Type Hunter Py

Type Hunter

Audit code for type design weaknesses — places where the type system could prevent bugs but doesn't because the types are too loose, too broad, or too clever. The goal: types express domain intent precisely so misuse is caught at check-time and refactors are safe.

This skill focuses on type design — whether the right types exist and are used. For enforcement questions (whether existing invariants hold post-construction, loose optionality, defensive access, error suppression), see invariant-hunter-py.

When to Use

• Reviewing type annotations for expressiveness and safety
• Tightening domain models before a refactor
• Reducing runtime validation by encoding invariants in the type system
• Preparing for stricter mypy/pyright configuration
• Auditing Python codebases transitioning from untyped to typed

Core Principles

1. Types encode domain rules. str says nothing about what a value represents. OrderId (via NewType) says it's an order identifier — the type checker prevents mixing it with a user ID. Narrow types encode business meaning and let the compiler catch misuse.

2. Make illegal states unrepresentable. If an Order can be in state "shipped" but tracking_number is Optional[str], the type allows shipped orders without tracking numbers. Better: model ShippedOrder as a separate dataclass that requires tracking_number: str.

3. Unions should be exhaustively handled. A Union[Success, Failure, Pending] is only as good as the handling of each variant. If new variants are added, the type checker should flag unhandled cases — use assert_never() and pattern matching with exhaustiveness checking.

4. Don't fight the type system. cast(), # type: ignore, and Any are escape hatches, not design tools. If you need them frequently, the types are misaligned with the actual data flow. Fix the types, not the checker.

5. Type aliases should clarify, not obscure. UserId = str is documentation; NewType("UserId", str) is enforcement. Callback = Callable[[int, str, bool, Optional[dict]], Awaitable[Optional[str]]] is a puzzle — name the parameters via a Protocol.

What to Hunt

1. Primitive Obsession

Using str, int, float, dict, list where a domain type would be safer and more meaningful.

Signals:

• Functions accepting str for IDs, codes, slugs, URLs, emails, paths
• Functions accepting int for quantities, amounts, indices, timestamps
• dict[str, Any] as a function parameter or return type (domain data without shape)
• list[str] where the items have domain meaning (e.g., list of order IDs)
• Multiple parameters of the same primitive type that could be accidentally swapped: def transfer(amount: int, from_id: str, to_id: str) — from_id and to_id are interchangeable to the type checker

Action: Recommend NewType, dataclass, or TypedDict for domain-specific types. For ID types, NewType prevents accidental mixing while having zero runtime cost.

2. Stringly-Typed APIs

Using string literals where Literal unions, Enum, or union types would provide type safety.

Signals:

• str parameter with runtime checks like if status not in ("active", "inactive", "banned")
• Dict keys used as a discriminator without Literal or TypedDict narrowing
• Error codes as bare strings instead of Literal union or Enum
• **kwargs: Any hiding structured options that should be typed
• Configuration dicts (dict[str, Any]) instead of typed config dataclasses

Action: Replace with Literal["active", "inactive", "banned"] for small closed sets, or enum.Enum / enum.StrEnum for larger sets with behavior. Use TypedDict or dataclass for structured dictionaries.

3. Over-Broad Unions and Optional Overuse

Unions that are wider than the actual possible values, or Optional used where the type definition should not permit absence.

Boundary with invariant-hunter: type-hunter owns "this type should not be Optional — redesign the model." invariant-hunter owns "given a correctly non-Optional type, downstream code still does unnecessary is not None checks." If the type definition is wrong, it belongs here. If the type is right but consumers don't trust it, it belongs in invariant-hunter.

Signals:

• Optional[X] on a field that is always set after __init__ or after a specific lifecycle point — the type definition itself is too loose
• Union[str, int, float, bool, None] — too broad, indicates unclear data model
• Return type Optional[X] where None means "not found" and also means "error" — conflated semantics
• X | None passed through multiple layers requiring is not None checks at every level
• Optional on fields that should force the caller to provide a value

Action: Narrow unions to the actual possible types. Split Optional into different return paths (e.g., raise exception for errors, return empty collection instead of None). Use @overload to narrow return types based on input. Use separate dataclass variants instead of Optional fields that depend on state.

4. Structural vs. Nominal Confusion

Misuse of Protocol (structural typing) where a nominal type (ABC/base class) would be safer, or vice versa.

Signals:

• Protocol used for domain types where accidental structural matches are dangerous (e.g., any object with a .process() method satisfies Processor, even if it's unrelated)
• ABC/base class used for adapter interfaces where structural typing via Protocol would allow easier extension
• isinstance() checks in code that should use Protocol or union narrowing
• Overuse of Any to bridge between incompatible types that should share a proper protocol

Action:

• Use nominal types (ABC, base class) for domain concepts where identity matters: Order, User, Payment.
• Use structural types (Protocol) for capability interfaces: Serializable, Renderable, Repository. These describe what an object can do, not what it is.

