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domain archive
title Jupyter Live Kernel
verification metadata-normalized
source skill-pipeline
status draft
created 2026-05-09

背景

（此 lesson 从 skill jupyter-live-kernel 自动提取，待补全）

根因

（待补充）

修复

Jupyter Live Kernel (hamelnb)

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-

## 验证

（待补充）