CAD Skills vs Medley: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CAD Skills and Medley — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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CAD Skills
earthtojake
Open-source library of agent skills for CAD, robotics, fabrication, and simulation — generate STEP/STL, URDF/SRDF/SDF, DXF, and slicer G-code from prompts.
Key features
- Prompt-to-CAD Generation: The CAD skill creates and edits parametric CAD models from natural language or image requests, exporting to STEP, STL, 3MF, and GLB.
- URDF Robot Description: Writes robot structure files with links, joints, limits, inertials, and meshes ready for ROS-based stacks.
- SRDF for MoveIt2: Adds planning groups, end effectors, named poses, and collision rules on top of a URDF for MoveIt2 planning.
- SDF Simulation Worlds: Creates simulator models and worlds with frames, physics, sensors, and lights.
- 2D DXF Drawings: Produces cut-ready DXF profiles, templates, gaskets, and layouts from Python or CAD geometry.
- G-code Slicing: Slices supported mesh files into validated, printer-profiled FDM G-code using real slicer CLIs.
- Bambu Lab Print Jobs: Dry runs, uploads, and cautiously starts local Bambu Lab prints from validated G-code.
- CAD Viewer Previews: Local browser previews for CAD, G-code, URDF, and other robot files for fast agent iteration.
Best for
- Agentic Mechanical Design: Let a coding agent iterate on brackets, flanges, and enclosures directly from natural-language specs.
- Robot Description Authoring: Generate URDF/SRDF/SDF files for new robot arms or mobile bases without hand-writing XML.
- Rapid Prototyping: Take a CAD model to sliced, printer-ready G-code and kick off a Bambu Lab print from within an agent workflow.
- Custom Part Fabrication: Produce DXF/STEP files pre-checked for SendCutSend, then order laser-cut or CNC parts.
- Simulation Setup: Author SDF worlds for physics simulators with the right frames, sensors, and lighting for robotics research.
- Off-the-Shelf Sourcing: Use step.parts to pull ready-made STEP models for common hardware like screws, bearings, and connectors.
Medley
Medley
Claude Code plugin that decomposes prompts into coordinated multi-agent plans and visualizes the plan at a shareable URL.
Key features
- Slash-Command Integration: Activates directly inside Claude Code via the /mission command to produce a plan without leaving the chat interface.
- Prompt Decomposition: Breaks a single user prompt into discrete subtasks with clear dependencies to turn vague requests into actionable steps.
- Multi-Agent Coordination: Generates a coordinated plan that assigns roles or agent responsibilities and sequences work across multiple agents to tackle complex tasks.
- Plan Visualization URL: Renders the produced plan structure at a shareable URL so users can inspect, review, and share the full task graph and execution plan.
- Task Assignment & Sequencing: Determines ordering and handoffs between subtasks so parallel and dependent work is organized for execution by different agents.
- Shareable Workflow Export: Enables distribution of the decomposed plan via link for collaboration, review, or external execution tracking.
- Decomposes a single prompt into a coordinated multi-agent plan
- Invoked within Claude Code via the /mission command
- Generates a structured plan view accessible at a shareable URL
- Orchestrates multiple agents/subtasks rather than relying on a single model
- Focus on readable plan structure for inspection and collaboration
Best for
- Complex Project Breakdown: Converting a high-level product or research brief into a multi-step plan with assigned agent roles and dependencies for coordinated execution.
- Multi-step Code Development: Decomposing a feature request into design, implementation, testing, and deployment tasks that can be executed or reviewed by specialized agents.
- Data Analysis Pipelines: Breaking down an analysis prompt into data-cleaning, transformation, modeling, and visualization subtasks that are assigned and sequenced.
- Content Creation Workflows: Orchestrating ideation, drafting, editing, fact-checking, and formatting steps across different agents to produce polished content.
- Collaborative Review & Handoff: Sharing the generated plan URL with teammates or stakeholders to review responsibilities, timelines, and handoffs before execution.
- Experiment Orchestration: Designing and coordinating multi-step experiments or research tasks where different agents perform measurements, aggregation, and interpretation.
- Breaking complex prompts into executable subtasks for multi-agent workflows
- Orchestrating LLM agents to collaborate on a single objective
- Sharing and reviewing decomposition and task assignments via a URL
- Improving reliability and coverage by distributing work across multiple agents
- Prompt engineering for complex, multi-step automation tasks