Map Python node and trait declarations to a live TypeGraph and instance graph.
npx skills add https://github.com/atopile/atopile --skill fabll
Please help me install this Agent Skill. Skill: FabLL Source: https://github.com/atopile/atopile/tree/main/.claude/skills/fabll Command: npx skills add https://github.com/atopile/atopile --skill fabll
FabLL bridges Python node and trait declarations to the TypeGraph and the instance graph, enabling clean modeling of hardware components by preserving field invariants and trait semantics.
High-intent search queries and answers about installing and using this skill.
To map Python node declarations to a TypeGraph, create a GraphView and TypeGraph, define a Node subclass, and use bind_typegraph to generate a bound instance for hardware modeling.
TypeGraph binding enforces field and trait invariants by automatically maintaining validation and resolution rules during instance creation, ensuring predictable composition and reuse for hardware components.
Yes, you can bind and validate hardware component graph structures in Python by defining Node subclasses and relying on the TypeGraph backend to automatically maintain field and trait invariants.
The best way to instantiate a hardware component from a Python Node subclass is to call bind_typegraph on your Node class, then invoke create_instance with the active GraphView.
No, you do not need to manually maintain trait semantics; the TypeGraph backend automatically preserves and enforces trait semantics and field invariants during instance graph generation.
You should use TypeGraph instance creation instead of standard Python classes when you need strict type binding, automatic trait resolution, and enforced field invariants for complex hardware composition.
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