databricks-lakebase-autoscale
Patterns and best practices for Lakebase Autoscaling (next-gen managed PostgreSQL). Use when creating or managing Lakebase Autoscaling projects, configuring autoscaling compute or scale-to-zero, working with database branching for dev/test workflows, implementing reverse ETL via synced tables, or connecting applications to Lakebase with OAuth credentials.
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 identifies the product domain (Lakebase Autoscaling / managed PostgreSQL), lists specific capabilities, and provides explicit trigger guidance via a well-structured 'Use when...' clause. It uses third-person voice appropriately and includes enough product-specific terminology to be highly distinctive among a large set of skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: creating/managing projects, configuring autoscaling compute, scale-to-zero, database branching for dev/test, reverse ETL via synced tables, and connecting with OAuth credentials. | 3 / 3 |
Completeness | Clearly answers both 'what' (patterns and best practices for Lakebase Autoscaling managed PostgreSQL) 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', 'Autoscaling', 'PostgreSQL', 'scale-to-zero', 'database branching', 'dev/test', 'reverse ETL', 'synced tables', 'OAuth credentials'. Good coverage of domain-specific terms a user working with this product would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with product-specific terminology ('Lakebase Autoscaling', 'synced tables', 'scale-to-zero') that clearly distinguishes it from generic PostgreSQL or database skills. Very 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 well-structured, highly actionable skill with excellent progressive disclosure and concrete, executable code examples covering all major Lakebase Autoscaling operations. Its main weaknesses are moderate verbosity (some repeated information across sections, unnecessary explanatory text) and lack of explicit validation checkpoints in multi-step workflows. The Common Issues table and CLI reference are valuable additions that enhance practical utility.
Suggestions
Add explicit validation/verification steps to the Quick Start workflow (e.g., verify project status after creation, test connection before proceeding)
Remove the 'When to Use' section and the Overview paragraph since they largely restate the frontmatter description and feature table; let the feature table speak for itself
Consolidate the 'Notes' section into the relevant tables/sections above to reduce repetition (e.g., CU RAM info is in both the feature table and Notes)
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary sections like 'When to Use' (which restates the description), the Overview paragraph that re-explains what Lakebase is, and the 'Notes' section that partially duplicates information from tables above. The feature table and comparison table are useful but some content is repeated across sections. | 2 / 3 |
Actionability | Excellent actionability with fully executable Python code examples for all major operations (create project, generate tokens, connect, create branches, resize compute), complete CLI commands, and concrete MCP tool invocations with specific parameter examples. Code is copy-paste ready with real SDK imports and method calls. | 3 / 3 |
Workflow Clarity | Individual operations are clearly shown but there's no explicit multi-step workflow with validation checkpoints. For example, the Quick Start shows project creation but doesn't guide through the full workflow of create → verify → connect → validate connection. The Common Issues table helps with troubleshooting but there are no feedback loops or explicit validation steps in the code patterns. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear overview in the main file and well-signaled one-level-deep references to detailed files (projects.md, branches.md, computes.md, connection-patterns.md, reverse-etl.md). Related skills are also clearly linked. The main file provides enough to get started while pointing to specifics. | 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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