PortalJS is a framework for building data portals that present searchable datasets through a home page, catalog, and dataset showcase. It is for teams that want to choose a data backend, scaffold a Next.js portal, load CSV or JSON data, connect services such as CKAN, and deploy the result. The catalogue entries are agent commands, skills, instructions, and a plugin that guide portal creation and data loading.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datopian/portaljs --skill portaljs-architectgit clone --depth 1 https://github.com/datopian/portaljsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datopian/portaljs/portaljs-architect)<a href="https://agentmods.dev/skills/datopian/portaljs/portaljs-architect"><img src="https://agentmods.dev/badge/skills/datopian/portaljs/portaljs-architect/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datopian/portaljs/portaljs-architect"><img src="https://agentmods.dev/badge/skills/datopian/portaljs/portaljs-architect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.01550 |
| Opus 5.5 | $0.00024 | $0.00620 |
| Sonnet 5.5 | $0.00012 | $0.00310 |
| Haiku 4.5 | $0.00006 | $0.00155 |
Grade A, and why
portaljs-architect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Parse
$ARGUMENTSfor 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 withdu -shand line counts to ground size guesses. - 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-wideDATA_QUERYmode (flat | duckdb). - Echo the architecture brief (stack, reasoning per slot, deviations from default, deferred items) and wait for confirmation ("go") or corrections.
- On confirmation, persist the brief to
./ARCHITECTURE.mdin the working directory. - 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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 108 lines · 60 tokens per session scan A 0d1b37b22199
portaljs-architect is a skill published in the GitHub repository datopian/portaljs (2,370 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,550 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
data-mesh-expert
Expert-level data mesh architecture, domain-oriented ownership, data products, federated governance, and self-serve platforms. Use when the user mentions architecture, domain driven, data products, governance, or platform, or when the task involves Data Mesh Principles, Domain-Oriented Data Ownership, Data as a…
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
rag-knowledge
Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…
architecture-paradigm-pipeline
Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.
ai-news-intelligence
AI News Intelligence pipeline for ForThePeople.in. Use this skill when building, debugging, or extending the automated news analysis system that reads Indian news, extracts civic data, and updates the district dashboard. Triggers: 'AI intelligence', 'news pipeline', 'Gemini analyzer', 'auto-update from news'…
lightcad
Use when extending the FeiYang LightCAD platform (C#/.NET 8) — implementing ILcPlugin, registering ElementType / ElementActions, [CommandClass] + [CommandMethod] commands, plugins.json loading, DwgReader.Import / SaveDwg workflows; also when a plugin DLL is not loaded, dispatching to a command does nothing, or a build…