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databricks-genie-agents

Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is databricks-genie-agents in databricks/databricks-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

72%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-structured router-style SKILL.md that excels at progressive disclosure, with a lifecycle table that clearly signals when to load each reference and concrete CLI entry points per phase. Its main gaps are the absence of validation checkpoints for the batch export/migrate workflow in the body and slight redundancy in the overview sections.

Suggestions

Add an explicit validation/verification checkpoint to the export/migrate row of the lifecycle table (e.g., after create-space, verify the imported Agent answers a sample question identically to the source before cutover), since batch/cross-workspace migration currently sequences get-space → remap → create-space with no verification step.

State the Genie-Agent-vs-Genie-One distinction once (either the Overview or the Related Skills section) instead of twice, and drop the opening line that duplicates the frontmatter description.

Include one copy-paste-ready CLI example for the most common action (e.g., a full `databricks genie create-space ...` invocation) in the body so the primary task is executable without first loading a reference file.

DimensionReasoningScore

Conciseness

The body is a lean router with a dense, useful lifecycle table, but has minor trimmable redundancy: the Genie-Agent-vs-Genie-One distinction is stated in both the Overview and Related Skills sections, and the opening line repeats the frontmatter description.

4 / 5

Actionability

The lifecycle table gives concrete CLI tokens ("discover-schema", "get-space --include-serialized-space", "genie-create-eval-run") and gating instructions ("Always load before creating or updating", "gather space ID + failing question + observed behavior first"), but full copy-paste-ready command syntax is deferred to the reference files.

4 / 5

Workflow Clarity

The typical flow (create → query/validate → diagnose → optimize) and per-phase gating are clear, but the export/migrate phase — which explicitly covers batch and cross-workspace migration — sequences "get-space → remap → create-space" with no validation or verification step in the body, so the batch-operation cap applies.

3 / 5

Progressive Disclosure

Exemplary structure: the lifecycle table maps each phase to a real reference file with explicit "Load when" conditions, all seven referenced paths exist on disk one level deep, and sibling cross-links between references serve only as signposting.

5 / 5

Total

16

/

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, specific description that names concrete lifecycle actions, includes the former 'Genie Spaces' name as a trigger synonym, and explicitly disambiguates from the general data-discovery skill. Its only weakness is that the 'when to use' guidance is expressed negatively (when to use the sibling skill instead) rather than as a positive 'Use when...' trigger clause.

DimensionReasoningScore

Specificity

Lists multiple concrete actions covering the full lifecycle — "build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API" — with no filler, matching the comprehensive-coverage anchor rather than 4's 'minor gaps'.

5 / 5

Completeness

The 'what' is fully explicit, and the 'when' is addressed via an explicit boundary clause ("For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead") rather than a positive 'Use when...' trigger phrase, placing it above anchor 3 but below 5's concrete positive triggers.

4 / 5

Trigger Term Quality

Good keyword coverage including the natural synonyms "Databricks Genie Agents", "formerly Genie Spaces", "natural-language agents", and "Conversation API", but misses common user phrasings like 'text-to-SQL' or 'chat with your data'.

4 / 5

Distinctiveness Conflict Risk

Clear niche (curated, per-data Genie Agents) with explicit disambiguation against the closest sibling skill, making wrong-skill triggering unlikely.

5 / 5

Total

18

/

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

relative_links

Relative link issues: 6 suspicious

Warning

Total

15

/

16

Passed

Repository

databricks/devhub

Path

.databricks/aitools/skills/databricks-genie-agents/SKILL.md

Reviewed

Table of Contents

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