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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.

70

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

88%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 comprehensive, well-structured analytical skill body with concrete actionable guidance, a clear sequenced workflow, and useful checklists. The main weakness is that everything lives inline in one long file with no progressive disclosure to reference materials.

Suggestions

Move the detailed 'Specific Check Guidance' and 'Core Checks' catalogs into a reference file (e.g., references/checks.md) and link to it from SKILL.md to reduce body length and improve progressive disclosure.

Tighten the overlap between the 'Run core quality checks' workflow step and the 'Core Checks'/'Specific Check Guidance' standards sections to remove redundant framing.

Add a brief worked SQL or Python profiling snippet (row count, null rate, duplicate rate) in the 'Build a compact profile' step to make the most common first action copy-paste executable.

DimensionReasoningScore

Conciseness

Dense, specialized guidance that assumes Claude's competence and avoids explaining basic concepts, with only minor redundancy between the 'Run core quality checks' and 'Specific Check Guidance' sections that could be tightened.

4 / 5

Actionability

Highly concrete, actionable instruction guidance — exact checks, sentinel values ('', 'unknown', 'n/a', 0, -1), cross-field contradictions (is_cancelled = false with non-null cancelled_at), shape-specific check lists, and explicit tool routing ($jupyter-notebooks, $build-report, structured_data) — covering the common cases.

5 / 5

Workflow Clarity

A clearly sequenced 8-step workflow with explicit checkpoints ('Confirm grain before interpreting anomalies', 'infer cautiously and label assumptions'), supporting checklists (Core Checks, shape-specific checks), and a severity rubric that functions as validation for findings.

5 / 5

Progressive Disclosure

Well-organized into clear sections (Workflow, Standards, Defaults) with no nested references, but the body is a single ~160-line monolith with no offloaded reference files, so detailed check catalogs are inlined rather than split out.

4 / 5

Total

18

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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, well-formed description that explicitly states both capability and trigger conditions with concrete, natural-language phrases. Minor gaps in action coverage and synonym terms keep it just below a perfect score on specificity and trigger quality.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy', 'reconcile conflicting sources or metric definitions', 'decide whether evidence is safe to cite' — but stops short of comprehensive coverage of the full data-quality action set (profiling, checks, remediation).

4 / 5

Completeness

Clearly answers both 'what' (assess whether data/evidence is trustworthy enough to use) and 'when' with an explicit 'Use when...' clause listing concrete triggers (check data quality, reconcile conflicting sources or metric definitions, decide whether evidence is safe to cite).

5 / 5

Trigger Term Quality

Good natural keyword coverage with 'data quality', 'reconcile conflicting sources', 'metric definitions', and 'evidence is safe to cite', though common synonyms like 'validate', 'QA', or 'trust' are only weakly represented.

4 / 5

Distinctiveness Conflict Risk

Clear niche around data/evidence trustworthiness with minimal conflict risk, though the 'check data quality' trigger has some overlap with closely related QA/validation 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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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