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databricks-lakeflow-connect

Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.

75

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is databricks-lakeflow-connect in databricks/databricks-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

92%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 strong overview-style SKILL.md: executable CLI/JSON examples throughout, wrong-vs-right anti-patterns for the three most likely failure modes, and a clean split of deep content into four well-signaled reference files that actually exist and go only one level deep. The only costs are minor redundancy in the catalog/decision pointers and validation detail that lives in the troubleshooting reference rather than inline in the workflow.

DimensionReasoningScore

Conciseness

The body is dense and assumes Databricks competence — no space is spent explaining what Unity Catalog, Delta, or CDC are, and the catalog/decision tables are token-efficient. Not a 5 because of small redundancies: the decision-tree reference is pointed to twice ("Is Lakeflow Connect the right tool?" and again under "Beta and Private Preview"), and the source list from the description is re-enumerated in the GA connector table.

4 / 5

Actionability

A fully executable `databricks pipelines create --json` example with real connection/table objects, concrete bundle run-by-key commands, imperative start/poll commands with the run asymmetry called out, and wrong-vs-right JSON anti-patterns (libraries vs ingestion_definition, continuous:true rejection). Commands are copy-paste ready and cover the common cases.

5 / 5

Workflow Clarity

The six-step workflow is clearly sequenced with validation checkpoints (verify prerequisites, poll the first run via start-update/get-update, watch the event log with the troubleshooting SQL a reference away). Not a 5 because the error-recovery loop — what to do when the first run fails — is delegated to reference 5 rather than stated inline in the workflow, and there is no explicit "only proceed when" gate analogous to the rubric's top anchor.

4 / 5

Progressive Disclosure

SKILL.md is a true overview: decision table, connector catalog, minimal example, and workflow, with deep material split into four real reference files (all verified present, one level deep, no nested references). The detailed-guides table labels each file with a "When to read" column, making navigation easy; the unreferenced assets and missing #3 reference are inconsequential.

5 / 5

Total

18

/

20

Passed

Description

95%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.

An exemplary description: a single concrete capability statement followed by an explicit 'Use when' clause enumerating the exact sources and target that a user would naturally name. Trigger coverage, completeness, and distinctiveness are all at the top anchor; the only minor limitation is that a single action verb covers the capability.

DimensionReasoningScore

Specificity

"Build managed ingestion pipelines into Databricks using Lakeflow Connect" names the domain and the concrete action, with the target ("into Unity Catalog with serverless pipelines") and source classes made explicit. Not a 5 because only one capability verb (build pipelines) is stated — the rest of the description enumerates triggers rather than additional actions like monitoring or scheduling pipelines.

4 / 5

Completeness

The first sentence answers "what" concretely ("Build managed ingestion pipelines into Databricks using Lakeflow Connect") and the second gives an explicit "Use when" clause with concrete trigger phrases (named SaaS apps, databases, Unity Catalog target). Both are present, explicit, and specific — matching the top anchor.

5 / 5

Trigger Term Quality

"ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog" comprehensively covers the natural source names a user would say when requesting this. The abbreviated "PuPr" (Public Preview) is the only jargon-adjacent term, but it sits alongside the plain phrase, so it does not lower coverage.

5 / 5

Distinctiveness Conflict Risk

The description carves a clear niche — managed pull ingestion from named SaaS/database sources into Unity Catalog via Lakeflow Connect — which is distinct from adjacent skills (file ingestion, push ingestion, federation) and unlikely to trigger for the wrong skill. Named-source triggers leave minimal conflict risk.

5 / 5

Total

19

/

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/devhub
Reviewed

Table of Contents

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