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databricks-jobs

Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation. For compute (serverless, performance mode, classic), also use databricks-serverless.

Quality

87%

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-jobs in databricks/databricks-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

86%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-engineered skill body: executable examples across DABs/SDK/CLI, real and correctly anchored reference files, explicit compute routing, and a troubleshooting table. The remaining gains are tightening duplicated CLI listings and adding an explicit validate-fix-retry loop plus a caution on auto-approved destroys.

Suggestions

Collapse the "Common Operations" CLI listing and the "Development Workflow" steps into one section to remove the repeated validate/deploy/run commands.

Add an explicit feedback loop: "If bundle validate fails, fix the reported issue and re-validate before deploying."

Add a warning or confirmation step before `databricks bundle destroy --auto-approve`, since it irreversibly removes deployed resources.

DimensionReasoningScore

Conciseness

The body is dense and almost every section carries non-obvious specifics (run_if conditions, permission levels, the run-now --json idiom, "no cluster block = serverless"). Minor trimmable content exists — the full project-structure tree and the re-listing of CLI commands in both "Common Operations" and "Development Workflow" — but no padding explaining concepts Claude already knows, placing it above the midpoint, not at the lean-anchor 5.

4 / 5

Actionability

Copy-paste-ready, complete examples in all three management modes: a non-interactive `databricks bundle init` heredoc, DABs YAML, a runnable Python SDK snippet with imports, and exact CLI commands including the non-obvious `jobs run-now --json '{"job_id": ...}'` form. The common cases (create, list, run, cancel, delete, validate, deploy) are all covered with real commands.

5 / 5

Workflow Clarity

The Development Workflow gives a clear ordered sequence (validate → deploy → run → check status) with `bundle validate` as a pre-deploy checkpoint and a Common Issues table for error recovery. It stops short of the 5 anchor because there is no explicit validate→fix→retry loop, and the destructive `databricks bundle destroy --auto-approve` appears without any confirmation guidance.

4 / 5

Progressive Disclosure

Textbook progressive disclosure: a Reference Files table maps use cases to four real one-level-deep files (all verified present), the task-type and trigger tables link to verified section anchors within those files, the body keeps only summaries inline, and routing to sibling skills (databricks-core, databricks-serverless) is clearly signaled at the top.

5 / 5

Total

18

/

20

Passed

Description

83%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 that answers what and when explicitly, uses third person, and includes an unusual but useful routing clause for the sibling serverless skill. Its only weaknesses are slightly summarized action verbs and a small set of missing natural synonyms (workflow, ETL, schedule).

DimensionReasoningScore

Specificity

The description lists several concrete actions — "Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI" — and names the concrete artifact types (notebooks, Python wheels, SQL, dbt, pipelines). Falls short of a 5 only because "develop and deploy" summarizes rather than enumerating distinct actions.

4 / 5

Completeness

Both questions are answered explicitly: the 'what' is "Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI" and the 'when' is a concrete "Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines" trigger clause, plus explicit routing guidance ("Invoke BEFORE starting implementation", compute delegated to databricks-serverless).

5 / 5

Trigger Term Quality

Good natural-keyword coverage: "data engineering jobs", "notebooks", "SQL", "dbt", "pipelines", "Databricks" are phrases users would actually say. A few common variations are missing (e.g., "workflow", "ETL", "orchestration", "schedule a job"), so it is not the comprehensive anchor at 5; it is clearly above the midpoint.

4 / 5

Distinctiveness Conflict Risk

The Lakeflow Jobs niche is distinct and it explicitly defers compute to databricks-serverless, minimizing overlap. Minor residual risk: it lists "pipelines" as a trigger, which overlaps the databricks-pipelines sibling skill for pure pipeline (non-job) requests.

4 / 5

Total

17

/

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

plugins/databricks/claude/skills/databricks-jobs/SKILL.md

Reviewed

Table of Contents

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