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/).
89
86%
Does it follow best practices?
Impact
Pending
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 the natural terms a Databricks user would use. | 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/, audit logs, lineage, and billing tables makes it very 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 with strong actionability—concrete SQL, MCP tool signatures, and clear reference file organization. Its main weaknesses are some verbosity in the introductory sections and a lack of explicit validation/verification steps in workflows. The progressive disclosure pattern is exemplary with a clean reference table and appropriately scoped inline content.
Suggestions
Trim the 'When to Use This Skill' section to 3-4 key bullets or remove it entirely since the reference table already communicates scope.
Add a validation step after the GRANT statements (e.g., 'Verify access: SELECT * FROM system.access.audit LIMIT 1') to improve workflow clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The 'When to Use This Skill' section is somewhat verbose—listing 8 bullet points that largely restate what the reference files cover. The 'Best Practices' section contains generic advice Claude already knows (e.g., 'grant minimal access'). However, the quick start examples and tool tables are efficient. | 2 / 3 |
Actionability | Provides fully executable SQL queries, concrete MCP tool calls with exact parameter names and paths, and copy-paste ready grant statements. The tool usage table gives specific function signatures with realistic arguments. | 3 / 3 |
Workflow Clarity | The skill presents individual operations clearly but lacks sequenced workflows with validation checkpoints. For example, the 'Enable System Tables Access' section doesn't verify that grants succeeded, and there's no workflow connecting the steps (grant access → query → validate results). | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear reference table pointing to three detailed files (system tables, volumes, data profiling) at one level deep. The SKILL.md serves as a well-organized overview with quick start content inline and deeper topics properly delegated to reference files. | 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
02aac8c
- Reviewed
Table of Contents
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