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databricks-unity-gateway

Use when querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.

70

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

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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 overview skill: correct routing tables, executable CLI workflow, and exemplary progressive disclosure with condition-triggered one-level-deep references. The main gap is workflow validation — operations including deletes and permission changes lack post-operation verification or error-recovery checkpoints in the main body — plus some redundant disambiguation text that could be trimmed.

Suggestions

Add a post-operation verification step to the CLI Workflow (e.g., re-run `get-model-service` after create/update, or confirm grants with the permissions reference's check command) so destructive operations like delete and revoke have an explicit feedback loop.

Consolidate the databricks-model-serving disambiguation, which currently appears three times (description, gateway table, and the 'An explicit request…' paragraph), into the Choose the Correct Gateway section alone.

Merge the Service Types table's per-row 'read references/…' mentions with the 'Querying and permissions' bullet list, which currently repeats the same pointers, into a single navigation block.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence — no basic-concept explanations, and product-specific facts (securables, the legacy `ai-gateway` command-group name) are things Claude would not know. Minor trimming opportunities: the databricks-model-serving disambiguation is repeated in the description, the gateway table, and the "An explicit request…" paragraph, and the Service Types table rows duplicate the "Before any … operation, read references/…" bullets.

4 / 5

Actionability

Concrete, runnable bash commands (`databricks --version`, `databricks ai-gateway --help`, `databricks ai-gateway <operation> --json @payload.json --profile <PROFILE>`) plus exact per-service operation names in the Service Types table. Falls short of 5 because the commands are templated — payload construction and per-operation contracts are deferred to the reference files rather than shown with a worked example.

4 / 5

Workflow Clarity

The CLI Workflow gives a clear numbered sequence with pre-flight checkpoints (verify CLI version, inspect `--help` before building a payload), but there are no post-operation verification steps or error-recovery guidance at the SKILL.md level. Since the operation set includes destructive actions (`delete-model-service`, `delete-mcp-service`, grant/revokes), the missing validation feedback loop caps this at 3.

3 / 5

Progressive Disclosure

Verified against the actual bundle: all 8 files in references/ are cited from the body with conditional "Before X, read references/Y.md" signals keyed to the specific task (querying, permissions, budgets, account-scoped ops, migration), no reference file points to another reference (one level deep), and bulk detail is correctly kept out of the overview. External authoritative-doc links are also clearly grouped.

5 / 5

Total

16

/

20

Passed

Description

100%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.

An excellent description: concrete capabilities, explicit and varied use-when triggers with synonyms and error-code triggers, and an explicit boundary against the nearest competing skill. The length is justified because every clause carries distinct trigger information rather than padding.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "querying or calling pay-per-token foundation models", "creating, updating, listing, or querying Unity Gateway … services", "managing spend budgets and usage alerts", and "migrating from legacy AI Gateway" — giving comprehensive coverage of the skill's operations with no vague filler.

5 / 5

Completeness

Both questions are explicitly answered: the what (CRUD on Unity Gateway services, querying models, budgets, migration) and multiple explicit "Use when…" trigger clauses, plus an explicit negative trigger ("Not for configuring legacy AI Gateway on Model Serving endpoints") directing to the right alternative skill.

5 / 5

Trigger Term Quality

It covers the natural terms a user would say for this domain comprehensively, including the synonym pair "Unity Gateway (also called Unity AI Gateway)", "MCP services", "model provider services", "system.ai", "spend budgets", "usage alerts", and even the concrete failure signals "CAN_QUERY" and "PERMISSION_DENIED" plus the name format "catalog.schema.service". Not level 4 because no commonly used variation is missing.

5 / 5

Distinctiveness Conflict Risk

The Databricks Unity Gateway niche is clear and the description actively disambiguates against the closest neighbor — "use databricks-model-serving for that" — and explains the serving-endpoint-permission vs. gateway-service distinction, minimizing wrong-skill triggering.

5 / 5

Total

20

/

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-unity-gateway/SKILL.md

Reviewed

Table of Contents

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