Claire chatted to Josie Hughes from École Polytechnique Fédérale de Lausanne about using AI to develop new designs for robotic manipulators.
Josie Hughes is an Assistant Professor at EPFL, where she established the CREATE Lab in 2021. She completed her PhD in the Bio-inspired Robotics Lab at the University of Cambridge, examining the role of passivity in bio-inspired manipulators. Her research focuses on developing novel design paradigms for designing robot structures that exploit their physicality and interactions with the environment. This includes the development of robotic hands, soft manipulators, and automation systems for applications focused on sustainability and science.
The hardest part of building against a new platform is teaching your tools about it. Your coding agent doesn’t know the SDK’s conventions. Your IDE doesn’t know the CLI commands. Your terminal doesn’t know the auth pattern. Every gap is a context switch, and every context switch is time spent away from the work. DataRobot Skills close those gaps inside the tools you already use. Our marketplace listings in Anthropic, Gemini and Cursor close them at the install step.
A Skill is a folder with a SKILL.md file. The frontmatter tells the agent when the skill applies. The body tells it how to do the work. The agent loads only the skills relevant to the current task, so context stays clean and reasoning stays sharp.
DataRobot Skills are Agent Context Protocol definitions. They work across Claude Code, Cursor, Codex, Gemini CLI, Amp, VS Code Copilot, Goose, Letta, Kilo Code, and OpenCode. They are not slash commands and not MCP tools. They are the procedural knowledge your agent needs to use the SDK, the CLI, and the platform correctly, every time.
The point of the marketplace listings is to remove the install step entirely so developers find DataRobot at the moment they’re picking tools, not after they’ve already committed to a workflow.
The repo currently ships 10+ official Skills, with new Skills being added every week. Each one corresponds to a core part of the DataRobot agent building workflow.
| Skill | What it teaches the agent |
datarobot-setup | The on-ramp. Installs the DataRobot CLI, Python SDK and Agent Assist, configures the endpoint and API key, and verifies connectivity. Run this first and you don’t need to worry about a single thing around setup. |
datarobot-agent-assist | The full agent lifecycle. Generates agent_spec.md from a guided design conversation, rehearses tool calls before any code is written, scaffolds against the Agentic Starter template, runs local tests, and deploys to the DataRobot platform. |
datarobot-model-training | Project creation, AutoML configuration, target leakage checks, partitioning patterns. |
datarobot-predictions | Batch and real-time prediction generation, template scaffolding for prediction APIs. |
datarobot-model-deployment | Deploying and managing models, including governance settings and deployment metadata. |
datarobot-feature-engineering | Feature analysis, transformations, derived feature workflows. |
datarobot-model-monitoring | Performance and data drift monitoring, accuracy tracking, alert configuration. |
datarobot-model-explainability | Prediction explanations, feature impact, model diagnostics. |
datarobot-data-preparation | Dataset upload, validation, schema checks, and registry workflows. |
datarobot-app-framework-cicd | CI/CD pipelines for DataRobot application templates. |
datarobot-external-agent-monitoring | OpenTelemetry instrumentation for external agents reporting into DataRobot. |
Two of these change the experience the most. datarobot-setup is the on-ramp: before it existed, a developer who installed the Skills still had to manually authenticate, point the SDK at the right base URL, and confirm everything was wired up. Now the setup phase becomes another thing the agent does, not another thing the developer does.
datarobot-agent-assist brings the spec-driven design loop into the same context. Instead of switching to a different tool to design an agent, the developer asks for help, the skill activates, and dr assist runs from inside the same IDE conversation, producing an agent_spec.md and rehearsing the tool calls before any code is written. Design, test and deploy, all inside the agent loop.
Open the Cursor marketplace entry for DataRobot, click the “Add to Cursor” button, and Cursor handles the rest. The Skills register against the workspace and become available in any chat. If you’d rather pin to the repo and version-control the install, open the repo as your workspace and Cursor reads AGENTS.md automatically.
Gemini CLI now treats DataRobot as an extension with bundled Skills. From your terminal:
gemini extensions install https://github.com/datarobot-oss/datarobot-agent-skillsThe Skills land in ~/.gemini/extensions/datarobot-agent-skills/skills and load on session start. Use /skills list inside a Gemini session to confirm. Environment variables propagate automatically.
For Claude users, DataRobot Skills are available through the plugin marketplace listing and you can run this command:
claude plugin install datarobot-agent-skills@claude-plugins-officialThe universal installer is the answer if you are running an AI IDE or CLI not listed above:
npx ai-agent-skills install datarobot-oss/datarobot-agent-skillsThe marketplace listings are the first step in distributing DataRobot’s developer surface the same way modern infrastructure tools distribute theirs. Expect the catalog to grow: more skills around the agent lifecycle, more bundled flows for Agent Assist, deeper coverage of governance and observability patterns that today live in docs rather than in agent context.
