Document vertical skill workflow

Start with notebooks/vertical_skill.ipynb and inspect the existing sidebar in docs/_quarto.yml, then read workflow.qmd, data-structures.qmd, and ADR-011 for terminology and links. Add docs/user-guide/vertical.qmd, place it beside network.qmd, and cross-link the two guide pages. Done means the page covers the listed workflows, caveats, examples, return type, and depth selection clearly in the rendered documentation.

Vertical skill landed in #585 but has no user-facing documentation. The notebooks/vertical_skill.ipynb is a useful starting point but not part of the rendered docs.

Scope

• Add a docs/user-guide/vertical.qmd page covering:
  • When to use VerticalObservation / VerticalModelResult (sigma/sigma-z columns, fixed (x, y))
  • Reading observations from dfs0 / DataFrame / xarray (z_item, item, aux_items)
  • The "pre-extracted column" requirement — link to ADR-011 and show the mikeio extraction snippet users need to run before constructing a VerticalModelResult
  • Matching: nearest-time + linear interp on depth, default tolerance = half median model dt
  • .vertical accessor: .skill(bins=...), .mean()/.min()/.max(), .plot.profile(), .plot.hovmoller()
  • SkillProfile return type
  • cmp.sel(z=...) for slicing depth ranges or selecting a layer
• Add the page to docs/_quarto.yml sidebar (next to network.qmd)
• Cross-link from workflow.qmd and data-structures.qmd

Known caveats to call out in the docs

• match(max_model_gap=...) is not yet supported for vertical matching — raises NotImplementedError (see src/modelskill/matching.py:421)
• Default depth-bin count for vertical.skill() is 5, same convention as gridded_skill
• No spatial check between obs (x, y) and model column (x, y) — see ADR-011

Reference material

• notebooks/vertical_skill.ipynb — has plots and example flows worth lifting
• Survey results in #585 PR description (~40 responses) — informs what users actually want documented