Portaljs Deploy

OrganizationPopular

datopian

portaljs-deploy

Deploy a PortalJS portal to PortalJS Arc — Datopian-managed static hosting on Cloudflare. Builds a static export, uploads it, and returns a live SLUG.arc.portaljs.com URL. One command, one target. Use when a portal is ready to publish or redeploy to a live URL.

Overview

Publisher	datopian
Repository	`portaljs`
Skill name	`portaljs-deploy`
Stars	2.4K
Forks	332
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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 Deploy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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-deploy .claude/skills/portaljs-deploy

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Portaljs Deploy 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 Deploy 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 Deploy 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 — Deploy

Overview

Publish an existing PortalJS portal to PortalJS Arc — Datopian's managed static hosting on Cloudflare. Build a static export, upload it to the Arc API, and print a live https://SLUG.arc.portaljs.com URL. Re-running redeploys the same portal (idempotent on the slug). This is a single-target skill — it deploys to Arc only. For self-hosting, run npm run build and upload out/ to any static host; no skill required for that path. Arc serves static exports only — SSR is not hosted on Arc yet.

Prerequisites

A PortalJS portal directory with a package.json that lists next as a dependency.
Node 18+ and npm on PATH (the Arc device-login flow uses Node's global fetch).
curl and tar available for packaging and upload.
A PortalJS Arc token — read from PORTALJS_TOKEN, or ~/.portaljs/credentials ({"token":"…"}). If neither exists, the skill signs in on demand via a device-code flow; no manual token copying required.
If the portal stores large data in Git LFS/R2, do not run git lfs pull before deploying — large datasets are served from Cloudflare R2 via absolute URLs in datasets.json, not copied into the export.

Instructions

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

Gather input — portal directory (default .) and slug (default from package.json name or directory name, slugified). Confirm the directory is a Next.js project; reject reserved slugs (www, api, admin, staging, arc).
Resolve the Arc token: read PORTALJS_TOKEN, else ~/.portaljs/credentials; if missing, run the device-authorization sign-in flow and save the returned token.
Ensure next.config.js sets output: 'export' and images: { unoptimized: true }, then run npm run build; stop if the build fails.
Verify the export carries no dataset bytes — run npm run check-export (or scripts/check-export.mjs) to catch Git LFS pointer leaks and oversized data files.
Tar the out/ directory and POST it to $PORTALJS_ARC_API/v1/deploy?slug=<slug> with the bearer token; handle 200/401/409/400/413 responses distinctly.
Report the live URL, file count, upload size, and R2-vs-inline dataset counts.

Output

Modified (if needed): next.config.js — adds output: 'export' and images: { unoptimized: true } when absent, preserving the rest of the config.
Created (on first sign-in): ~/.portaljs/credentials (mode 0600).
Verified: npm run build exits 0, out/index.html exists, the export-hygiene check passes.
Result: the portal is live at https://SLUG.arc.portaljs.com; re-running updates the same slug in place.

Error Handling

Symptom	Cause	Fix
`NOT_A_PORTAL` error	No `next` dependency found in `PORTAL_DIR/package.json`	Run from a valid portal directory, or pass the correct path.
Slug rejected	Derived slug is reserved (`www`, `api`, …) or not a valid DNS label	Pass an explicit `--slug <name>`.
Build fails (non-zero exit)	App/config error surfaced in `npm run build`	Print the log, fix the error, never deploy a failing build.
`check-export` fails	Git LFS pointer leaked into `out/`, or a data file exceeds the size budget	Reference large data by absolute R2 URL via `portaljs-add-dataset`; don't `git lfs pull` before building.
`401` on upload	Token invalid, expired, or revoked	Re-run the device sign-in flow once, retry the upload; stop if it 401s again.
`409` on upload	Slug already taken by another account	Choose a different `--slug`.
`400` / `413` on upload	Malformed slug or export too large	Read the JSON `error` field and address the specific cause.

Examples

Example 1 — Deploy the current directory with the default slug


/portaljs-deploy

Example 2 — Deploy with an explicit slug


/portaljs-deploy --slug my-open-data

Example 3 — Non-interactive deploy from CI with a token env var

bash
export PORTALJS_TOKEN=arc_live_xxxxxxxx
/portaljs-deploy ./portals/city-budget --slug city-budget

Resources

Full workflow: .claude/commands/portaljs-deploy.md
Deploy internals reference: references/reference.md
Related skills: portaljs-new-portal, portaljs-add-dataset, portaljs-connect-ckan
PortalJS Arc dashboard (sign in, manage tokens): https://arc.portaljs.com

Bundled files

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

references/reference.md4 KB

Frequently asked questions

What does the Portaljs Deploy AI skill do?

Why use Portaljs Deploy on TypingMind?

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

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

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 Deploy?

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

Is the Portaljs Deploy 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

PortalJS — Deploy

Overview

Prerequisites

Instructions

Output

Error Handling

Examples

Example 1 — Deploy the current directory with the default slug

Example 2 — Deploy with an explicit slug

Example 3 — Non-interactive deploy from CI with a token env var

Resources

Bundled files

Frequently asked questions

Related AI Skills

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