CAD Agent

> Give your AI agent eyes for CAD work.

7 stars

Best use case

CAD Agent is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

> Give your AI agent eyes for CAD work.

Teams using CAD Agent should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/cad-agent/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/clawd-maf/cad-agent/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/cad-agent/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How CAD Agent Compares

Feature / AgentCAD AgentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

> Give your AI agent eyes for CAD work.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# CAD Agent

> Give your AI agent eyes for CAD work.

## Description

CAD Agent is a rendering server that lets AI agents see what they're building. Send modeling commands → receive rendered images → iterate visually.

**Use when:** designing 3D-printable parts, parametric CAD, mechanical design, build123d modeling

## Architecture

**Critical:** All CAD logic runs inside the container. You (the agent) only:
1. Send commands via HTTP
2. View the returned images
3. Decide what to do next

```
YOU (agent)                     CAD AGENT CONTAINER
─────────────                   ───────────────────
Send build123d code      →      Executes modeling
                         ←      Returns JSON status
Request render           →      VTK renders the model
                         ←      Returns PNG image
*Look at the image*
Decide: iterate or done
```

**Never** do STL manipulation, mesh processing, or rendering outside the container. The container handles everything — you just command and observe.

## Setup

### 1. Clone the Repository

```bash
git clone https://github.com/clawd-maf/cad-agent.git
cd cad-agent
```

### 2. Build the Docker Image

```bash
docker build -t cad-agent:latest .
```

Or using docker-compose:

```bash
docker-compose build
```

### 3. Run the Server

```bash
# Using docker-compose (recommended)
docker-compose up -d

# Or using docker directly
docker run -d --name cad-agent -p 8123:8123 cad-agent:latest serve
```

### 4. Verify Installation

```bash
curl http://localhost:8123/health
# Should return: {"status": "healthy", ...}
```

> **Docker-in-Docker caveat:** In nested container environments (e.g., Clawdbot sandbox), host networking may not work—`curl localhost:8123` will fail even though the server binds to `0.0.0.0:8123`. Use `docker exec cad-agent python3 -c "..."` commands instead. On a normal Docker host, localhost access works fine.

## Workflow

### 1. Create Model

```bash
curl -X POST http://localhost:8123/model/create \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my_part",
    "code": "from build123d import *\nresult = Box(60, 40, 30)"
  }'
```

### 2. Render & View

```bash
# Get multi-view (front/right/top/iso)
curl -X POST http://localhost:8123/render/multiview \
  -d '{"model_name": "my_part"}' -o views.png

# Or 3D isometric
curl -X POST http://localhost:8123/render/3d \
  -d '{"model_name": "my_part", "view": "isometric"}' -o iso.png
```

**Look at the image.** Does it look right? If not, modify and re-render.

### 3. Iterate

```bash
curl -X POST http://localhost:8123/model/modify \
  -d '{
    "name": "my_part", 
    "code": "result = result - Cylinder(5, 50).locate(Pos(20, 10, 0))"
  }'

# Re-render to check
curl -X POST http://localhost:8123/render/3d \
  -d '{"model_name": "my_part"}' -o updated.png
```

### 4. Export

```bash
curl -X POST http://localhost:8123/export \
  -d '{"model_name": "my_part", "format": "stl"}' -o part.stl
```

## Endpoints

| Endpoint | What it does |
|----------|--------------|
| `POST /model/create` | Run build123d code, create model |
| `POST /model/modify` | Modify existing model |
| `GET /model/list` | List models in session |
| `GET /model/{name}/measure` | Get dimensions |
| `POST /render/3d` | 3D shaded render (VTK) |
| `POST /render/2d` | 2D technical drawing |
| `POST /render/multiview` | 4-view composite |
| `POST /export` | Export STL/STEP/3MF |
| `POST /analyze/printability` | Check if printable |

## build123d Cheatsheet

```python
from build123d import *

# Primitives
Box(width, depth, height)
Cylinder(radius, height)
Sphere(radius)

# Boolean
a + b   # union
a - b   # subtract
a & b   # intersect

# Position
part.locate(Pos(x, y, z))
part.rotate(Axis.Z, 45)

# Edges
fillet(part.edges(), radius)
chamfer(part.edges(), length)
```

## Important

- **Don't bypass the container.** No matplotlib, no external STL libraries, no mesh hacking.
- **Renders are your eyes.** Always request a render after changes.
- **Iterate visually.** The whole point is you can see what you're building.

## Design File Safety

The project has safeguards against accidentally committing CAD outputs:
- `.gitignore` blocks *.stl, *.step, *.3mf, etc.
- Pre-commit hook rejects design files
- User's designs stay local, never versioned

## Links

- [Repository](https://github.com/clawd-maf/cad-agent)
- [build123d docs](https://build123d.readthedocs.io/)
- [VTK](https://vtk.org/)

Related Skills

paylock

7
from Demerzels-lab/elsamultiskillagent

Non-custodial SOL escrow for AI agent deals.

agent-reputation

7
from Demerzels-lab/elsamultiskillagent

summary: Cross-platform AI agent reputation checker with trust scoring and PayLock escrow recommendations.

Telecom Agent Skill

7
from Demerzels-lab/elsamultiskillagent

Turn your AI Agent into a Telecom Operator. Bulk calling, ChatOps, and Field Monitoring.

OpenClaw-Finnhub

7
from Demerzels-lab/elsamultiskillagent

OpenClaw skill for real-time stock quote, and financials via Finnhub API.

```markdown

7
from Demerzels-lab/elsamultiskillagent

# OpenClaw-Last.fm

security-operator

7
from Demerzels-lab/elsamultiskillagent

Runtime security guardrails for OpenClaw agents.

operator-humanizer

7
from Demerzels-lab/elsamultiskillagent

Transform AI-generated text into authentic human writing.

kit-email-operator

7
from Demerzels-lab/elsamultiskillagent

**AI-powered email marketing for Kit (ConvertKit)**.

agora

7
from Demerzels-lab/elsamultiskillagent

Trade prediction markets on Agora — the prediction market exclusively for AI agents. Register, browse markets, trade YES/NO, create markets, earn reputation via Brier scores.

surf-check

7
from Demerzels-lab/elsamultiskillagent

Surf forecast decision engine.

jinko-flight-search

7
from Demerzels-lab/elsamultiskillagent

Search flights and discover travel destinations using the Jinko MCP server. Provides two core capabilities: (1) Destination discovery — find where to travel based on criteria like budget, climate, or activities when the user has no specific destination in mind, and (2) Specific flight search — compare flights between two known cities/airports with flexible dates, cabin classes, and budget filters. Use this skill when the user wants to: search for flights, find cheap flights, discover travel destinations, compare flight prices, plan a trip, find deals from a specific city, or explore where to go. Triggers on any flight-booking, travel-planning, or destination-discovery request. Requires the Jinko MCP server connected at https://mcp.gojinko.com.

mlx-whisper

7
from Demerzels-lab/elsamultiskillagent

Local speech-to-text with MLX Whisper (Apple Silicon optimized, no API key).