Agent skill · data analytics · aiskillstore
bigquery
Comprehensive guide for using BigQuery CLI (bq) to query and inspect tables in Monzo's BigQuery projects, with emphasis on data sensitivity and INFORMATION_SCHEMA queries.
Why this skill is useful
Provides specific BigQuery CLI commands and patterns tailored for Monzo's data sensitivity and project structure that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
BigQuery CLI Skill This skill provides comprehensive guidance on using the BigQuery CLI (bq) for querying and inspecting data in Monzo's BigQuery projects. Core Principles 1. Always specify the project explicitly using --projectid=PROJECTNAME 2. Always use Standard SQL with --uselegacysql=false 3. Respect data sensitivity - avoid querying actual content from sensitive tables 4. Use INFORMATIONSCHEMA for metadata queries (schemas, columns, tables) Common Query Patterns 1. Check Table Schema (INFORMATIONSCHEMA) Use this to inspect column names, types, and structure without accessing sensitive data: Examples: 2. Count Rows (Safe for Sensitive Tables) Use COUNT() to check table size without exposing data: Example: 3. List All Tables in a Dataset Example: 4. Export Schema to File Useful for programmatic processing of table schemas: Example: 5. Check Table Metadata Get table creation time, size, and other metadata: 6. Find Tables by Pattern Search for tables matching a naming pattern: Example: 7. Get Detailed Column Information Get comprehensive column metadata including descriptions: 8. Sample Data (Non-Sensitive Tables Only) ⚠️ WARNING: Only use this on non-sensitive tables. Never query actual content from people/staff/PII tables. Output Formatting Options Control how results are displayed: Common Projects and Datasets Main Analytics Projects monzo-analytics - Main analytics warehouse monzo-analytics-v2 - New OOM architecture models monzo-analytics-pii - PII-containing data (use with caution) sanitized-events-prod - Sanitised event data raw-analytics-events-prod - Raw event data Common Datasets dims - Dimension tables prod - Production tables lending - Lending-specific tables slurpee - Slurpee data Data Sensitivity Guidelines ✅ SAFE Operations (Always Allowed) 1. INFORMATIONSCHEMA queries - These only return metadata, not actual data 2. COUNT() queries - These only return row counts 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/bigquery