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yaml-driven-architecture

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Apply YAML-driven architecture-as-code patterns for system definition, configuration management, and automated generation. Use when designing configuration schemas, building code generators, or implementing declarative architecture definitions.

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YAML-Driven Architecture Patterns

Core Pipeline

YAML → Validation → Model → Transform → Output
  1. Parse: YAML to data structures
  2. Validate: Schema compliance (Pydantic)
  3. Model: Type-safe internal representation
  4. Transform: Apply templates
  5. Generate: Code, docs, configs

YAML Design Rules

  • Maximum 4 levels deep (use references for deeper)
  • Convention over configuration (provide sensible defaults)
  • Filename conventions for type detection: system.yaml → SystemContext, components.yaml → ComponentView

Schema Definition

class Component(BaseModel):
    id: str
    name: str
    class Config:
        extra = "forbid"  # Catch typos
  • Use extra="forbid" to catch typos
  • Use kebab-case IDs
  • Validate relationships bidirectionally (ensure all references exist)

Generation Patterns

Stateless generators: Pure functions taking model → output

def generate_module(module: ModuleView) -> str:
    return template.render(module=module)

Multi-output: Single source generates code, docs, tests

Error Handling

  • Provide context: file, line number, field name
  • Suggest corrections: "Unknown field 'typ'. Did you mean 'type'?"
  • Fail fast with clear messages

Best Practices

  • Validate early at YAML level
  • Use anchors for repeated configurations
  • Version schemas for breaking changes
  • Keep logic in generators, YAML for declaration only

Remember: YAML is for configuration, not computation. Keep it declarative.