CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

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.

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

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, navigation-first skill body: lean tables, executable command patterns, and exemplary progressive disclosure with all references resolving to real, one-level-deep files. The main weaknesses are repeated boundary guidance that could be consolidated and the absence of an explicit validation/verification step for destructive lifecycle operations (delete), which caps workflow clarity.

Suggestions

Add an explicit verification step after create/update/delete operations (e.g. run the corresponding get or list operation to confirm the change) with a retry loop on failure, which would lift workflow clarity above the destructive-operation cap.

Consolidate the legacy Model Serving boundary guidance, which currently appears in the Choose the Correct Gateway table, the paragraph beneath it, and the CLI Workflow section, into a single statement to save tokens.

Include one fully concrete worked example (a real operation name with a small real payload) in the CLI Workflow section so the command pattern is copy-paste ready.

DimensionReasoningScore

Conciseness

Largely efficient: the body is a lean overview of tables, three short command snippets, and pointed navigation, with no explanation of concepts Claude already knows. It sits at anchor 4 rather than 5 because the Model-Serving boundary guidance is repeated across three places (the gateway-choice table, the paragraph after it, and again in the CLI Workflow section) and could be consolidated to trim tokens.

4 / 5

Actionability

Concrete, executable commands ("databricks --version", "databricks ai-gateway --help", "databricks ai-gateway <operation> --json @payload.json --profile <PROFILE>") plus a complete per-service-type operation listing in a table. It stops short of anchor 5 because no fully concrete example with a real operation name and a real payload is shown inline; the placeholders (<operation>, @payload.json) delegate the specifics to the reference files.

4 / 5

Workflow Clarity

The CLI Workflow has a clear numbered sequence (verify version, inspect help, construct payload) with a pre-flight checkpoint ("Inspect the command and the relevant operation before constructing a payload"), but the skill manages destructive operations (delete-model-service, delete-mcp-service, delete-model-provider-service) and the body contains no explicit post-operation verification or error-recovery loop. Per the rubric, missing validation steps for destructive operations caps workflow clarity at 3 even though the sequence itself is well laid out.

3 / 5

Progressive Disclosure

The body is a clear overview that appropriately splits detail into eight real one-level-deep reference files, each signaled by an explicit "Before X, read [references/Y.md]" directive tied to the operation being performed. Verified against the actual bundle: all referenced paths exist in references/, and cross-links between references are peer navigation rather than nested chains, so nothing is buried two levels deep.

5 / 5

Total

16

/

20

Passed

Description

92%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: concrete capabilities, explicit multi-clause triggers, synonym coverage, and an explicit negative boundary against the closest competing skill. The only notable gap is the omission of the "databricks ai-gateway" CLI command name as a trigger term, which the body text itself identifies as an explicit trigger for this skill.

Suggestions

Add the CLI command name "databricks ai-gateway" as an explicit trigger phrase, since users often invoke the skill by typing the command.

Consider mentioning "system.ai services" alongside a more natural phrasing users would type (e.g. "query a model by its three-part name") to broaden natural trigger coverage.

DimensionReasoningScore

Specificity

Multiple specific concrete actions with comprehensive coverage: "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". Full resource lifecycle is enumerated, matching the anchor for comprehensive coverage; nothing here is vague or generic.

5 / 5

Completeness

Both questions are answered explicitly: the "what" lists concrete capabilities (create/update/list/query services, manage budgets and alerts, migrate from legacy AI Gateway) and the "when" is given through multiple explicit "Use when..." / "Also use for..." clauses with concrete trigger phrases, including a specific error-scenario trigger. This matches anchor 5 and exceeds anchor 4's "'when' could be more explicit".

5 / 5

Trigger Term Quality

Good natural-term coverage including the product synonyms "Unity Gateway (also called Unity AI Gateway)", "MCP services", "model provider services", "spend budgets", "usage alerts", and the error-state trigger "PERMISSION_DENIED querying a three-part model name". It falls short of anchor 5 because the most common invocation phrase, the CLI command "databricks ai-gateway" (which the body itself calls an explicit trigger), is absent from the description.

4 / 5

Distinctiveness Conflict Risk

A clear niche with minimal conflict risk: it distinguishes Unity Gateway from the adjacent databricks-model-serving skill with an explicit exclusion ("Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that") and even disambiguates the naming overlap between Unity Gateway and legacy AI Gateway. Well above anchor 4's "minor overlap risk".

5 / 5

Total

19

/

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

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.