Name: databricks-aibi-dashboards
Rating: 68.8 (1 reviews)
Author: databricks

databricks-aibi-dashboards

Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.

Quality

85%

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Securityby

Passed

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Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-structured, highly actionable skill with clear multi-step workflow, explicit validation checkpoints, and excellent progressive disclosure into real reference files. The only meaningful gap is some repetition of the same rules across sections that could be consolidated to save tokens.

Suggestions

Consolidate the bare-table-name / FROM rule into one canonical location and reference it elsewhere instead of restating it three times (Quick Reference, Step 2, Implementation Guidelines).

The create-command flag-only vs JSON-only guidance appears in both the Quick Reference table and Step 5; keep the detailed explanation in one place and link to it.

The palette-design rules section is long; consider moving the full starter-palette list into a reference file and keeping only the 60/30/10 mental model inline.

Dimension	Reasoning	Score
Conciseness	The body is information-dense with executable commands and JSON skeletons and does not pad with basic concepts, but the bare-table-name rule and the create-command flag-only/JSON-only warnings are repeated across the Quick Reference, Step 2, Step 5, and Implementation Guidelines; matches anchor 4 (efficient, minor instances that could be trimmed) rather than 5 (every token earns its place).	4 / 5
Actionability	Provides copy-paste-ready CLI commands ("databricks lakeview create ..."), complete JSON skeletons, and a runnable parallel-probe bash block; matches anchor 5 (fully executable, specific examples cover common cases).	5 / 5
Workflow Clarity	The NEW DASHBOARD CREATION WORKFLOW (Steps 1–5) has explicit validation ("You MUST test ALL SQL queries via CLI BEFORE deploying", Step 3 "Verify Data Matches Story"), a feedback loop (fix query / adjust story before deploying), and a 12-item Quality Checklist; matches anchor 5 (clear sequence, explicit validation, error-recovery loop, checklist).	5 / 5
Progressive Disclosure	SKILL.md is an overview with one-level-deep references to five real reference files (verified present: 1-widget-specifications.md, 2-advanced-widget-specifications.md, 3-filters.md, 4-examples.md, 5-troubleshooting.md), each signaled via a Widget Index table and a task→file Reference Files table; matches anchor 5 (clear overview, well-signaled one-level-deep references, appropriately split, easy navigation).	5 / 5
	Total	19 / 20 Passed

Description

78%Weight 40%Scale 1-5

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 clearly states what the skill does and when to use it, with strong distinctiveness and good trigger terms. It is held back from top marks by redundant phrasing ("as Databricks Dashboard have a unique json structure") and a lack of broader natural synonyms beyond "dashboards"/"Lakeview".

Suggestions

Reword the grammatically awkward and somewhat redundant clause "as Databricks Dashboard have a unique json structure" into a cleaner benefit statement or drop it.

Add a few broader natural trigger phrases users might say, e.g. "Use when the user mentions Databricks dashboards, Lakeview, or wants to visualize/KPI governed data".

Confirm the description stays in third person throughout and lead with the core action to keep it crisp.

Dimension	Reasoning	Score
Specificity	Quotes "Create Databricks AI/BI dashboards" and "creating, updating, or deploying Lakeview dashboards" — names the domain plus three concrete actions; matches anchor 4 (several specific actions, minor gaps) rather than 5 which demands comprehensive coverage of all dashboard actions.	4 / 5
Completeness	Answers both what ("Create Databricks AI/BI dashboards") and when ("Must use when creating, updating, or deploying Lakeview dashboards"); the awkward "as Databricks Dashboard have a unique json structure" clause and lack of broader user-mention trigger phrases keep it at anchor 4 rather than 5.	4 / 5
Trigger Term Quality	Includes natural terms "Databricks AI/BI dashboards" and "Lakeview dashboards" that users would say; matches anchor 4 (good keyword coverage, a few natural terms missing) rather than 5 which requires comprehensive synonyms and file extensions.	4 / 5
Distinctiveness Conflict Risk	"Databricks AI/BI dashboards" and "Lakeview dashboards" carve a clear niche with distinct triggers and minimal conflict risk, matching anchor 5; not 4 because overlap with other skills is minimal rather than a minor risk.	5 / 5
	Total	17 / 20 Passed

Validation

81%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (513 lines); consider splitting into references/ and linking	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 3 missing, 5 suspicious	Warning

	Total	13 / 16 Passed

Repository: databricks/devhub
Path: .databricks/aitools/skills/databricks-aibi-dashboards/SKILL.md
Commit: ade58e4

Reviewed: 5 days ago

Table of Contents

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