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

Tool	Use When
This skill	Iterative exploration, state across steps, data science, ML, "let me try this and check"
execute_code	One-shot scripts needing hermes tool access (web_search, file ops). Stateless.
terminal	Shell commands, builds, installs, git, process management

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.