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checking-freshness

checking-freshness

github.com/astronomerData & databases4571.0k installsUpdated 3w ago
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About checking-freshness

A data-freshness skill for data users that checks table timestamps and recent row activity, then reports whether the data is current, stale, or unknown. It also outlines Airflow checks when data appears stale.

Key features

  1. 01Locate likely timestamp columns
  2. 02Calculate time since last update
  3. 03Review recent row counts
  4. 04Classify freshness status
  5. 05Check Airflow runs when stale

Use cases

  1. 01Verify a table before using it in a meeting
  2. 02Check whether recent daily loads occurred
  3. 03Investigate stale data via pipeline status

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/astronomer/agents --skill checking-freshness

After installing, type/checking-freshnessin Claude Code to confirm it loaded.

Details

From the project README

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

Freshness Status

Report status using this scale:

StatusAgeMeaning
Fresh< 4 hoursData is current
Stale4-24 hoursMay be outdated, check if expected
Very Stale> 24 hoursLikely a problem unless batch job
UnknownNo timestampCan't determine freshness

If Data is Stale

Check Airflow for the source pipeline:

  1. Find the DAG: Which DAG populates this table? Use af dags list and look for matching names.

  2. Check DAG status:

    • Is the DAG paused? Use af dags get <dag_id>
    • Did the last run fail? Use af dags stats
    • Is a run currently in progress?

  3. Diagnose if needed: If the DAG failed, use the debugging-dags skill to investigate.

On Astro

If you're running on Astro, you can also:

  • DAG history in the Astro UI: Check the deployment's DAG run history for a visual timeline of recent runs and their outcomes
  • Astro alerts for SLA monitoring: Configure alerts to get notified when DAGs miss their expected completion windows, catching staleness before users report it
On OSS Airflow
  • Airflow UI: Use the DAGs view and task logs to verify last successful runs and SLA misses

Output Format

Provide a clear, scannable report:

FRESHNESS REPORT
================

TABLE: database.schema.table_name
Last Update: 2024-01-15 14:32:00 UTC
Age: 2 hours 15 minutes
Status: Fresh

TABLE: database.schema.other_table
Last Update: 2024-01-14 03:00:00 UTC
Age: 37 hours
Status: Very Stale
Source DAG: daily_etl_pipeline (FAILED)
Action: Investigate with **debugging-dags** skill

Quick Checks

If user just wants a yes/no answer:

  • "Is X fresh?" -> Check and respond with status + one line
  • "Can I use X for my 9am meeting?" -> Check and give clear yes/no with context

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