Feedback wanted: does FlowGuard's model-first workflow check make sense?

[liuyingxuvka]

FlowGuard is an early Python framework for model-first function-flow engineering.

It is meant for AI coding agents and engineers who want to model risky workflow behavior before changing production code. The core shape is:

F: Input x State -> Set(Output x State)

It currently checks finite deterministic behavior for things like duplicate side effects, broken idempotency, cache/source-of-truth mismatch, stuck states, loop/progress issues, contract boundaries, and conformance replay through adapters.

If you try it, I would especially like feedback on:

1. Is the concept understandable from the README?
2. Could you run the examples?
3. Which example explains the value best?
4. Is the model-first workflow too heavy for real coding-agent work?
5. What should come next: PyPI package, cleaner CLI, more examples, or better Codex/agent integration docs?
6. Do you have a retry/cache/dedup/idempotency workflow that this should be tested against?

Useful links:

• Repo: https://github.com/liuyingxuvka/FlowGuard
• Latest release: https://github.com/liuyingxuvka/FlowGuard/releases/latest

If something breaks, please include:

• OS:
• Python version:
• Install command:
• Example command:
• Error output, with private paths/secrets removed: