Top OpenClaw Skills for AI Floor Plan and Layout Generation

Architecture firms using AI generative tools report a 73% reduction in design iteration time, according to a 2024 industry survey compiled by Gitnux. Yet most AI floor plan generators are standalone SaaS apps. You generate a layout in Maket or Planner 5D, download the file, and email it to a colleague. The workflow breaks the moment you need to chain steps together: generate a parametric layout, render it in 3D, populate it with furniture, and deliver the final package to a client portal.

AI floor plan generators use machine learning to create 2D and 3D architectural layouts from room dimensions, photos, or text descriptions. OpenClaw agents can connect these capabilities through skills on ClawHub, the public registry with over 5,400 community-built skills as of May 2026. Instead of switching between apps, your agent calls one skill to generate geometry, another to produce a diagram, and a third to store and share the result. The "ai floor plan generator" keyword draws 1,900 monthly US searches at $3.61 CPC, reflecting real demand for tools in this space.

We evaluated each skill for three criteria: how well it handles architectural geometry, what formats it exports, and whether it fits into an automated pipeline without manual file handling.

Floor plan skills are not a standalone category in ClawHub, so we pulled the most relevant tools from Development, Image and Video Generation, and Productivity categories. Each skill covers a different part of the pipeline, from early concept diagrams to dimensionally precise CAD output.

The cad-agent skill by clawd-maf runs as a Dockerized rendering server that gives your OpenClaw agent visual feedback while building parametric models. The agent writes build123d code, sends it over HTTP to a container, and receives a rendered PNG image from VTK. It inspects the render, adjusts dimensions, and iterates until the geometry matches the brief.

For floor plan work, this translates to modeling room boundaries, wall thicknesses, door openings, and window placements as parametric geometry. Each iteration renders a multi-view composite so the agent sees the layout from multiple angles before committing to a change. The visual feedback loop distinguishes cad-agent from one-shot generators: the agent catches dimension errors, misaligned walls, or missing features during modeling rather than after export.

The trade-off is speed. Every render cycle requires a round trip to the Docker container, so complex floor plans with many rooms take longer than a single-pass generator would. For projects where dimensional accuracy matters more than turnaround time, the iterative approach pays for itself by reducing rework downstream. A four-room residential layout might take a dozen render cycles, but each cycle confirms the agent is on track before adding the next room.

ClawHub: clawskills.sh/skills/clawd-maf-cad-agent

Strengths:

• Visual feedback loop lets agents inspect rendered previews at each design step
• Parametric modeling through build123d supports precise wall dimensions and room boundaries
• Exports to STL, STEP, and 3MF for downstream CAD and fabrication tools

Limitations:

• Requires Docker installed and running on the host machine
• Agents need familiarity with build123d syntax to produce useful floor plan geometry

Best for: Teams that need dimensionally accurate floor plan models with iterative visual verification.

Pricing: Free, open source.

The openclaw-openscad-3d skill by Averroeskw converts natural language descriptions into parametric 3D models using OpenSCAD. Describe a room layout in plain English, the LLM generates OpenSCAD code, and the skill compiles it into a model with a PNG preview.

Format support is what makes this skill stand out for architectural work. OpenSCAD reads and writes DXF, the standard exchange format for 2D architectural drawings. An agent can generate a floor plan as parametric geometry, export it as DXF, and hand it to a drafter or BIM tool for refinement. The skill also outputs STL, 3MF, OBJ, OFF, AMF, SVG, PNG, CSG, PLY, and GLB, covering 12+ formats total.

Beyond generation, the skill analyzes existing models for physical properties: dimensions, volume, and printability. It converts between formats and estimates fabrication costs. All rendering runs on CPU through OpenSCAD, so no GPU is required. This makes it practical for agents running on standard cloud instances or developer laptops without dedicated graphics hardware.

For a floor plan pipeline, openclaw-openscad-3d pairs well with cad-agent. Use OpenSCAD for initial layout geometry and DXF export, then switch to cad-agent when you need visual iteration on the 3D representation. The two skills complement each other: OpenSCAD handles the precision drafting, cad-agent handles the visual refinement.

GitHub: Averroeskw/openclaw-openscad-3d

Strengths:

• DXF export for 2D architectural drawings compatible with standard CAD drafting tools
• Text-to-parametric generation with PNG preview rendering
• Model analysis for dimensions, volume, and format conversion between 12+ file types

Limitations:

• Requires OpenSCAD installed locally on the host machine
• Parametric output suits geometric layouts rather than organic architectural forms

Best for: Agents generating 2D floor plan outlines that need to works alongside existing CAD workflows through DXF.

Pricing: Free, MIT licensed.

The 3d-model-generation skill by eftalyurtseven generates 3D models from text descriptions or reference images using the each::sense AI API. It covers a range of asset types including architectural elements, furniture, and environment scenes.

For floor plan workflows, this skill fills the gap between a flat 2D layout and a client-ready 3D visualization. After generating floor plan geometry with cad-agent or OpenSCAD, an agent can use 3d-model-generation to create the furniture, fixtures, and architectural details that populate the space. Upload a reference photo of a kitchen island or a specific chair, and the skill produces a 3D model in GLB for web viewing, OBJ for legacy tools, FBX for game engines, or USDZ for Apple AR Quick Look.

Two quality modes are available: "max" for highest detail and "eco" for faster turnaround during early iterations. The skill handles generation polling internally, so agents send a prompt and receive a completed file when the job finishes. This fire-and-forget approach keeps the pipeline moving without manual status checks.

