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name: portaljs-architect description: Recommend a data-portal architecture (storage, compute, catalog, access, hosting, metadata) from stated needs, then hand off to the build skills. The advisory entry point. Use when starting a new data-portal project and the underlying architecture has not yet been decided. allowed-tools: Read, Write, Bash(du:), Bash(wc:), Bash(ls:), Bash(head:) version: 1.0.0 author: Datopian hello@datopian.com license: MIT compatibility: Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs clone. tags:

• portaljs
• data-portal
• architecture
• advisory
• ckan
• nextjs

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PortalJS — Architect

Overview

The advisory entry point for a PortalJS project. Before anything gets scaffolded, this skill works out what to build: given the kind of portal, the shape of the data, and its purpose, it fills six architecture slots (storage, catalog, compute, access, hosting, metadata), resolves two build-time knobs (per-dataset data tier and the portal-wide DATA_QUERY mode), and hands off to the concrete build skills. It decides; it does not build. When the brief is thin it interviews in short rounds and never dead-ends — every question has a sensible default, reachable by replying "use defaults."

Prerequisites

• A rough idea of the portal's purpose and data (exact numbers are not required — the interview supplies defaults for anything missing).
• Optional: local files or a directory of sample data to inspect for size and shape.
• No PortalJS project needs to exist yet; this skill runs before scaffolding.

Instructions

The canonical, full step-by-step workflow lives in .claude/commands/portaljs-architect.md — that file is the single source of truth. Follow it when executing this skill:

1. Parse $ARGUMENTS for anything already specified, then interview for what's missing, one round at a time: (1) what's being built, (2) what the data is, (3) what it's for, (4) constraints. Accept "use defaults" at any point. Inspect named files/directories with du -sh and line counts to ground size guesses.
2. Derive the recommendation by matching the answers against the decision tables — Storage/Catalog/Compute by data volume and query needs, Access/Hosting by public vs. private, Metadata by standards-compliance needs — then resolve the two build-time knobs: per-dataset data tier (inline | LFS | external) and the portal-wide DATA_QUERY mode (flat | duckdb).
3. Echo the architecture brief (stack, reasoning per slot, deviations from default, deferred items) and wait for confirmation ("go") or corrections.
4. On confirmation, persist the brief to ./ARCHITECTURE.md in the working directory.
5. Hand off to the build skills — /portaljs-new-portal, /portaljs-add-dataset, /portaljs-connect-ckan, /portaljs-define-schema, /portaljs-deploy — mapped from the brief, and offer to run the first one.

Output

• Created: ./ARCHITECTURE.md documenting the six slots, the two build-time knobs, the reasoning, and anything deferred to a later build step.
• Modified: nothing else — this skill is advisory only.
• Verified: the brief was echoed back and confirmed before being persisted.
• Result: a concrete, named sequence of follow-up skill invocations (e.g. /portaljs-new-portal → /portaljs-add-dataset → /portaljs-deploy).

Error Handling

Symptom	Cause	Fix
Skill keeps asking rounds of questions	Brief was thin or $ARGUMENTS omitted	Answer inline, or reply "use defaults" to accept the opinionated default stack
Recommendation looks generic	Rounds were skipped without real data details	Give actual size/shape/cadence, or point at files for du -sh inspection
ARCHITECTURE.md never appears	Confirmation step was skipped	Reply "go" once the echoed brief looks right
Scaffolded portal has the wrong DATA_QUERY	Flat downgrade wasn't applied	Run the perl -pi -e one-liner from the command file against lib/datasets.ts
Hand-off names a skill that doesn't exist	Decision maps to a (planned) skill (e.g. /connect-openmetadata)	Treat it as designed-in/built-later; proceed with the closest available skill

Examples

Example 1 — National statistics office, DCAT-AP harvesting

/portaljs-architect We're a national statistics office. ~200 datasets, mostly large
CSVs (some GBs), updated quarterly, all public, and we must publish DCAT-AP for the
EU data portal.

Infers a multi-publisher, analytics-grade portal. Recommends Parquet on R2 + DuckLake + DuckDB, static Cloudflare Pages, Frictionless + DCAT-AP metadata, owner namespace, data tier external for the Parquet, DATA_QUERY=duckdb. Writes ARCHITECTURE.md and hands off to /portaljs-new-portal then /portaljs-add-dataset.

Example 2 — Small nonprofit, no arguments given

/portaljs-architect

Runs the full four-round interview since nothing was pre-filled. Accepting defaults at each round lands on the opinionated default stack: repo files or Git-LFS + R2 storage, datasets.json catalog, DuckDB compute, static access on Cloudflare Pages, Frictionless metadata, theme namespace, data tier LFS, DATA_QUERY=duckdb.

Example 3 — Internal catalog with restricted datasets

/portaljs-architect Internal engineering data catalog, single team, dozens of CSVs,
some of it access-controlled to specific roles.

The private-data answer in Round 2 flips Access/Hosting to runtime + backend RBAC on Cloudflare Workers — flagged as the larger, opt-in build — while Storage/Catalog/Compute still follow the volume-based defaults.

Resources

• Full workflow: .claude/commands/portaljs-architect.md
• Reference: references/reference.md
• Decision framework: site/content/docs/architecture/decision-framework.md
• Related skills: /portaljs-new-portal, /portaljs-add-dataset, /portaljs-connect-ckan, /portaljs-define-schema, /portaljs-deploy