Gcp Iam
- 433 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
gcp-iam is a cloud security skill that configures and audits Google Cloud IAM roles and bindings for developers wiring least-privilege access for apps, agents, and CI pipelines.
About
gcp-iam is a cloud-gcp cluster skill in alphaonedev/openclaw-graph, part of a 312-skill graph-native OpenClaw workspace, that manages Google Cloud identity and access control. It delegates provisioning to official Google Cloud Python client libraries—google-cloud-python core plus service packages such as BigQuery, Storage, Pub/Sub, Cloud Run, and Vertex AI—while supplying architecture guidance on least privilege, Workload Identity Federation, cost modeling, and IaC trade-offs. Agents consult gcp-iam for service-account permissions, conditional bindings, policy simulation, and CI/CD access patterns alongside Vertex AI Agent Engine and ADK integration notes. Reach for gcp-iam when onboarding developers to GCP projects, scoping service accounts for Workers or agents, auditing bindings for compliance, or preparing Workload Identity Federation before automated deploys.
- GCP IAM role and binding guidance
- Service account and workload identity setup
- Least-privilege policy patterns
- Cross-project and org-level access scoping
- Audit-ready permission reviews
Gcp Iam by the numbers
- 433 all-time installs (skills.sh)
- Ranked #378 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
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| Installs | 433 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
How do you audit GCP IAM roles for CI pipelines?
Configure and audit GCP IAM roles, bindings, and service accounts when wiring cloud access for apps, agents, and CI deploy pipelines.
Who is it for?
Cloud and platform engineers configuring GCP IAM for applications, AI agents, or CI/CD who need policy guidance plus Google Cloud Python SDK execution paths.
Skip if: Teams managing AWS IAM or Azure RBAC without Google Cloud resources in scope.
When should I use this skill?
User asks to configure GCP IAM, service account roles, Workload Identity Federation, or audit cloud access for agents and CI pipelines.
What you get
IAM role and binding recommendations, service account permission plans, Workload Identity Federation pre-flight checklist, and SDK install guidance.
- IAM role binding plan
- service account permission matrix
- Workload Identity Federation checklist
By the numbers
- Belongs to openclaw-graph, a catalog of 312 skills across 27 clusters
- Documents pip installs for google-cloud-python and multiple service-specific clients
- Tags include gcp, iam, and access-control with claude-sonnet model_hint
Files
gcp-iam
Google Cloud Integration
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
# Core GCP client library
pip install google-cloud-python
# Vertex AI + Agent Engine (AI/ML workloads)
pip install google-cloud-aiplatform
# Specific service clients (install only what you need)
pip install google-cloud-bigquery # BigQuery
pip install google-cloud-storage # Cloud Storage
pip install google-cloud-pubsub # Pub/Sub
pip install google-cloud-run # Cloud RunSDK Docs: https://github.com/googleapis/google-cloud-python Vertex AI SDK: https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk
Use the Google Cloud Python SDK for all GCP provisioning and operational actions. This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.
Architecture Guidance
Consult this skill for:
- GCP service selection and trade-off analysis
- Cost estimation and optimization (committed use discounts, sustained use)
- Pre-flight IAM / Workload Identity Federation requirements
- IaC approach (Terraform AzureRM vs Deployment Manager vs Config Connector)
- Integration patterns with Google Workspace and other GCP services
- Vertex AI Agent Engine for multi-agent workflow design
Agent & AI Capabilities
| Capability | Tool |
|---|---|
| LLM agents | Vertex AI Agent Engine |
| Model serving | Vertex AI Model Garden |
| RAG | Vertex AI Search + Embeddings API |
| Multi-agent | Agent Development Kit (google/adk-python) |
| MCP | Vertex AI Extensions (MCP-compatible) |
Reference
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Use gcp-iam for IAM policy design and audits on Google Cloud; pair with gcp-terraform in the same cluster when provisioning bindings as code.
FAQ
Which SDK does gcp-iam use for GCP operations?
gcp-iam delegates provisioning to official Google Cloud Python client libraries, starting with google-cloud-python and optional installs such as google-cloud-storage, google-cloud-bigquery, google-cloud-pubsub, and google-cloud-aiplatform for Vertex AI workloads.
What architecture topics does gcp-iam cover?
gcp-iam covers GCP service selection, cost estimation, Workload Identity Federation pre-flight, IaC approach trade-offs, Google Workspace integration patterns, and Vertex AI Agent Engine design for multi-agent workflows.
Where does gcp-iam live in openclaw-graph?
gcp-iam is a cloud-gcp cluster skill inside alphaonedev/openclaw-graph, a graph-native OpenClaw workspace catalog with 312 skills across 27 clusters, using Neo4j Cypher directives instead of flat markdown files.