Is my table data analyzed by this skill?

No, the skill only queries the INFORMATION_SCHEMA metadata regarding job history and bytes processed; it does not read the actual content of your database tables.

How far back can the analysis timeframe go?

You can specify any timeframe in hours. For example, use 24 for a daily audit, 168 for a weekly view, or 720 for a full 30-day usage analysis.

Can I save the generated analysis reports?

Yes, after performing the analysis, the skill offers to save a detailed, timestamped Markdown report locally for sharing or historical tracking.

What permissions are required to use this skill?

You need the Google Cloud SDK (bq tool) installed and authenticated via 'gcloud auth login', with at least bigquery.jobs.list permissions to access the project's metadata.

Does this skill work with BigQuery flat-rate pricing?

The skill calculates cost estimates based on standard on-demand pricing ($6.25/TB), but the performance metrics and usage patterns remain highly relevant for flat-rate users.

BigQuery Usage Analysis

Name: BigQuery Usage Analysis
Author: openshift-eng

byopenshift-eng

•

분석 및 모니터링

Performs deep analysis of Google BigQuery usage patterns and costs to identify optimization opportunities and performance bottlenecks.

This skill provides a comprehensive auditing tool for Google BigQuery environments, enabling developers and data engineers to gain visibility into query performance and project expenses. By querying project metadata via INFORMATION_SCHEMA, it identifies high-cost query patterns, tracks usage across specific service accounts, and detects common anti-patterns like inefficient SELECT statements or missing partitioning. It generates a prioritized report with actionable recommendations to help reduce GCP billing and improve overall data processing efficiency.

주요 기능

01Identification of heavy users and automated service accounts driving costs

0229 GitHub stars

03Generation of structured Markdown reports with prioritized optimization fixes

04Automatic cost estimation using standard BigQuery on-demand pricing models

05Granular analysis of individual query patterns and execution frequency

06Detection of SQL anti-patterns such as 'SELECT *' and missing partitioning filters

사용 사례

01Identifying inefficient querying behavior across large team environments

02Debugging performance bottlenecks in automated data pipelines and dashboards

03Auditing monthly BigQuery spend to reduce cloud infrastructure costs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add openshift-eng/ai-helpers analyze-usage

For use in Claude.ai and ChatGPT

Download Skill