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databricks-apps

Build apps on Databricks Apps platform. Use when asked to create dashboards, data apps, analytics tools, or visualizations. Invoke BEFORE starting implementation.

68

Quality

83%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

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 well-structured skill that serves effectively as a hub document for Databricks Apps development. Its strengths are the clear phase-based reference table, concrete CLI commands, explicit workflow ordering with validation checkpoints, and a useful decision matrix for choosing approaches. Minor verbosity from repeated warnings and some redundant constraints slightly reduce token efficiency, but overall the content is high quality and actionable.

DimensionReasoningScore

Conciseness

The skill is mostly efficient and avoids explaining basic concepts Claude already knows, but there's some repetition (e.g., app name constraints mentioned twice, multiple reminders about reading parent skill, repeated warnings about not guessing). The tables and structure help, but some sections could be tightened.

2 / 3

Actionability

Provides concrete, executable CLI commands for scaffolding, manifest inspection, and validation. The workflow steps are specific with exact commands, flag names, and file paths. The common mistakes section with ❌/✅ examples is particularly actionable. The 'When to Use What' decision matrix gives clear, specific guidance.

3 / 3

Workflow Clarity

The development workflow is clearly sequenced with numbered steps and explicit ordering constraints ('DO NOT write UI code before running typegen'). Validation checkpoints are present (validate before deploying, update smoke tests before validation). The scaffolding workflow has a clear two-phase process (manifest first, then init). The 'When to Use What' section provides clear decision paths with anti-pattern warnings.

3 / 3

Progressive Disclosure

Excellent use of a phase-based reference table at the top that maps tasks to specific reference files. The skill serves as a clear overview/hub with well-signaled one-level-deep references to SQL queries, frontend, AppKit SDK, tRPC, Lakebase, platform guide, and other frameworks. Content is appropriately split between the overview here and detailed guides in referenced files.

3 / 3

Total

11

/

12

Passed

Description

82%

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 description that clearly communicates both what the skill does and when to use it, with good trigger terms tied to the Databricks ecosystem. The main weaknesses are that the capability descriptions are somewhat high-level and the trigger terms like 'dashboards' and 'visualizations' could overlap with non-Databricks visualization skills. The timing instruction ('BEFORE starting implementation') is a useful operational detail.

Suggestions

Add more specific concrete actions to improve specificity, e.g., 'Build and deploy Streamlit/Gradio/Flask apps on Databricks Apps platform, configure app permissions, connect to Unity Catalog data sources'.

Strengthen distinctiveness by emphasizing Databricks-specific terms in the trigger clause, e.g., 'Use when the user mentions Databricks Apps, Databricks dashboards, or building apps within a Databricks workspace'.

DimensionReasoningScore

Specificity

Names the domain (Databricks Apps platform) and some actions (create dashboards, data apps, analytics tools, visualizations), but these are somewhat high-level categories rather than deeply specific concrete actions like 'configure data sources, set up authentication, deploy to workspace'.

2 / 3

Completeness

Clearly answers both what ('Build apps on Databricks Apps platform') and when ('Use when asked to create dashboards, data apps, analytics tools, or visualizations'), with an additional timing instruction ('Invoke BEFORE starting implementation').

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'Databricks', 'dashboards', 'data apps', 'analytics tools', 'visualizations'. These are terms users would naturally use when requesting this type of work.

3 / 3

Distinctiveness Conflict Risk

The Databricks-specific framing provides some distinctiveness, but terms like 'dashboards', 'analytics tools', and 'visualizations' are generic enough to potentially overlap with other dashboard/visualization skills (e.g., a general Streamlit or Plotly skill).

2 / 3

Total

10

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
databricks/devhub
Reviewed

Table of Contents

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