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.
npx skills add https://github.com/datopian/portaljs --skill portaljs-deploy
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.
package.json that lists next as a dependency.fetch).curl and tar available for packaging and upload.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.
git lfs pull beforedeploying — large datasets are served from Cloudflare R2 via absolute URLs in
datasets.json, not copied into the export.
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:
.) and slug (default from package.jsonname or directory name, slugified). Confirm the directory is a Next.js project; reject
reserved slugs (www, api, admin, staging, arc).
PORTALJS_TOKEN, else ~/.portaljs/credentials; ifmissing, run the device-authorization sign-in flow and save the returned token.
next.config.js sets output: 'export' and images: { unoptimized: true },then run npm run build; stop if the build fails.
npm run check-export (orscripts/check-export.mjs) to catch Git LFS pointer leaks and oversized data files.
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.
next.config.js — adds output: 'export' andimages: { unoptimized: true } when absent, preserving the rest of the config.
~/.portaljs/credentials (mode 0600).npm run build exits 0, out/index.html exists, the export-hygienecheck passes.
https://SLUG.arc.portaljs.com; re-running updatesthe same slug in place.
| 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. |
/portaljs-deploy
/portaljs-deploy --slug my-open-data
export PORTALJS_TOKEN=arc_live_xxxxxxxx
/portaljs-deploy ./portals/city-budget --slug city-budget
.claude/commands/portaljs-deploy.mdreferences/reference.mdportaljs-new-portal, portaljs-add-dataset, portaljs-connect-ckanAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take datopian/portaljs-deploy from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.