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testland/data-quality-conventions

Use when designing data-quality coverage for a new data product or auditing an existing one: choosing dbt tests vs Great Expectations vs Soda, column vs table-level checks, severity tiers, SLA and freshness conventions, and anti-patterns to avoid.

Quality

85%

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Overview
Quality
Evals
Security
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Quality

Content

75%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 lean, opinionated conventions catalog that tells Claude exactly which checks to author, at what severity, and how to name and retire them, with concrete rules-of-thumb throughout. Its main weaknesses are mildly redundant prose in a few sections, vague per-engine severity specifics that sit awkwardly between concrete guidance and clean deferral, and no explicit multi-step workflow with checkpoints (though the task type doesn't demand them).

Suggestions

Tighten the intro paragraph and the freshness rationale ('the most common silent failure') so each point is made once — this would lift conciseness to fully lean.

For the severity-tiering section, either give one concrete config snippet per engine (e.g. a dbt `severity: warn` block and a SodaCL `warn:`/`fail:` example) or drop the per-engine specifics entirely and point straight to the engine skills, since the current half-specific description is the least actionable passage.

Consider moving the common-anti-patterns table and the engine-selection justifications into a references/ file (e.g. anti-patterns.md), keeping SKILL.md as a tighter overview — this would also give the skill a genuine progressive-disclosure layer.

DimensionReasoningScore

Conciseness

Dense, table-driven reference with no explanations of concepts Claude already knows; only minor trimmable prose remains (the intro's 'those tell you the **how**... this tells you **which** checks' framing and phrases like 'the shop should treat distribution monitoring as a separate concern'). Not 5 because a few justification sentences restate their point twice.

4 / 5

Actionability

Gives concrete, directly applicable guidance: a named starter set (`not_null`, `unique`, `relationships`, `accepted_values`), an explicit threshold rule ('The freshness threshold is **2× the expected cadence**... A daily pipeline gets `freshness < 2d`'), naming examples (`not_null_orders_email`, `missing_count(email) = 0`), and a promotion policy ('Land new checks at `warn` for two cycles, then promote'). Not 5 because the per-engine severity mechanics are vague ('suite-level metadata', 'the alert configuration syntax') with no concrete config example.

4 / 5

Workflow Clarity

A clear authoring sequence is signaled explicitly ('Use this matrix to pick an engine **before authoring any checks**', 'start at the **column level** for the keys and required fields, then add **table-level** invariants') and the skill closes with an explicit check-lifecycle/retirement workflow. Not 5 because there are no validation-style checkpoints; not 3 because this is a design-reference skill rather than a destructive/batch-execution workflow, so the missing-feedback-loop cap does not apply.

4 / 5

Progressive Disclosure

Single well-organized file with consistent section structure, and it correctly defers engine-specific syntax to the sibling skills ('Each engine's specifics are in its SKILL.md') instead of inlining it. Not 5 because at ~125 lines it exceeds the simple-skill threshold and content like the anti-patterns table and engine-comparison rows could arguably live in reference files; no bundle files exist to verify navigation against.

4 / 5

Total

16

/

20

Passed

Description

88%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 description that explicitly covers both what the skill provides and when to use it, with an unusually concrete enumeration of its scope (engine choice, check levels, severity, SLA/freshness, anti-patterns) in third-person imperative style. Its only weaknesses are missing common synonym triggers and deliberate overlap with the named engine-specific skills, which creates minor routing ambiguity.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete capabilities: 'choosing dbt tests vs Great Expectations vs Soda, column vs table-level checks, severity tiers, SLA and freshness conventions, and anti-patterns to avoid' — comprehensive coverage of the skill's scope, matching the top anchor rather than anchor 4's 'minor gaps in coverage'.

5 / 5

Completeness

Explicitly answers both questions: the what is the enumerated list after the colon (engine selection, check-level coverage, severity tiers, SLA/freshness conventions, anti-patterns), and the when is a concrete 'Use when designing data-quality coverage for a new data product or auditing an existing one' clause with specific trigger conditions — matching the top anchor, clearly above anchor 4's 'when could be more explicit'.

5 / 5

Trigger Term Quality

Good natural keyword coverage — 'data-quality coverage', 'auditing', 'dbt tests', 'freshness', 'SLA', 'data product' are phrases users in this domain would actually say. Not 5 because common synonyms and variations are missing (e.g. 'data validation', 'data checks', 'data quality tests'); not 3 because coverage clearly goes beyond 'some relevant keywords'.

4 / 5

Distinctiveness Conflict Risk

The design-vs-execution niche ('choosing... conventions' rather than running checks) is distinct, but the description explicitly names 'dbt tests', 'Great Expectations', and 'Soda', so a request like 'add dbt tests to my project' could plausibly pull this skill instead of an engine-specific one — minor overlap risk with closely related skills, matching anchor 4 rather than anchor 5's 'minimal conflict risk'.

4 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

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