Name: databricks-jobs
Rating: 72.8 (1 reviews)
Author: databricks

databricks-jobs

Develop and deploy Lakeflow Jobs on Databricks. Use when creating data engineering jobs with notebooks, Python wheels, or SQL tasks. Invoke BEFORE starting implementation.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Advisory

Suggest reviewing before use

Quality

Content

80%

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

The content is highly actionable and token-efficient, with concrete executable commands and configs throughout. Its weaknesses are the absence of an explicit validate→fix→retry feedback loop in the deploy workflow and the lack of progressive disclosure into bundle reference files.

Suggestions

Add an explicit error-recovery loop to the Development Workflow (e.g., 'If `bundle validate` fails, read the error, fix the config, and re-run validate before deploying') to lift workflow_clarity to 3.

Move the large inline CLAUDE.md/AGENTS.md template and/or the detailed task-configuration examples into reference files under references/ and link to them one level deep, improving progressive_disclosure.

Consolidate the two scheduling examples (periodic vs. quartz cron) or move the secondary one to a reference file to tighten conciseness.

Dimension	Reasoning	Score
Conciseness	The body is code/config-dense with no lecturing on basic Databricks concepts; the embedded CLAUDE.md/AGENTS.md template is justified copy-paste material and every section earns its place, matching the lean score-3 anchor rather than the padded score-2 example.	3 / 3
Actionability	It provides fully executable, copy-paste-ready commands (`databricks bundle init` with complete config), runnable YAML job definitions, notebook Python, and `uv run pytest`, matching the score-3 anchor.	3 / 3
Workflow Clarity	The Development Workflow lists Validate→Deploy→Run→Check status with a `databricks bundle validate` checkpoint, but lacks an explicit error-recovery feedback loop; per the scoring notes, missing feedback loops for batch/deploy operations caps this at 2 rather than 3.	2 / 3
Progressive Disclosure	It is a single ~180-line file with no bundle references (references/scripts/assets absent) and all detailed content (full templates, multiple scheduling variants) inline; sections are well organized so it is not a wall of text, but no content is split into one-level-deep reference files, so it does not reach 3.	2 / 3
	Total	10 / 12 Passed

Description

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.

The description is concise, specific, and includes an explicit "Use when" trigger covering both what the skill does and when to invoke it. Voice is third-person/imperative throughout, so no specificity penalty applies.

Dimension	Reasoning	Score
Specificity	"Develop and deploy Lakeflow Jobs" plus task types "notebooks, Python wheels, or SQL tasks" list multiple specific concrete actions, matching the score-3 anchor; it is comprehensive rather than naming only some actions.	3 / 3
Completeness	It states what ("Develop and deploy Lakeflow Jobs on Databricks") and an explicit when ("Use when creating data engineering jobs..."), clearly answering both with an explicit trigger; not score 2 because the when clause is present, not merely implied.	3 / 3
Trigger Term Quality	"Lakeflow Jobs", "Databricks", and "data engineering jobs with notebooks, Python wheels, or SQL tasks" give good coverage of natural terms a user would actually say, matching the score-3 anchor rather than a single keyword.	3 / 3
Distinctiveness Conflict Risk	"Lakeflow Jobs on Databricks" carves a clear niche with distinct triggers unlikely to fire for unrelated skills, matching the score-3 anchor.	3 / 3
	Total	12 / 12 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/devhub
Commit: 131db95

Reviewed: 5 days ago

Table of Contents

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