Query BigQuery for customer usage and health metrics into structured JSON.
npx skills add https://github.com/al-stev/field-eng-skills --skill bigquery-al-stev
Please help me install this Agent Skill. Skill: bigquery Source: https://github.com/al-stev/field-eng-skills/tree/main/.claude/skills/bigquery Command: npx skills add https://github.com/al-stev/field-eng-skills --skill bigquery-al-stev
Provide a reliable, automated way to fetch and aggregate customer usage and health metrics from W&B's BigQuery so engineers and CS teams avoid manual queries and multi-tab copy-paste when preparing dashboards, QBRs, and adoption analyses. The skill centralizes seat utilization, Weave ingestion, tracked hours, account health, product-area adoption, power-user identification, and support ticket summaries into a single structured output. It handles missing sections gracefully and enriches results with SFDC entitlement data when available.
Run the bigquery skill to produce usage JSON for a customer, for example: uv run --project .claude/skills/bigquery python .claude/skills/bigquery/scripts/usage.py --customer GResearch
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Query BigQuery for customer usage metrics by running parameterized queries against the wandb-production dataset to extract seat utilization, Weave ingestion, and tracked hours. The process returns structured JSON matching the INTELLIGENCE_DATA.usage schema for direct use in dashboards and reports.
Aggregated customer health metrics include seat utilization history, Weave ingestion volumes, product-area adoption breakdowns, power user lists, account health scores, and support ticket summaries. These categories are consolidated into a single structured JSON payload to streamline QBR and adoption analysis.
Application Default Credentials are required to authenticate and run parameterized BigQuery queries for usage reporting. The skill enforces this authentication method to securely access the wandb-production BigQuery project and retrieve customer analytics data.
Automate seat utilization and Weave ingestion reporting by executing a Python script that runs parameterized BigQuery queries and outputs structured JSON. This centralizes multi-tab copy-paste workflows into a single command, producing dashboard-ready data without manual SQL.
When BigQuery usage data is missing for certain sections, the query pipeline applies graceful degradation to return partial results without failing. It also enriches available BigQuery snapshots with SFDC entitlement fields to maintain accurate headline metrics.
BigQuery reporting integrates with pandas as a core dependency to process and structure customer analytics data. The workflow queries BigQuery, returns structured JSON, and leverages pandas alongside pyyaml and google-cloud-bigquery to support usage reporting pipelines.
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