Validate Python Stack

Choose the validation path that matches the current directory and report exact failures without hiding skipped checks.

Template Repository

1. Run uv sync --all-groups.
2. Run uv run python scripts/compile_catalog.py --check.
3. Run uv run ruff check . and uv run ruff format --check ..
4. Run uv run pytest -n auto. Use focused tests first while iterating, but finish with the full suite.
5. For every directory under skills/, run the skill-creator validator when available and run npx --yes skills add . --list to verify discovery.
6. Generate the changed presets or custom combinations and exercise their real CLI, API, MCP, training, serving, or deployment entry point as applicable.
7. If GitHub workflows changed and Docker is available, run the repository's local Actions validation before pushing.

Generated Project

1. Read .copier-answers.yml, readme.md, and pyproject.toml to identify enabled layers.
2. Run uv sync --all-groups, uv run ruff check ., uv run ruff format --check ., and uv run pytest.
3. Run npx --yes skills add . --list; expect project-workflow and only the conditional skills appropriate to the selected stack.
4. Exercise the primary entry point. For a containerized target, build the Docker image and verify its health route locally when Docker is available.
5. Validate generated JSON, TOML, YAML, Python, and HCL as structured data rather than with string-only assertions.

Reporting

Separate passed, failed, and unavailable checks. Include the command, relevant error, and whether the failure belongs to the template, the generated project, local infrastructure, or an external service.