checking-freshness

Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.

Quality

83%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a solid, actionable skill with clear workflows and executable SQL examples. Its main strength is the well-sequenced process from freshness check through escalation, with concrete thresholds and output formatting. Minor weaknesses include some verbosity in platform-specific sections and the lack of bundle files for content that could benefit from progressive disclosure.

Suggestions

Consider moving the Astro and OSS Airflow platform-specific guidance into a separate reference file to keep the main skill leaner and improve progressive disclosure.

Trim the list of common timestamp column names to the top 3-4 most common patterns, since Claude can infer variations.

Dimension	Reasoning	Score
Conciseness	Generally efficient but includes some unnecessary detail. The list of common timestamp column names is helpful but slightly verbose. The Astro/OSS Airflow sections add moderate bulk that could be trimmed. The freshness status table and output format are well-structured and earn their space.	2 / 3
Actionability	Provides fully executable SQL queries with clear placeholders, a concrete freshness status table with specific thresholds, specific CLI commands for Airflow investigation (af dags list, af dags get), and a clear output format template. Guidance is copy-paste ready and specific.	3 / 3
Workflow Clarity	Clear three-step sequence (find column → query last update → check row counts) with a well-defined escalation path when data is stale (check Airflow → find DAG → diagnose). The workflow includes a feedback loop by referencing the debugging-dags skill for further investigation. The freshness status table serves as a validation checkpoint for interpreting results.	3 / 3
Progressive Disclosure	Content is well-organized with clear sections and headers, and appropriately references the debugging-dags skill for deeper investigation. However, the Astro and OSS Airflow platform-specific sections could be split into separate reference files rather than inlined, and there are no bundle files to support progressive disclosure despite the content length (~90 lines).	2 / 3
	Total	10 / 12 Passed

Description

89%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a solid description with excellent trigger terms and completeness, clearly answering both what and when. Its main weakness is that the 'what' portion is quite brief ('Quick data freshness check') and could benefit from listing more specific concrete actions the skill performs. Overall it would perform well in skill selection scenarios.

Suggestions

Expand the 'what' portion with more specific actions, e.g., 'Checks data freshness by querying table metadata, comparing timestamps, and reporting last update times.'

Dimension	Reasoning	Score
Specificity	It names the domain (data freshness/currency checking) and implies actions like checking if data is up to date or when a table was last updated, but doesn't list multiple concrete actions (e.g., query metadata, compare timestamps, check ETL logs).	2 / 3
Completeness	Clearly answers both what ('Quick data freshness check') and when ('Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it') with explicit trigger guidance.	3 / 3
Trigger Term Quality	Includes strong natural trigger terms users would actually say: 'up to date', 'last updated', 'stale', 'data currency', 'data freshness'. Good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	The focus on data freshness/staleness/currency is a clear niche that is unlikely to conflict with other skills. The trigger terms are specific to this particular use case.	3 / 3
	Total	11 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: astronomer/agents
Commit: 789b454

Reviewed: 8 days ago

Table of Contents

Discovery Implementation Validation

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