EAS service (paid). Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.
npx skills add https://github.com/expo/skills --skill eas-workflows
> EAS service - costs apply. EAS Workflows run on Expo Application Services, a paid product with free-tier limits. Each workflow job consumes your plan's build/compute minutes, and jobs that build or submit also need paid Apple Developer and Google Play accounts. Review https://expo.dev/pricing before triggering runs.
Help developers write and edit EAS CI/CD workflow YAML files.
Fetch these resources before generating or validating workflow files. First resolve this skill's directory, then use the fetch script in its scripts/ directory. It is implemented using Node.js and caches responses using ETags for efficiency:
# Fetch resources
node <skill-dir>/scripts/fetch.js <url>
Do not rely on memorized values; these resources evolve as new features are added.
Workflows live in .eas/workflows/*.yml (or .yaml).
A workflow file has these top-level keys:
name — Display name for the workflowon — Triggers that start the workflow (at least one required)jobs — Job definitions (required)defaults — Shared defaults for all jobsconcurrency — Control parallel workflow runsConsult the schema for the full specification of each section.
Use ${{ }} syntax for dynamic values. The schema defines available contexts:
github.* — GitHub repository and event informationinputs.* — Values from workflow_dispatch inputsneeds.* — Outputs and status from dependent jobsjobs.* — Job outputs (alternative syntax)steps.* — Step outputs within custom jobsworkflow.* — Workflow metadataWhen generating or editing workflows:
needs and after exist in the workflowif conditions respect the schema's length constraintsAfter generating or editing a workflow file, validate it against the schema:
# Install dependencies if missing
[ -d "<skill-dir>/scripts/node_modules" ] || npm install --prefix <skill-dir>/scripts
node <skill-dir>/scripts/validate.js <workflow.yml> [workflow2.yml ...]
The validator fetches the latest schema and checks the YAML structure. Fix any reported errors before considering the workflow complete.
When users ask about available options (job types, triggers, runner types, etc.), fetch the schema and derive the answer from it rather than relying on potentially outdated information.
If you encounter errors, misleading or outdated information in this skill, report it so Expo can improve:
npx --yes submit-expo-feedback@latest --category skills --subject "eas-workflows" "<actionable feedback>"
Only submit when you have something specific and actionable to report. Include as much relevant context as possible.
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 expo/eas-workflows 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.