AI tools for USATLAS analysis

US ATLAS maintains a marketplace of plugins for AI coding assistants. The plugins load ATLAS-specific context into Claude Code, Cursor, and Codex: what tools exist, how they fit together, and when to use each one. In practice this means the assistant already knows that ATLAS NTuples use MeV, how to write a pyhf workspace, and how to find datasets on the grid, so you spend less time correcting it.

Installation

/plugins marketplace add usatlas/marketplace

git clone https://github.com/usatlas/marketplace.git ~/usatlas-marketplace
mkdir -p ~/.agents/skills
ln -s ~/usatlas-marketplace/plugins/atlas/skills ~/.agents/skills/atlas
ln -s ~/usatlas-marketplace/plugins/analysis-facilities/skills \
      ~/.agents/skills/analysis-facilities
ln -s ~/usatlas-marketplace/plugins/hep-python-tools/skills \
      ~/.agents/skills/hep-python-tools

Plugins

analysis-facilities

Skills for working at each USATLAS Analysis Facility.

BNL and SLAC facility skills are in progress.

atlas

The main ATLAS analysis plugin. Five subagents and 25 skills, covering everything from dataset discovery to publication-ready statistical fits.

The subagents activate when their domain comes up, or you can invoke them directly:

Skills by category:

hep-python-tools

Two skills for writing self-contained Python scripts and CLIs.

MCP servers

The atlas plugin configures three Model Context Protocol servers. Start them alongside your assistant session and it can query live ATLAS catalogs rather than rely on training data.

Rucio

Dataset and file replica discovery.

pixi exec rucio-mcp serve --read-only

RUCIO_ACCOUNT has no default. Set it before launching:

export RUCIO_ACCOUNT=yourusername   # your CERN/grid username
export RUCIO_AUTH_TYPE=x509_proxy   # or "oidc" or "userpass"
voms-proxy-init --voms atlas

See the rucio-mcp documentation for authentication options.

AMI

Dataset tags, cross-sections, and generator parameters from AMI.

pixi exec ami-mcp serve

See the ami-mcp documentation.

ATLAS Open Data

The ATLAS Open Data catalog, for educational and public datasets.

uvx atlasopenmagic-mcp serve

See the atlasopenmagic-mcp repository.

Jupyter (your running notebook at UChicago)

If you have a JupyterLab session running at af.uchicago.edu, your MCP client can drive it directly — listing cells, executing code, reading kernel output. The server runs inside your singleuser pod, so authentication reuses your existing notebook credential (JupyterHub API token with the access:servers scope for jupyterhub.af.uchicago.edu, or the per-pod URL token for the af.uchicago.edu/jupyterlab portal).

See Connecting an MCP client to your notebook for the full setup, including the difference in token lifetime between the two launch surfaces.

Example: Claude Code with NRP models + Jupyter MCP

Claude Code does not have to talk to Anthropic's API. You can point it at the National Research Platform (NRP) LLM endpoint to run open-weight models such as qwen3, and — in the same session — attach the Jupyter MCP server so the model can drive a live notebook. This walkthrough wires both together against a BinderHub-launched notebook.

You need three things:

1. Claude Code, configured to use NRP's LLM endpoint
2. An NRP account with an LLM API token
3. A BinderHub repo with jupyter-server-proxy and jupyter-server-mcp installed (the MCP server exposes the notebook on port 3001)

Step 1 — get an NRP LLM token

Create an account at nrp.ai, then mint an LLM token at https://nrp.ai/llmtoken/. This token is your ANTHROPIC_AUTH_TOKEN in the steps below.

Step 2 — configure Claude Code

Edit your Claude Code config (~/.claude.json or equivalent) to route requests to NRP and register the Jupyter MCP server:

{
    "env": {
        "ANTHROPIC_BASE_URL": "https://ellm.nrp-nautilus.io/anthropic",
        "ANTHROPIC_API_KEY": "YOUR_NRP_TOKEN_HERE",
        "ANTHROPIC_DEFAULT_OPUS_MODEL": "qwen3",
        "ANTHROPIC_DEFAULT_SONNET_MODEL": "qwen3",
        "ANTHROPIC_DEFAULT_HAIKU_MODEL": "qwen3",
        "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1",
        "CLAUDE_CODE_DISABLE_FEEDBACK_SURVEY": "1",
        "CLAUDE_CODE_ENABLE_TELEMETRY": "0",
        "API_TIMEOUT_MS": "3000000",
        "DISABLE_TELEMETRY": "1"
    },
    "mcpServers": {
        "jupyter": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://<YOUR_JUPYTERHUB_URL>/user/<your-username>/proxy/3001/mcp?token=<YOUR_JUPYTER_TOKEN>",
                "--transport",
                "http-only"
            ]
        }
    }
}

The three model slots all map to qwen3 so every Claude Code model tier is served by the same NRP model.

Step 3 — launch a BinderHub notebook

Launch your Binder at https://binderhub.ssl-hep.org/, wait for the server to start, and note the server name from the launch URL. Once it is running, construct the MCP URL:

https://<jupyterhub-host>/user/<your-username>/<server-name>/proxy/3001/mcp?token=<token>

• The server name comes from your BinderHub launch URL.
• The token is a JupyterHub API token from https://<jupyterhub-host>/hub/token.

Plug this URL into the jupyter MCP entry from Step 2.

Step 4 — start Claude Code

Launch without triggering the Anthropic login flow:

export ANTHROPIC_AUTH_TOKEN="YOUR_NRP_TOKEN_HERE"
export ANTHROPIC_BASE_URL="https://ellm.nrp-nautilus.io/anthropic"
unset ANTHROPIC_API_KEY
claude

Reference