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:
- 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.
- Validate the portal has the metadata contract (
datasets.json, lib/metadata/types.ts,
the showcase route); proceed anyway if lib/metadata/ predates the contract.
- 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.
- Echo the inferred schema as a table for confirmation; offer to go beyond L0 only if
warranted.
- Ask for optional Data Package metadata: license, source(s), keywords, version.
- 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.
- Optionally validate the schema against the data's rows via the profile's
validate().
- Verify with
npx next build; fix malformed JSON or an invalid FieldType before
reporting success.
- 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