Agent skill · datadrivenconstruction
generative-ai-design
Generative design for construction: text-to-BIM concepts, option generation, and AI-assisted design iteration with cost and carbon feedback. Use when exploring early design options with AI.
What it needs
About 1k tokens when loaded.
What this skill does
Generative AI Design for Construction (2026) What is real in 2026 Generative design in construction is option generation with feedback, not autonomous architecture: given site constraints, program and budget, an AI generates massing/typology options and scores them on cost, carbon and buildability — the human designer selects and refines. The loop Toolchain Stage Tools --- --- Massing generation parametric tools (Rhino/Grasshopper, Dynamo) + LLM sketches Text-to-concept image models (Midjourney/DALL·E) for moodboards; text-to-BIM is early-stage (Hypar, Finch, qbiq) Quantification OpenConstructionERP BIM takeoff (oce-bim-takeoff) Cost scoring CWICR cost bases (oce-load-cost-bases) Carbon scoring embodied-carbon-esg Prompt pattern for concept generation Then quantify and rank: Option GFA FAR Cost/m² kgCO₂e/m² Verdict --- --- --- --- --- --- A 11,800 2.9 1,050 € 310 lowest cost B 12,400 3.1 1,180 € 285 lowest carbon C 12,100 3.0 1,120 € 295 balanced Guardrails AI options are starting points, always human-reviewed and code-checked. Cost/carbon scores come from real databases (CWICR + EPD), not LLM guesses. Keep every option's inputs logged (reproducibility, AI Act transparency). Text-to-BIM models are not yet permit-grade — treat outputs as concepts. Resources Finch: https://finch3d.com · Hypar: https://hypar.io · qbiq: https://www.qbiq.ai Generative design overview: https://www.autodesk.com/solutions/generative-design
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/generative-ai-design