jupyter-notebook

# 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=$HOME/notebooks \ --IdentityProvider.token='' --ServerApp.password='' > ~/.hermes/cache/scratch/jupyter.log 2>&1 & sleep 3 ``` Note: Token/password disabled for local agent access. The server runs headless. ### Creating a Notebook for REPL Use If you just need a REPL (no existing notebook), create a minimal notebook file: ``` mkdir -p ~/notebooks ``` Write a minimal .ipynb JSON file with one empty code cell, then start a kernel session via the Jupyter REST API: ``` curl -s -X POST http://127.0.0.1:8888/api/sessions \ -H "Content-Type: application/json" \ -d '{"path":"scratch.ipynb","type":"notebook","name":"scratch.ipynb","kernel":{"name":"python"}}' ``` ## Core Workflow All commands return structured JSON. Always use `--compact` to save tokens. ### 1. Discover servers and notebooks ``` uv run "$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.