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Portaljs New Portal

OrganizationPopular
datopian
portaljs-new-portal

Scaffold a new PortalJS data portal from a brief. Copies the canonical template from examples/portaljs-catalog and substitutes project tokens. Use when starting a brand-new data portal project from scratch.

Overview

Publisherdatopian
Repositoryportaljs
Skill nameportaljs-new-portal
Stars
2.4K
Forks
332
Bundled files
1
LicenseMIT
Links
  • 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 New Portal 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-new-portal .claude/skills/portaljs-new-portal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Portaljs New Portal 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 New Portal 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 New Portal 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 — New Portal

Overview

Scaffold a production-ready PortalJS data portal from a brief. The skill is interactive: if the brief is thin it interviews the user in three short rounds (mapped to the template's three surfaces — Home, Catalog, Showcase), echoes a brief back for confirmation, then copies examples/portaljs-catalog (locally or via a remote tiged fetch), substitutes placeholder tokens, sets the namespace mode, seeds any datasets named in the interview, installs dependencies, and verifies the scaffold with a type check.

Prerequisites

  • Node.js >=22 and npm available on PATH.
  • Network access, unless a current local checkout of the portaljs repo is available (the resolver defaults to a remote fetch of the template).
  • A destination directory name that does not already contain files (or user consent to overwrite one that does).

Instructions

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

  1. Interview the user in up to three rounds — Home/basics, Catalog & discovery (datasets, namespace mode theme vs owner), Showcase/views — skipping any round already answered by the input brief. Accept "use defaults" at any point.
  2. Confirm a short brief (name, slug, description, namespace, datasets, views) before building.
  3. Resolve the template source: prefer a remote tiged fetch of examples/portaljs-catalog at main (or PORTALJS_TEMPLATE_REF); use a local checkout only when it is current (has pages/[owner]/[slug].tsx) and the destination is outside that repo.
  4. Materialize the template into ./PROJECT_SLUG, asking first if the destination already exists and is non-empty.
  5. Substitute __PROJECT_NAME__, __PROJECT_SLUG__, __DESCRIPTION__ tokens across all files with perl -pi, escaping /, \, and & in the values.
  6. Set NAMESPACE_TYPE ('theme' or 'owner') in lib/datasets.ts per the interview.
  7. Seed datasets captured in Round 2 (via /portaljs-add-dataset or by hand), or clear datasets.json to [] if none were named.
  8. Run npm install inside the scaffolded portal.
  9. Verify with npx tsc --noEmit (never next build here — it would corrupt a running dev server's .next/ directory).
  10. Report the scaffolded routes, namespace mode, and next steps.

Output

  • Created: a new directory ./PROJECT_SLUG/ containing the full examples/portaljs-catalog template with tokens substituted.
  • Modified: lib/datasets.ts (NAMESPACE_TYPE); datasets.json (seeded datasets or cleared to []).
  • Verified: npx tsc --noEmit passes inside the scaffolded portal.
  • Result: a runnable portal at ./PROJECT_SLUG with Home (/), Catalog (/search), and Showcase (/@<namespace>/<slug>) surfaces wired up.

Error Handling

SymptomCauseFix
DIR_EXISTS./PROJECT_SLUG already exists and is non-emptyAsk the user for a different name or consent to remove it; then proceed.
Remote fetch failsBad PORTALJS_TEMPLATE_REF, network outage, or tiged unavailableTell the user plainly and ask to retry, use a different ref, or check network.
Stale local scaffold (pages/datasets/[slug].tsx)Old local clone missing pages/[owner]/[slug].tsxResolver already falls back to remote in this case — do not force local mode.
npm install failsNode <22 or no networkReport the error and ask the user to check Node version and connectivity.
tsc --noEmit failsToken substitution or manifest errorPrint the log and fix before reporting success — never report success with a failing type check.

Examples

Example 1 — Full brief up front

/portaljs-new-portal Auckland Open Data Portal — datasets published by several council
departments (multiple publishers). Start with ./data/parks.csv and ./data/budget.csv.

Infers name + description, picks NAMESPACE_TYPE = 'owner', asks for namespace values (e.g. parks-dept, finance), confirms the brief, scaffolds the template, and seeds both datasets at /@parks-dept/parks and /@finance/budget.

Example 2 — No arguments, full interview

/portaljs-new-portal

Runs all three interview rounds from scratch, accepting "use defaults" for any round, then confirms the brief before scaffolding.

Example 3 — Single-publisher portal with no datasets yet

/portaljs-new-portal Reference Data Hub — a single-team reference catalog, no data yet.

Picks NAMESPACE_TYPE = 'theme' with namespace reference, clears datasets.json to [], and reports /portaljs-add-dataset as the next step.

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 New Portal AI skill do?

Scaffold a new PortalJS data portal from a brief. Copies the canonical template from examples/portaljs-catalog and substitutes project tokens. Use when starting a brand-new data portal project from scratch.

Why use Portaljs New Portal on TypingMind?

Because you install it once and use it with any model. Portaljs New Portal 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 New Portal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datopian/portaljs/tree/main/skills/portaljs-new-portal. 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 New Portal?

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 New Portal?

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

Is the Portaljs New Portal 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.

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