nanobrain-testing-debugging

Debug a failing nanobrain workflow trigger by checking for trigger silent-failures and misconfigurations. Use the testing primitives and policy-checker scripts to identify root causes like missing async definitions or incorrect Python interpreter settings.

Nanobrain import errors typically stem from environment misconfigurations or incorrect Python interpreters. Diagnose these failures by validating your test setup against the framework's strict architectural policy using the provided policy-checker scripts.

Run non-mocked nanobrain integration tests by following the provided testing taxonomy that validates components against real backends. This approach uses pytest and asyncio to verify production-grade reliability without misleading mock data.

Structure nanobrain tests using a clear taxonomy of smoke, integration, and unit tests. This methodology ensures components are verified against real backends and strict architectural policies, avoiding misleading mocks and identifying common failure modes.

Yes, pytest and asyncio are required dependencies for executing the nanobrain testing methodology. These frameworks support the asynchronous execution and policy validation needed to verify components against real backends.

Resolve nanobrain initialization errors by identifying missing async definitions or environment-specific import issues. The skill provides a comprehensive diagnostic guide to trace these common failure modes using policy-checker scripts.