Portaljs Add Dcat logo

Portaljs Add Dcat

OrganizationPopular
datopian
portaljs-add-dcat

Make a PortalJS portal harvestable by national/EU/US open-data portals — emit standards-compliant DCAT catalog feeds (DCAT 2/3, DCAT-AP, DCAT-US, national profiles) in JSON-LD, Turtle, and RDF/XML at build, with autodiscovery and per-profile conformance checking. Use when a portal needs to be harvested by data.europa.eu, data.gov, or a national open-data catalog.

Overview

Publisherdatopian
Repositoryportaljs
Skill nameportaljs-add-dcat
Stars
2.4K
Forks
332
Bundled files
1
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by datopian on GitHub. Read the source before you install it.

Installation

Install the Portaljs Add Dcat AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/datopian/portaljs.git /tmp/portaljs
mkdir -p .claude/skills
cp -r /tmp/portaljs/skills/portaljs-add-dcat .claude/skills/portaljs-add-dcat
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Portaljs Add Dcat in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Portaljs Add Dcat on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Portaljs Add Dcat is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PortalJS — Add DCAT

Overview

Turn an existing PortalJS (portaljs-catalog) portal into a harvestable data catalog. PortalJS is Frictionless-native — a dataset is a Data Package (see portaljs-define-schema) — and DCAT is the serialization + harvest layer on top (lib/metadata/dcat.ts + lib/metadata/dcat-profiles.ts). This skill selects one or more DCAT application profiles, maps every dataset's metadata to them, and writes static feed files at build time — JSON-LD, Turtle, and RDF/XML — so external catalogs (data.europa.eu, data.gov, national portals) can harvest the datasets automatically, on any static host, with no runtime.

Prerequisites

  • A scaffolded PortalJS portal with datasets.json, package.json, and lib/metadata/ (the metadata-profile contract) present.
  • lib/metadata/dcat.ts (the DCAT-3 core) already in place — profiles augment it.
  • Node 18+ and npm available in the portal directory.
  • For DCAT-AP / DCAT-US: a publishing organization (name + homepage) and a contact (name + email) — both profiles require dct:publisher and dcat:contactPoint.
  • Optional but recommended: network access to run SHACL conformance checks against the official EU ITB validator or pyshacl.

Instructions

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

  1. Gather input from $ARGUMENTS (interview if thin): portal directory (default .), profiles (default ["dcat-3"]), site URL, publisher, contact, license, themes, languages, access level.
  2. Validate the portal directory: confirm datasets.json, package.json, and lib/metadata/ exist; stop with an ERROR: if the metadata contract is missing.
  3. Ensure the DCAT profile layer is present — dcat-profiles.ts, dcat-rdf.ts, dcat-validate.ts — copying canonical versions from examples/portaljs-catalog if the portal predates this skill.
  4. Ensure scripts/generate-dcat.ts is wired to predev/prebuild and emits per-profile x serialization feeds from dcat.config.json.
  5. Write dcat.config.json with the gathered profiles, publisher, contact, license, themes, and access level.
  6. Add feed autodiscovery: a <link rel="alternate" type="application/ld+json"> to pages/_document.tsx pointing at /catalog.jsonld.
  7. Generate the feeds and check conformance: run npm run generate:dcat and surface any missing mandatory fields.
  8. Verify the RDF: confirm the JSON-LD parses, and cross-check that JSON-LD, Turtle, and RDF/XML agree; run SHACL validation (ITB for DCAT-AP, pyshacl for DCAT-US) when network/tooling allow.
  9. Verify the build with npx next build; fix errors before reporting success.
  10. Report the profiles emitted, feed paths, conformance status, and next steps (register with the harvester, run portaljs-deploy).

Output

  • Created/modified: dcat.config.json (committed config).
  • Generated (build artifacts, gitignored): public/catalog.jsonld /.ttl/.rdf (canonical feed), public/catalog.<profile>.{jsonld,ttl,rdf} per configured profile, public/catalog-feeds.json (feed index).
  • Modified: pages/_document.tsx (autodiscovery <link>), package.json (generate:dcat script wired to predev/prebuild).
  • Verified: feeds are valid JSON-LD/Turtle/RDF-XML, conformance status reported, npx next build passes.