5. Weak Discriminated Unions

Union types that lack a reliable discriminator field, forcing unsafe isinstance checks or hasattr() calls.

Signals:

• Union[A, B, C] where there's no common field with Literal type to distinguish variants
• isinstance() chains to narrow a union that should have a discriminator
• hasattr() checks to determine which variant of a union is present
• type: str field that should be type: Literal["a"] in each variant

Action: Add a Literal discriminator field to each variant. Use @dataclass variants with a shared type field that narrows via Literal. Leverage pattern matching with match/case for exhaustive variant handling.

Example of well-discriminated union:

from dataclasses import dataclass
from typing import Literal, Union

@dataclass
class Success:
    type: Literal["success"] = "success"
    value: str

@dataclass
class Failure:
    type: Literal["failure"] = "failure"
    error: str

Result = Union[Success, Failure]

def handle(result: Result) -> None:
    match result:
        case Success(value=v):
            print(v)
        case Failure(error=e):
            print(e)

6. Type Alias Soup

Type aliases that obscure rather than clarify, or missing aliases where they'd improve readability.

Signals:

• Deeply nested generics: dict[str, list[tuple[int, Optional[str]]]] used inline instead of named
• TypeAlias that just renames a primitive: Name: TypeAlias = str — provides documentation but no safety
• Multiple aliases for the same shape: UserMap = dict[str, User] and UserDict = dict[str, User]
• Callable types with 3+ parameters used inline: Callable[[int, str, bool, dict[str, Any]], Awaitable[str]]

Action:

• Replace TypeAlias = str with NewType("X", str) when the alias should prevent mixing
• Name complex generics: OrderIndex: TypeAlias = dict[str, list[Order]]
• For complex Callable signatures, define a Protocol with __call__ for named parameters
• Eliminate duplicate aliases — one name per shape

7. Unsafe Narrowing and Type Guards

Incorrect or missing type narrowing that forces unsafe assertions or casts.

Signals:

• cast() used to narrow a type that could be narrowed via isinstance(), pattern matching, or TypeGuard
• # type: ignore on assignments that would pass with proper narrowing
• Missing TypeGuard function for custom narrowing logic (e.g., checking a dict has certain keys)
• assert isinstance(x, Foo) used for narrowing in production code (disabled by -O flag)
• x: Any followed by field access without narrowing — no type checking on the access

Action: Replace cast() with isinstance() checks or TypeGuard functions. Use pattern matching for union narrowing. Reserve cast() for situations where the type checker genuinely cannot infer the correct type and runtime checking is impossible or too expensive.

8. Generic Type Misuse

Overuse, underuse, or incorrect use of TypeVar, Generic, and ParamSpec.

Signals:

• TypeVar without constraints or bounds that should be constrained: T = TypeVar("T") where only int | str is valid — should be T = TypeVar("T", int, str) or T = TypeVar("T", bound=Numeric)
• Generic[T] class where T is only used once (doesn't relate input to output)
• Overly generic function where a simple union or overload would be clearer
• Missing TypeVar where a function should preserve the input type in its return
• ParamSpec / Concatenate used where a simpler Protocol would suffice

Action: Add bounds or constraints to TypeVar when only specific types are valid. Remove unnecessary generics (use union or overload instead). Add generics when a function's return type truly depends on its input type.

Audit Workflow

Phase 1: Gain Context

1. Resolve audit surface. The prompt may specify the scope as:
   • Diff: files changed on the current branch vs base (main/master)
   • Path: specific files, folders, or layers
   • Codebase: the entire project If unspecified, default to codebase. For diff mode, resolve the file list:

   BASE=$(git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@' || echo main)
   SCOPE=$(git diff --name-only $(git merge-base HEAD $BASE)...HEAD)
   

   Constrain all subsequent scans to the resolved surface.

2. Identify the type checking configuration (mypy, pyright, pytype) and strictness level (strict, basic, off). Check pyproject.toml, mypy.ini, or pyrightconfig.json.
3. Identify domain models — data classes, TypedDicts, Pydantic models. These are the priority for type design.

Phase 2: Scan for Type Design Signals

# Primitive-heavy function signatures
rg 'def \w+\(.*: (str|int|float|bool)(,|\))' --type py

# dict[str, Any] usage (untyped dicts)
rg 'dict\[str,\s*Any\]|Dict\[str,\s*Any\]' --type py

# Optional overuse
rg 'Optional\[|: .+ \| None' --type py

# Type escape hatches
rg '(: Any\b|cast\(|# type: ignore|# pyright: ignore)' --type py

# Stringly typed checks
rg '(if .+ (==|!=|in|not in) ["\x27]|\.get\(["\x27])' --type py

# isinstance chains (potential weak discrimination)
rg 'isinstance\(' --type py | head -30

# TypeVar and Generic usage
rg '(TypeVar|Generic\[|ParamSpec)' --type py

# NewType usage (or lack thereof)
rg 'NewType\(' --type py

# Enum usage
rg '(class \w+\(.*Enum\)|class \w+\(.*StrEnum\))' --type py

# Complex inline types (long Callable or deeply nested generics)
rg 'Callable\[\[.{40,}\]' --type py

Phase 3: Evaluate Domain Models

1. Review dataclasses, TypedDicts, and Pydantic models for:

   • Primitive fields that should be NewType or domain types
   • Optional fields that should be required or split into separate models
   • Missing discriminator fields on union variants
   • Mutable fields that should be frozen=True