The generative AI in architecture market reached $2.07 billion in 2026, growing at 40.9% CAGR according to Research and Markets. Much of that growth comes from exactly this use case: turning design intent into visual assets without manual 3D modeling work.

ClawHub: clawskills.sh/skills/eftalyurtseven-3d-model-generation

Strengths:

• Accepts both text descriptions and reference images as generation input
• Five output formats covering web, AR/VR, game engines, and 3D printing
• Fire-and-forget generation with internal polling and automatic file delivery

Limitations:

• Requires an each::sense API key with associated usage costs
• Generates individual 3D objects, not complete multi-room floor plans

Best for: Populating floor plan scenes with 3D furniture and architectural elements from text or photo references.

Pricing: Free skill; each::sense API usage costs apply.

Keep floor plan files organized across agent sessions

generous storage with semantic search across your architectural files. Upload floor plans, share revisions with clients, and hand off project deliverables to your team. No credit card, no expiration.

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The MCP Diagram Generator is not a traditional ClawHub skill. It runs as a Model Context Protocol server that OpenClaw connects to for structured diagram generation. Describe what you need in natural language, and the server produces diagram files in your chosen format.

For floor plans, this tool works best at the conceptual stage. An agent can generate room layouts as draw.io files for collaborative editing, Mermaid diagrams for embedding in project documentation, Excalidraw JSON for whiteboard-style brainstorming, or PNG exports for immediate sharing. The output is 2D and schematic rather than dimensionally precise, which makes it better suited for early space planning than construction documents.

A floor plan rendered as a flowchart-style diagram shows room adjacencies and circulation paths , even without exact measurements. For agents building proposals or presenting layout options to clients, this speed advantage outweighs the lack of metric precision. You can generate three layout variations in the time it takes cad-agent to refine one.

Two related tools complement this approach. The beautiful-mermaid skill by ntlx renders Mermaid diagrams as themed SVG or ASCII art with zero dependencies and 15 built-in themes, useful when you want polished output without a rendering server. OpenClaw's built-in image generation tool adds photorealistic concept renders from text prompts, showing what a space might look like before any geometry is modeled. Pair diagram generation for the spatial layout with image generation for the visual concept, and you cover both the functional and aesthetic sides of a floor plan presentation.

Source: The Ultimate Guide to OpenClaw Diagram Generation

Strengths:

• Outputs in four formats: draw.io, Mermaid, Excalidraw, and PNG
• Natural language input with no template configuration required
• Pairs with beautiful-mermaid and image generation for richer presentations

Limitations:

• 2D schematic output without dimensional precision
• Better for conceptual layouts than construction-grade documentation

Best for: Early-stage space planning, room adjacency diagrams, and client presentation materials.

Pricing: Free.

Fastio is not a floor plan generator. It is the workspace where generated floor plans, models, and diagrams land after the pipeline runs. When agents produce DXF files, 3D models, rendered diagrams, and concept images through the skills above, those deliverables need persistent storage, version tracking, and a reliable path to human reviewers.

Fastio's MCP server exposes tools for file management, uploads, AI chat, and workspace operations. Intelligence Mode auto-indexes uploaded files for semantic search, so finding "the second-floor layout with the open kitchen" works without browsing folder hierarchies. Agents create workspaces per project or per client, organize deliverables by revision, and transfer ownership to project leads when a design is ready for review.

The practical value for floor plan workflows is session persistence. An agent that generates a floor plan today can access the same workspace tomorrow without re-generating previous files. CAD exports, 3D renders, and schematic diagrams stay organized and searchable across sessions. Branded shares let you deliver floor plan packages to clients through a portal rather than scattered email attachments.

The Business Trial includes 50GB storage, included credits, and 5 workspaces with no credit card required. For teams running generation pipelines that produce dozens of files per project, the persistent workspace eliminates re-downloading and re-generating assets between sessions.

ClawHub: clawhub.ai/dbalve/fast-io

Strengths:

• generous storage and monthly credits during the trial with no credit card required to start
• Semantic search across indexed floor plan files via Intelligence Mode
• Ownership transfer: agents build the project library, humans receive and review it

Limitations:

• Intelligence Mode consumes AI credits when indexing large file sets
• Not a generation tool; pairs with the other skills on this list

Best for: Teams connecting agent-generated floor plans to human review and client delivery workflows.

Pricing: Business Trial. Pro plans at fast.io/pricing.

The five tools on this list cover different pipeline stages. One practical sequence for a floor plan project:

Start with MCP Diagram Generator for the conceptual layout. Describe room adjacencies and circulation in natural language, generate a draw.io or Excalidraw diagram, and share it with stakeholders for early feedback before committing to precise geometry.

Move to openclaw-openscad-3d or cad-agent for parametric modeling once the concept is approved. OpenSCAD handles 2D floor plan outlines with DXF export for drafting tools. cad-agent's visual feedback loop works better for 3D walkthroughs where the agent needs to verify geometry at each step.

Use 3d-model-generation to populate the approved layout with furniture and fixtures. Reference photos of specific items produce better results than generic text prompts like "modern sofa." The awesome-openclaw-skills repository groups skills by category, so you can find additional generation and rendering tools as your pipeline needs grow.

Store everything in a Fastio workspace. Each revision lives in a versioned workspace. When the design is complete, transfer the workspace to the client or project lead for final review. Fastio's MCP documentation covers the full tool surface for programmatic uploads and workspace management.

All five tools are free to install. 3d-model-generation is the only one requiring a paid API key for generation. cad-agent, openclaw-openscad-3d, MCP Diagram Generator, and Fastio all run without paid API dependencies.