Deploying Expo apps to iOS App Store, Android Play Store, web hosting, and API routes
npx skills add https://github.com/openai/plugins --skill expo-deployment
This skill covers deploying Expo applications across all platforms using EAS (Expo Application Services).
Consult these resources as needed:
npm install -g eas-cli
eas login
npx eas-cli@latest init
This creates eas.json with build profiles.
# iOS App Store build
npx eas-cli@latest build -p ios --profile production
# Android Play Store build
npx eas-cli@latest build -p android --profile production
# Both platforms
npx eas-cli@latest build --profile production
# iOS: Build and submit to App Store Connect
npx eas-cli@latest build -p ios --profile production --submit
# Android: Build and submit to Play Store
npx eas-cli@latest build -p android --profile production --submit
# Shortcut for iOS TestFlight
npx testflight
Deploy web apps using EAS Hosting:
# Deploy to production
npx expo export -p web
npx eas-cli@latest deploy --prod
# Deploy PR preview
npx eas-cli@latest deploy
Standard eas.json for production deployments:
{
"cli": {
"version": ">= 16.0.1",
"appVersionSource": "remote"
},
"build": {
"production": {
"autoIncrement": true,
"ios": {
"resourceClass": "m-medium"
}
},
"development": {
"developmentClient": true,
"distribution": "internal"
}
},
"submit": {
"production": {
"ios": {
"appleId": "[email protected]",
"ascAppId": "1234567890"
},
"android": {
"serviceAccountKeyPath": "./google-service-account.json",
"track": "internal"
}
}
}
}
npx testflight for quick TestFlight submissionseas credentialsUse EAS Workflows for CI/CD:
# .eas/workflows/release.yml
name: Release
on:
push:
branches: [main]
jobs:
build-ios:
type: build
params:
platform: ios
profile: production
submit-ios:
type: submit
needs: [build-ios]
params:
platform: ios
profile: production
See ./reference/workflows.md for more workflow examples.
EAS manages version numbers automatically with appVersionSource: "remote":
# Check current versions
eas build:version:get
# Manually set version
eas build:version:set -p ios --build-number 42
# List recent builds
eas build:list
# Check build status
eas build:view
# View submission status
eas submit:list
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).
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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 openai/expo-deployment 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.
The instructions reference npm, npx.
Without those the skill loads but fails at the first command.