databricks-metric-views
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
94
92%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 strong skill description that clearly communicates what the skill does (define, create, query, manage metric views in YAML) and when to use it (building KPIs, revenue metrics, order analytics, reusable metrics). It uses third person voice, includes natural trigger terms, and occupies a distinct niche with Unity Catalog metric views.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'define, create, query, and manage governed business metrics in YAML.' Also specifies the domain clearly (Unity Catalog metric views) and the format (YAML). | 3 / 3 |
Completeness | Clearly answers both 'what' (define, create, query, and manage governed business metrics in YAML) and 'when' (explicit 'Use when' clause covering building standardized KPIs, revenue metrics, order analytics, or reusable business metrics needing consistent definitions). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'KPIs', 'revenue metrics', 'order analytics', 'business metrics', 'YAML', 'Unity Catalog', 'metric views', 'reusable', 'consistent definitions'. Good coverage of terms a user working in this domain would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Unity Catalog metric views in YAML format. The combination of 'Unity Catalog', 'metric views', 'YAML', and 'governed business metrics' creates a very specific trigger profile unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, well-structured skill that provides highly actionable guidance with executable examples for both SQL and MCP tool approaches. The progressive disclosure is excellent with clear references to deeper content. The main weakness is moderate verbosity—some conceptual comparison tables and the integrations list could be trimmed to improve token efficiency.
Suggestions
Remove or significantly condense the 'Dimensions vs Measures' and 'Why Metric Views vs Standard Views' comparison tables, as Claude already understands these concepts and they consume tokens without adding actionable guidance.
Remove the 'Integrations' bullet list—it's informational but not actionable and could be a single sentence or moved to a reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally well-structured but includes some content that could be trimmed. The 'Dimensions vs Measures' and 'Why Metric Views vs Standard Views' comparison tables explain concepts Claude likely already understands. The 'Integrations' section is a simple list that adds little actionable value. The YAML quick reference partially duplicates the full example above it. | 2 / 3 |
Actionability | The skill provides fully executable SQL and MCP tool examples for creating, querying, describing, granting, and managing metric views. The code is copy-paste ready with realistic field names and complete syntax, covering both SQL and programmatic approaches. | 3 / 3 |
Workflow Clarity | The workflow is clearly sequenced: inspect source table schema first, then create the metric view, then query it. The 'Common Issues' table serves as a validation/troubleshooting checklist. For a non-destructive operation (creating views), the level of workflow guidance is appropriate and well-ordered. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview with a quick start, then points to one-level-deep reference files (yaml-reference.md and patterns.md) via a well-organized table. External documentation links are cleanly separated in a Resources section. Content is appropriately split between the overview and detailed references. | 3 / 3 |
Total | 11 / 12 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- databricks-solutions/ai-dev-kit
- Commit
02aac8c
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.