Python Excellence Prover Connector for AI agents.

It acts as a quality gate that forces your AI to follow high-level Python standards like type hinting, async I/O, and Pydantic validation.

Yes, it specifically targets common errors like bare except blocks and mutable default arguments that often cause silent failures in production.

It requires it for all external data validation to ensure your agent isn't just passing around raw, untyped dictionaries.

You can, but it's really designed for production systems where you care about performance, clean architecture, and long-term maintainability.

It forces the use of async I/O for network and file operations and encourages generators for handling large datasets.

Definitely. It enforces Pythonic idioms like f-strings, pathlib, and clear exception hierarchies that make it easier for humans to read.

No. The agent writes the code. The capability VALIDATES that it meets senior Python standards. type hints + Pydantic, structured error handling, clean architecture, and optimized async patterns. It catches five failure modes before code is committed.

Because untyped Python is a shell script. Without type hints, mypy can't catch bugs, IDEs can't autocomplete, and Pydantic can't validate data boundaries. Type safety is the foundation. error handling, architecture, and performance all depend on knowing what types flow through the code.

23 consistency rules catching: bare except (catches SystemExit), mutable default args (def f(x=[])), os.path instead of pathlib, string concatenation instead of f-strings, print() instead of structured logging, open() without context manager, sync I/O in async context, global mutable state, blanket # type: ignore, and magic values.