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Parametric design methodology, data structures, constraint systems, Grasshopper and Dynamo patterns, parameter space exploration, and associative geometry for AEC computational design
marcinfinitesimal533/Claude-skills-for-Computational-Designers · ★ 1 · AI & Automation · score 74
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# Parametric Modeling for AEC Computational Design This skill provides a complete reference for parametric design methodology as applied to architecture, engineering, and construction. It covers the intellectual framework, data structures, constraint logic, tool-specific taxonomies for Grasshopper and Dynamo, associative geometry patterns, and professional best practices. --- ## 1. Parametric Thinking Methodology ### 1.1 What Makes Design Parametric vs. Static A static design is a fixed artifact — a single geometric configuration with hard-coded dimensions. Changing one element requires manually adjusting every dependent element. A parametric design is a system of relationships: a directed graph of inputs, transformations, and outputs where modifying any input propagates changes through the entire dependency chain automatically. The distinction is not merely about sliders. Parametric thinking means encoding **design intent** rather than design outcome. The designer authors a set of rules that describe a family of possible designs, not a single instance. **Key characteristics of parametric models:** - **Dependency awareness** — Every element knows what it depends on and what depends on it. - **Reversibility** — Changes propagate backward and forward through the logic chain without loss of intent. - **Multiplicity** — A single definition produces an infinite set of valid design instances within the parameter space. - **Traceability** — Every output can be traced back thr