Jupyter Live Kernel (hamelnb)

Harness-agent tools

This skill was ported from Octop. Use these harness/deepagents tools:

Concept	Tool
Shell	execute
Read / write / edit files	read_file, write_file, edit_file
Find files / search content	glob, grep
Fetch URLs	web_fetch
Browser automation	browser_use
Subagent work	task
Memory	memory_store, memory_recall, memory_search

Builtin skill files live under /_builtin_skills/<name>/. User-installed skills live under /skills/<name>/.

Gives you a stateful Python REPL via a live Jupyter kernel. Variables persist across executions. Use this instead of execute 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 (Python one-shot)	Short scripts, installs, file transforms — stateless between calls
execute (shell)	git, builds, system commands

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 (clone once, or use the copy under this skill after sync):

SCRIPT="_builtin_skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py"

If hamelnb is not bundled, clone upstream:

git clone https://github.com/hamelsmu/hamelnb.git ~/.agent-skills/hamelnb
SCRIPT="$HOME/.agent-skills/hamelnb/skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py"

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.

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.