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analyzing-data

Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show me Z", "find customers", "what is the count").

69

Quality

85%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

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

A highly actionable, well-structured skill body with concrete commands and a clear workflow. Its main weakness is progressive disclosure: the two external reference files it points to are missing from the bundle.

Suggestions

Create the missing reference/discovery-warehouse.md and reference/common-patterns.md files (or remove the broken links) so the References section actually resolves.

Add an explicit validation checkpoint after query execution (e.g. verify row counts / inspect dtypes before presenting findings) to strengthen the workflow's feedback loop.

Trim mechanism details like '(Snowflake async / connection-pool fan-out)' and '(preserving state until then)' that Claude does not need to act on the commands.

DimensionReasoningScore

Conciseness

The body is mostly lean and command-driven with no padding about what SQL or a warehouse is, but a few explanations could be trimmed (e.g. 'they execute concurrently (Snowflake async / connection-pool fan-out)' and 'preserving state until then').

4 / 5

Actionability

Provides copy-paste-ready `uv run scripts/cli.py ...` commands throughout the workflow and a fully documented kernel function table with signatures and return types, covering the common cases executable as-is.

5 / 5

Workflow Clarity

A clear 6-step numbered workflow with a feedback loop in step 1 (pattern record --success/--failure), but query execution lacks an explicit 'validate the result before presenting' checkpoint, leaving a minor validation gap.

4 / 5

Progressive Disclosure

Structure is clean with signaled one-level references in a References section, but both referenced files — reference/discovery-warehouse.md and reference/common-patterns.md — do not exist on disk, so the navigation is broken and the disclosure fails to resolve.

3 / 5

Total

16

/

20

Passed

Description

91%Weight 40%Scale 1-5

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

A strong, third-person description that clearly answers both what the skill does and when to use it, with excellent natural trigger phrases. Minor room to tighten the slightly broad 'anything that needs warehouse data' phrasing.

DimensionReasoningScore

Specificity

Names the domain ('Queries the data warehouse with SQL and answers business questions about data') and lists several concrete operations — 'counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis' — giving broad but not perfectly comprehensive coverage of distinct skill actions.

4 / 5

Completeness

Explicitly states both what ('Queries the data warehouse with SQL and answers business questions about data') and when ('Use when answering anything that needs warehouse data ...') with concrete trigger phrases, matching the score-5 anchor.

5 / 5

Trigger Term Quality

Includes natural user utterances as examples ('who uses X', 'how many Y', 'show me Z', 'find customers', 'what is the count') alongside synonyms like 'counts, metrics, trends, aggregations, data lookups', covering how users actually phrase these requests.

5 / 5

Distinctiveness Conflict Risk

The 'data warehouse with SQL' niche is distinct with clear triggers and low conflict risk, but 'answering anything that needs warehouse data' is slightly broad, leaving minor overlap risk with general analytics skills.

4 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 3 missing

Warning

Total

15

/

16

Passed

Repository
astronomer/agents
Reviewed

Table of Contents

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