AST-based static analyzer that identifies type signatures that reduce
code clarity and AI-readability. Targets:
- Dict[str, Any] / dict[str, Any] (302 findings)
- list[dict[...]] (115 findings)
- Optional[dict[...]] / Optional[tuple[...]] (11 findings)
- Tuple[...]/tuple[...] as anonymous structs (4 findings)
- Return tuples and assign tuples (4 findings)
The script also counts POSITIVE patterns (TypeAlias, NamedTuple,
@dataclass, pydantic.BaseModel) that already exist in the codebase.
Current count: 0. The codebase has zero strong type aliases.
Usage: python scripts/audit_weak_types.py [--json] [--top N] [--verbose]
Exits 0 (informational); exits 1 only on usage error.
Initial run on src/ found 430 weak sites across 29 files. The 4 most
common unique type strings (list[dict[str, Any]], dict[str, Any],
Dict[str, Any], List[Dict[str, Any]]) account for 86% of findings.
A focused track adding 4-6 type aliases would eliminate the vast
majority of the noise.
Output modes:
- human-readable (default): top N files with category breakdowns
- JSON (--json): machine-readable for tooling
- verbose (--verbose): every finding inline
Exit codes:
- 0: audit ran successfully (regardless of findings)
- 1: usage error (bad args, source dir not found)
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