Python Types and Contracts

Overview

Treat type hints as interface design, not decoration. Focus on explicit contracts, stable public APIs, and boundary-safe modeling.

These are preferred defaults for common cases, not universal rules. When a default conflicts with project constraints, suggest a better-fit alternative and explain tradeoffs and compensating controls.

When to Use

• Public API signatures lack type annotations or use overly broad types.
• Pydantic models are scattered throughout internal logic instead of at trust boundaries.
• Contract changes risk breaking downstream consumers without migration paths.
• Interfaces accept Any, object, or untyped dicts where narrower types apply.
• Schema boundaries between layers (API, DB, domain) are implicit or inconsistent.
• Adding or evolving protocols, abstract base classes, or structural subtyping.

When NOT to use:

• Pure implementation-level code with no public interface.
• Throwaway scripts or one-off data munging where type rigor adds no value.
• Performance-critical inner loops where typing overhead matters more than safety.

Quick Reference

• Type public APIs and keep contracts explicit.
• Prefer narrow interfaces and boundary protocols over broad parameter types.
• Use pydantic at trust boundaries by default, not everywhere.
• Make compatibility and migration impact explicit for any contract change.
• Favor Protocol for structural subtyping over deep inheritance hierarchies.
• Return concrete types from public functions; accept protocols or unions as inputs.

Common Mistakes

• Typing everything identically. Internal helpers don’t need the same rigor as public APIs. Over-annotating private code adds noise without safety.
• Pydantic everywhere. Using pydantic models for internal data flow instead of reserving them for validation at trust boundaries (API ingress, config loading, external data).
• Broad return types. Returning Any or dict from public functions forces callers to guess structure. Return concrete types or TypedDicts.
• Breaking contracts silently. Changing function signatures, removing fields, or narrowing accepted types without versioning, deprecation warnings, or migration notes.
• Ignoring None. Omitting Optional or union with None when a value can legitimately be absent, hiding null-safety bugs until runtime.

Scope Note

• Treat these recommendations as preferred defaults for common cases, not universal rules.
• If a default conflicts with project constraints or worsens the outcome, suggest a better-fit alternative and explain why it is better for this case.
• When deviating, call out tradeoffs and compensating controls (tests, observability, migration, rollback).

Invocation Notice

• Inform the user when this skill is being invoked by name: python-design-modularity.

References

• references/typing-policy.md
• references/contract-evolution.md
• references/pydantic-boundaries.md