data-querying

Write and verify SQL queries with BigQuery. Use when executing bq commands, writing SQL queries, or including query results in documents. Trigger on "bq", "BigQuery", "query".

$ Installer

git clone https://github.com/yusuke-suzuki/dotfiles /tmp/dotfiles && cp -r /tmp/dotfiles/.claude/skills/data-querying ~/.claude/skills/dotfiles

// tip: Run this command in your terminal to install the skill

SKILL.md

View on GitHub →

name: data-querying description: Write and verify SQL queries with BigQuery. Use when executing bq commands, writing SQL queries, or including query results in documents. Trigger on "bq", "BigQuery", "query".

Data Querying

Query Process

Dry run: Validate syntax and check cost
```
bq query --project_id=<PROJECT_ID> --use_legacy_sql=false --dry_run "SELECT ..."
```
Cost: ~$5/TB. <1GB is light, 2GB+ needs optimization.

Execute: Run and confirm results

bq query --project_id=<PROJECT_ID> --use_legacy_sql=false --format=csv "SELECT ..."

Learn: Study existing queries in project docs for patterns.

Query Design

Specify exact date ranges
Filter partitioned tables by partition key
Avoid correlated subqueries (use JOINs/CTEs)
Filter early with CTEs before joining large tables

Authentication

If bq query fails with authentication error, prompt user to run gcloud auth login and resume after login.