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 occupies a distinct niche that minimizes conflict risk with other skills.
| 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' (explicit 'Use when' clause listing five specific 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 authentication', '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, Databricks, and PostgreSQL-specific use cases 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 through executable code examples covering all major use cases. The progressive disclosure is well-handled with appropriate references to detailed files. 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 provisioning and connecting.
Suggestions
Remove or significantly trim the Overview section's feature table and description paragraph — Claude doesn't need to be told what managed PostgreSQL or OAuth authentication are.
Add a sequenced workflow with validation steps for the common end-to-end flow (create instance → verify state → connect → validate connection), especially checking instance.state before attempting connections.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview table and 'When to Use' section explain things Claude could infer. The feature table, available regions list, and some commentary ('Lakebase Provisioned is Databricks' managed PostgreSQL database service for OLTP...') add tokens without much value. However, the code examples themselves are lean and the overall structure is reasonably efficient. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples for all major patterns: instance creation, OAuth token generation, notebook connection, SQLAlchemy with token refresh, Unity Catalog registration, MLflow resources, and CLI commands. SDK version requirements and pip install commands are included. | 3 / 3 |
Workflow Clarity | Individual patterns are clear but there's no explicit multi-step workflow with validation checkpoints. The token refresh pattern is well-documented, but operations like creating an instance, registering with Unity Catalog, and setting up reverse ETL lack a sequenced workflow with verification steps (e.g., checking instance state before connecting, validating registration succeeded). | 2 / 3 |
Progressive Disclosure | The skill provides a clear overview with well-organized sections, references to detailed files (connection-patterns.md, reverse-etl.md) that are one level deep, and links to related skills. The MCP tools are summarized in tables rather than fully detailed inline. Content is appropriately split between the main file and reference files. | 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
b4071a0
- Reviewed
Table of Contents
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