jupyter-live-kernel

---
name: jupyter-live-kernel
description: >
  Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb.
  Load this skill when the task involves exploration, iteration, or inspecting
  intermediate results — data science, ML experimentation, API exploration, or
  building up complex code step-by-step. Uses terminal to run CLI commands against
  a live Jupyter kernel. No new tools required.
version: 1.0.0
author: Hermes Agent
license: MIT
metadata:
  hermes:
    tags: [jupyter, notebook, repl, data-science, exploration, iterative]
    category: data-science
---

# 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-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.

## Activation Keywords

- "jupyter-live-kernel"
- "jupyter live kernel"
- "use jupyter live kernel"
- "jupyter live kernel help"
- "jupyter live kernel tool"

## Tools Used

- `Read` - Read existing files and documentation
- `Write` - Create new files and documentation
- `Bash` - Execute commands when needed

## Instructions for Agents

1. Identify user's intent and specific requirements
2. Gather necessary context from files or user input
3. Execute appropriate actions using available tools
4. Provide clear results and suggest next steps

## Examples

### Basic Jupyter Live Kernel usage
```
User: "Help me with jupyter live kernel"
→ Understand requirements → Execute actions → Provide results
```

### Advanced usage
```
User: "I need detailed jupyter live kernel assistance"
→ Clarify scope → Provide comprehensive solution → Follow up
```