>- AWS cloud resource monitoring including EC2, RDS, Lambda, ECS/EKS, VPC networking, load balancers, S3, DynamoDB, SQS/SNS, and cost optimization. Use when analyzing AWS infrastructure, resource inventory, security compliance, capacity planning, or cost savings. "Lambda functions", "ECS services", "security groups", "unattached EBS volumes", "AWS load balancer topology", "publicly accessible databases", "AWS dashboards". Do NOT use for explaining existing queries, product documentation questions, generic host CPU/memory metrics (use dt-obs-hosts), application-level tracing (use dt-obs-tracing), or log analysis (use dt-obs-logs).
npx skills add https://github.com/Dynatrace/dynatrace-for-ai --skill dt-obs-aws
Monitor and analyze AWS resources using Dynatrace Smartscape and DQL. Query AWS services, optimize costs, manage security, and plan capacity across your AWS infrastructure.
Use this skill when the user needs to work with AWS resources in Dynatrace. Load the reference file for the task type:
AWS resources use the AWS_* prefix and can be queried using the smartscapeNodes function. All AWS entities are automatically discovered and modeled in Dynatrace Smartscape.
Compute: AWS_EC2_INSTANCE, AWS_LAMBDA_FUNCTION, AWS_ECS_CLUSTER, AWS_ECS_SERVICE, AWS_EKS_CLUSTER
Networking: AWS_EC2_VPC, AWS_EC2_SUBNET, AWS_EC2_SECURITYGROUP, AWS_EC2_NATGATEWAY, AWS_EC2_VPCENDPOINT
Database: AWS_RDS_DBINSTANCE, AWS_RDS_DBCLUSTER, AWS_DYNAMODB_TABLE, AWS_ELASTICACHE_CACHECLUSTER
Storage: AWS_S3_BUCKET, AWS_EC2_VOLUME, AWS_EFS_FILESYSTEM
Load Balancing: AWS_ELASTICLOADBALANCINGV2_LOADBALANCER, AWS_ELASTICLOADBALANCINGV2_TARGETGROUP
Messaging: AWS_SQS_QUEUE, AWS_SNS_TOPIC, AWS_EVENTS_EVENTBUS, AWS_MSK_CLUSTER
All AWS entities include:
aws.account.id - AWS account identifieraws.region - AWS region (e.g., us-east-1)aws.resource.id - Unique resource identifieraws.resource.name - Resource nameaws.arn - Amazon Resource Nameaws.vpc.id - VPC identifier (for VPC-attached resources)aws.subnet.id - Subnet identifieraws.availability_zone - Availability zoneaws.security_group.id - Security group IDs (array)tags - Resource tags (use tags[TagName])AWS-originated logs (fetch logs) carry these fields — no exploration needed:
aws.region, aws.account.id, aws.service, aws.log_group, aws.log_streamcontent, loglevel, timestamp, k8s.*, dt.smartscape.*AWS-originated bizevents (fetch bizevents) carry:
aws.region, aws.account.id, event.type, event.providerUse filter isNotNull(aws.region) to scope to AWS-originated records.
AWS entities use these relationship types:
is_attached_to - Exclusive attachment (e.g., volume to instance)uses - Dependency relationship (e.g., instance uses security group)runs_on - Vertical relationship (e.g., instance runs on AZ)is_part_of - Composition (e.g., instance in cluster)belongs_to - Aggregation (e.g., service belongs to cluster)balances - Load balancing (e.g., target group balances instances)balanced_by - Inverse load-balancing relationship (e.g., load balancer balanced by target group)Dynatrace ingests AWS CloudWatch metrics using this pattern:
cloud.aws.<service>.<MetricName>.By.<DimensionName>
The <service> is the lowercase AWS service name, <MetricName> is the CloudWatch metric name (case-preserved), and <DimensionName> is the CloudWatch dimension.
Examples: cloud.aws.ec2.CPUUtilization.By.InstanceId, cloud.aws.lambda.Invocations.By.FunctionName, cloud.aws.rds.CPUUtilization.By.DBInstanceIdentifier
Use timeseries, not fetch, for these metrics. Group by dt.smartscape_source.id to split by entity.
→ See references/metrics-performance.md for the complete metric catalog by service with DQL query templates.
