PortalJS — Define Schema

Overview

Define a dataset's metadata profile — the authoring skill for the metadata-profile contract (lib/metadata). Where portaljs-add-dataset registers that a dataset exists, this skill describes what its data means: infer a Frictionless Table Schema (fields, types, constraints) from sampled data, add the Data Package fields a catalog surfaces (title, licenses, sources, keywords), and write them onto the dataset's entry in datasets.json. The showcase at /@<namespace>/<slug> then renders a typed field table instead of a bare preview. The model is Frictionless-native; DCAT is a serialization layer built on top later, not authored here.

The skill runs on a profile ladder — reach for higher levels only when needed:

Level	What it is	When
L0	Default frictionless-tabular profile; declare schema + metadata.	Default. Standard tabular CSV/TSV.
L1	L0 plus extra descriptive package fields.	Extra metadata, standard validation is fine.
L2	Fully custom profile (own schema template + validate()).	A dataset type needing custom validation rules.
L3	Multiple registered profiles, resolved per dataset.	A portal mixing dataset types.

The skill is interactive and never dead-ends: if input is thin it interviews in short rounds, infers defaults from the data, echoes the schema for confirmation, and accepts "use defaults" to proceed with the inferred schema as-is.

Prerequisites

• A scaffolded PortalJS portal with the metadata contract (lib/metadata/types.ts, pages/[owner]/[slug].tsx); see portaljs-new-portal.
• The target dataset already registered in datasets.json (see portaljs-add-dataset).
• For tabular schema inference, the dataset's CSV/TSV file present under PORTAL_DIR/public/data/. JSON/GeoJSON datasets get package metadata only — no fields.
• Node 18+; tsx optional, used for the schema-validation check.

Instructions

The canonical, full step-by-step workflow is .claude/commands/portaljs-define-schema.md — the single source of truth. Read and follow it when executing. Summary:

1. Gather PORTAL_DIR, DATASET (slug or namespace/slug), and LEVEL (default L0) from input; if DATASET is missing, list the portal's slugs and ask.
2. Validate the portal has the metadata contract (datasets.json, lib/metadata/types.ts, the showcase route); proceed anyway if lib/metadata/ predates the contract.
3. For tabular datasets, sample the header and ~50 rows from public/data/<file> and infer each field's type, constraints (required, unique, pattern), and a primary key.
4. Echo the inferred schema as a table for confirmation; offer to go beyond L0 only if warranted.
5. Ask for optional Data Package metadata: license, source(s), keywords, version.
6. Write the schema and metadata onto the dataset's entry in datasets.json in place, preserving all other fields; for L2/L3, scaffold and register a custom profile module.
7. Optionally validate the schema against the data's rows via the profile's validate().
8. Verify with npx next build; fix malformed JSON or an invalid FieldType before reporting success.
9. Report the profile, fields, metadata set, and the showcase URL.

Output

• Modified: datasets.json (target entry gains profile, schema, licenses, sources, keywords, version — unset fields omitted).
• Created (L2/L3 only): lib/metadata/<profile-id>.ts; lib/metadata/registry.ts updated with a registerProfile(...) call.
• Verified: npx next build succeeds.
• Result: /@<namespace>/<slug> renders a typed field table in place of a bare preview.

Error Handling

Symptom	Cause	Fix
Dataset not found in datasets.json	Wrong slug or missing namespace/ prefix	List available slugs and re-prompt.
lib/metadata/ missing	Portal predates the metadata-profile contract	Proceed anyway — schema fields are optional and ignored by older showcases.
No fields schema produced	Dataset is JSON/GeoJSON, not tabular	Expected — capture Data Package metadata only.
Validation reports type errors	Sampled values don't coerce to the inferred type	Relax the type or drop the offending required/pattern constraint.
next build fails on datasets.json	Stray comma or a type outside FieldType	Fix the JSON/type and rebuild before reporting success.

Examples

Example 1 — Default L0 schema for a CSV dataset

/portaljs-define-schema population-2022

Infers fields (e.g. country: string, population: integer), drafts titles, asks for a license and source, and writes the schema under the default frictionless-tabular profile.

Example 2 — Metadata only for a GeoJSON dataset

/portaljs-define-schema neighborhoods-geo

GeoJSON has no tabular fields; the skill captures license, sources, and keywords onto the entry and skips schema inference.

Example 3 — Custom L2 profile with its own validation

/portaljs-define-schema co2-emissions level=L2

Scaffolds lib/metadata/co2-emissions-profile.ts with a custom validate(), registers it in lib/metadata/registry.ts, and sets "profile": "co2-emissions-profile" on the entry.

Resources

• Full workflow: .claude/commands/portaljs-define-schema.md
• Field-type and troubleshooting reference: references/reference.md
• Related skills: portaljs-add-dataset, portaljs-add-dcat, portaljs-check-data-quality
• Frictionless Table Schema specification: https://datapackage.org/standard/table-schema/