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.
81
58%
Does it follow best practices?
Impact
97%
1.46xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/data-engineering/skills/data-quality-frameworks/SKILL.mdQuality
Discovery
75%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description is structurally sound with a clear 'what' and 'when' clause, and it carves out a distinct niche around data quality validation tooling. Its main weakness is that the specific actions are somewhat high-level ('implement', 'building', 'establishing') rather than listing granular capabilities, and the trigger terms could cover more natural user phrasings.
Suggestions
Add more concrete actions such as 'create expectation suites, configure checkpoints, write dbt schema tests, define data contract schemas' to improve specificity.
Expand trigger terms to include common variations like 'data testing', 'schema validation', 'data profiling', 'data checks', or 'GE expectations' to improve discoverability.
| 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', 'building', and 'establishing'. It lacks granular actions like 'create expectation suites', 'configure checkpoints', or 'define schema contracts'. | 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 an explicit 'Use when...' clause. | 3 / 3 |
Trigger Term Quality | Includes relevant keywords like 'Great Expectations', 'dbt tests', 'data contracts', 'data quality', and 'validation rules'. However, it misses common variations users might say such as 'data testing', 'data profiling', 'schema validation', 'data checks', 'GE suites', or 'dbt schema tests'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of Great Expectations, dbt tests, and data contracts creates a clear niche focused specifically on data quality validation tooling. This is unlikely to conflict with general data engineering, ETL, or other data skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides highly actionable, executable examples across Great Expectations, dbt, and data contracts, which is its primary strength. However, it is excessively verbose — much of the content (concept explanations, exhaustive pattern variations) could be condensed or split into referenced files. The lack of progressive disclosure and a clear orchestration workflow connecting the patterns weakens its effectiveness as a skill document.
Suggestions
Reduce the content to a concise overview (under 100 lines) with quick-start examples, and move detailed patterns (GE suites, dbt tests, data contracts, pipeline code) into separate referenced files like GREAT_EXPECTATIONS.md, DBT_TESTS.md, DATA_CONTRACTS.md.
Remove the 'Core Concepts' section (data quality dimensions table and testing pyramid) — Claude already knows these concepts and they consume tokens without adding actionable value.
Add an explicit end-to-end workflow section showing how to sequence the tools: e.g., 1) Define contract → 2) Generate GE suite from contract → 3) Configure checkpoint → 4) Validate → 5) If failures, review report → 6) Fix and re-validate.
Remove the 'When to Use This Skill' bullet list — this information belongs in the YAML frontmatter description, not the body content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at 400+ lines. It explains basic concepts like data quality dimensions that Claude already knows, includes a redundant ASCII testing pyramid, and provides exhaustive code examples that could be significantly condensed. The 'Core Concepts' section and 'When to Use This Skill' list add little value for Claude. | 1 / 3 |
Actionability | The skill provides fully executable code examples across multiple frameworks — Great Expectations Python suites, dbt YAML tests, custom SQL tests, data contract YAML, and a complete quality pipeline class. All examples are copy-paste ready with realistic configurations. | 3 / 3 |
Workflow Clarity | While individual patterns are well-structured, there's no clear end-to-end workflow showing how to sequence these tools together. The automated pipeline (Pattern 6) includes a failure check but lacks explicit validation checkpoints between steps (e.g., verify GE context setup before running, verify suite creation before checkpoint). For batch/pipeline operations, the missing feedback loops cap this at 2. | 2 / 3 |
Progressive Disclosure | This is a monolithic wall of content with no references to external files. At 400+ lines, the detailed GE suite configurations, dbt test examples, data contract specs, and pipeline code should be split into separate reference files. Everything is inline with no navigation structure beyond section headers. | 1 / 3 |
Total | 7 / 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 (584 lines); consider splitting into references/ and linking | Warning |
Total | 10 / 11 Passed | |
- Repository
- wshobson/agents
- Commit
27a7ed9
- Reviewed
Table of Contents
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