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.
89
86%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
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 an excellent skill description that clearly defines its scope around Lakebase Provisioned PostgreSQL, lists specific concrete use cases, and includes an explicit 'Use when...' clause with multiple natural trigger scenarios. The description is concise yet comprehensive, uses third-person voice appropriately, and is highly distinctive due to the specificity of the product domain.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: creating Lakebase instances, connecting applications to PostgreSQL, implementing reverse ETL via synced tables, storing agent/chat memory, and configuring OAuth authentication. | 3 / 3 |
Completeness | Clearly answers both 'what' (patterns and best practices for Lakebase Provisioned PostgreSQL for OLTP workloads) and 'when' with an explicit 'Use when...' clause listing five distinct trigger scenarios. | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'Lakebase', 'PostgreSQL', 'OLTP', 'reverse ETL', 'synced tables', 'agent memory', 'chat memory', 'OAuth', 'Databricks Apps'. These cover a wide range of natural user queries related to this domain. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Lakebase Provisioned (Databricks managed PostgreSQL) is a very specific product/service. The combination of Lakebase, PostgreSQL, reverse ETL, synced tables, and OAuth authentication makes it unlikely to conflict with other skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, comprehensive skill with excellent actionability—nearly every pattern includes executable code and CLI commands. The main weaknesses are some unnecessary explanatory content that Claude doesn't need (overview descriptions, feature tables) and a lack of explicit validation checkpoints in multi-step workflows like instance creation and connection setup. Progressive disclosure is well-handled with appropriate references to detailed files.
Suggestions
Remove or significantly trim the 'Overview' section—the feature table and description explain concepts Claude can infer from the code examples and 'When to Use' section.
Add explicit validation steps to the Quick Start workflow, e.g., checking instance.state before attempting connection, and verifying the connection succeeds before proceeding to schema creation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview section with the feature table and the 'When to Use' section explain things Claude could infer. The available regions list and some descriptive text add tokens without much value. However, the code examples themselves are lean and the tables are efficient. | 2 / 3 |
Actionability | The skill provides fully executable Python code examples for every major pattern (creating instances, generating tokens, connecting via psycopg, SQLAlchemy with token refresh, Unity Catalog registration), plus complete CLI commands. Code is copy-paste ready with real SDK calls. | 3 / 3 |
Workflow Clarity | Individual patterns are clear, but there's no explicit end-to-end workflow with validation checkpoints. For instance, after creating an instance there's no step to verify it's running before connecting. The token refresh pattern is well-documented but the overall provisioning-to-connection flow lacks explicit sequencing and validation steps. | 2 / 3 |
Progressive Disclosure | The skill has a clear Quick Start section, organized common patterns, and appropriately references external files (connection-patterns.md, reverse-etl.md) for detailed content. Related skills are well-linked. Content is structured with clear headers and tables for quick scanning. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- databricks-solutions/ai-dev-kit
- Commit
02aac8c
- Reviewed
Table of Contents
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