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Développement · SKILL.md

repo-development

repo-development

Maintain segmentation_models_pytorch source, docs, and focused tests safely inside an SMP checkout.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: repo-development description: "Maintain segmentation_models_pytorch source, docs, and focused tests safely inside an SMP checkout." disable-model-invocation: true metadata: disco-role: operating license: MIT

Repo Development

Use this sub-skill when the task is to edit this repository rather than simply use the installed package: add or change a decoder/model architecture, encoder, loss, metric, docs table, tests, pytest marker selection, Makefile workflow, or contributor-facing docs.

Do not use this sub-skill for ordinary training/inference examples, release publishing, private Hugging Face credentials, or broad benchmark/model generation. For usage-level questions, route to the appropriate sibling runtime sub-skill instead of changing repository files.

Start Here

  1. Identify the changed surface from the request and touched paths: decoder/model, encoder, loss/metric, base module, docs, or tests.
  2. Read references/maintainer-guide.md for source ownership, metadata, Make targets, docs table generation, and safe maintainer boundaries.
  3. Use references/test-selection.md to choose the smallest useful pytest, ruff, and docs commands before considering broad make test or marker-heavy runs.
  4. Run python sub-skills/repo-development/scripts/list_changed_test_focus.py <changed-path>... from the generated skill root, or copy the script path from this sub-skill, to get JSON command suggestions from explicit changed files.
  5. If a command fails or looks unexpectedly slow/networked, check references/troubleshooting.md before widening scope.

Editing Checklist

  • Keep package code under segmentation_models_pytorch/ and tests under tests/; update docs in docs/*.rst and generated tables only when the changed surface requires it.
  • Preserve public API exports in segmentation_models_pytorch/__init__.py, decoder package __init__.py files, segmentation_models_pytorch/encoders/__init__.py, losses/__init__.py, and metrics/__init__.py when adding new public objects.
  • Prefer focused tests first: one decoder test file for a decoder change, one encoder test file for an encoder change, tests/test_losses.py for losses, tests/test_preprocessing.py for preprocessing metadata, and base tests for shared modules.
  • Treat logits_match, compile, torch_export, and torch_script as opt-in markers; they can be slow, version-gated, or network/cache-sensitive.
  • Use make fixup or ruff check --fix plus ruff format for repository style; the configured Ruff settings live in pyproject.toml.
  • Do not run or recommend misc/generate_test_models.py unless the user explicitly asks for maintainer-only Hugging Face model fixture regeneration.
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