Dt Obs Gcp logo

Dt Obs Gcp

Organization
Dynatrace
dt-obs-gcp

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.

Overview

PublisherDynatrace
Repositorydynatrace-for-ai
Skill namedt-obs-gcp
Stars
156
Forks
30
Bundled files
9
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Dynatrace on GitHub. Read the source before you install it.

Installation

Install the Dt Obs Gcp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dynatrace/dynatrace-for-ai.git /tmp/dynatrace-for-ai
mkdir -p .claude/skills
cp -r /tmp/dynatrace-for-ai/skills/dt-obs-gcp .claude/skills/dt-obs-gcp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dt Obs Gcp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dt Obs Gcp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dt Obs Gcp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GCP Cloud Infrastructure

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.

When to Use This Skill

Use this skill when the user needs to work with GCP resources in Dynatrace. Load the reference file for the task type:

TaskFile to load
Inventory and topology queries(no additional file — use core patterns above)
Compute Engine instances, machine types, IP addressesLoad references/compute-instances.md
GKE clusters, node pools, pods, deployments, services, RBACLoad references/kubernetes-gke.md
Cloud Run services, revisions, executionsLoad references/serverless-containers.md
VPC networks, subnets, routes, DNS recordsLoad references/networking-dns.md
Pub/Sub topicsLoad references/messaging-pubsub.md
IAM service accounts, roles, Secret ManagerLoad references/iam-security.md
Monitoring dashboards, logging, saved queriesLoad references/monitoring-logging.md
GCP projects, regions, organizational hierarchyLoad references/resource-management.md
Resource ownership, GCP labels, organizational structureLoad references/resource-ownership.md

Core Concepts

Entity Types

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

Common GCP Fields

All GCP entities include:

  • gcp.project.id — GCP project identifier
  • gcp.region — GCP region (e.g., us-central1)
  • gcp.zone — GCP zone (e.g., us-central1-a)
  • gcp.organization.id — GCP organization identifier
  • gcp.resource.name — Resource name
  • gcp.resource.type — Resource type identifier
  • gcp.asset.type — GCP asset type
  • gcp.object — JSON blob containing full resource configuration

GCP Organizational Hierarchy

GCP resources are organized in a hierarchy:

  • Organization — Top-level container (gcp.organization.id)
  • Folder — Logical grouping within an organization
  • Project — Resource container (gcp.project.id)
  • Region/Zone — Physical location (gcp.region, gcp.zone)

Entity Naming Convention

GCP entity types follow the pattern GCP_<SERVICE_API>_<RESOURCE>:

  • Service API maps to the Google API domain (e.g., compute.googleapis.comCOMPUTE_GOOGLEAPIS_COM)
  • Resource is the specific resource type (e.g., INSTANCE, NETWORK)

Examples:

  • GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE — Compute Engine VM
  • GCP_K8S_IO_POD — GKE pod
  • GCP_RUN_GOOGLEAPIS_COM_SERVICE — Cloud Run service

Query Patterns

All GCP queries build on four core patterns. Master these and adapt them to any entity type.

Pattern 1: Resource Discovery

List resources by type, filter by project/region/zone, summarize counts:

dql
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.

Pattern 2: Configuration Parsing

Parse gcp.object JSON for detailed configuration fields:

dql
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.

Pattern 3: Relationship Traversal

Follow relationships between resources:

dql
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.

Pattern 4: Label-Based Ownership

Group resources by GCP labels for ownership and organizational tracking:

dql
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.


Reference Guide

Load reference files for detailed queries when the core patterns above need service-specific adaptation.

ReferenceWhen to loadKey content
compute-instances.mdCompute Engine VMs, machine types, IP addresses, disksInstance inventory, machine type distribution, status checks
kubernetes-gke.mdGKE clusters, node pools, pods, deployments, services, RBACCluster topology, workload distribution, RBAC bindings
serverless-containers.mdCloud Run services, revisions, executionsService inventory, revision tracking, execution analysis
networking-dns.mdVPC networks, subnets, routes, DNS recordsNetwork topology, subnet analysis, route tables, DNS record sets
messaging-pubsub.mdPub/Sub topicsTopic inventory, messaging topology
iam-security.mdIAM service accounts, roles, Secret ManagerService account audit, role analysis, secret version tracking
monitoring-logging.mdMonitoring dashboards, logging, saved queriesDashboard inventory, saved query analysis
resource-management.mdGCP projects, regions, organizational hierarchyProject inventory, region distribution, hierarchy mapping
resource-ownership.mdResource ownership, GCP labels, organizational structureLabel-based grouping, project-level summaries, chargeback

Best Practices

Configuration Parsing

  1. Always parse gcp.object with JSON parser: parse gcp.object, "JSON:gcpjson"
  2. Access primary resource attributes via gcpjson[configuration][resource][...]
  3. Access extended properties via gcpjson[configuration][additionalAttributes][...]
  4. Check for null values after parsing with isNotNull()

GCP Hierarchy

  1. Organization → Folder → Project → Region/Zone
  2. Use gcp.project.id as the primary scoping filter
  3. Use gcp.organization.id for cross-project queries
  4. Use gcp.region and gcp.zone for location-based analysis

Entity Naming

  1. Entity types follow the GCP_<SERVICE_API>_<RESOURCE> format
  2. Service API maps to the Google API domain with underscores replacing dots and hyphens
  3. Use specific entity types (avoid "GCP_*" wildcards when possible)

Labels

  1. GCP labels must be accessed via backtick syntax: `tags:gcp_labels`
  2. Use isNotNull(tags:gcp_labels) for label-based filtering
  3. Track label coverage with summarize operations

Relationship Traversal

  1. Use "*" as the relationship name — GCP entities do not have named relationship types
  2. Use fieldsKeep to maintain important fields through traversal
  3. Access traversal history with dt.traverse.history[-N]
  4. Complex topologies may require multiple traverse operations

Limitations and Notes

Smartscape Limitations

  • Smartscape data reflects the most recent scan; there may be a delay between GCP changes and Dynatrace visibility
  • Not all GCP services are represented as entity types
  • Some configuration fields may be null depending on resource setup
  • Resource discovery depends on GCP integration configuration

GCP-Specific Notes

  • GCP labels must be accessed via backtick syntax: `tags:gcp_labels`
  • GCP entities use "*" for relationship traversal (no named relationship types)
  • GCP object configuration requires parsing with parse gcp.object, "JSON:gcpjson"
  • Configuration fields nest under gcpjson[configuration][resource][...] (differs from AWS pattern)

General Tips

  • Filter early by project and region for better performance
  • Use isNotNull() and isNull() for graceful null handling
  • Combine project and region filters for large environments
  • Use countDistinct() for unique resource counts
  • Limit results with | limit N during exploration

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dt Obs Gcp AI skill do?

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.

Why use Dt Obs Gcp on TypingMind?

Because you install it once and use it with any model. Dt Obs Gcp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dt Obs Gcp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-obs-gcp. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dt Obs Gcp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dt Obs Gcp?

As many as you like. As long as a model supports skills, you can use Dt Obs Gcp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dt Obs Gcp AI skill free?

Yes. It is published on GitHub by Dynatrace under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