Name: data-quality-auditor
Rating: 66.4 (1 reviews)
Author: alirezarezvani

data-quality-auditor

Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and produce an actionable remediation plan. Use when the user asks to check data quality, profile a dataset, hunt outliers or missing values, or validate data before analysis or model training.

Quality

80%

Does it follow best practices?

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Securityby

Passed

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The canonical home for this skill is data-quality-auditor in alirezarezvani/claude-skills

Quality

Content

72%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 well-structured, highly actionable skill body with concrete commands and clear modes, weakened by missing validation checkpoints in the batch audit workflow and referenced bundle files that are absent from the bundle.

Suggestions

Add an explicit validate/verify checkpoint to Mode 1 (e.g., confirm profiler JSON output parsed correctly before computing the DQS) to satisfy the batch-operation feedback-loop requirement.

Ship the referenced bundle files (scripts/data_profiler.py, missing_value_analyzer.py, outlier_detector.py, references/data-quality-concepts.md) so the one-level-deep references actually resolve.

Trim the motivational preamble in the opening paragraph to tighten token efficiency.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with well-earned tables and bullets that assume Claude's intelligence, but carries some motivational preamble ('You move fast, think in impact, and never let good enough data quietly poison a model or dashboard') that could be trimmed, placing it just below the lean score-5 anchor.	4 / 5
Actionability	Provides copy-paste-ready, executable bash commands with concrete flags for all three scripts and covers the common cases (CSV input, column filtering, JSON output, monitoring), directly matching the score-5 anchor.	5 / 5
Workflow Clarity	The three modes have clear sequenced steps, but the core audit workflow (a batch operation: profiling, auto-scoring, remediating) has no explicit validation checkpoint confirming script output before reporting DQS, so per the batch-operation cap it cannot exceed 3.	3 / 5
Progressive Disclosure	Internal section structure is good and references are one level deep and clearly signaled, but the referenced bundle files (scripts/*.py and references/data-quality-concepts.md) do not exist on disk, so navigation to detailed materials is partially broken, keeping it below the score-4 anchor.	3 / 5
	Total	15 / 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, specific description that clearly states concrete capabilities and an explicit 'Use when' trigger clause. Minor room to improve on trigger-term breadth and overlap with adjacent analytics skills.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Audit datasets for completeness, consistency, accuracy, and validity', 'Profile data distributions, detect anomalies and outliers, surface structural issues', 'produce an actionable remediation plan' — with comprehensive coverage and no real gaps, matching the score-5 anchor.	5 / 5
Completeness	Explicitly answers both 'what' (audit/profile/detect/remediate) and 'when' via a 'Use when...' clause with concrete trigger phrases, directly matching the score-5 anchor.	5 / 5
Trigger Term Quality	Includes natural user phrases ('check data quality', 'profile a dataset', 'hunt outliers or missing values', 'validate data before analysis or model training') but lacks broader synonyms or file extensions, placing it just below the comprehensive score-5 anchor.	4 / 5
Distinctiveness Conflict Risk	Has a clear data-quality-audit niche with distinct triggers, but the broad 'validate data before analysis or model training' framing creates minor overlap with adjacent analytics/modeling skills, so it sits just below the minimal-conflict score-5 anchor.	4 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 15 missing	Warning

	Total	15 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/data-quality-auditor/SKILL.md
Commit: 19392f7

Reviewed: ~6 hours ago

Table of Contents

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