Bigquery Sql

Schema Mapping

gemini-cli-extensions

Plans source-to-target schema mappings for ETL, ELT, or data integration work, producing a documented Mapping Manifesto.

Resolving Mcp Region Configs

gemini-cli-extensions

Fixes unreplaced region placeholders in regional Google Cloud MCP server configs so missing MCP tools register.

Notebook Guidance

gemini-cli-extensions

This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.

Ml Best Practices

gemini-cli-extensions

Guides machine learning notebooks with step-by-step plans for clustering, forecasting, classification, regression and model comparison.

Managing Python Dependencies

gemini-cli-extensions

Guides agents to detect a Python project's dependency manager and install packages correctly instead of using global pip.

Google Cloud Storage Fuse

gemini-cli-extensions

Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when you need to interact with gcsfuse — decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size the file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or GCS bill spikes on existing mounts with gcsfuse metrics. Covers mount semantics, the gcsfuse CLI and config file, the GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or for fully POSIX-compliant shared file systems (Filestore, Managed Lustre).

Spanner Basics

google

Featured

Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.

Gke Cost Analysis

google

Featured

Answers natural-language questions about GKE cluster and workload costs using BigQuery billing exports and live cluster metrics.

Datalineage Bigquery Asset Impact Analysis

google

Featured

Guides an agent through downstream impact (blast radius) analysis for a BigQuery table or view using Data Lineage.

Bigquery Troubleshooting

google

Featured

Diagnoses failing, slow, or unexpectedly expensive BigQuery jobs through structured root-cause workflows.

Bigquery Optimization

google

Featured

Guides BigQuery cost and performance optimization across capacity editions, storage layout, and SQL queries.

Bigquery Bigframes

google

Featured

Guides writing Python code with BigQuery DataFrames (BigFrames) for data processing, analysis, and machine learning.