Compute table metrics such as row counts and null counts.
npx skills add https://github.com/starlake-ai/starlake --skill metrics-starlake-ai
Please help me install this Agent Skill. Skill: metrics Source: https://github.com/starlake-ai/starlake/tree/main/.agent/skills/metrics Command: npx skills add https://github.com/starlake-ai/starlake --skill metrics-starlake-ai
This skill computes metrics for a table to support data quality checks and analytics.
Use starlake metrics --domain <domain> --schema <schema> to compute metrics for your table.
High-intent search queries and answers about installing and using this skill.
Table metrics for data quality checks are computed by applying the analytics skill to a defined domain and schema, yielding row counts, null counts, and distinct value counts to monitor schema health.
To monitor data completeness, you can calculate basic table metrics including row counts, null counts, distinct value counts, and simple aggregates to validate data against expected tolerances and surface anomalies.
Computing schema metrics via command line requires running the tool with domain and schema inputs, such as specifying the target domain and schema parameters to generate the required table analytics output.
Authentication credentials are optional when computing metrics on secure data sources, allowing the analytics workflow to safely access and monitor data within a defined domain and schema if security configurations demand it.
Table metrics can be applied in analytics pipelines for anomaly detection by validating data against expected tolerances, surfacing irregularities in simple aggregates, and assessing data quality within your defined schema.
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