databricks-lakebase
Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.
71
87%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
89%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 solid skill description with excellent trigger terms and completeness, clearly identifying both what the skill covers and when to use it. The main weakness is that the 'what' portion lists topic areas rather than concrete actions, making it read more like a topic index than a capability description. Overall it would perform well in skill selection scenarios.
Suggestions
Replace topic nouns with action-oriented phrases, e.g., 'Configures Lakebase Postgres projects, manages scaling, sets up connectivity, syncs tables, and integrates via Data API' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (Databricks Lakebase Postgres) and lists several topic areas (projects, scaling, connectivity, synced tables, Data API), but these are more like categories than concrete actions. It doesn't use action verbs like 'configure', 'create', or 'troubleshoot'. | 2 / 3 |
Completeness | Clearly answers both 'what' (Databricks Lakebase Postgres covering projects, scaling, connectivity, synced tables, Data API) and 'when' with an explicit 'Use when...' clause specifying Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks. | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would actually say: 'Lakebase', 'Postgres', 'Databricks', 'OLTP', 'Data API', 'synced tables', 'connecting apps'. These cover multiple natural variations of how users might phrase requests about this topic. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche — Databricks Lakebase Postgres is a very specific product/feature area. The combination of 'Lakebase', 'Postgres on Databricks', and 'OLTP' makes it unlikely to conflict with general database or Postgres skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a high-quality skill that provides comprehensive, actionable guidance for Lakebase Postgres with excellent workflow clarity and progressive disclosure. The main weakness is length—some content (migration comparison table, extensive troubleshooting) could be moved to reference files to improve conciseness. Overall, it strikes a strong balance between being a useful quick-start guide and a thorough reference.
Suggestions
Move the 'Key Differences from Lakebase Provisioned' comparison table and migration notes to a reference file to reduce main skill length.
Consider moving the longer troubleshooting entries (especially synced table issues and DABs note) to a dedicated troubleshooting reference file, keeping only the most common errors inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is generally efficient and avoids explaining basic concepts Claude already knows (e.g., what Postgres is), but it's quite long (~300 lines) with some sections that could be tightened—the troubleshooting table is extensive, the 'Key Differences from Lakebase Provisioned' table could be in a reference file, and some explanatory notes (e.g., compliance line, cloud support repeated) add marginal value. | 2 / 3 |
Actionability | The skill provides fully executable CLI commands with concrete flags, JSON payloads, and copy-paste-ready bash scripts. Connection workflows include specific JSON paths for extracting values, and the scriptable version for psql connection is immediately usable. SQL examples for extensions and grants are also concrete and complete. | 3 / 3 |
Workflow Clarity | Multi-step processes are clearly sequenced with explicit validation checkpoints—project creation includes post-creation verification steps, the schema permissions workflow has a clear 'deploy first' sequence with error recovery options (A and B), and the SQL connection workflow is numbered with clear dependencies between steps. Destructive operations (delete project, drop schema) include explicit warnings and user confirmation requirements. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview with well-signaled one-level-deep references to six specific reference files (computes-and-scaling.md, connectivity.md, synced-tables.md, lakehouse-sync.md, pgvector.md, off-platform.md). The main file covers the essential workflows while appropriately deferring detailed topics like Data API, synced tables configuration, and vector search to dedicated reference documents. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- databricks/databricks-agent-skills
- Commit
caf71f1
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.