Uv Pytest Unit Testing

Uv Pytest Unit Testing

Overview

Use this skill to standardize pytest setup and execution for uv-managed Python repositories, including single-project repos and uv workspaces.

Workflow

1. Detect repository mode.

• Treat repo as workspace when pyproject.toml defines [tool.uv.workspace].
• Treat repo as single project otherwise.

2. Bootstrap pytest dependencies and baseline config.

• Run scripts/bootstrap_pytest_uv.sh --workspace-root <repo>.
• Add --package <member-name> for workspace member package setup.
• Add --with-cov when pytest-cov should be installed and baseline coverage flags added.
• Add --dry-run to preview all actions without mutating files.

3. Run tests with uv.

• Run scripts/run_pytest_uv.sh --workspace-root <repo> for root-project execution.
• Run scripts/run_pytest_uv.sh --workspace-root <repo> --package <member-name> for workspace member execution.
• Pass through pytest selectors/options after --, for example:
  • scripts/run_pytest_uv.sh --workspace-root <repo> -- --maxfail=1 -q
  • scripts/run_pytest_uv.sh --workspace-root <repo> --package api --path tests/unit -- -k auth -m "not slow"

4. Apply balanced quality gates.

• Require passing test runs before concluding work.
• Recommend coverage reporting as guidance, not a hard threshold, unless user explicitly requests enforced minimum coverage.

5. Troubleshoot failures in this order.

• Confirm command context: root vs --package run target.
• Confirm test discovery layout: tests/, test_*.py, *_test.py.
• Confirm marker registration in tool.pytest.ini_options.markers when custom markers are used.
• Confirm import path assumptions (package install mode, working directory, and module names).

Test Authoring Guidance

• Keep fast unit tests under tests/unit and integration-heavy tests under tests/integration when repo size warrants separation.
• Use fixtures for setup reuse, and keep fixture scope minimal (function by default).
• Use @pytest.mark.parametrize for matrix-style cases instead of hand-written loops.
• Use monkeypatch for environment variables and runtime dependency replacement.
• Register custom marks in config to avoid marker warnings.

Automation Suitability

• Codex App automation: High. Strong recurring fit for test health checks, failure triage, and drift detection.
• Codex CLI automation: High. Strong fit for non-interactive test setup and targeted test sweeps.

Codex App Automation Prompt Template

Use $uv-pytest-unit-testing.

Scope boundaries:
- Work only inside <REPO_PATH>.
- Operate only on pytest setup and test execution tasks.
- Do not perform unrelated code refactors.

Task:
1. Detect repository mode from <WORKSPACE_ROOT>/pyproject.toml.
2. If <DRY_RUN_BOOTSTRAP:TRUE|FALSE> is TRUE, run:
   `scripts/bootstrap_pytest_uv.sh --workspace-root <WORKSPACE_ROOT> <PACKAGE_FLAG> <WITH_COV_FLAG> --dry-run`
3. If <DRY_RUN_BOOTSTRAP:TRUE|FALSE> is FALSE, run:
   `scripts/bootstrap_pytest_uv.sh --workspace-root <WORKSPACE_ROOT> <PACKAGE_FLAG> <WITH_COV_FLAG>`
4. Run tests with:
   `scripts/run_pytest_uv.sh --workspace-root <WORKSPACE_ROOT> <PACKAGE_FLAG> <TEST_PATH_FLAG> -- <PYTEST_ARGS>`
5. Keep package-targeted runs explicit when <PACKAGE_NAME_OR_EMPTY> is set.

Output contract:
1. STATUS: PASS or FAIL
2. SETUP: what bootstrap actions ran
3. TEST_RESULTS: concise pass/fail summary
4. FAILURES: grouped likely causes
5. NEXT_STEPS: minimal remediation actions

Codex CLI Automation Prompt Template

codex exec --full-auto --sandbox workspace-write --cd "<REPO_PATH>" "<PROMPT_BODY>"

<PROMPT_BODY> template:

Use $uv-pytest-unit-testing.
Limit scope to pytest setup and execution in <WORKSPACE_ROOT>.
Run bootstrap in dry-run or real mode based on <DRY_RUN_BOOTSTRAP:TRUE|FALSE>.
Run tests with explicit package targeting when <PACKAGE_NAME_OR_EMPTY> is set.
Return STATUS, setup actions, concise test summary, grouped likely causes for failures, and minimal next steps.

Customization Placeholders

• <REPO_PATH>
• <WORKSPACE_ROOT>
• <PACKAGE_NAME_OR_EMPTY>
• <PACKAGE_FLAG>
• <TEST_PATH_OR_EMPTY>
• <TEST_PATH_FLAG>
• <PYTEST_ARGS>
• <WITH_COV_FLAG:--with-cov|EMPTY>
• <DRY_RUN_BOOTSTRAP:TRUE|FALSE>

Interactive Customization Workflow

1. Ask whether users want bootstrap mode or run mode.
2. Gather workspace root and optional package target.
3. For bootstrap mode, gather with_cov and dry_run.
4. For run mode, gather optional test path and optional pytest args.
5. Return both:

• A YAML profile for durable reuse.
• The exact command to run.

6. Use this precedence order:

• CLI flags
• --config profile file
• .codex/profiles/uv-pytest-unit-testing/customization.yaml
• ~/.config/gaelic-ghost/python-skills/uv-pytest-unit-testing/customization.yaml
• Script defaults

7. If users want temporary reset behavior:

• --bypassing-all-profiles
• --bypassing-repo-profile
• --deleting-repo-profile

8. If users provide no customization or profile files, keep existing script defaults unchanged.
9. See references/interactive-customization.md for schema and examples.

References

• Use references/pytest-workflow.md for pytest conventions, config keys, fixtures, markers, and troubleshooting.
• Use references/uv-workspace-testing.md for uv workspace execution patterns (uv run, uv run --package, package-targeted test runs).

Resources

scripts/

• scripts/bootstrap_pytest_uv.sh: Install pytest dev dependencies and append baseline tool.pytest.ini_options when missing.
• scripts/run_pytest_uv.sh: Run pytest via uv for root project or workspace member package, with passthrough args.

references/

• references/pytest-workflow.md: Practical pytest setup and usage guidance.
• references/uv-workspace-testing.md: uv execution guidance for single-project and workspace repositories.

Source: prabhulkarraj05/python-skills — distributed by TomeVault.