` exists with the matching stem
      (or confirmed n/a — about to hand off to `organize-ml-workspace`)
- [ ] Test category picked: smoke | regression | distribution | …
- [ ] Subskill dispatched: `smoke-test-ml-pipeline` | …
- [ ] Test file stem decided: `tests/<category>/test_NN_<short_name>.py`
- [ ] pytest is on the project's dependency manifest (per
      `data-science-python-stack` § Tier 1)

Stop conditions — read before anything else

• No test without an approved design note. Never create tests/<category>/test_NN_*.py if the matching journal/NN_*.md isn't on disk and at least approved. The design note is the contract; the test asserts the contract holds. Reverse order is incoherent.
• The stem rule is hard. Test file basename is test_NN_<short_name>.py (with the test_ prefix that pytest expects); the NN_<short_name> portion matches the experiment exactly. One experiment → one test file per category. No test_<NN>_v2.py, no test_NN_<short_name>_2.py. If a test needs to evolve, edit it in place; the pairing must stay 1:1.
• One subskill per category. This skill only places the empty test file and hands off. Don't write assertion bodies, fixture construction, or test-specific logic in this skill — that belongs to the matching subskill (smoke-test-ml-pipeline, etc.).
• pytest is the runner. Tests are pytest tests, not jupytext-style # %% scripts. The experiment scripts live in experiments/ and stay # %%-style for interactive iteration; the tests are binary pass/fail and benefit from pytest's reporting. Don't mix the two conventions.

Layout this skill owns

project/
└── tests/
    ├── smoke/
    │   ├── test_01_baseline.py        # ↔ experiments/01_baseline.py
    │   ├── test_02_short_name.py
    │   └── ...
    └── (future: regression/, distribution/, …)

The pairing rule:

journal/NN_<short_name>.md
experiments/NN_<short_name>.py
tests/<category>/test_NN_<short_name>.py

— same NN_<short_name> stem in all three. The test_ prefix on the test file basename is the pytest naming convention; everything after it tracks the experiment.

The tests/<category>/ subfolder lets the workspace grow more test types without renaming anything. tests/smoke/ is the only required category for v1.

Test categories — Dispatch table

Use the user's signal first; fall back to the defaults at the bottom.

The current implementation only ships smoke-test-ml-pipeline. Future categories will land as sibling subskills; the dispatch table is the contract for adding them.

The required-test-per-experiment rule

Every approved experiment must have a passing smoke test before it can be marked done in JOURNAL.md. This is enforced by iterate-ml-experiment § 3 (after design-note approval, before script creation) and § 4 (before recording outcome): the test is part of the iteration loop, not an afterthought. If the smoke test fails, the iteration that follows is on the pipeline (re-enter build-ml-pipeline), not on the model — a smoke-test failure is almost always a structural problem with how the DataOps graph is laid out, not a metric problem with the predictor.

The smoke test is non-optional for every experiment in the workspace. Other test categories (regression, distribution, …) are opt-in and added when the workspace's needs warrant them.

Decision flow

1. Is there an approved journal/NN_<short_name>.md and a matching experiments/NN_<short_name>.py?
   • No → hand off to iterate-ml-experiment (design note first) or organize-ml-workspace (script first). Stop.
   • Yes → continue.
2. Identify the test category from the user's signal (Dispatch table). Default: smoke.
3. Place the empty test file at tests/<category>/test_NN_<short_name>.py with the pytest scaffolding (one def test_*(): function, empty body, a # TODO: filled in by <subskill> marker). If the file already exists, do not overwrite.
4. Hand off to the subskill (smoke-test-ml-pipeline etc.). The subskill writes the assertions, fixture construction, and any helpers it needs.
5. Confirm with the user that the subskill's draft is correct before running the test (the subskill owns this loop; this skill only does placement + dispatch).

What this skill does NOT do

• Run pytest. Test execution is the user's call (or CI's).
• Write assertion bodies. Each subskill owns the assertions for its category.
• Decide whether a test is required. The required-test-per-experiment rule (smoke at every iteration) is fixed by this skill; adding optional categories is a workspace-level decision the user makes, not this skill.
• Touch files outside tests/. The companion-skill edits to iterate-ml-experiment / organize-ml-workspace / build-ml-pipeline / evaluate-ml-pipeline are on those skills, not this one.

Companion skills

• smoke-test-ml-pipeline — owns the smoke test contract (fixture construction, the diagnostic-by-construction property, assertions, failure semantics). The only required test category in v1.
• organize-ml-workspace — scaffolds tests/<category>/ alongside journal/ / experiments/ at workspace creation time. The placeholder test files are placed by this skill, not by organize-ml-workspace.
• iterate-ml-experiment — drives the iteration loop. After design-note approval, dispatches to this skill (which dispatches to smoke-test-ml-pipeline) to draft the matching test. After the experiment runs, requires the smoke test to pass before the experiment can flip to done.
• build-ml-pipeline — pipeline declaration. The smoke test is the executable proof that build-ml-pipeline's X-marker rule (mark X early, featurize after, history references via upstream nodes) was followed. Smoke test failure typically means the pipeline shape is wrong; route back here.
• evaluate-ml-pipeline — owns the CV protocol. CV is necessary but not sufficient when the pipeline has history-dependent features; smoke-test-ml-pipeline fills the gap CV doesn't.
• data-science-python-stack — declares pytest as a Tier 1 mandatory dependency for any workspace that uses this skill.