Guide for building, configuring, and deploying microfrontends on Vercel. Use this skill when the user mentions microfrontends, multi-zones, splitting an app across teams, independent deployments, cross-app routing, incremental migration, composing multiple frontends under one domain, microfrontends.json, @vercel/microfrontends, the microfrontends local proxy, or path-based routing between Vercel projects. Also use when the user asks about shared layouts across projects, navigation between microfrontends, fallback environments, asset prefixes, or feature flag controlled routing.
npx skills add https://github.com/vercel/vercel-plugin --skill microfrontends
Split a large application into independently deployable units that render as one cohesive app. Vercel handles routing on its global network using microfrontends.json.
Core concepts: default app (has microfrontends.json, serves unmatched requests) · child apps (have routing path patterns) · asset prefix (prevents static-asset collisions) · independent deployments.
Frameworks: Next.js (App Router + Pages Router), SvelteKit, React Router, Vite — all via @vercel/microfrontends.
CLI (vercel microfrontends / vercel mf):
create-group — create a new group; interactive by default, or fully non-interactive with --non-interactive (options: --name, --project (repeatable), --default-app, --default-route, --project-default-route (repeatable, format: <project>=<route>, required for each non-default project in non-interactive mode), --yes to skip confirmation prompt); note: --non-interactive is blocked if adding the projects would exceed the free tier limit — the user must confirm billing changes interactivelyadd-to-group — add the current project to an existing group; requires interactive terminal (options: --group, --default-route)remove-from-group — remove the current project from its group; requires interactive terminal (option: --yes skips project-link prompt only)delete-group — delete a group and all its settings, irreversible; requires interactive terminal (option: --group to pre-select group)inspect-group — retrieve group metadata (project names, frameworks, git repos, root dirs); useful for automating setup (options: --group, --format=json, --config-file-name)pull — pull remote microfrontends.json for local development (option: --dpl)microfrontends proxy — local dev proxy · microfrontends port — print auto-assigned portThis skill includes detailed reference docs in the references/ directory. Do not read all references upfront. Instead, search or grep the relevant file when the user asks about a specific topic:
| Topic | Reference file |
|---|---|
| Getting started, quickstart, framework setup, microfrontends.json schema, fields, naming, examples | references/configuration.md |
| Path expressions, asset prefixes, flag-controlled routing, middleware | references/path-routing.md |
| Local proxy setup, polyrepo config, Turborepo, ports, deployment protection | references/local-development.md |
| Inspecting groups (inspect-group), adding/removing projects, fallback environments, navigation, observability | references/managing-microfrontends.md |
| Testing utilities (validateMiddlewareConfig, validateRouting, etc.), debug headers, common issues | references/troubleshooting.md |
| Deployment protection, Vercel Firewall, WAF rules for microfrontends | references/security.md |
When the user asks about a specific topic, use grep or search over the relevant reference file to find the answer without loading all references into context.
Assess 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 vercel/microfrontends 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.