jupyter-live-kernel

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• SKILL.md

SKILL.md (first lines)

## Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

- `terminal` => `bash`
- `web_extract` => `web_fetch`, plus `web_search` when discovery is needed
- `search_files` => `grep_search` and `glob_search`
- `browser_*` tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from `${KHEISH_SKILL_DIR}`.

# 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 Kheish 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

This imported skill does not bundle the external `hamelnb` helper repository. Keep
the helper checkout separate from Kheish and point `SCRIPT` at that checkout:
```
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 "$HOME/.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

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