AWS SQS message queue service for decoupled architectures. Use when creating queues, configuring dead-letter queues, managing visibility timeouts, implementing FIFO ordering, or integrating with Lambda.
npx skills add https://github.com/itsmostafa/aws-agent-skills --skill sqs
Amazon Simple Queue Service (SQS) is a fully managed message queuing service for decoupling and scaling microservices, distributed systems, and serverless applications.
| Type | Description | Use Case |
|------|-------------|----------|
| Standard | At-least-once, best-effort ordering | High throughput |
| FIFO | Exactly-once, strict ordering | Order-sensitive processing |
| Setting | Description | Default |
|---------|-------------|---------|
| Visibility Timeout | Time message is hidden after receive | 30 seconds |
| Message Retention | How long messages are kept | 4 days (max 14) |
| Delay Seconds | Delay before message is available | 0 |
| Max Message Size | Maximum message size | 256 KB |
Queue for messages that failed processing after maxReceiveCount attempts.
AWS CLI:
aws sqs create-queue \
--queue-name my-queue \
--attributes '{
"VisibilityTimeout": "60",
"MessageRetentionPeriod": "604800",
"ReceiveMessageWaitTimeSeconds": "20"
}'
boto3:
import boto3
sqs = boto3.client('sqs')
response = sqs.create_queue(
QueueName='my-queue',
Attributes={
'VisibilityTimeout': '60',
'MessageRetentionPeriod': '604800',
'ReceiveMessageWaitTimeSeconds': '20' # Long polling
}
)
queue_url = response['QueueUrl']
aws sqs create-queue \
--queue-name my-queue.fifo \
--attributes '{
"FifoQueue": "true",
"ContentBasedDeduplication": "true"
}'
# Create DLQ
aws sqs create-queue --queue-name my-queue-dlq
# Get DLQ ARN
DLQ_ARN=$(aws sqs get-queue-attributes \
--queue-url https://sqs.us-east-1.amazonaws.com/123456789012/my-queue-dlq \
--attribute-names QueueArn \
--query 'Attributes.QueueArn' --output text)
# Set redrive policy on main queue
aws sqs set-queue-attributes \
--queue-url https://sqs.us-east-1.amazonaws.com/123456789012/my-queue \
--attributes "{
\"RedrivePolicy\": \"{\\\"deadLetterTargetArn\\\":\\\"${DLQ_ARN}\\\",\\\"maxReceiveCount\\\":\\\"3\\\"}\"
}"
import boto3
import json
sqs = boto3.client('sqs')
queue_url = 'https://sqs.us-east-1.amazonaws.com/123456789012/my-queue'
# Send single message
sqs.send_message(
QueueUrl=queue_url,
MessageBody=json.dumps({'order_id': '12345', 'action': 'process'}),
MessageAttributes={
'MessageType': {
'DataType': 'String',
'StringValue': 'Order'
}
}
)
# Send to FIFO queue
sqs.send_message(
QueueUrl='https://sqs.us-east-1.amazonaws.com/123456789012/my-queue.fifo',
MessageBody=json.dumps({'order_id': '12345'}),
MessageGroupId='order-12345',
MessageDeduplicationId='unique-id-12345'
)
# Batch send (up to 10 messages)
sqs.send_message_batch(
QueueUrl=queue_url,
Entries=[
{'Id': '1', 'MessageBody': json.dumps({'id': 1})},
{'Id': '2', 'MessageBody': json.dumps({'id': 2})},
{'Id': '3', 'MessageBody': json.dumps({'id': 3})}
]
)
import boto3
import json
sqs = boto3.client('sqs')
queue_url = 'https://sqs.us-east-1.amazonaws.com/123456789012/my-queue'
while True:
# Long polling (wait up to 20 seconds)
response = sqs.receive_message(
QueueUrl=queue_url,
MaxNumberOfMessages=10,
WaitTimeSeconds=20,
MessageAttributeNames=['All'],
AttributeNames=['All']
)
messages = response.get('Messages', [])
for message in messages:
try:
body = json.loads(message['Body'])
print(f"Processing: {body}")
# Process message...
# Delete on success
sqs.delete_message(
QueueUrl=queue_url,
ReceiptHandle=message['ReceiptHandle']
)
except Exception as e:
print(f"Error processing message: {e}")
# Message will become visible again after visibility timeout
# Create event source mapping
aws lambda create-event-source-mapping \
--function-name my-function \
--event-source-arn arn:aws:sqs:us-east-1:123456789012:my-queue \
--batch-size 10 \
--maximum-batching-window-in-seconds 5
Lambda handler:
def handler(event, context):
for record in event['Records']:
body = json.loads(record['body'])
message_id = record['messageId']
try:
process_message(body)
except Exception as e:
# Raise to put message back in queue
raise
return {'batchItemFailures': []}
| Command | Description |
|---------|-------------|
| aws sqs create-queue | Create queue |
| aws sqs delete-queue | Delete queue |
| aws sqs list-queues | List queues |
| aws sqs get-queue-url | Get queue URL by name |
| aws sqs get-queue-attributes | Get queue settings |
| aws sqs set-queue-attributes | Update queue settings |
| Command | Description |
|---------|-------------|
| aws sqs send-message | Send single message |
| aws sqs send-message-batch | Send up to 10 messages |
| aws sqs receive-message | Receive messages |
| aws sqs delete-message | Delete message |
| aws sqs delete-message-batch | Delete up to 10 messages |
| aws sqs purge-queue | Delete all messages |
| Command | Description |
|---------|-------------|
| aws sqs change-message-visibility | Change timeout |
| aws sqs change-message-visibility-batch | Batch change |
Causes:
Debug:
# Check queue attributes
aws sqs get-queue-attributes \
--queue-url $QUEUE_URL \
--attribute-names All
# Check approximate message counts
aws sqs get-queue-attributes \
--queue-url $QUEUE_URL \
--attribute-names \
ApproximateNumberOfMessages,\
ApproximateNumberOfMessagesNotVisible,\
ApproximateNumberOfMessagesDelayed
Causes:
Redrive from DLQ:
# Enable redrive allow policy on source queue
aws sqs set-queue-attributes \
--queue-url $MAIN_QUEUE_URL \
--attributes '{"RedriveAllowPolicy": "{\"redrivePermission\":\"allowAll\"}"}'
# Start redrive
aws sqs start-message-move-task \
--source-arn arn:aws:sqs:us-east-1:123456789012:my-queue-dlq \
--destination-arn arn:aws:sqs:us-east-1:123456789012:my-queue
Solutions:
# Check event source mapping
aws lambda list-event-source-mappings \
--function-name my-function
# Check for errors
aws lambda get-event-source-mapping \
--uuid <mapping-uuid>
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 itsmostafa/sqs 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.