## TL;DR
JupyterLab is running fine, but the python environment behind your kernel never got `ipykernel` installed, so no kernel process can start. Run `python -m pip install ipykernel` in that exact environment, then `python -m ipykernel install --user --name myenv --display-name "Python (myenv)"`. The kernel then appears in the launcher and connects on the first try.

```text
ModuleNotFoundError: No module named 'ipykernel'
```

## Use this when
- JupyterLab opens but starting any python kernel fails with ModuleNotFoundError for ipykernel.
- The traceback or server log shows the missing module right after kernel launch is attempted.
- A fresh conda env or venv shows up in kernelspec list but cannot start.

## Not for this skill when
- The kernel starts and then dies mid-cell. That is a crash, not a missing package.
- JupyterLab itself will not launch. That is a server-side install problem.

## Steps
1. Find which python the kernel spec uses: `jupyter kernelspec list`, then read the `argv` in the spec's kernel.json. Verify: you know the exact python path.
2. Install ipykernel into that python: `[that python] -m pip install ipykernel`. Verify: `[that python] -c "import ipykernel"` succeeds.
3. Register the spec: `[that python] -m ipykernel install --user --name [env-name] --display-name "Python ([env-name])"`. Verify: `jupyter kernelspec list` shows the entry.
4. Restart JupyterLab and pick the new kernel in the launcher. Verify: the kernel status dot goes solid and `1+1` executes.
5. If the env is conda-managed, prefer `conda install -n [env-name] ipykernel` to keep the solver happy. Verify: `conda list -n [env-name] ipykernel` shows it.

## Variant phrasings
### no module named ipykernel jupyter
The generic search. Same missing-package fix.

### ipykernel not installed kernel error
Log-style phrasing. Install into the kernel's python, not the server's.

### jupyter lab kernel not found ipykernel
Launcher-side phrasing. The registration step is what makes it appear.

Compatibility: JupyterLab 3/4, notebook 6/7, ipykernel 6.x. The install-and-register flow is the same on pip and conda.

## Why it happens
JupyterLab and the kernel are separate processes, often from separate environments. The Lab server only needs jupyter_server; the kernel needs ipykernel. Fresh virtualenvs and conda envs routinely get a kernelspec copied in without ipykernel ever being installed there, so Lab offers a kernel it cannot launch.

## Edge cases / pitfalls
- Installing ipykernel into the Lab server's environment does not fix a kernel in another env. Match the python exactly.
- Do not edit kernel.json by hand to point at a different python. Reinstall the spec from the right python instead.
- `--user` installs go to your home kernels dir. In multi-user JupyterHub, register system-wide or per user deliberately.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_UGKVg6h107NbMy6arNR_pA