Deploys and manages Laravel applications on Laravel Cloud using the `cloud` CLI. Use when the user wants to deploy an app, ship to cloud, create/manage environments, databases, caches, domains, instances, background processes, check billing/usage/spend, or any Laravel Cloud infrastructure. Triggers on deploy, ship, cloud management, environment setup, database provisioning, billing/usage queries, and similar cloud operations.
npx skills add https://github.com/laravel/agent-skills --skill deploying-laravel-cloud
composer global require laravel/cloud-cli
cloud auth -n
Commands follow a CRUD pattern: resource:list, resource:get, resource:create, resource:update, resource:delete.
Available resources: application, environment, instance, database-cluster, database, cache, bucket, domain, websocket-cluster, background-process, command, deployment.
Some resources have additional commands (e.g., domain:verify, database:open, instance:sizes, cache:types). Discover these via cloud -h.
Never hardcode command signatures. Always run cloud <command> -h to discover options at runtime.
Always add -n to every command — prevents the CLI from hanging.
Never use -q or --silent — they suppress all output.
Flag combos per operation:
:list, :get) → --json -n:create) → --json -n:update) → --json -n --force:delete) → -n --force (no --json)-n --force-n with all options passed explicitly (no --json)Determine the task and follow the matching path:
First deploy? → cloud ship -n (discover options via cloud ship -h)
Existing app? →
cloud repo:config
cloud deploy {app_name} {environment} -n --open
cloud deploy:monitor -n
Environment variables? → cloud environment:variables -n --force
Provision infrastructure? → cloud <resource>:create --json -n
Custom domain? → cloud domain:create --json -n then cloud domain:verify -n
For multi-step operations, see reference/checklists.md.
Not sure what the user needs? → ask them before running anything.
:list --json -n or :get --json -ncloud auth -nAlways run cloud deploy:monitor -n after every deploy. If it fails, show the user what went wrong before attempting a fix.
Delegate high-output operations to subagents (using the Task tool) to keep the main context window small. Only the summary comes back — verbose output stays in the subagent's context.
Delegate these to a subagent:
cloud deploy:monitor -n — deployment logs can be very longcloud deployment:get --json -n — full deployment detailscloud <resource>:list --json -n — listing many resources produces large JSONWebFetchKeep in the main context:
:create, :delete, :update — output is smallcloud deploy -n — you need the deployment ID immediatelyFollow exact steps:
Use your judgment:
Run PHP code directly in a Cloud environment:
cloud tinker {environment} --code='Your PHP code here' --timeout=60 -n
--code — PHP code to execute (required in non-interactive mode)--timeout — max seconds to wait for output (default: 60)The code must explicitly output results using echo, dump, or similar — expressions alone produce no output.
Always pass --code and -n to avoid interactive prompts.
Run shell commands on a Cloud environment:
cloud command:run {environment} --cmd='your command here' -n
--cmd — the command to run (required in non-interactive mode)--no-monitor — skip real-time output streaming--copy-output — copy output to clipboardReview past commands:
cloud command:list {environment} --json -n — list command historycloud command:get {commandId} --json -n — get details and output of a specific commandDelegate command:run to a subagent when output may be long.
View billing and usage for the current organization:
cloud usage --json -n
--period=current|previous|1|2|3 — billing period (default current; 1/2/3 are N periods back, max 3). Anything else errors out.--environment=<id> — filter usage to a single environment--detailed — include per-application, per-resource, and per-add-on breakdowns--json — machine-readable output (always pair with -n)Common queries:
cloud usage --json -n | jq '.currentSpendCents'cloud usage --period=previous --json -ncloud usage --environment=<id> --detailed --json -nAll amounts are in cents. Keys are camelCase at every level (e.g. currentSpendCents, bandwidth.allowanceBytes, databases[].totalCents, applications[].totalCostCents).
Delegate --detailed --json to a subagent — the payload includes every database, cache, bucket, websocket, and application and can get large.
~/.config/cloud/config.json — auth tokens and preferences.cloud/config.json — app and environment defaults (set by cloud repo:config)Laravel Cloud Docs: https://cloud.laravel.com/docs/llms.txt
When the user asks how something works or needs an explanation of a Laravel Cloud feature, fetch the docs from the URL above using WebFetch and use it to provide accurate answers.
cloud <command> -h for any command's optionscloud -h to discover commandsAssess 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 laravel/deploying-laravel-cloud 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.