Name: databricks-apps-python
Rating: 66.4 (1 reviews)
Author: databricks

databricks-apps-python

Builds Databricks applications. Prefers AppKit (TypeScript + React SDK) for new apps; falls back to Python frameworks (Dash, Streamlit, Gradio, Flask, FastAPI, Reflex) when Python is required. Handles OAuth authorization, app resources, SQL warehouse and Lakebase connectivity, model serving, foundation model APIs, and deployment. Use when building web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions AppKit, Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app.

Quality

81%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

67%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.

A well-organized, actionable skill body with concrete commands, code, and clear reference navigation. Its main weaknesses are redundant doc/runtime sections and a broken reference to a non-existent examples/llm_config.py file.

Suggestions

Consolidate the overlapping 'Quick Reference', 'Platform Constraints', and 'Official Documentation' sections to remove repeated runtime specs and documentation URLs.

Either add the missing examples/llm_config.py file or remove the reference to it in the Foundation Models and Workflow sections.

Add an inline post-deploy verification checkpoint in the main Workflow so the deployment step has an explicit validation gate rather than deferring entirely to references/4-deployment.md.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and assumes Claude's competence (no basic concept explanations), but contains redundant sections — 'Quick Reference', 'Platform Constraints', and 'Official Documentation' repeat runtime/doc-URL information that could be consolidated.	3 / 5
Actionability	Provides executable commands ('databricks apps init/deploy') and concrete code (backend toggle, SQL warehouse connection, Pydantic models) with exact app.yaml commands per framework, but the backend-toggle snippet references backend_mock/backend_real modules that are not provided and examples/llm_config.py is referenced but absent.	4 / 5
Workflow Clarity	The Workflow section gives a clear decision-tree sequence ('New app from scratch? → Use AppKit') plus a Required Steps checklist, though verification checkpoints for deployment are deferred to references rather than stated inline in the main flow.	4 / 5
Progressive Disclosure	Well-structured overview with clearly signaled, one-level-deep references to six real reference files (each with keywords), but the body also points to examples/llm_config.py which does not exist in the bundle.	4 / 5
	Total	15 / 20 Passed

Description

95%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.

A strong, third-person description that explicitly covers what the skill does and when to use it with concrete, natural trigger terms. The only minor weakness is that capabilities are listed as handled domains rather than crisp discrete actions.

Dimension	Reasoning	Score
Specificity	Names the domain plus many concrete capability areas ('Handles OAuth authorization, app resources, SQL warehouse and Lakebase connectivity, model serving, foundation model APIs, and deployment'), but these are framed as handled domains rather than discrete discrete actions, sitting just below fully comprehensive.	4 / 5
Completeness	Explicitly answers both 'what' ('Builds Databricks applications... Handles...') and 'when' with a concrete 'Use when building web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions...' trigger clause.	5 / 5
Trigger Term Quality	Comprehensive natural terms including named frameworks (AppKit, Streamlit, Dash, Gradio, Flask, FastAPI, Reflex) plus app types (web apps, dashboards, ML demos, REST APIs) and 'Databricks app' that users would naturally say.	5 / 5
Distinctiveness Conflict Risk	Clear Databricks-apps niche with distinct, specific triggers (named frameworks and 'Databricks app'); minimal overlap risk with unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
relative_links	Relative link issues: 2 missing, 1 suspicious	Warning

	Total	14 / 16 Passed

Repository: databricks/devhub
Path: .databricks/aitools/skills/databricks-apps-python/SKILL.md
Commit: ade58e4

Reviewed: 5 days ago

Table of Contents

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