GCP cloud resources including Compute Engine, GKE, Cloud Run, Pub/Sub, VPC networking, DNS, IAM, Secret Manager, and monitoring. Monitor GCP infrastructure, analyze resource usage, audit security posture, and manage organizational hierarchy across projects and folders.
npx skills add https://github.com/Dynatrace/dynatrace-for-ai --skill dt-obs-gcp
Monitor and analyze GCP resources using Dynatrace Smartscape and DQL. Query GCP services, manage organizational hierarchy, audit security posture, and track resource ownership across your GCP infrastructure.
Use this skill when the user needs to work with GCP resources in Dynatrace. Load the reference file for the task type:
| Task | File to load |
|---|---|
| Inventory and topology queries | (no additional file — use core patterns above) |
| Compute Engine instances, machine types, IP addresses | Load references/compute-instances.md |
| GKE clusters, node pools, pods, deployments, services, RBAC | Load references/kubernetes-gke.md |
| Cloud Run services, revisions, executions | Load references/serverless-containers.md |
| VPC networks, subnets, routes, DNS records | Load references/networking-dns.md |
| Pub/Sub topics | Load references/messaging-pubsub.md |
| IAM service accounts, roles, Secret Manager | Load references/iam-security.md |
| Monitoring dashboards, logging, saved queries | Load references/monitoring-logging.md |
| GCP projects, regions, organizational hierarchy | Load references/resource-management.md |
| Resource ownership, GCP labels, organizational structure | Load references/resource-ownership.md |
GCP resources use the GCP_* prefix and can be queried using the smartscapeNodes function. All GCP entities are automatically discovered and modeled in Dynatrace Smartscape.
Compute: GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE, GCP_COMPUTE_GOOGLEAPIS_COM_ADDRESS
Networking: GCP_COMPUTE_GOOGLEAPIS_COM_NETWORK, GCP_COMPUTE_GOOGLEAPIS_COM_SUBNETWORK, GCP_COMPUTE_GOOGLEAPIS_COM_ROUTE, GCP_DNS_GOOGLEAPIS_COM_RESOURCERECORDSET
Kubernetes (GKE): GCP_K8S_IO_POD, GCP_K8S_IO_NODE, GCP_K8S_IO_SERVICE, GCP_K8S_IO_SERVICEACCOUNT, GCP_K8S_IO_PERSISTENTVOLUMECLAIM, GCP_APPS_K8S_IO_DEPLOYMENT, GCP_APPS_K8S_IO_STATEFULSET, GCP_CONTAINER_GOOGLEAPIS_COM_NODEPOOL, GCP_RBAC_AUTHORIZATION_K8S_IO_CLUSTERROLEBINDING, GCP_RBAC_AUTHORIZATION_K8S_IO_ROLEBINDING
Serverless: GCP_RUN_GOOGLEAPIS_COM_SERVICE, GCP_RUN_GOOGLEAPIS_COM_REVISION, GCP_RUN_GOOGLEAPIS_COM_EXECUTION
IAM & Security: GCP_IAM_GOOGLEAPIS_COM_SERVICEACCOUNT, GCP_IAM_GOOGLEAPIS_COM_ROLE, GCP_SECRETMANAGER_GOOGLEAPIS_COM_SECRETVERSION
Messaging: GCP_PUBSUB_GOOGLEAPIS_COM_TOPIC
Monitoring: GCP_MONITORING_GOOGLEAPIS_COM_DASHBOARD, GCP_LOGGING_GOOGLEAPIS_COM_SAVEDQUERY
Infrastructure: GCP_REGION
All GCP entities include:
gcp.project.id — GCP project identifiergcp.region — GCP region (e.g., us-central1)gcp.zone — GCP zone (e.g., us-central1-a)gcp.organization.id — GCP organization identifiergcp.resource.name — Resource namegcp.resource.type — Resource type identifiergcp.asset.type — GCP asset typegcp.object — JSON blob containing full resource configurationGCP resources are organized in a hierarchy:
gcp.organization.id)gcp.project.id)gcp.region, gcp.zone)GCP entity types follow the pattern GCP_<SERVICE_API>_<RESOURCE>:
compute.googleapis.com → COMPUTE_GOOGLEAPIS_COM)INSTANCE, NETWORK)Examples:
GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE — Compute Engine VMGCP_K8S_IO_POD — GKE podGCP_RUN_GOOGLEAPIS_COM_SERVICE — Cloud Run serviceAll GCP queries build on four core patterns. Master these and adapt them to any entity type.
