decorator

The `decorator` package solves one specific problem well: creating decorators that preserve function signatures and introspection. In production, this is crucial when you have monitoring tools, documentation generators, or debug tooling that rely on accurate function metadata. The core `decorator()` function adds negligible runtime overhead compared to manual functools.wraps() approaches.

Day-to-day usage is straightforward - you write decorator factories that maintain proper `__name__`, `__doc__`, and argument signatures automatically. This becomes essential when decorating functions with complex signatures or keyword-only arguments. The library is stable with minimal breaking changes between versions, which is exactly what you want for low-level infrastructure code.

The main limitation is its narrow scope - it's not a full decorator utilities library. No built-in retry logic, rate limiting, or caching decorators. You're getting signature preservation, period. Documentation is adequate but sparse on edge cases. Memory footprint is minimal, no connection pooling or resource management needed since it's purely functional transformation code.

The `decorator` package solves one specific problem exceptionally well: creating decorators that preserve function signatures for introspection. If you've ever written `@wraps(func)` and found that `help()` or IDE autocomplete still doesn't work right, this is your solution. The main `decorator` function lets you write decorators that maintain proper signatures, which is crucial for documentation tools, type checkers, and debugging.

The learning curve is reasonable if you already understand decorators, though the documentation could use more real-world examples. The README covers basic usage, but I found myself needing to experiment to understand the `caller` pattern. Error messages are straightforward when you pass wrong types, though they won't hold your hand if you misunderstand decorator mechanics generally.

Day-to-day usage is smooth once you grasp the pattern. It's particularly valuable when building libraries where good introspection matters. Community support is limited due to the package's narrow scope—most questions are answered in existing GitHub issues, but don't expect quick responses. For most projects, Python's built-in `functools.wraps` is sufficient, but when you need perfect signature preservation, this delivers.

The decorator package solves one specific problem well: preserving function signatures when writing decorators. In practice, this means your decorated functions maintain proper introspection, which is critical for documentation tools, type checkers, and frameworks that inspect callables. The library is essentially a thin wrapper around functools with better ergonomics for the signature-preservation use case.

From a security perspective, this package has an excellent profile. It's a small, focused codebase with minimal dependencies (essentially none beyond stdlib), which dramatically reduces supply chain risk. The code is straightforward Python with no network operations, file I/O, or dangerous dynamic evaluation. It doesn't handle user input directly or perform any authentication/authorization, so the attack surface is negligible.

The main practical gotcha is understanding when you actually need it versus just using functools.wraps. For most modern Python code (3.7+), functools.wraps is sufficient unless you need the decorator module's specific signature manipulation features. The documentation could be clearer about this trade-off, and error messages when decorators are misapplied don't always point you to the root cause.