dropseed

annotations

@dropseed/annotations
dropseed
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Updated 1/18/2026
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Workflow for adding type annotations to Plain packages. Use this when adding or improving type coverage.

Installation

$skills install @dropseed/annotations
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Details

Repositorydropseed/plain
Path.claude/skills/annotations/SKILL.md
Branchmaster
Scoped Name@dropseed/annotations

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

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Skill Instructions


name: annotations description: Workflow for adding type annotations to Plain packages. Use this when adding or improving type coverage.

Type Annotation Workflow

We are gradually adding type annotations using Python 3.13+.

Workflow

  1. Check current coverage:

    uv run plain code annotations <directory> --details
    
  2. Add annotations: Focus on function/method signatures (parameters and return types)

  3. Type check:

    ./scripts/type-check <directory>
    
  4. Format: ./scripts/fix

  5. Test: ./scripts/test <package>

  6. Verify improvement:

    uv run plain code annotations <directory>
    
  7. Add to validation: Once a directory reaches 100% coverage, add it to FULLY_TYPED_PATHS in scripts/type-validate

Guidelines

  • Add from __future__ import annotations when necessary
  • Focus on public APIs and user-facing methods first
  • Don't annotate __init__ return types (type checkers infer None)
  • Use explicit return None for functions with -> Type | None return 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%