unit-test-workflow

name	unit-test-workflow
description	Run and validate cdflib unit tests after code changes. Use when editing Python code, verifying regressions, or preparing a PR. Covers default local pytest runs, optional remote-data tests, and failure triage.
argument-hint	Describe what changed and whether you want full, targeted, or remote-data validation.

cdflib Unit Test Workflow

What This Skill Produces

• A repeatable test workflow after code changes.
• A clear choice between quick local validation and slower remote-data validation.
• A pass/fail decision with explicit completion checks.

When to Use

• After modifying code under cdflib/.
• Before opening or updating a pull request.
• When investigating possible regressions in parsing, writing, epochs, or xarray behavior.

Repository Test Model

• Unit tests live in tests/.
• Default local validation command is:

pytest

• In this repository, the default pytest run is the normal fast path and does not run the remote-data suite.
• Remote-data tests depend on downloading sample files and are slower (~15 minutes).
• Remote-data coverage is also exercised by CI on a weekly schedule (Monday 06:00 UTC) in .github/workflows/remote-tests.yaml.

Procedure

1. Choose Test Scope

• If code changes are functional (logic, parsing, writing, time conversion, xarray integration), run full local tests with pytest.
• If the change is docs-only or non-runtime metadata, tests may be optional unless requested by maintainers.
• If code touches behavior known to depend on external sample data, include remote-data validation when practical.

2. Run Default Local Suite

Use:

pytest

Expected outcome:

• Exit code 0.
• No new failures introduced by the change.

3. Branch for Remote-Data Validation (Optional/Deeper)

Use this branch when changes affect data-read compatibility across real-world files or when maintainers request deeper confidence.

Recommended command path from project docs:

pytest -m remote_data

Alternative CI-style invocation:

pytest --remote-data

4. Interpret Results and Act

• If tests pass: report completion and summarize what scope was run.
• If tests fail:
  • Identify first failing test and traceback.
  • Determine whether failure is caused by the new changes or a pre-existing issue.
  • Fix code/tests as appropriate.
  • Re-run pytest until green.

5. Completion Checks

A testing cycle is complete when all of the following are true:

• The intended test scope was run (default local suite at minimum).
• pytest passed for that scope.
• Any failures encountered were triaged and addressed.
• The final summary states exactly which command(s) were run.

Quick Decision Table

Situation	Command	Notes
Standard code change	pytest	Default, expected for most edits
Suspected remote-data impact	pytest -m remote_data or pytest --remote-data	Slower, network-dependent
Pre-merge confidence check	pytest (+ remote-data when relevant)	Match change risk to test depth

Agent Output Format

When using this skill, report:

1. What changed.
2. Which test command(s) were run.
3. Whether tests passed.
4. If failures occurred, what was fixed and what re-runs succeeded.