Pythonic Code

Pythonic Code

Write Python that makes domain behavior obvious to human and AI readers. Apply a WWGD lens: choose the simplest correct design that feels native to Python and is easy to verify.

The preferred design method is Constructive Domain Modeling: define the valid values and outcomes a program can construct, then let their types carry obligations to the code that consumes them.

Orient Before Coding

1. Read the repository's AGENTS.md or CLAUDE.md instructions.
2. Read docs/ENGINEERING_STYLE.md when present.
3. Read docs/DOMAIN_MODEL.md when the change touches domain language, ownership, identity, source-of-truth rules, or lifecycle behavior.
4. Read each target file completely before editing it.
5. Identify the supported Python version, configured tools, surrounding patterns, and behavior that must remain stable.

Let local project rules override generic style advice.

Make Decisions In This Order

1. Preserve correctness, domain invariants, and public behavior.
2. Respect repository conventions and compatibility constraints.
3. Make data flow, control flow, errors, and side effects obvious.
4. Choose the smallest abstraction that reduces cognitive load now.
5. Use Python idioms when they clarify intent rather than merely shorten code.
6. Prove the result with types, tests, and repository tooling.

Model The Positive Space

Constructive Domain Modeling describes what the program supports instead of starting with a broad representation and a growing list of invalid combinations.

• Represent one valid state with a product of required fields, usually a frozen dataclass.
• Represent meaningful alternatives with a closed union using a Python 3.12 type alias.
• Use Pydantic models and discriminated unions at API, CLI, MCP, configuration, and persistence boundaries where untrusted values require runtime validation or serialization.
• Parse or classify a broad boundary shape once, then pass the narrower domain value internally. Do not make every consumer rediscover the invariant through checks and casts.
• Consume a closed union with explicit match cases. Use typing.assert_never when it proves exhaustive handling, and avoid catch-all cases that hide a newly added variant.
• Prefer a total function over a partial one. When a case is expected, either narrow the input so the case is impossible or widen the return union so the caller must handle it.
• Return explicit variants for recoverable domain outcomes when callers can respond differently. Keep exceptions for broken invariants, cancellation, and unpredictable filesystem, network, queue, or database failures.
• Choose the simplest model that rules out a real error. Do not add wrapper-only IDs, Result types around every operation, or maximum-precision unions that cost more than they clarify.

Before narrowing an ORM model or compatibility schema, trace its writers and serialized forms. Storage may remain broad while a parser constructs a safer domain value for the core workflow.

Prefer Functions Before Hierarchies

• Start with an ordinary, fully typed function.
• Pair functions with a dataclass when related state or an operation result needs a name.
• Use callbacks, closures, or functools.partial when binding behavior is clearer than creating another object.
• Use functools.singledispatch only when behavior genuinely varies by the first argument's runtime type and open registration is an intentional extension point.
• Use a narrow Protocol for genuine replaceable behavior. Do not use property-only protocols to describe internal result data; return a concrete frozen dataclass unless callers truly require structural interoperability.
• Use a concrete class when identity, cohesive mutable state, lifecycle, or resource ownership requires one.
• Use an abstract base class only when runtime-enforced subclassing or shared skeletal behavior is part of the current design.

Do not replace one class hierarchy with clever functional machinery. Prefer the form with the fewest concepts, hidden rules, and call hops.

Keep Reasoning Local

• Keep a straightforward workflow together when top-to-bottom reading is clearest.
• Extract a helper only when its name captures a domain operation, it isolates a side effect or constraint, it removes meaningful duplication, or it forms a cohesive testable computation.
• Do not extract helpers merely to shorten a function.
• Treat a class dominated by private methods as a signal that behavior may belong in explicit module-level functions operating on typed values.
• Treat long chains of _prepare_*, _resolve_*, _apply_*, and _build_* calls as a prompt to reconsider the data flow or name one meaningful phase object.
• Avoid manager, factory, base, adapter, strategy, and registry abstractions with only one real implementation.
• Avoid dynamic registration, metaprogramming, and decorator-driven control flow unless the product currently needs that extension mechanism.

If extracting a helper makes the reader navigate more but understand no less, keep the logic local.