Gcp Bigquery
- 35 installs
- 6 repo stars
- Updated March 13, 2026
- alphaonedev/openclaw-graph
gcp-bigquery is a skill that provides architecture guidance and cost modeling for Google BigQuery while delegating provisioning to the google-cloud-bigquery Python client.
About
This skill provides architecture guidance, cost modeling, and pre-flight IAM requirements for Google BigQuery, a serverless data warehouse for SQL queries on large datasets. A developer uses it to plan BigQuery usage while delegating actual provisioning to the official google-cloud-bigquery Python client. It also covers Vertex AI integration patterns for AI and RAG workloads.
- Architecture guidance for BigQuery serverless data warehouse
- Delegates execution to google-cloud-bigquery Python client
- Cost modeling and IAM pre-flight requirements
Gcp Bigquery by the numbers
- 35 all-time installs (skills.sh)
- Ranked #477 of 911 Databases skills by installs in the Skillselion catalog
- Data as of Jul 7, 2026 (Skillselion catalog sync)
gcp-bigquery capabilities & compatibility
Free guidance; GCP billing applies to BigQuery usage via the SDK
- Capabilities
- architecture guidance · cost modeling · iam preflight
- Works with
- gcp
- Use cases
- data analysis · database
- Pricing
- Bring your own API key
What gcp-bigquery says it does
This skill delegates all GCP provisioning and operations to the official Google Cloud Python client libraries.
This skill provides architecture guidance, cost modeling, and pre-flight requirements — the SDK handles execution.
Google Cloud's BigQuery is a serverless, fully managed data warehouse for running SQL queries on large datasets.
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| Installs | 35 |
|---|---|
| repo stars | ★ 6 |
| Last updated | March 13, 2026 |
| Repository | alphaonedev/openclaw-graph ↗ |
What it does
Plan BigQuery data-warehouse architecture and cost while the google-cloud-bigquery Python SDK handles execution.
Who is it for?
Planning BigQuery architecture, cost, and IAM before executing with the official SDK.
Skip if: Direct provisioning without the Google Cloud Python SDK, or non-GCP data warehouses.
When should I use this skill?
You need to design a BigQuery data warehouse and estimate its cost and IAM setup.
What you get
A BigQuery architecture plan with cost model and IAM pre-flight, executed via the Python SDK.
- Architecture and service-selection guidance
- Cost estimates
- IAM pre-flight requirements
Files
gcp-bigquery
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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Does this skill provision BigQuery itself?
No, it delegates all provisioning and operations to the official google-cloud-bigquery Python client and provides architecture and cost guidance.
What is BigQuery?
It is a serverless, fully managed data warehouse for running SQL queries on large datasets.