List resources by type, filter by project/region/zone, summarize counts:
smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| fields name, gcp.project.id, gcp.region, gcp.zone, gcp.resource.name
To list all GCP resource types, replace with "GCP_*" and add | summarize count = count(), by: {type} | sort count desc. Add filters like | filter gcp.project.id == "<PROJECT_ID>" or | filter gcp.region == "<REGION>" to scope results.
Parse gcp.object JSON for detailed configuration fields:
smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| parse gcp.object, "JSON:gcpjson"
| fieldsAdd machineType = gcpjson[configuration][resource][machineType],
status = gcpjson[configuration][resource][status]
| fields name, gcp.project.id, machineType, status
GCP configuration fields are nested under gcpjson[configuration][resource][...] for primary resource attributes and gcpjson[configuration][additionalAttributes][...] for extended properties.
Follow relationships between resources:
smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| traverse "*", "GCP_COMPUTE_GOOGLEAPIS_COM_SUBNETWORK"
| fields name, gcp.project.id
GCP entities use "*" as the relationship name in traversals because GCP entities do not have named relationship types. Use fieldsKeep to carry fields through traversals and dt.traverse.history[-N] to access ancestor fields.
Group resources by GCP labels for ownership and organizational tracking:
smartscapeNodes "GCP_*"
| filter isNotNull(`tags:gcp_labels`)
| fields name, gcp.project.id, `tags:gcp_labels`
GCP labels are exposed via the tags:gcp_labels field and must be accessed using backtick syntax. Replace "GCP_*" with a specific type to scope to one service.
Load reference files for detailed queries when the core patterns above need service-specific adaptation.
| Reference | When to load | Key content |
|---|---|---|
| compute-instances.md | Compute Engine VMs, machine types, IP addresses, disks | Instance inventory, machine type distribution, status checks |
| kubernetes-gke.md | GKE clusters, node pools, pods, deployments, services, RBAC | Cluster topology, workload distribution, RBAC bindings |
| serverless-containers.md | Cloud Run services, revisions, executions | Service inventory, revision tracking, execution analysis |
| networking-dns.md | VPC networks, subnets, routes, DNS records | Network topology, subnet analysis, route tables, DNS record sets |
| messaging-pubsub.md | Pub/Sub topics | Topic inventory, messaging topology |
| iam-security.md | IAM service accounts, roles, Secret Manager | Service account audit, role analysis, secret version tracking |
| monitoring-logging.md | Monitoring dashboards, logging, saved queries | Dashboard inventory, saved query analysis |
| resource-management.md | GCP projects, regions, organizational hierarchy | Project inventory, region distribution, hierarchy mapping |
| resource-ownership.md | Resource ownership, GCP labels, organizational structure | Label-based grouping, project-level summaries, chargeback |
gcp.object with JSON parser: parse gcp.object, "JSON:gcpjson"gcpjson[configuration][resource][...]gcpjson[configuration][additionalAttributes][...]isNotNull()gcp.project.id as the primary scoping filtergcp.organization.id for cross-project queriesgcp.region and gcp.zone for location-based analysisGCP_<SERVICE_API>_<RESOURCE> format"GCP_*" wildcards when possible) tags:gcp_labels isNotNull(tags:gcp_labels) for label-based filtering"*" as the relationship name — GCP entities do not have named relationship typesfieldsKeep to maintain important fields through traversaldt.traverse.history[-N] tags:gcp_labels "*" for relationship traversal (no named relationship types)parse gcp.object, "JSON:gcpjson"gcpjson[configuration][resource][...] (differs from AWS pattern)isNotNull() and isNull() for graceful null handlingcountDistinct() for unique resource counts| limit N during explorationAssess 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-gcp 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.