Hamelnb

hamelnb

Use this skill when a local notebook kernel already holds useful state and you do not want to rerun expensive setup.

Starting JupyterLab

When launching JupyterLab for use with this skill, disable token and password auth so the script can access the server API without browser session cookies:

jupyter lab --IdentityProvider.token='' --ServerApp.password=''

If the server is already running but returns 403 errors, restart it with these flags.

Core Loop

Run from the repo root.

SCRIPT=skills/jupyter-live-kernel/scripts/jupyter_live_kernel.py

Prefer uv for this skill. The helper script declares its runtime dependencies inline, so the default invocation is:

uv run "$SCRIPT" --help

Because the script uses inline metadata, uv run "$SCRIPT" stays self-contained even when you launch it from inside this repo.

If the script is executable and uv is on PATH, direct execution also works:

"$SCRIPT" --help

Fallback:

• Use python3 "$SCRIPT" ... only if uv is unavailable and the required packages are already installed.
• If you need to add a new runtime dependency to the helper script, prefer uv add --script "$SCRIPT" <package>.

1. Discover reachable servers.

uv run "$SCRIPT" servers --compact

2. Find live notebooks.

uv run "$SCRIPT" notebooks --port 8899 --compact

3. Inspect the saved notebook.

uv run "$SCRIPT" contents --port 8899 --path demo.ipynb --compact

4. Execute code incrementally in the live kernel.

uv run "$SCRIPT" execute \
  --port 8899 \
  --path demo.ipynb \
  --code $'x = 41\nprint("hello")\nx + 1' \
  --compact

5. Edit saved notebook cells. Use a real cell ID returned by contents.

uv run "$SCRIPT" edit \
  --port 8899 \
  --path demo.ipynb \
  replace-source \
  --cell-id <cell-id> \
  --source $'x = 42\nx' \
  --compact

To insert a new cell, use insert with --at-index, --before <int>, or --after <int>. Note: --before/--after accept integer indices, not cell IDs.

uv run "$SCRIPT" edit \
  --port 8899 \
  --path demo.ipynb \
  insert \
  --at-index 1 \
  --cell-type code \
  --source $'print("hello")' \
  --compact

The available edit subcommands are: replace-source, insert, delete, move, clear-outputs.

Core guidance:

• Prefer --cell-id over --index for existing cells (replace-source, delete, move).
• For insert, use --at-index (integer) — cell IDs are not accepted by --before/--after.
• Use execute for the normal loop.
• Keep restart, run-all, and restart-run-all for explicit verification or reset requests, not routine iteration.
• Use --save-outputs with run-all or restart-run-all to persist cell outputs into the notebook file so they appear in the JupyterLab UI.
• When using execute with --cell-id, outputs are automatically saved to the notebook file (no need to pass --save-outputs separately). Without --cell-id, outputs are not saved.
• To use the kernel as a pure REPL without persisting outputs, pass --no-save-outputs. This suppresses the automatic save even when --cell-id is provided. Only use this flag when the user explicitly says they don't need the notebook updated (e.g. "run this headlessly", "I don't care about the notebook output"). Default to saving outputs.

Target Selection And Ambiguity

Always make the live target explicit before edits or execution. Never guess when more than one live option exists.

Required behavior:

1. Resolve server first.
   • If multiple reachable servers are discovered, ask the user to choose one.
   • After selection, pass --port (or --server-url) on all follow-up commands.
2. Resolve notebook path second.
   • If the user already specified --path, use it.
   • Otherwise run notebooks for the selected server and collect candidates.
   • If there is exactly one live notebook, state it explicitly and proceed.
   • If there are multiple notebook candidates, ask the user to choose one.
3. Resolve session/kernel when needed.
   • If multiple live sessions exist for the chosen path, ask the user to choose a session.
   • Then pass --session-id on execute/restart/run-all/variables commands to pin the exact kernel.

Claude Code ambiguity flow:

• Use AskUserQuestion for each ambiguity point (server, notebook, session) as a picker.
• Keep each picker to one short question with a short header (Server, Notebook, Session).
• Option labels must include enough context to disambiguate:
  • Server: port, base URL.
  • Notebook: path, port.
  • Session: session id, kernel id, path.
• After selection, confirm in plain language (for example: Using port 8888, notebook notebooks/tiny-demo.ipynb, session 1234...), then continue.

Codex ambiguity flow:

• Ask a direct clarifying question in plain text listing candidates.
• Continue only after the user confirms a specific target.

Once selected, keep using the same port + path and, when applicable, session_id until the user asks to switch.

Advanced

Inspect live Python-kernel variables:

uv run "$SCRIPT" variables --port 8899 --path demo.ipynb list --compact
uv run "$SCRIPT" variables --port 8899 --path demo.ipynb preview --name x --compact

Verification commands:

uv run "$SCRIPT" restart --port 8899 --path demo.ipynb --compact
uv run "$SCRIPT" run-all --port 8899 --path demo.ipynb --compact
uv run "$SCRIPT" run-all --port 8899 --path demo.ipynb --save-outputs --compact
uv run "$SCRIPT" restart-run-all --port 8899 --path demo.ipynb --save-outputs --compact

Advanced guidance:

• variables is Python-only.
• run-all and restart-run-all require a notebook-backed live session.
• run-all and restart-run-all exit non-zero when a cell fails.
• run-all and restart-run-all verify a saved snapshot loaded at the start.
• Pass --save-outputs to run-all or restart-run-all to persist cell outputs and execution counts back into the notebook file. Without this flag outputs are not written back.

Transport

execute, variables, run-all, and restart-run-all default to --transport auto.

• websocket: use Jupyter Server kernel channels at /api/kernels/<kernel_id>/channels
• zmq: fall back to the local kernel connection file with jupyter_client
• auto: try websocket first, then use local ZMQ fallback only when the websocket request did not already reach the kernel

Prefer auto unless you are debugging transport behavior.

Limits

• contents returns the saved notebook file, not unsaved browser edits.
• edit writes through the Contents API and changes the saved notebook on disk.
• The stale-write guard is best-effort, not atomic.
• Concurrent notebook edits can invalidate a verification run after it starts.
• Concurrent execution from other clients can affect kernel state during verification.
• Variable listing is bounded to --limit <= 100.
• Variable preview is bounded to --max-chars <= 2000 and avoids arbitrary repr(...) calls for non-scalar objects.
• Workspace-derived notebook tabs are a persisted JupyterLab snapshot, not guaranteed real-time UI truth.
• Workspace-derived notebook tabs can include historical workspaces for the same relative path.
• The skill assumes local Jupyter runtime metadata is visible to the current user.

Resources

• Script: jupyter_live_kernel.py
• Architecture notes: jupyter-hooks.md