docs(website): dedicated page per bundled + optional skill (#14929)

Generates a full dedicated Docusaurus page for every one of the 132 skills
(73 bundled + 59 optional) under website/docs/user-guide/skills/{bundled,optional}/<category>/.
Each page carries the skill's description, metadata (version, author, license,
dependencies, platform gating, tags, related skills cross-linked to their own
pages), and the complete SKILL.md body that Hermes loads at runtime.

Previously the two catalog pages just listed skills with a one-line blurb and
no way to see what the skill actually did — users had to go read the source
repo. Now every skill has a browsable, searchable, cross-linked reference in
the docs.

- website/scripts/generate-skill-docs.py — generator that reads skills/ and
  optional-skills/, writes per-skill pages, regenerates both catalog indexes,
  and rewrites the Skills section of sidebars.ts. Handles MDX escaping
  (outside fenced code blocks: curly braces, unsafe HTML-ish tags) and
  rewrites relative references/*.md links to point at the GitHub source.
- website/docs/reference/skills-catalog.md — regenerated; each row links to
  the new dedicated page.
- website/docs/reference/optional-skills-catalog.md — same.
- website/sidebars.ts — Skills section now has Bundled / Optional subtrees
  with one nested category per skill folder.
- .github/workflows/{docs-site-checks,deploy-site}.yml — run the generator
  before docusaurus build so CI stays in sync with the source SKILL.md files.

Build verified locally with `npx docusaurus build`. Only remaining warnings
are pre-existing broken link/anchor issues in unrelated pages.

website/docs/user-guide/skills/bundled/data-science/data-science-jupyter-live-kernel.md

@@ -0,0 +1,183 @@
+---
+title: "Jupyter Live Kernel — Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb"
+sidebar_label: "Jupyter Live Kernel"
+description: "Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb"
+---
+
+{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
+
+# Jupyter Live Kernel
+
+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.
+
+## Skill metadata
+
+| | |
+|---|---|
+| Source | Bundled (installed by default) |
+| Path | `skills/data-science/jupyter-live-kernel` |
+| Version | `1.0.0` |
+| Author | Hermes Agent |
+| License | MIT |
+| Tags | `jupyter`, `notebook`, `repl`, `data-science`, `exploration`, `iterative` |
+
+## Reference: full SKILL.md
+
+:::info
+The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
+:::
+
+# 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.