databricks-aibi-dashboards
Create Databricks AI/BI dashboards. Use when creating, updating, or deploying Lakeview dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.
72
87%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
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 with clear trigger terms and explicit 'Use when' guidance targeting a specific product domain. The main weakness is that the capability description could be more specific about what kinds of dashboard operations are supported. The 'CRITICAL' and 'Follow guidelines strictly' portions are internal instructions that don't help with skill selection and add noise.
Suggestions
Expand the capability list with more specific actions (e.g., 'create charts, configure filters, define datasets, manage layouts') to improve specificity.
Remove internal process instructions ('CRITICAL: You MUST test ALL SQL queries...', 'Follow guidelines strictly') from the description since these don't aid skill selection and belong in the skill body.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Databricks AI/BI dashboards) and mentions actions like creating, updating, deploying, and testing SQL queries, but doesn't list specific concrete capabilities like chart types, layout configuration, data source setup, etc. | 2 / 3 |
Completeness | Clearly answers both 'what' (create Databricks AI/BI dashboards, test SQL queries, deploy) and 'when' ('Use when creating, updating, or deploying Lakeview dashboards'), with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms: 'Databricks', 'AI/BI', 'dashboards', 'Lakeview', 'SQL queries', 'deploying'. Users asking about Databricks dashboards would naturally use these terms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific product names (Databricks, Lakeview, AI/BI) that create a clear niche unlikely to conflict with other skills like generic SQL or dashboard tools. | 3 / 3 |
Total | 11 / 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 for creating Databricks dashboards. The mandatory validation workflow with explicit checkpoints is excellent for preventing deployment failures. Minor verbosity from repeated warnings and some redundancy between sections slightly reduces token efficiency, but the content is overwhelmingly useful and domain-specific.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and avoids explaining basic concepts, but has some redundancy—the critical warnings are repeated multiple times (field name matching rule appears in both section 2 and the checklist, the validation mandate is stated in multiple places). The ASCII workflow box and some table formatting add tokens without proportional value. However, most content is genuinely instructive and domain-specific. | 2 / 3 |
Actionability | Provides concrete, executable JSON patterns, specific tool invocations with parameters, exact SQL function names to use (and avoid), precise grid dimensions, and copy-paste-ready code examples. The field matching examples with correct/wrong patterns are particularly actionable. Every section gives specific, implementable guidance rather than abstract descriptions. | 3 / 3 |
Workflow Clarity | The 5-step mandatory workflow is clearly sequenced with explicit validation at step 3, a feedback loop (fix and re-test before proceeding), and a final quality checklist before deployment. The workflow diagram makes the sequence unambiguous, and the warning about skipping validation is well-placed. This is a destructive/deployment operation and the validation checkpoints are thorough. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview with well-signaled one-level-deep references to four supporting files (widget specs, filters, examples, troubleshooting) organized in a table by use case. The main SKILL.md contains the essential workflow and guidelines while appropriately deferring detailed specifications to reference files. Related skills are also linked at the bottom. | 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
93cb4e3
- Reviewed
Table of Contents
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