nanobrain-config-yaml

To validate a YAML configuration file for a Nanobrain workflow, use the composer-side validator and spec-mode composer to enforce strict schema integrity, class-path resolution, and framework compliance before execution.

Nanobrain workflow configuration fails silently due to schema violations and incorrect class-path resolution. Use strict Pydantic-based configuration guidance to enforce required class/config indirection patterns and prevent these path resolution errors.

Structure step definitions and executor references in a workflow YAML by applying the required class/config indirection patterns. This enforces strict class-path resolution and ensures data units and executors pass framework linting standards.

Yes, you can use Pydantic models for Nanobrain configuration management. The framework requires Pydantic-based configuration to enforce strict schema integrity and validate class-path resolution for defined workflow steps and executors.

Production hardening in Nanobrain YAML configs requires compliance with framework-specific security primitives. Strict schema validation ensures your step definitions and executor references meet these production hardening requirements during workflow authoring.

You need to use spec-mode composer for workflow configuration when validating generated workflows against framework standards. It ensures your YAML configuration meets strict schema integrity and class-path resolution requirements before execution.