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checking-freshness

Determines whether a table's data is fresh by locating timestamp columns and calculating age from the most recent value.

2|Updated Apr 8, 2026
Search Tags:#data quality#observability#data freshness#stale data#etl monitoring#airflow dags#sql timestamp
One-click install
npx skills add https://github.com/miptah21/skills --skill checking-freshness-miptah21
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: checking-freshness
Source: https://github.com/miptah21/skills/tree/main/.agents/skills/checking-freshness
Command: npx skills add https://github.com/miptah21/skills --skill checking-freshness-miptah21

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the problem of determining whether a dataset is current enough to use, preventing decisions based on stale or out-of-date data.

Core Features & Use Cases

  • Timestamp-column discovery: Identifies common freshness indicators like _loaded_at, updated_at, created_at, ingestion_time, or business date fields such as event_date.
  • Last-update and age calculation: Computes the most recent timestamp value and measures how long ago it occurred.
  • Recent activity validation: Checks recent row volume by day to detect whether updates are actually happening.
  • Operational triage guidance: When data is stale, helps trace the upstream pipeline by locating and checking the relevant Airflow DAG (including pointers for further debugging).

Quick Start

Use checking-freshness to assess whether table data is fresh enough for your next analysis by evaluating its last update time and update activity.

Frequently Asked Questions about checking-freshness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check data freshness and determine if a SQL table is stale?▼

To check data freshness, the Skill locates an appropriate timestamp column like _loaded_at or updated_at, calculates the age of the most recent timestamp relative to current time, and maps the result to a Fresh, Stale, or Very Stale status.

What is the best way to monitor ETL data freshness and troubleshoot stale data?▼

Monitoring ETL data freshness involves querying last update timestamps and validating recent row counts by day to confirm updates are occurring, then mapping the results to a freshness status report for operational triage.

How does this approach identify the correct timestamp column for calculating data age?▼

Calculating data age requires discovering common freshness indicators within the schema, automatically identifying columns such as _loaded_at, updated_at, created_at, ingestion_time, or business date fields like event_date to measure the most recent update time.

Can I trace stale data issues back to specific Airflow DAGs for pipeline debugging?▼

Yes, when data is stale, the Skill provides operational triage guidance by helping trace the upstream pipeline, locating the relevant Airflow DAG, and offering pointers for further ETL debugging and investigation.

Does this data observability method work without requiring external dependencies or components?▼

Yes, this data observability method operates independently without external dependencies or components, requiring only SQL access to query last update timestamps and recent row volumes to validate dataset currency before reporting.

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