data-quality-checker
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
38
Quality
7%
Does it follow best practices?
Impact
96%
1.00xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/data-quality-checker/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is essentially a placeholder with no substantive content. It names the skill and category but provides zero information about what the skill actually does, what specific data quality checks it performs, or when Claude should select it. The duplicate trigger term suggests auto-generated or incomplete content.
Suggestions
Add specific capabilities: list concrete actions like 'validates data schemas, detects null values, identifies duplicates, checks data type consistency, flags outliers and anomalies'
Add a 'Use when...' clause with natural trigger terms: 'Use when the user asks to validate data, check data quality, find data issues, detect anomalies, or verify data integrity in pipelines'
Include relevant file types or data formats: mention CSV, JSON, database tables, or specific pipeline contexts where this skill applies
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the skill ('Data Quality Checker') without describing any concrete actions. There are no specific capabilities listed like 'validates schemas', 'detects anomalies', or 'checks for null values'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and provides no 'when should Claude use it' guidance. The 'Triggers on' section just repeats the skill name rather than providing meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('data quality checker, data quality checker'). Missing natural user terms like 'validate data', 'check for nulls', 'data validation', 'anomaly detection', 'schema validation'. | 1 / 3 |
Distinctiveness Conflict Risk | The 'Data Pipelines' category and 'Data Quality' focus provide some specificity, but without concrete actions or triggers, it could easily conflict with other data-related skills. The category mention helps slightly with disambiguation. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a placeholder with no actionable content. It describes what a data quality checker skill would do in abstract marketing language but provides zero concrete guidance, code examples, validation rules, or workflows. Claude would gain nothing from this skill that it doesn't already know.
Suggestions
Add concrete code examples for common data quality checks (null checks, type validation, range validation, uniqueness constraints) with executable Python/SQL snippets
Define a clear workflow: 1) Define quality rules, 2) Run validation, 3) Handle failures, 4) Generate reports - with specific commands or function calls at each step
Remove all generic boilerplate ('provides automated assistance', 'follows best practices') and replace with specific techniques like Great Expectations, dbt tests, or custom validation patterns
Add example input data and expected validation output to make the skill immediately actionable
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actionable information. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does in abstract terms but never shows how to actually check data quality - no validation rules, no code examples, no specific techniques. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. For a data quality checker, there should be clear steps for defining quality rules, running checks, handling failures, and reporting results. None of this is present. | 1 / 3 |
Progressive Disclosure | The content is organized into sections with headers, but there are no references to detailed documentation, no links to examples or advanced features. The structure exists but contains no substantive content to disclose. | 2 / 3 |
Total | 5 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
0c08951
- Reviewed
Table of Contents
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