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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.

70

Quality

86%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

Content

72%

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

This is a well-structured skill with strong actionability and excellent progressive disclosure through reference file organization. The main weaknesses are moderate verbosity (inline summary tables that partially duplicate reference content, lengthy CLAUDE.md template) and a development workflow that lacks explicit error recovery feedback loops. Overall it serves as an effective overview document that guides Claude to the right reference materials.

Suggestions

Add error recovery guidance to the Development Workflow section (e.g., 'If validate fails: check YAML syntax and resource references; if deploy fails: verify profile and permissions').

Consider removing or condensing the Task Types Summary and Trigger Types Summary tables since they largely duplicate the reference file table at the top — a single sentence pointing to the reference would suffice.

DimensionReasoningScore

Conciseness

The skill is generally well-structured but includes some content that could be tightened. The CLAUDE.md/AGENTS.md template block is verbose, the task types and trigger types summary tables duplicate what's in the reference files, and some sections like compute configuration go into detail that could be deferred to references. However, it avoids explaining basic concepts Claude already knows.

2 / 3

Actionability

The skill provides fully executable code examples across all three interfaces (DABs YAML, Python SDK, CLI), with copy-paste ready commands for scaffolding, creating jobs, running them, and managing them. The scaffolding command with config-file is particularly actionable and specific.

3 / 3

Workflow Clarity

The Development Workflow section provides a clear 4-step sequence (validate → deploy → run → check status), but lacks explicit validation checkpoints and error recovery feedback loops. There's no 'if validation fails, do X' guidance, and the workflow for scaffolding a new project doesn't include verification steps after creating files.

2 / 3

Progressive Disclosure

Excellent progressive disclosure with a clear reference files table at the top pointing to task-types, triggers-schedules, notifications-monitoring, and examples. The main body provides a concise overview with summary tables that link to specific sections in reference files. Navigation is one level deep and well-signaled.

3 / 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.

This is a strong skill description that clearly identifies a specific domain (Lakeflow Jobs on Databricks), lists concrete methods and technologies, and provides explicit trigger guidance with a 'Use when' clause. The inclusion of multiple natural trigger terms (DABs, dbt, Python wheels, pipelines) ensures good coverage, and the behavioral instruction to invoke before starting implementation adds useful procedural context.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions and technologies: 'Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI' names the platform, the artifact type, and three distinct methods of interaction.

3 / 3

Completeness

Clearly answers both 'what' (develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or CLI) and 'when' (when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines), plus adds a behavioral directive ('Invoke BEFORE starting implementation').

3 / 3

Trigger Term Quality

Includes strong natural keywords users would say: 'Lakeflow Jobs', 'Databricks', 'DABs', 'Python SDK', 'CLI', 'notebooks', 'Python wheels', 'SQL', 'dbt', 'pipelines', 'data engineering jobs'. These cover a wide range of terms a user working in this domain would naturally use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive with a clear niche: Lakeflow Jobs on Databricks is a very specific domain. The combination of 'Lakeflow', 'Databricks', 'DABs' creates strong unique triggers unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
databricks/databricks-agent-skills
Reviewed

Table of Contents

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