analyzing-data

Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.

Quality

91%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Critical

Do not install without reviewing

Quality

Content

100%

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

This is an excellent skill file that efficiently teaches Claude a complex multi-step data analysis workflow. It balances conciseness with completeness—the workflow is clear, every command is executable, and advanced content is appropriately delegated to reference files. The caching strategy (concepts, patterns, table schemas) adds a sophisticated learning loop that makes the skill more effective over time.

Dimension	Reasoning	Score
Conciseness	The content is lean and efficient. It assumes Claude knows what SQL, Polars, and Pandas are. Every section serves a purpose—no unnecessary explanations of concepts, libraries, or data warehousing basics. The CLI reference tables are compact and scannable.	3 / 3
Actionability	Every step includes concrete, copy-paste-ready CLI commands. The workflow provides exact bash commands for each phase (pattern lookup, concept lookup, execution, caching). Function signatures with return types are clearly specified.	3 / 3
Workflow Clarity	The 6-step workflow is clearly sequenced with logical progression: check cache → discover → execute → cache learnings → present. It includes feedback loops (pattern record success/failure) and conditional branching (if cache misses, search codebase). The validation step of recording outcomes provides error tracking.	3 / 3
Progressive Disclosure	The SKILL.md provides a clear overview with the workflow and CLI reference inline (appropriately, as they're essential), while pointing to one-level-deep references for advanced topics: discovery-warehouse.md for large table handling and common-patterns.md for SQL templates. Navigation is well-signaled with descriptive labels.	3 / 3
	Total	12 / 12 Passed

Description

82%

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 strong trigger term coverage and good completeness, clearly stating both what the skill does and when to use it. Its main weaknesses are moderate specificity in describing concrete actions and some risk of overlap with other data-related skills due to broad terms like 'metrics' and 'trends'.

Suggestions

Add more specific concrete actions like 'generates SQL queries, aggregates metrics, joins tables across schemas, produces summary reports' to improve specificity.

Narrow the distinctiveness by specifying the data warehouse technology or clarifying boundaries with other data skills (e.g., 'Use this for ad-hoc data warehouse queries, not for dashboard creation or data pipeline management').

Dimension	Reasoning	Score
Specificity	Names the domain (data warehouse querying) and a general action (queries data warehouse, answers business questions), but doesn't list multiple specific concrete actions like 'generate SQL queries, join tables, aggregate metrics, export results'. The actions remain somewhat high-level.	2 / 3
Completeness	Clearly answers both 'what' (queries data warehouse and answers business questions about data) and 'when' (handles questions requiring database/warehouse queries including specific trigger phrases). The trigger guidance is explicit and well-enumerated.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would actually say: 'who uses X', 'how many Y', 'show me Z', 'find customers', 'what is the count', 'data lookups', 'metrics', 'trends', 'SQL analysis'. These closely match real user phrasing patterns.	3 / 3
Distinctiveness Conflict Risk	While 'data warehouse' and 'SQL' provide some specificity, terms like 'data lookups', 'metrics', 'trends' could overlap with analytics, reporting, or dashboard skills. The broad scope of 'answers business questions about data' could conflict with other data-oriented skills.	2 / 3
	Total	10 / 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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