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databricks-lakebase-provisioned

Patterns and best practices for Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. Use when creating Lakebase instances, connecting applications or Databricks Apps to PostgreSQL, implementing reverse ETL via synced tables, storing agent or chat memory, or configuring OAuth authentication for Lakebase.

68

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

The body is highly actionable with executable examples across SDK, CLI, and MCP tooling, but carries mild verbosity, lacks validation checkpoints around destructive operations, and references bundle files that are not present.

Suggestions

Add explicit verify/confirm steps before destructive actions like `manage_lakebase_database(action="delete", force=True)` and `manage_lakebase_sync(action="delete")`.

Either provide the referenced connection-patterns.md and reverse-etl.md files or remove the dangling Reference Files links.

Tighten the Overview paragraph and Notes section to reduce restatement of the feature table and drop the non-provisioned Autoscaling aside.

DimensionReasoningScore

Conciseness

Mostly efficient with abundant executable code, but the Overview prose, feature table, and Notes section restate information and include context (e.g., the Autoscaling aside) that could be trimmed.

2 / 3

Actionability

Provides fully executable, copy-paste-ready Python, CLI commands, and MCP tool examples with concrete parameters — e.g., the SQLAlchemy token-refresh loop and databricks CLI snippets.

3 / 3

Workflow Clarity

Sections are well organized but destructive operations (delete database, delete synced table, force=True) lack explicit validation/verification checkpoints, capping clarity at 2 per the destructive-operations guideline.

2 / 3

Progressive Disclosure

A Reference Files section signals connection-patterns.md and reverse-etl.md, but those files do not exist in any bundle directory, and a large amount of content remains inline rather than being split out.

2 / 3

Total

9

/

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.

The description is specific, trigger-rich, and clearly distinguishes the skill with an explicit 'Use when' clause. It answers both what and when without padding.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'creating Lakebase instances, connecting applications or Databricks Apps to PostgreSQL, implementing reverse ETL via synced tables, storing agent or chat memory, or configuring OAuth authentication' — matching the multi-action anchor.

3 / 3

Completeness

Explicitly states what the skill covers ('Patterns and best practices for Lakebase Provisioned... for OLTP workloads') and an explicit 'Use when...' trigger clause enumerating scenarios, answering both what and when.

3 / 3

Trigger Term Quality

Covers natural terms a user would say — 'Lakebase', 'PostgreSQL', 'Databricks Apps', 'reverse ETL', 'agent or chat memory', 'OAuth' — with good breadth of phrasings.

3 / 3

Distinctiveness Conflict Risk

Targets a clear niche (Databricks managed PostgreSQL / Lakebase Provisioned) with distinct triggers unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 2 missing, 4 suspicious

Warning

Total

15

/

16

Passed

Repository
databricks-solutions/ai-dev-kit
Reviewed

Table of Contents

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