bigquery-graph

Provides guidelines and best practices for querying and defining property graphs and semantic graphs in BigQuery using GQL (Graph Query Language). Use when creating property graphs or querying graph topologies in BigQuery.

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Repository: gemini-cli-extensions/data-agent-kit-starter-pack
Last source activity: July 24, 2026 at 23:01
Detected SKILL.md language: English
Stars: 172
Forks: 41

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name	bigquery-graph
metadata	{"version":"v1"}
description	Provides guidelines and best practices for querying and defining property graphs and semantic graphs in BigQuery using GQL (Graph Query Language). Use when creating property graphs or querying graph topologies in BigQuery.

BigQuery Graph Analytics

BigQuery supports Graph Analytics through property graph queries (using GQL) and semantic graphs. Property graphs allow you to query topology, node/edge connections, and graph relationships directly in BigQuery SQL.

Reference Directory

GQL Querying: graph_queries.md - Standard GQL syntax and pattern matching.
Semantic Queries: semantic_queries.md - Semantic graph operations and expand functions.
Schema Best Practices: best_practices.md - Performance and indexing best practices for graph schemas.
DDL Reference: ddl_reference.md - CREATE PROPERTY GRAPH DDL syntax.
Feature Parity & Limitations: feature_parity.md - GQL limitations and feature parity.
Graph Schema Advisor: graph_schema_ddl_advisor.md - Assistant guidelines for designing graph schemas.

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gcp-managed-airflow-dag-authoring

Guides the authoring and validation of Apache Airflow DAGs for Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.

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gcp-managed-airflow-recommendations

Provides recommendations and best practices for creating, configuring, tuning and optimizing Managed Service for Apache Airflow (MSAA, Cloud Composer) environments. Use when the user asks for guidance, recommendations, or best practices on configuring Cloud Composer, scaling Airflow environments, preventing workload restarts, or analyzing system health.

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gcp-spark

Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.

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name	bigquery-graph
metadata	{"version":"v1"}
description	Provides guidelines and best practices for querying and defining property graphs and semantic graphs in BigQuery using GQL (Graph Query Language). Use when creating property graphs or querying graph topologies in BigQuery.

BigQuery Graph Analytics

Reference Directory

GQL Querying: graph_queries.md - Standard GQL syntax and pattern matching.
Semantic Queries: semantic_queries.md - Semantic graph operations and expand functions.
Schema Best Practices: best_practices.md - Performance and indexing best practices for graph schemas.
DDL Reference: ddl_reference.md - CREATE PROPERTY GRAPH DDL syntax.
Feature Parity & Limitations: feature_parity.md - GQL limitations and feature parity.
Graph Schema Advisor: graph_schema_ddl_advisor.md - Assistant guidelines for designing graph schemas.

bigquery-graph

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BigQuery Graph Analytics

Reference Directory

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More from this repository

BigQuery Graph Analytics

Reference Directory