Jupyter Notebook

Jupyter Notebook (hamelnb live kernel)

Gives you a stateful Python REPL via a live Jupyter kernel. Variables persist across executions. Use this instead of execute_code when you need to build up state incrementally, explore APIs, inspect DataFrames, or iterate on complex code.

When to Use This vs Other Tools

Rule of thumb: If you'd want a Jupyter notebook for the task, use this skill.

Prerequisites

1. uv must be installed (check: which uv)
2. JupyterLab must be installed: uv tool install jupyterlab
3. A Jupyter server must be running (see Setup below)

Setup

The hamelnb script location:

SCRIPT="$HOME/.agent-skills/hamelnb/skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py"

If not cloned yet:

git clone https://github.com/hamelsmu/hamelnb.git ~/.agent-skills/hamelnb

Starting JupyterLab

Check if a server is already running:

uv run "$SCRIPT" servers

If no servers found, start one:

jupyter-lab --no-browser --port=8888 --notebook-dir=$HOME/notebooks \
  --IdentityProvider.token='' --ServerApp.password='' > /tmp/jupyter.log 2>&1 &
sleep 3

Note: Token/password disabled for local agent access. The server runs headless.

Creating a Notebook for REPL Use

If you just need a REPL (no existing notebook), create a minimal notebook file:

mkdir -p ~/notebooks

Write a minimal .ipynb JSON file with one empty code cell, then start a kernel session via the Jupyter REST API:

curl -s -X POST http://127.0.0.1:8888/api/sessions \
  -H "Content-Type: application/json" \
  -d '{"path":"scratch.ipynb","type":"notebook","name":"scratch.ipynb","kernel":{"name":"python3"}}'

Core Workflow

All commands return structured JSON. Always use --compact to save tokens.

1. Discover servers and notebooks

uv run "$SCRIPT" servers --compact
uv run "$SCRIPT" notebooks --compact

2. Execute code (primary operation)

uv run "$SCRIPT" execute --path <notebook.ipynb> --code '<python code>' --compact

State persists across execute calls. Variables, imports, objects all survive.

Multi-line code works with $'...' quoting:

uv run "$SCRIPT" execute --path scratch.ipynb --code $'import os\nfiles = os.listdir(".")\nprint(f"Found {len(files)} files")' --compact

3. Inspect live variables

uv run "$SCRIPT" variables --path <notebook.ipynb> list --compact
uv run "$SCRIPT" variables --path <notebook.ipynb> preview --name <varname> --compact

4. Edit notebook cells

# View current cells
uv run "$SCRIPT" contents --path <notebook.ipynb> --compact

# Insert a new cell
uv run "$SCRIPT" edit --path <notebook.ipynb> insert \
  --at-index <N> --cell-type code --source '<code>' --compact

# Replace cell source (use cell-id from contents output)
uv run "$SCRIPT" edit --path <notebook.ipynb> replace-source \
  --cell-id <id> --source '<new code>' --compact

# Delete a cell
uv run "$SCRIPT" edit --path <notebook.ipynb> delete --cell-id <id> --compact

5. Verification (restart + run all)

Only use when the user asks for a clean verification or you need to confirm the notebook runs top-to-bottom:

uv run "$SCRIPT" restart-run-all --path <notebook.ipynb> --save-outputs --compact

Practical Tips from Experience

1. First execution after server start may timeout — the kernel needs a moment to initialize. If you get a timeout, just retry.

2. The kernel Python is JupyterLab's Python — packages must be installed in that environment. If you need additional packages, install them into the JupyterLab tool environment first.

3. --compact flag saves significant tokens — always use it. JSON output can be very verbose without it.

4. For pure REPL use, create a scratch.ipynb and don't bother with cell editing. Just use execute repeatedly.

5. Argument order matters — subcommand flags like --path go BEFORE the sub-subcommand. E.g.: variables --path nb.ipynb list not variables list --path nb.ipynb.

6. If a session doesn't exist yet, you need to start one via the REST API (see Setup section). The tool can't execute without a live kernel session.

7. Errors are returned as JSON with traceback — read the ename and evalue fields to understand what went wrong.

8. Occasional websocket timeouts — some operations may timeout on first try, especially after a kernel restart. Retry once before escalating.

9. If websocket consistently times out on this host, force zmq transport: uv run "$SCRIPT" execute --transport zmq .... Symptom: every execute returns "Websocket execution may already have reached the kernel, so auto fallback was skipped". The kernel actually ran fine (REST shows execution_state=idle and execution_count increments) — only the websocket reply channel is broken. zmq transport uses jupyter_client directly and sidesteps the issue.

10. When starting a fresh server for REST-only use, add --ServerApp.disable_check_xsrf=True — otherwise POST /api/sessions returns "'_xsrf' argument missing from POST" and kernel session creation fails.

Timeout Defaults

The script has a 30-second default timeout per execution. For long-running operations, pass --timeout 120. Use generous timeouts (60+) for initial setup or heavy computation.

Source: NousResearch/hermes-agent → optional-skills/data-science/jupyter-notebook/SKILL.md