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databricks-apps

Build apps on Databricks Apps platform. Use when asked to create dashboards, data apps, analytics tools, or visualizations. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

74

Quality

92%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

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 as an effective orchestration hub for Databricks Apps development. Its greatest strengths are the clear decision gates, concrete CLI commands, and excellent progressive disclosure through well-organized reference links. The main weakness is moderate verbosity — some guidance is repeated across sections (Lakebase mentions, data access patterns), and the scaffolding rules protocol could be more concise.

Suggestions

Consolidate the overlapping Lakebase guidance — the State Storage Guidance, Decision Gate, 'When to Use What' routing table, and Lakebase apps workflow section all partially repeat each other; merge into fewer, non-redundant sections.

DimensionReasoningScore

Conciseness

The skill is fairly efficient and avoids explaining basic concepts Claude already knows (React, TypeScript, SQL), but it's quite long with some repetition — the Data Access Decision Gate table and routing table overlap, Lakebase guidance appears in multiple sections, and some warnings are restated. The scaffolding rules protocol section is verbose for what could be a shorter directive.

2 / 3

Actionability

The skill provides concrete, executable CLI commands for every step (manifest, init, validate, deploy, logs), specific flag syntax with examples, anti-pattern examples with ❌/✅ corrections, and clear routing tables for decision-making. The scaffolding workflow is copy-paste ready with real command structures.

3 / 3

Workflow Clarity

The development workflow is clearly sequenced with explicit ordering (numbered steps for analytics apps, decision gates before scaffolding, phase ordering for scaffolding rules). Validation checkpoints are explicit ('Run typegen BEFORE writing UI', 'validate before deploying', 'update smoke test selectors BEFORE running validation'), and there are feedback loops (conflict detection → ask user, validation failure → fix → re-validate).

3 / 3

Progressive Disclosure

The skill is an excellent overview that delegates detailed content to well-organized reference files via a clear phase-based table at the top. References are one level deep, clearly signaled with descriptive links (SQL Queries Guide, Frontend Guide, Lakebase Guide, etc.), and the routing table at the bottom maps use cases to specific reference files. The structure supports discovery without overwhelming.

3 / 3

Total

11

/

12

Passed

Description

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 skill description that clearly identifies the platform (Databricks Apps), lists concrete use cases (dashboards, data apps, analytics tools, visualizations), and provides explicit trigger guidance. The inclusion of the timing directive ('Invoke BEFORE starting implementation') adds useful operational context. The description is concise yet comprehensive.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'Build apps on Databricks Apps platform', 'create dashboards, data apps, analytics tools, or visualizations', 'Evaluates data access patterns', 'scaffolding'. These are concrete, actionable capabilities.

3 / 3

Completeness

Clearly answers both what ('Build apps on Databricks Apps platform, evaluates data access patterns before scaffolding') and when ('Use when asked to create dashboards, data apps, analytics tools, or visualizations'). Also includes a timing directive ('Invoke BEFORE starting implementation').

3 / 3

Trigger Term Quality

Includes natural keywords users would say: 'dashboards', 'data apps', 'analytics tools', 'visualizations', 'Databricks Apps'. Also includes domain-specific but relevant terms like 'Lakebase synced tables' and 'data access patterns' that power users would mention.

3 / 3

Distinctiveness Conflict Risk

Clearly scoped to the Databricks Apps platform specifically, with distinct triggers like 'Databricks', 'Lakebase synced tables', and platform-specific scaffolding. Unlikely to conflict with generic app-building or dashboard skills due to the Databricks specificity.

3 / 3

Total

12

/

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/databricks-agent-skills
Reviewed

Table of Contents

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