Azure Functions, Cosmos DB modeling, Service Bus patterns, Bicep templates
npx skills add https://github.com/vibeeval/vibecosystem --skill azure-patterns
import { app, HttpRequest, HttpResponseInit, InvocationContext } from '@azure/functions';
app.http('getOrder', {
methods: ['GET'],
authLevel: 'function',
route: 'orders/{orderId}',
handler: async (request: HttpRequest, context: InvocationContext): Promise<HttpResponseInit> => {
const orderId = request.params.orderId;
context.log(`Processing order: ${orderId}`);
try {
const order = await orderService.getById(orderId);
if (!order) {
return { status: 404, jsonBody: { error: 'Order not found' } };
}
return { status: 200, jsonBody: order };
} catch (error) {
context.error('Failed to get order', error);
return { status: 500, jsonBody: { error: 'Internal server error' } };
}
},
});
// Service Bus trigger with retry
app.serviceBusTopic('processOrderEvent', {
topicName: 'order-events',
subscriptionName: 'order-processor',
connection: 'ServiceBusConnection',
handler: async (message: unknown, context: InvocationContext) => {
const event = message as OrderEvent;
context.log(`Processing event: ${event.type} for order ${event.orderId}`);
await processEvent(event);
},
});
import * as df from 'durable-functions';
df.app.orchestration('orderWorkflow', function* (context) {
const orderId = context.df.getInput() as string;
// Step 1: Validate order
const order = yield context.df.callActivity('validateOrder', orderId);
// Step 2: Reserve inventory (with retry)
const retryOptions = new df.RetryOptions(5000, 3); // 5s interval, 3 attempts
yield context.df.callActivityWithRetry('reserveInventory', retryOptions, order);
// Step 3: Charge payment
yield context.df.callActivity('chargePayment', order);
// Step 4: Wait for shipping confirmation (with timeout)
const deadline = new Date(context.df.currentUtcDateTime.getTime() + 24 * 60 * 60 * 1000);
const shippingEvent = context.df.waitForExternalEvent('shippingConfirmed');
const timeout = context.df.createTimer(deadline);
const winner = yield context.df.Task.any([shippingEvent, timeout]);
if (winner === timeout) {
yield context.df.callActivity('escalateShipping', orderId);
}
return { orderId, status: 'completed' };
});
// Partition key design: use tenant/user ID for multi-tenant
interface OrderDocument {
id: string; // Unique document ID
partitionKey: string; // = tenantId (good distribution)
type: 'order'; // Discriminator for polymorphic container
customerId: string;
items: OrderItem[]; // Embed frequently accessed together
total: number;
status: string;
createdAt: string;
_etag?: string; // Optimistic concurrency
}
// Optimistic concurrency with ETags
async function updateOrder(order: OrderDocument): Promise<void> {
const container = cosmosClient.database('shop').container('orders');
try {
await container.item(order.id, order.partitionKey).replace(order, {
accessCondition: { type: 'IfMatch', condition: order._etag },
});
} catch (error) {
if (error.code === 412) {
throw new ConflictError('Order was modified by another process');
}
throw error;
}
}
// Change feed processor for event-driven updates
const changeFeedProcessor = cosmosClient
.database('shop')
.container('orders')
.getChangeFeedProcessorBuilder('orderProcessor')
.setFeedProcessorOptions({ startFromBeginning: false, maxItemCount: 25 })
.setLeaseContainer(leaseContainer)
.setProcessChanges(async (changes, context) => {
for (const doc of changes) {
await publishEvent({ type: 'order.updated', data: doc });
}
})
.build();
import { ServiceBusClient, ServiceBusMessage } from '@azure/service-bus';
const client = new ServiceBusClient(process.env.SERVICEBUS_CONNECTION!);
// Send with deduplication and scheduling
async function sendOrderEvent(event: OrderEvent): Promise<void> {
const sender = client.createSender('order-events');
const message: ServiceBusMessage = {
body: event,
messageId: `${event.orderId}-${event.type}-${Date.now()}`, // Dedup key
subject: event.type,
applicationProperties: { priority: event.priority },
timeToLive: 24 * 60 * 60 * 1000, // 24h TTL
};
await sender.sendMessages(message);
await sender.close();
}
// Receive with sessions (ordered processing per entity)
async function processWithSessions(): Promise<void> {
const receiver = client.acceptSession('order-events', 'order-processor', 'session-1');
const messages = await receiver.receiveMessages(10, { maxWaitTimeInMs: 5000 });
for (const msg of messages) {
try {
await handleMessage(msg.body);
await receiver.completeMessage(msg);
} catch {
await receiver.abandonMessage(msg); // Retry via Service Bus
}
}
await receiver.close();
}
@description('Azure Function App with Service Bus and Cosmos DB')
param location string = resourceGroup().location
param appName string
resource cosmosAccount 'Microsoft.DocumentDB/databaseAccounts@2024-05-15' = {
name: '${appName}-cosmos'
location: location
kind: 'GlobalDocumentDB'
properties: {
databaseAccountOfferType: 'Standard'
consistencyPolicy: { defaultConsistencyLevel: 'Session' }
locations: [{ locationName: location, failoverPriority: 0 }]
}
}
resource serviceBus 'Microsoft.ServiceBus/namespaces@2022-10-01-preview' = {
name: '${appName}-bus'
location: location
sku: { name: 'Standard', tier: 'Standard' }
}
resource functionApp 'Microsoft.Web/sites@2023-12-01' = {
name: '${appName}-func'
location: location
kind: 'functionapp,linux'
identity: { type: 'SystemAssigned' }
properties: {
siteConfig: {
appSettings: [
{ name: 'CosmosDBConnection', value: cosmosAccount.listConnectionStrings().connectionStrings[0].connectionString }
{ name: 'ServiceBusConnection', value: listKeys('${serviceBus.id}/AuthorizationRules/RootManageSharedAccessKey', serviceBus.apiVersion).primaryConnectionString }
]
}
}
}
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 vibeeval/azure-patterns 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.