databricks-unity-catalog
Unity Catalog system tables and volumes. Use when querying system tables (audit, lineage, billing) or working with volume file operations (upload, download, list files in /Volumes/).
71
86%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
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, well-crafted description that concisely covers specific capabilities, includes natural trigger terms, and clearly delineates both what the skill does and when to use it. The domain-specific terminology (Unity Catalog, system tables, /Volumes/) makes it highly distinctive and unlikely to conflict with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: querying system tables (audit, lineage, billing) and volume file operations (upload, download, list files). These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what (Unity Catalog system tables and volumes) and when ('Use when querying system tables (audit, lineage, billing) or working with volume file operations (upload, download, list files in /Volumes/)'). Explicit 'Use when' clause is present. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'system tables', 'audit', 'lineage', 'billing', 'volume', 'upload', 'download', 'list files', '/Volumes/'. These cover common variations of how users would reference these features. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Unity Catalog system tables and volumes are specific Databricks concepts. The mention of /Volumes/ path, audit/lineage/billing tables, and volume file operations makes this unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill that serves effectively as a hub document with strong actionability through concrete SQL examples and MCP tool signatures. Its main weaknesses are some verbosity in the introductory sections and a lack of validation/error-handling steps in workflows. The progressive disclosure is excellent with clear navigation to supporting files.
Suggestions
Condense the 'When to Use This Skill' section—many bullets overlap (e.g., billing/compute/jobs/queries are all system table queries) and could be grouped into 2-3 categories.
Add validation steps after granting system table access (e.g., a test query to confirm permissions) and guidance for handling common errors like access denied or empty results.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'When to Use This Skill' section is somewhat verbose with 8 bullet points that could be condensed. The 'Best Practices' section contains generic advice Claude already knows (e.g., 'grant minimal access'). However, the code examples and tool tables are efficient and well-structured. | 2 / 3 |
Actionability | Provides fully executable SQL queries for lineage, audit, and billing use cases. MCP tool invocations are concrete with specific function signatures and parameters. The volume operations table gives copy-paste ready tool calls. | 3 / 3 |
Workflow Clarity | The skill presents individual operations clearly but lacks sequenced multi-step workflows with validation checkpoints. For example, granting system table access has no verification step to confirm the grants worked, and there's no error handling guidance for when queries return empty results or access is denied. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, quick-start content inline, and a well-organized reference table pointing to three detailed files (system tables, volumes, data profiling) plus related skills. References are one level deep and clearly signaled. | 3 / 3 |
Total | 10 / 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
93cb4e3
- Reviewed
Table of Contents
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