2. Review function signatures for:

   • str/int parameters that carry domain meaning
   • Return types that are too broad (e.g., dict[str, Any] when the shape is known)
   • Any parameters or returns where a Protocol or union would be correct

Phase 4: Evaluate Type Safety Points

1. For each cast(), # type: ignore, Any: could proper narrowing or a better type design eliminate it?
2. For each union: is there an exhaustive discriminator? Are all variants handled (check for assert_never())?
3. For each Protocol: is structural typing appropriate, or should this be nominal?
4. For each TypeVar: are constraints or bounds appropriate?

Phase 5: Produce Report

Output Format

Save as YYYY-MM-DD-type-hunter-audit-{$LLM-name}.md in the project's docs folder (or project root if no docs folder exists).

# Type Hunter Audit — {date}

## Scope

- Surface: {diff / path / codebase}
- Files: {count or list}
- Type checker: {mypy / pyright / pytype / none}
- Strictness: {strict / basic / off}
- Exclusions: {list}

## Findings

### Primitive Obsession

| # | Symbol | Location | Current Type | Suggested Type | Risk |
| - | ------ | -------- | ------------ | -------------- | ---- |
| 1 | `transfer()` args | file:line | `str, str` | `AccountId, AccountId` via NewType | High — IDs swappable |

### Stringly-Typed APIs

| # | Symbol | Location | String Usage | Suggested Type | Risk |
| - | ------ | -------- | ------------ | -------------- | ---- |
| 1 | `set_status()` | file:line | `status: str` checked at runtime | `Literal["active", "inactive"]` | Medium |

### Over-Broad Unions / Optional Overuse

| # | Symbol | Location | Current Type | Issue | Action |
| - | ------ | -------- | ------------ | ----- | ------ |
| 1 | `Order.tracking` | file:line | `Optional[str]` | Always set after shipping | Split into ShippedOrder |

### Structural vs. Nominal Confusion

| # | Symbol | Location | Current Design | Issue | Action |
| - | ------ | -------- | -------------- | ----- | ------ |
| 1 | `Processor` | file:line | Protocol | Domain type — accidental match risk | Use ABC |

### Weak Discriminated Unions

| # | Union | Location | Issue | Action |
| - | ----- | -------- | ----- | ------ |
| 1 | `Event` | file:line | No Literal discriminator | Add `type: Literal[...]` to each variant |

### Type Alias Issues

| # | Alias | Location | Issue | Action |
| - | ----- | -------- | ----- | ------ |
| 1 | `UserId = str` | file:line | Alias, not enforced | Use `NewType("UserId", str)` |

### Unsafe Narrowing

| # | Location | Escape Hatch | Action |
| - | -------- | ------------ | ------ |
| 1 | file:line | `cast(User, data)` | Use isinstance + TypeGuard |

### Generic Misuse

| # | Symbol | Location | Issue | Action |
| - | ------ | -------- | ----- | ------ |
| 1 | `process[T]` | file:line | Unbounded TypeVar, only int/str valid | Add constraint |

## Recommendations (Priority Order)

1. **Must-fix**: {primitive IDs, missing discriminators on critical unions, unsafe narrowing in domain logic}
2. **Should-fix**: {Optional overuse, stringly-typed APIs, broad unions}
3. **Consider**: {type alias cleanup, generic tightening, structural→nominal conversions}

Operating Constraints

• No code edits. This skill produces an audit report only. Implementation is a separate step.
• No empty sections. Include only categories with findings. Omit a heading, table, or list entirely when it would contain zero items — do not include empty tables, placeholder subsections, or negative statements like "no dead exports", "none found", or "no issues".
• Scope: type design only. Do not flag runtime enforcement gaps (→ invariant-hunter-py), structural complexity (→ simplicity-hunter-py), naming issues (→ slop-hunter-py), test gaps (→ test-hunter-py), security (→ security-hunter-py), module boundary violations (→ boundary-hunter-py), or documentation gaps (→ doc-hunter-py). If a finding doesn't answer "could a better type prevent a bug here?", it doesn't belong here.
• Evidence required. Every finding must cite file/path.py:line with the exact type definition or usage.
• Pragmatism beats purity. Not every str needs a NewType. Flag type weaknesses where a bug is likely — ID parameters that could be swapped, states modeled with Optional that should be discriminated, unions without exhaustive handling. Skip trivial cases where the primitive type is genuinely sufficient.
• Python version awareness. Note when suggestions require specific Python versions (e.g., match/case requires 3.10+, type statement requires 3.12+, TypeGuard requires 3.10+ or typing_extensions).