Workflow for adding type annotations to Plain packages. Use this when adding or improving type coverage.
Install
npx skillscat add dropseed/plain/annotations Install via the SkillsCat registry.
SKILL.md
Type Annotation Workflow
We are gradually adding type annotations using Python 3.13+.
Workflow
Check current coverage:
uv run plain code annotations <directory> --detailsAdd annotations: Focus on function/method signatures (parameters and return types)
Type check:
./scripts/type-check <directory>Format:
./scripts/fixTest:
./scripts/test <package>Verify improvement:
uv run plain code annotations <directory>Add to validation: Once a directory reaches 100% coverage, add it to
FULLY_TYPED_PATHSinscripts/type-validate
Guidelines
- Add
from __future__ import annotationswhen necessary - Focus on public APIs and user-facing methods first
- Don't annotate
__init__return types (type checkers inferNone) - Use explicit
return Nonefor functions with-> Type | Nonereturn type - Some Django-style ORM patterns are inherently difficult to type - that's okay
- Goal is progress, not perfection
Example
# Check coverage
uv run plain code annotations plain/plain/assets --details
# After adding annotations...
./scripts/type-check plain/plain/assets
./scripts/fix
./scripts/test plain
uv run plain code annotations plain/plain/assets # Should show 100%