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 a strong 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. The description is concise yet comprehensive, covering multiple distinct use cases with natural trigger terms that would help Claude accurately select this skill.
| 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', 'reverse ETL', 'synced tables', 'OAuth credentials', 'dev/test workflows'. 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 carves out a niche unlikely to conflict with generic database or PostgreSQL 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 redundant sections and explanations) and lack of explicit multi-step workflow sequencing with validation checkpoints. The troubleshooting table and comparison table add significant practical value.
Suggestions
Add an explicit end-to-end workflow section (e.g., 'First Project Setup') with numbered steps, .wait() validation checkpoints, and error recovery guidance for the create→configure→connect flow.
Remove or consolidate the 'When to Use' and 'Overview' sections which largely duplicate the opening paragraph and description; fold unique details into the feature table.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good use of tables and code examples, but includes some unnecessary sections like 'When to Use' (which restates the description), the Overview paragraph that repeats the intro, and explanatory text Claude already knows. The 'Notes' section partially duplicates information from tables above. | 2 / 3 |
Actionability | Excellent actionability with fully executable Python code examples for all major operations (create project, generate credentials, connect, create branch, resize compute), complete CLI commands, and specific SDK class/method names. Code is copy-paste ready with real import paths and parameter names. | 3 / 3 |
Workflow Clarity | Individual operations are clear, but there's no explicit end-to-end workflow with validation checkpoints. The Quick Start shows project creation but doesn't sequence through the full setup (create project → wait → get endpoint → generate credential → connect). The '.wait()' pattern is mentioned in Notes but not consistently shown as a validation step in workflows. Missing feedback loops for error recovery in multi-step operations. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure with a clear overview in SKILL.md and well-signaled one-level-deep references to detailed files (projects.md, branches.md, computes.md, connection-patterns.md, reverse-etl.md). The main file provides enough to get started while pointing to specifics. Related skills are also clearly linked. | 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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