How does the skill identify table update times?

It searches for common industry-standard timestamp patterns like updated_at, _loaded_at, or etl_timestamp within your table's metadata via INFORMATION_SCHEMA.

Does it support real-time data monitoring?

Yes, it calculates freshness down to the minute, making it suitable for both traditional daily batch jobs and high-frequency streaming updates.

Can this skill fix stale data automatically?

While it doesn't modify data, it identifies stale sources and provides direct links to Airflow DAGs to help you diagnose and restart failing pipelines.

Which databases are compatible with this skill?

The skill uses standard SQL syntax compatible with major data warehouses and databases including Snowflake, BigQuery, and PostgreSQL.

What is the criteria for 'Very Stale' data?

Data is categorized as 'Very Stale' if the most recent record is older than 24 hours, typically indicating a missed batch window or a failed ETL job.

Data Freshness Checker

Name: Data Freshness Checker
Author: astronomer

byastronomer

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Database Management

Verifies the currency and reliability of database tables by analyzing update timestamps and pipeline health.

About

The checking-freshness skill empowers Claude to autonomously validate the state of your data sources before performing critical analysis or engineering tasks. It systematically scans for common timestamp patterns, calculates data latency using SQL, and classifies table status from 'Fresh' to 'Very Stale.' By bridging the gap between database querying and Airflow pipeline monitoring, it ensures that your AI-driven workflows are always based on current, reliable information and helps diagnose root causes when data becomes stale.

Key Features

SQL-based data age and latency calculations
Standardized freshness status reporting (Fresh, Stale, Very Stale)
Historical row count analysis to verify update consistency
Automated timestamp column discovery across various ETL patterns
Direct integration with Airflow DAGs for pipeline troubleshooting
77 GitHub stars

Use Cases

Confirming data ingestion status after a scheduled batch job
Validating if a table is ready for reporting before generating insights
Detecting silent ETL failures by monitoring data lag over time

About

Key Features

SQL-based data age and latency calculations
Standardized freshness status reporting (Fresh, Stale, Very Stale)
Historical row count analysis to verify update consistency
Automated timestamp column discovery across various ETL patterns
Direct integration with Airflow DAGs for pipeline troubleshooting
77 GitHub stars

Use Cases

Confirming data ingestion status after a scheduled batch job
Validating if a table is ready for reporting before generating insights
Detecting silent ETL failures by monitoring data lag over time