data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Install with Tessl CLI
npx tessl i github:Dicklesworthstone/pi_agent_rust --skill data-quality-frameworks79
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
89%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 is a solid skill description that clearly communicates its purpose and when to use it. The explicit 'Use when...' clause and specific tool mentions (Great Expectations, dbt tests) provide good trigger coverage. The main weakness is that the capabilities could be more specific about what concrete actions are performed beyond 'implement' and 'validation'.
Suggestions
Expand specificity by listing concrete actions like 'create expectation suites, configure checkpoints, define schema tests, generate data quality reports'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (data quality validation) and specific tools (Great Expectations, dbt tests, data contracts), but doesn't list multiple concrete actions beyond 'implement' and 'validation rules' - lacks detail on what specific operations are performed. | 2 / 3 |
Completeness | Clearly answers both what ('Implement data quality validation with Great Expectations, dbt tests, and data contracts') and when ('Use when building data quality pipelines, implementing validation rules, or establishing data contracts') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Good coverage of natural terms users would say: 'data quality', 'validation', 'Great Expectations', 'dbt tests', 'data contracts', 'validation rules', 'data quality pipelines' - these are terms practitioners would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on data quality validation with specific tool mentions (Great Expectations, dbt tests) that distinguish it from general data processing or other validation skills. Unlikely to conflict with unrelated skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides highly actionable, production-ready code examples for data quality frameworks with Great Expectations, dbt, and data contracts. However, it's overly verbose for a skill file - explaining basic concepts Claude knows and including extensive inline examples that would be better as linked references. The workflow for actually implementing these patterns end-to-end lacks explicit sequencing and validation checkpoints.
Suggestions
Remove the 'Core Concepts' section (data quality dimensions table and testing pyramid) - Claude already understands these concepts
Split detailed patterns (3-6) into separate reference files like GREAT_EXPECTATIONS.md, DBT_TESTS.md, DATA_CONTRACTS.md and link from a concise overview
Add an explicit workflow section showing the sequence: 1) Set up GE context, 2) Create suite, 3) Run validation, 4) Handle failures, 5) Integrate with CI/CD - with validation checkpoints at each step
Condense the Best Practices section into the main workflow rather than a separate do's/don'ts list
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but verbose. The 'Core Concepts' section with the data quality dimensions table and testing pyramid explains concepts Claude likely knows. The extensive pattern examples are useful but could be more condensed. | 2 / 3 |
Actionability | Excellent executable code throughout - complete Python scripts, YAML configurations, SQL tests, and CLI commands that are copy-paste ready. Each pattern includes working, runnable examples with proper imports and context. | 3 / 3 |
Workflow Clarity | While individual patterns are clear, the overall workflow for implementing data quality is implicit. Missing explicit validation checkpoints and error recovery steps - Pattern 6 shows a pipeline but doesn't guide through the setup sequence or what to do when validations fail beyond raising an error. | 2 / 3 |
Progressive Disclosure | Content is reasonably structured with patterns and sections, but it's a monolithic document with 400+ lines. The detailed patterns (especially Patterns 3-6) could be split into separate reference files, with SKILL.md providing just the quick start and linking to detailed guides. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
skill_md_line_count | SKILL.md is long (591 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Reviewed
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