Checking Freshness

When your dashboard looks off or someone asks if they can trust yesterday's numbers, this checks table timestamps against a freshness scale (fresh under 4 hours, stale up to 24, very stale beyond that). It finds timestamp columns across common ETL patterns, queries for last update times, and reports status in a scannable format. If data is stale, it points you to the relevant Airflow DAG and suggests using the debugging-dags skill to investigate failures. Honestly just saves you from writing the same timestamp queries over and over, and the staleness scale gives you a shared vocabulary when something hasn't updated.

skills.sh installs: 1K
GitHub stars: 459
last updated: Aug 21, 2026

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Install

$ npx -y skills add astronomer/agents --skill checking-freshness --agent claude-code

Installs into .claude/skills of the current project.

Files

Data Freshness Check

Quickly determine if data is fresh enough to use.

Freshness Check Process

For each table to check:

1. Find the Timestamp Column

Look for columns that indicate when data was loaded or updated:

_loaded_at, _updated_at, _created_at (common ETL patterns)
updated_at, created_at, modified_at (application timestamps)
load_date, etl_timestamp, ingestion_time
date, event_date, transaction_date (business dates)

Query INFORMATION_SCHEMA.COLUMNS if you need to see column names.

2. Query Last Update Time

SELECT
    MAX(<timestamp_column>) as last_update,
    CURRENT_TIMESTAMP() as current_time,
    TIMESTAMPDIFF('hour', MAX(<timestamp_column>), CURRENT_TIMESTAMP()) as hours_ago,
    TIMESTAMPDIFF('minute', MAX(<timestamp_column>), CURRENT_TIMESTAMP()) as minutes_ago
FROM <table>

3. Check Row Counts by Time

For tables with regular updates, check recent activity:

SELECT
    DATE_TRUNC('day', <timestamp_column>) as day,
    COUNT(*) as row_count
FROM <table>
WHERE <timestamp_column> >= DATEADD('day', -7, CURRENT_DATE())
GROUP BY 1
ORDER BY 1 DESC