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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").

76

Quality

96%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-3

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

A tight, highly actionable skill body with an explicit workflow and dense CLI reference. Its one weakness is progressive disclosure: the two referenced markdown files are cited but are not present in the bundle.

Suggestions

Add the missing reference/discovery-warehouse.md and reference/common-patterns.md files to the bundle, or remove the broken links and inline the essential content.

Use a consistent directory name for references — the body links under reference/ while the skill convention is references/; pick one to avoid broken paths.

Consider noting inside SKILL.md which reference a given workflow step relies on (e.g., point step 3's table discovery explicitly at discovery-warehouse.md) so navigation is unambiguous.

DimensionReasoningScore

Conciseness

Lean and dense throughout: it assumes Claude knows what SQL/data warehouses are and spends tokens only on the kernel/CLI specifics Claude could not infer, with compact reference tables and brief notes.

3 / 3

Actionability

Every command is a copy-paste-ready `uv run scripts/cli.py ...` invocation with concrete flags and example payloads, and kernel functions are shown with real call signatures and return types.

3 / 3

Workflow Clarity

A clear six-step numbered sequence (pattern lookup → concept lookup → table discovery → execute → cache → present) with cache-then-record feedback loops; for this read-heavy query skill the checkpoints present are appropriate.

3 / 3

Progressive Disclosure

The body is well-sectioned and signals one-level-deep references, but the referenced files reference/discovery-warehouse.md and reference/common-patterns.md do not exist in the bundle, so the navigation points to missing material.

2 / 3

Total

11

/

12

Passed

Description

100%Weight 40%Scale 1-3

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, specific description with clear what/when framing and natural user-facing trigger phrasings. It names concrete analytical actions rather than gesturing vaguely, and the SQL-on-warehouse niche is distinctive.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "Queries the data warehouse with SQL and answers business questions", plus enumerated counts, metrics, trends, aggregations, joins across tables, data lookups, and ad-hoc SQL analysis.

3 / 3

Completeness

Explicitly answers both "what" (queries the warehouse with SQL, answers business questions) and "when" ("Use when answering anything that needs warehouse data...").

3 / 3

Trigger Term Quality

Includes natural phrasings a user would actually say — "who uses X", "how many Y", "show me Z", "find customers", "what is the count" — alongside concrete analytical terms.

3 / 3

Distinctiveness Conflict Risk

The warehouse-data/SQL-business-questions niche is specific, and the triggers (counts, aggregations, joins, ad-hoc SQL analysis) are unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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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