Error Handling

SymptomCauseFix
NO_METADATA_CONTRACTlib/metadata/ not foundPortal predates the metadata-profile contract; scaffold with portaljs-new-portal or add lib/metadata first.
NO_DCAT_CORElib/metadata/dcat.ts not foundThe DCAT-3 core is missing; update the portal template before adding profiles.
BAD_CONFIGdcat.config.json is not valid JSONFix the syntax and re-run npm run generate:dcat.
UNKNOWN_PROFILEProfile id not in the registryUse one of dcat-2, dcat-3, dcat-ap, dcat-us, geodcat-ap, croissant, dcat-ap-se, dcat-ap-ch, dcat-ap-de, or register a national profile first.
Feed flagged non-conformantpublisher/contactPoint missing for DCAT-AP or DCAT-USAsk the user for the publishing organization and contact, add to dcat.config.json, regenerate.
DCAT-US SHACL rejects the publisherPublisher has no IRI (blank node)Set publisher.uri (or homepage) in dcat.config.json.
next build fails after config changeMalformed JSON in dcat.config.json or datasets.jsonPrint the build log, fix the JSON, rebuild before reporting success.

Examples

Example 1 — Default DCAT-3 feed, no national harvesting

/portaljs-add-dcat

Emits the canonical public/catalog.jsonld/.ttl/.rdf under the default dcat-3 profile, adds autodiscovery to _document.tsx, and wires generate:dcat into predev/prebuild. No publisher/contact required.

Example 2 — EU harvesting via DCAT-AP

/portaljs-add-dcat profiles=dcat-ap site=https://data.example.org

Prompts for publisher (name + homepage) and contact (name + email) since DCAT-AP requires both, writes them into dcat.config.json, and emits public/catalog.dcat-ap.{jsonld,ttl,rdf} plus the canonical feed with absolute links.

Example 3 — US federal harvesting via DCAT-US

/portaljs-add-dcat profiles=dcat-us site=https://data.example.gov

Requires an IRI-identified publisher (publisher.uri) for SHACL conformance; emits catalog.dcat-us.{jsonld,ttl,rdf} and validates against the DCAT-US 3.0 SHACL shapes with pyshacl when available.

Example 4 — Multiple profiles plus a national extension

/portaljs-add-dcat profiles=dcat-ap,dcat-ap-de site=https://daten.example.de

Emits both catalog.dcat-ap.* and catalog.dcat-ap-de.* feeds from one config; the first profile listed also becomes the canonical, un-suffixed catalog.jsonld/.ttl/.rdf.

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Portaljs Add Dcat AI skill do?

Make a PortalJS portal harvestable by national/EU/US open-data portals — emit standards-compliant DCAT catalog feeds (DCAT 2/3, DCAT-AP, DCAT-US, national profiles) in JSON-LD, Turtle, and RDF/XML at build, with autodiscovery and per-profile conformance checking. Use when a portal needs to be harvested by data.europa.eu, data.gov, or a national open-data catalog.

Why use Portaljs Add Dcat on TypingMind?

Because you install it once and use it with any model. Portaljs Add Dcat is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Portaljs Add Dcat in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datopian/portaljs/tree/main/skills/portaljs-add-dcat. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Portaljs Add Dcat?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Portaljs Add Dcat?

As many as you like. As long as a model supports skills, you can use Portaljs Add Dcat with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Portaljs Add Dcat AI skill free?

Yes. It is published on GitHub by datopian under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Related AI Skills

View all
mattpocock logo

Triage

mattpocock
CommunityPopular

Move issues and external PRs through a state machine of triage roles, categorise, verify, grill if needed, and write agent-ready briefs.

22.3K
264.4K
3 files
View
mattpocock logo

Claude Handoff

mattpocock
CommunityPopular

Hand the current conversation off to a fresh background agent that picks up the work immediately.

22.3K
264.4K
1 files
View
obra logo

Finishing A Development Branch

obra
CommunityPopular

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

25.8K
288.1K
Instructions
View
obra logo

Receiving Code Review

obra
CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

25.8K
288.1K
Instructions
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