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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.

69

Quality

87%

Does it follow best practices?

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality operational skill body: executable commands dominate, workflows are sequenced with checkpoints and destructive-action gates, and detail is properly pushed into real, well-labeled reference files. Remaining improvements are small: surface the orphaned medallion-from-cdc.md reference, add post-change verification for app/branch updates, and trim the legacy-migration prose.

Suggestions

Add references/medallion-from-cdc.md to the 'Reference docs' list in the body so it is discoverable from the top level instead of only via a link inside lakehouse-sync.md.

Add an explicit verification step after 'databricks apps create-update' and after branch creation (e.g., re-run 'databricks apps get' / 'list-branches' and confirm the resource/branch state), matching the verification pattern already used after create-project.

Tighten the Provisioned-to-Autoscaling migration block: the mapping table duplicates information already in the retirement callout, and the intro prose ("For context when reading older configs") could be one line.

DimensionReasoningScore

Conciseness

Dense and operational throughout ("Do NOT guess command syntax", limits as compact tables) with no padding of known concepts, and legacy Provisioned info is quarantined in a clearly-marked retired block. Minor tightening is still possible in the app-attach prose and the Provisioned mapping table.

4 / 5

Actionability

Fully executable guidance: copy-paste CLI commands with --json payloads, a complete create-update JSON payload, SQL GRANT statements, and a single-copy-paste scriptable token/connection block. Error rows map specific error strings to specific fixes.

5 / 5

Workflow Clarity

Create-project and schema-permission workflows have explicit verification checkpoints and error-recovery loops, and destructive operations are gated with user-confirmation warnings. However, the apps create-update and branch-creation flows lack verification steps, leaving minor validation gaps.

4 / 5

Progressive Disclosure

The body is an overview with six annotated, well-signaled one-level-deep reference links that all exist on disk. Minor gaps: references/medallion-from-cdc.md is not surfaced in the body's reference index (reachable only via a hop from lakehouse-sync.md), and there are several cross-file and cross-skill links.

4 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete capability enumeration paired with an explicit, natural-language 'Use when' trigger clause. Only minor gaps in action phrasing and synonym coverage keep specificity and trigger terms just below full marks.

DimensionReasoningScore

Specificity

Names the domain plus several concrete capability areas ("projects, scaling, connectivity, Lakebase synced tables, and Data API"), though as topic nouns rather than actions and with minor gaps (branching, app integration not mentioned).

4 / 5

Completeness

Explicitly answers both what ("projects, scaling, connectivity, Lakebase synced tables, and Data API") and when ("Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks") with concrete trigger phrases.

5 / 5

Trigger Term Quality

"Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks" covers natural user phrasings well, but misses common variations such as "serverless Postgres" or "OLTP workload".

4 / 5

Distinctiveness Conflict Risk

"Databricks Lakebase Postgres" is a clear niche and "on Databricks" scopes it away from generic Postgres or database skills, so conflict risk is minimal.

5 / 5

Total

18

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

databricks/databricks-agent-skills

Path

skills/databricks-lakebase/SKILL.md

Reviewed

Table of Contents

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