Get all AWS resources by type:
smartscapeNodes "AWS_*"
| summarize count = count(), by: {type}
| sort count desc
Filter by account and region:
smartscapeNodes "AWS_*"
| filter aws.account.id == "123456789012" and aws.region == "us-east-1"
| fields type, name, aws.resource.id
Using tags for filtering:
smartscapeNodes "AWS_*"
| filter tags[Environment] == "production"
| summarize count = count(), by: {type, aws.region}
→ For complete resource inventory patterns, see references/resource-management.md
List all VPCs:
smartscapeNodes "AWS_EC2_VPC"
| fields name, aws.account.id, aws.region, aws.vpc.id
Find resources in a VPC:
smartscapeNodes "AWS_*"
| filter aws.vpc.id == "vpc-0be61db7c5d2d1bd1"
| summarize resource_count = count(), by: {type, aws.subnet.id}
| sort resource_count desc
Analyze security group usage:
smartscapeNodes "AWS_EC2_INSTANCE"
| filter contains(aws.security_group.id, "sg-abc123")
| fields name, aws.resource.id, aws.vpc.id, aws.subnet.id
→ For VPC networking, see references/vpc-networking-security.md
→ For security group patterns, see references/security-compliance.md
List all RDS instances:
smartscapeNodes "AWS_RDS_DBINSTANCE"
| fields name, aws.account.id, aws.region, aws.vpc.id, aws.availability_zone
Find Multi-AZ databases:
smartscapeNodes "AWS_RDS_DBINSTANCE"
| parse aws.object, "JSON:awsjson"
| fieldsAdd multiAZ = awsjson[configuration][multiAZ]
| filter multiAZ == true
| fields name, aws.resource.id, aws.region
Group by engine type:
smartscapeNodes "AWS_RDS_DBINSTANCE"
| parse aws.object, "JSON:awsjson"
| fieldsAdd engine = awsjson[configuration][engine]
| summarize db_count = count(), by: {engine, aws.region}
| sort db_count desc
→ For database monitoring, see references/database-monitoring.md
List Lambda functions:
smartscapeNodes "AWS_LAMBDA_FUNCTION"
| fields name, aws.account.id, aws.region, aws.vpc.id
Find ECS services in a cluster:
smartscapeNodes "AWS_ECS_SERVICE"
| traverse "belongs_to", "AWS_ECS_CLUSTER"
| fields name, aws.resource.id, aws.region
List EKS clusters:
smartscapeNodes "AWS_EKS_CLUSTER"
| fields name, aws.account.id, aws.region, aws.vpc.id
→ For serverless, see references/serverless-containers.md
→ For containers, see references/serverless-containers.md
Complete load balancer to instance mapping:
smartscapeNodes "AWS_ELASTICLOADBALANCINGV2_LOADBALANCER"
| parse aws.object, "JSON:awsjson"
| fieldsAdd dnsName = awsjson[configuration][dnsName], scheme = awsjson[configuration][scheme]
| filter scheme == "internet-facing"
| traverse "balanced_by", "AWS_ELASTICLOADBALANCINGV2_TARGETGROUP", direction:backward, fieldsKeep:{dnsName, id}
| fieldsAdd targetGroupName = aws.resource.name
| traverse "balances", "AWS_EC2_INSTANCE", fieldsKeep: {targetGroupName, id}
| fieldsAdd loadBalancerDnsName = dt.traverse.history[-2][dnsName],
loadBalancerId = dt.traverse.history[-2][id],
targetGroupId = dt.traverse.history[-1][id]
→ For load balancing, see references/load-balancing-api.md
Find unattached EBS volumes:
smartscapeNodes "AWS_EC2_VOLUME"
| parse aws.object, "JSON:awsjson"
| fieldsAdd state = awsjson[configuration][state]
| filter state == "available"
| fields name, aws.resource.id, aws.availability_zone, aws.account.id
Analyze EBS costs by type:
smartscapeNodes "AWS_EC2_VOLUME"
| parse aws.object, "JSON:awsjson"
| fieldsAdd volumeType = awsjson[configuration][volumeType],
size = awsjson[configuration][size],
state = awsjson[configuration][state]
| summarize total_volumes = count(), total_size_gb = sum(size), by: {volumeType, state}
| sort total_size_gb desc
→ For cost optimization, see references/cost-optimization.md
Find publicly accessible databases:
smartscapeNodes "AWS_RDS_DBINSTANCE"
| parse aws.object, "JSON:awsjson"
| fieldsAdd publiclyAccessible = awsjson[configuration][publiclyAccessible]
| filter publiclyAccessible == true
| fields name, aws.resource.id, aws.vpc.id, aws.account.id
Security group blast radius:
smartscapeNodes "AWS_EC2_INSTANCE"
| traverse "uses", "AWS_EC2_SECURITYGROUP"
| summarize instance_count = count(), by: {aws.resource.name, aws.vpc.id}
| sort instance_count desc
| limit 20
→ For security, see references/security-compliance.md
Find untagged resources:
smartscapeNodes "AWS_*"
| filter isNull(tags)
| fields type, name, aws.resource.id, aws.account.id, aws.region
Cost allocation by cost center:
smartscapeNodes "AWS_*"
| filter isNotNull(tags[CostCenter])
| summarize resource_count = count(), by: {tags[CostCenter], type}
| sort resource_count desc
→ For resource ownership, see references/resource-ownership.md
| Pattern | Template |
|---------|----------|
| Discovery | smartscapeNodes "AWS_*" \| fieldsAdd <attrs> \| filter <cond> \| summarize <agg> |
| Config parsing | smartscapeNodes "AWS_<T>" \| parse aws.object, "JSON:awsjson" \| fieldsAdd f = awsjson[configuration][field] |
| Traversal | smartscapeNodes "AWS_<SRC>" \| traverse "<rel>", "AWS_<TGT>" |
| Multi-type | smartscapeNodes "AWS_T1", "AWS_T2" \| filter <cond> \| summarize count(), by: {type} |
"AWS_*" wildcards when possible)| limit N for explorationisNotNull() checks before accessing nested fieldsaws.object with JSON parser: parse aws.object, "JSON:awsjson"fieldsAdd configField = awsjson[configuration][field]toString() for complex nested objectscontains() or expandaws.object for detailed security contextpubliclyAccessible, storageEncrypted, and similar flagstags[TagName] for filtering by specific tag valuetags is a JSON object, not an array — use isNull(tags) for untagged resources, never arraySize(tags)isNull(tags[TagName]) to find resources missing a specific tagparse aws.object, "JSON:awsjson"cloud.aws.* naming convention (see AWS Metric Naming Convention)direction:backward for reverse relationships (e.g., target group → load balancer)fieldsKeep to maintain important fields through traversaldt.traverse.history[-N]getNodeName() for human-readable resource namesisNotNull() and isNull()countDistinct() for unique resource countsThis skill uses progressive disclosure. Start here for 80% of use cases. Load reference files for detailed specifications when needed.
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 dynatrace/dt-obs-aws 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.