Name: databricks-pipelines
Rating: 66.4 (1 reviews)
Author: databricks

databricks-pipelines

Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.

Quality

80%

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Securityby

Passed

No findings from the security scan

Quality

Content

78%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 well-architected skill body: lean tables, concrete commands and API signatures, clearly separated workflows with validation, and exemplary one-level-deep progressive disclosure to a verified reference bundle. Slight room to tighten redundancy and consolidate the distributed validation guidance into explicit checklists.

Dimension	Reasoning	Score
Conciseness	The body is dense and table-driven with minimal conceptual padding (it does not explain what Databricks/Spark/Delta are) and assumes Claude's competence, though the sheer volume of API tables and a lightly redundant Reference Index could be trimmed slightly.	4 / 5
Actionability	Concrete CLI command blocks (e.g. 'databricks bundle deploy -t dev --profile <profile>') and exact API/decorator signatures ('@dp.table()', 'dp.create_auto_cdc_flow()') are present throughout, but most full executable Python/SQL examples are deferred to reference files rather than inlined, leaving minor gaps.	4 / 5
Workflow Clarity	Workflows A/B/C are clearly distinguished, Running a Pipeline gives a sequenced validate→deploy→run→poll flow with an explicit 'databricks bundle validate' checkpoint, and the Common Issues table provides error→fix feedback; however the validation/recovery steps are distributed across sections rather than one cohesive numbered checklist with retry loops.	4 / 5
Progressive Disclosure	The body is an overview of API tables and decision trees that consistently links to one-level-deep reference files (all referenced files exist in references/), with a dedicated Reference Index and clear per-(feature, language) navigation; no nested references.	5 / 5
	Total	17 / 20 Passed

Description

82%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 tight, well-targeted description that clearly states both capability and trigger conditions with good natural-language keywords and a distinct niche. The only minor gap is that it lists one action verb rather than enumerating several specific concrete actions.

Dimension	Reasoning	Score
Specificity	The description names the domain ('Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks') and gives parameters ('batch or streaming data pipelines with Python or SQL') but offers only one action verb ('Develop') rather than a list of several concrete actions, matching the anchor that names domain plus 1-2 concrete actions but is not comprehensive.	3 / 5
Completeness	It explicitly answers both what ('Develop Lakeflow Spark Declarative Pipelines ... on Databricks') and when ('Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural keyword coverage ('data pipelines', 'batch or streaming', 'Python or SQL', 'Databricks', 'Delta Live Tables', 'Lakeflow') including the legacy synonym users still say, though a few natural variations (e.g., 'ETL', 'medallion') are absent.	4 / 5
Distinctiveness Conflict Risk	The niche is highly specific (Lakeflow/SDP/DLT on Databricks) with distinct triggers and an explicit pointer to the parent databricks-core skill, minimizing conflict risk with unrelated skills.	5 / 5
	Total	17 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
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-pipelines/SKILL.md
Commit: 691deaa

Reviewed: 5 days ago

Table of Contents

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