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analyze-data-quality

Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.

66

Quality

79%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./plugins/data-analytics/skills/analyze-data-quality/SKILL.md
SKILL.md
Quality
Evals
Security

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 thorough, well-structured instruction skill that gives specific actionable guidance across data shapes, severities, and reporting. Its main weaknesses are mild redundancy between the workflow and standards sections, no executable code templates, and a single-file layout longer than ideal for progressive disclosure.

Suggestions

De-duplicate the check guidance between Workflow steps 4-5 and the Standards 'Core Checks'/'Specific Check Guidance' sections so each check type appears in one canonical place.

Add one or two short executable SQL/Python snippets for the most common checks (e.g. duplicate primary-key rate, null rate by partition) to lift actionability from concrete guidance to copy-paste ready.

Move the detailed 'Specific Check Guidance' catalog into a references/ file (e.g. CHECKS.md) and link to it from SKILL.md, keeping the body as a concise overview to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is a dense, high-signal catalog of checks, severities, and output structure with no basic-concept padding, but the check catalogs overlap between workflow steps 4-5 and the Standards section and the 'Compare rates, not just counts' guidance is restated, leaving minor trim opportunities.

4 / 5

Actionability

Guidance is concrete and specific — exact checks per data shape, a severity rubric, a report structure, and named tools ($jupyter-notebooks, structured_data, operations_logs) — but it is instruction-only with no copy-paste SQL/code templates, so it stops at 4 rather than 5.

4 / 5

Workflow Clarity

An unambiguous 8-step sequence runs from clarifying context through recommending fixes, with soft checkpoints ('Confirm grain before interpreting anomalies', label assumptions); it lacks an explicit validate-and-retry feedback loop, but the work is read-only analysis so the destructive/batch cap does not apply.

4 / 5

Progressive Disclosure

Sections are well-organized with clear cross-skill and tool pointers, but at over 160 lines the detailed Core/Specific check catalogs are inlined with no bundle files to offload them, which is a minor organization gap rather than ideal one-level-deep splitting.

4 / 5

Total

16

/

20

Passed

Description

83%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 clearly states the skill's purpose and gives concrete, naturally phrased triggers for when to invoke it. It is mostly distinct from related analysis skills with only minor overlap risk.

DimensionReasoningScore

Specificity

Names several concrete actions ('check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite'), but they stay at a conceptual rather than operational level, so it is not a full 5.

4 / 5

Completeness

Explicitly answers both 'what' (assess whether data/results/dashboards are trustworthy enough to use) and 'when' (Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite) with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural-term coverage ('data quality', 'reconcile conflicting sources', 'metric definitions', 'evidence is safe to cite', 'dashboards'); a few common synonyms (e.g. 'QA', 'validate') are absent, keeping it below 5.

4 / 5

Distinctiveness Conflict Risk

It carves out underlying-data trustworthiness from closely related QA and KPI-design skills, but 'check data quality' is broad enough to carry minor overlap risk with sibling skills.

4 / 5

Total

17

/

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

Validation for skill structure

No warnings or errors.

Repository
openai/plugins
Reviewed

Table of Contents

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