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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").

73

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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 tight, highly actionable skill body: executable commands throughout, a clearly sequenced cache-first workflow, and exemplary token efficiency with no filler. The two real defects are that both linked reference files are missing from the bundle, breaking progressive disclosure, and that no validation checkpoint exists before presenting query results.

Suggestions

Create reference/discovery-warehouse.md and reference/common-patterns.md (or fix the link paths) so the References section's clearly-signaled links actually resolve — both files are absent from the bundle, leaving the detailed material unreachable.

Add a validation step between 'Execute query' and 'Present findings', e.g. sanity-check result row counts / NULLs and re-run with adjusted filters when a result looks wrong, before answering the user.

Consider moving the bulk of the CLI Reference section into a reference file, keeping only the most-used commands (exec, concept/pattern lookup and learn) inline to reduce duplication with the Workflow section.

DimensionReasoningScore

Conciseness

The body is lean and command-first: a six-step workflow of exact commands, a table for kernel functions, and one-line operational notes ("fail-fast", timeout, idle kernel) with zero explanation of concepts Claude already knows — "pl (Polars) and pd (Pandas) are pre-imported" is a necessary fact, not padding. The only redundancy is that pattern/concept commands appear both in the Workflow and the CLI Reference, but that duplication serves distinct purposes (usage in context vs. syntax reference), so every token still earns its place.

5 / 5

Actionability

Guidance is fully executable: exact commands like `uv run scripts/cli.py exec "df = run_sql('SELECT ...')""`, a kernel-function table with return types, and concrete flags (-t 600, --stale-only); every listed subcommand was verified to exist in scripts/cli.py. Placeholders appear only where content is inherently user-determined (the SQL itself, the question text), which is justified flexibility, so this matches the copy-paste-ready top anchor.

5 / 5

Workflow Clarity

The six steps are clearly sequenced with cache hit/miss branching ("If a pattern exists, follow its strategy", "If cache misses") and an outcome-recording loop ("pattern record <name> --success # or --failure"). It is not a 5 because there is no explicit validation checkpoint on query results before "Present findings" (e.g., sanity-check counts/NULLs before answering), and error recovery mid-workflow is only implicit; since queries are read-only ad-hoc analysis rather than destructive or batch operations, the cap-at-3 rule does not apply.

4 / 5

Progressive Disclosure

The two references are clearly signaled and one level deep with accurate topic descriptions ("Large table handling, warehouse exploration, INFORMATION_SCHEMA queries"; "SQL templates for trends, comparisons, top-N, distributions, cohorts"), but neither reference/discovery-warehouse.md nor reference/common-patterns.md exists in the bundle — no reference directory is present — so navigation to the detailed material is broken. With 100% of the reference links dead and ~50 lines of CLI reference inlined, this exceeds the 'minor organization gaps' of the level-4 anchor.

3 / 5

Total

17

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20

Passed

Description

96%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.

An exemplary description: it states concrete capabilities in third person, gives an explicit 'Use when' trigger clause with natural keyword synonyms, and includes literal example questions users would ask. The only weakness is that some example trigger phrases are generic enough to slightly overlap with a plain SQL/database skill.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities — "Queries the data warehouse with SQL", "counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis" — giving comprehensive coverage of the domain's actions, matching the anchor for multiple specific concrete actions. It is not a 4 because the enumerated actions span the full range of warehouse analysis operations rather than leaving minor gaps, and it uses correct third-person voice ("Queries", "answers").

5 / 5

Completeness

Both questions are explicitly answered: what — "Queries the data warehouse with SQL and answers business questions about data"; when — "Use when answering anything that needs warehouse data" followed by concrete trigger phrases and example questions. This matches the anchor for clearly and explicitly answering both what AND when with concrete trigger phrases.

5 / 5

Trigger Term Quality

It combines natural keyword synonyms ("counts, metrics, trends, aggregations, joins across tables, data lookups, ad-hoc SQL analysis") with literal example user questions — "who uses X", "how many Y", "show me Z", "find customers", "what is the count" — that are exactly what a user would naturally say. This is comprehensive natural-term coverage including paraphrases, matching the top anchor.

5 / 5

Distinctiveness Conflict Risk

The warehouse/SQL-analytics niche is mostly distinct ("data warehouse", "ad-hoc SQL analysis"), but generic example triggers like "find customers", "show me Z", and the broad "answers business questions about data" create minor overlap risk with a generic SQL or database skill. It fits 'mostly distinct; minor overlap risk with closely related skills' rather than the minimal-conflict top anchor.

4 / 5

Total

19

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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.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 3 missing

Warning

Total

15

/

16

Passed

Repository

astronomer/agents

Path

skills/analyzing-data/SKILL.md

Reviewed

Table of Contents

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