If you want to see what’s there now or contribute a pattern your team uses, the source of truth is the repo: github.com/datarobot-oss/datarobot-agent-skills. The marketplace listings track it.
The developer experience DataRobot is building is one where the platform shows up in the surface you already chose, with the on-ramp baked in. Skills are how that promise reaches the agent in your IDE. The marketplaces are how it reaches you.
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The recently launched Robotics Café is a weekly online seminar series to bring together researchers, students and industry practitioners working in the field of autonomous robotics. One of the key aims of the initiative is to provide a dedicated platform for students to present and disseminate their work, enabling broader visibility and impact across academia and industry.
Organised by P.B. Sujit (IISER Bhopal), Sandeep Manjanna (Plaksha University) and Aditya Paranjape (Monash University), the talks take place every Thursday from 17:00-18:00 Indian Standard Time. The link to watch the presentations live via Google Meet is here. Alternatively, you can catch the recordings on the Robotics Café YouTube channel.
The series kicked off with a talk from Professor Debasish Ghose (Indian Institute of Science) entitled: AERObotics: The Art of Catching Objects in the Air. The presentation explored the fascinating intersection of guidance theory and aerial robotics.
In the second lecture, Professor Arun Kumar Singh (University of Tartu) spoke about leveraging predictive uncertainty for model-based planning and control.
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You shouldn’t have to leave Cursor to build, deploy, or monitor a production-grade agent. You can wire together LangChain, a vector DB, a monitoring tool, and a deployment pipeline yourself, but you’ll spend more time on that plumbing than on the agent itself. DataRobot is the shortcut. It now lives where you build, integrating directly into your IDE across the coding agent, tool layer, and model gateway.
Picture what this unlocks. A platform engineer at a fintech company wires up the Global MCP, points their existing LangGraph agent at it, and ships a governed deployment with monitoring and tracing all before lunch, without touching their agent code.
DataRobot is the pathway to that workflow. Four pieces, one per layer of the stack:
datarobot-agent-skills ships Agent Context Protocol folders for the things developers ask DataRobot to do: model training, predictions, deployment, feature engineering, monitoring, explainability, data prep. One install reaches Claude Code, Cursor, Codex, Gemini CLI, Amp, VS Code Copilot, Goose, Letta, Kilo Code, and OpenCode:
npx ai-agent-skills install datarobot-oss/datarobot-agent-skillsAfter that, “create a customer churn project and start AutoML” works in your IDE without memorizing the SDK pattern. DataRobot is also in the Cursor marketplace for one-click install.
The Global MCP is auto-deployed to every DataRobot instance. Five lines in .cursor/mcp.json and you’re done:
{
"mcpServers": {
"datarobot-mcp": {
"url": "https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp",
"headers": { "Authorization": "Bearer <YOUR_API_KEY>" }
}
}
}
Need custom tools or internal services exposed over MCP? The af-component-datarobot-mcp template is a FastMCP scaffold with @dr_mcp_tool decorators and Pulumi-managed deployment as a Custom Model App. Local on port 8080, production on DataRobot serverless.
Agents stop containing tool code. They ask the server what’s available and call it when they need it.
That’s the architectural payoff: add or change tools without redeploying the agent. See the LangGraph integration pattern for the mcp_tools property that auto-converts MCP tools into LangChain tools.
datarobot-agent-templates provides scaffolds for CrewAI, LangGraph, and LlamaIndex. Each one ships with Pulumi infrastructure, a dev server, OpenTelemetry tracing, and the necessary plumbing that turns a local agent into a governed DataRobot deployment.
Agent Assist (dr assist) is the design-before-you-code path. It walks through agent specification, generates agent_spec.md, simulates tool-calling so you can validate model and tool choice without burning real LLM calls, then scaffolds against the templates.
Underneath all of it: the LLM Gateway, an OpenAI-compatible endpoint at {DATAROBOT_URL}/api/v2/genai/llmgw. Agents written against the OpenAI Python SDK work as-is. Switching providers is a model-string change. Metering, governance, and credentialing happen at the gateway. All four interfaces share auth through a documented credential resolution order, with separate Personal, Application, and Agent API key types when you need scoped service-to-service calls.
| Layer | You use | DataRobot plugs in |
|---|---|---|
| Coding agent | Cursor, Claude Code, Codex, Copilot, Gemini CLI | Skills + Cursor marketplace |
| Tool layer | Any MCP client | Global MCP or standalone template |
| Agent code | CrewAI, LangGraph, LlamaIndex | Templates + Agent Assist |
| Model access | OpenAI SDK | LLM Gateway |
In Cursor: install Skills, clone a LangGraph template, point your OpenAI client at the LLM Gateway, expose tools via Global MCP, run dr task run deploy. Result: a governed DataRobot deployment with monitoring and tracing.
Every capability available in the DataRobot UI is also available to your IDE and CI pipeline, so you can choose the surface that fits the task.
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