무료GitHub에 공개

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills

설치 방법 보기

포함된 파일(12)

SKILL.md6.9 KB
agents/openai.yaml325 B
assets/databricks.png15.0 KB
assets/databricks.svg582 B
references/account-access.md1.9 KB
references/budgets.md1.7 KB
references/mcp-services.md5.8 KB
references/model-provider-services.md9.8 KB
references/model-services.md8.9 KB
references/model-serving-migration.md4.1 KB
references/permissions.md5.2 KB
references/querying.md10.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Unity Gateway

Use the parent databricks-core skill for CLI authentication and profile selection.

Unity Gateway provides Unity Catalog securables for governed access to AI services. The CLI command group keeps the earlier product name: databricks ai-gateway --help.

Choose the Correct Gateway

Request	Use
Unity Gateway model service, MCP service, or model provider service	This skill and `databricks ai-gateway`
AI Gateway configuration on a Model Serving endpoint, including `put-ai-gateway`	`databricks-model-serving` and `databricks serving-endpoints`
Lakeflow Connect ingestion gateway	`databricks-lakeflow-connect`

Treat Unity AI Gateway as an earlier name for Unity Gateway when the resource is a Unity Catalog securable. Do not translate legacy Model Serving AI Gateway resources into Unity Gateway resources unless the user explicitly requests a migration.

CLI Workflow

Use the Databricks CLI for resource lifecycle and permission management. Do not substitute direct REST calls or a Databricks SDK for those operations. Python clients are supported for querying model and model provider services; read Query services.

An explicit request for Unity Gateway, Unity AI Gateway, or databricks ai-gateway belongs to this skill. Do not switch to databricks-model-serving merely because the request involves a model service or a workspace. Use that skill only for a legacy per-endpoint AI Gateway operation or when a provisioned-throughput destination requires Model Serving endpoint details.

Verify the CLI meets the minimum version:
```
databricks --version
```
Inspect the command and the relevant operation before constructing a payload:
```
databricks ai-gateway --help
databricks ai-gateway <operation> --help
```
Pass create and update configuration through --json. Use an explicit profile when profile-based authentication is required:
```
databricks ai-gateway <operation> --json @payload.json --profile <PROFILE>
```

Do not invent JSON fields from similarly named legacy APIs. Build the payload from the installed CLI help and the Unity Gateway documentation for the selected service type.

Service Types

Service type	CLI operations
Model service	`create-model-service`, `get-model-service`, `list-model-services`, `update-model-service`, `delete-model-service`; read Model services
MCP service	`create-mcp-service`, `get-mcp-service`, `list-mcp-services`, `update-mcp-service`, `delete-mcp-service`; read MCP services
Model provider service	`create-model-provider-service`, `get-model-provider-service`, `list-model-provider-services`, `update-model-provider-service`, `delete-model-provider-service`; read Model provider services

Querying and permissions

Before writing Python that invokes a model or model provider service, read references/querying.md.
Before checking, granting, or revoking Unity Gateway access, read references/permissions.md.
Before any model-service operation, read references/model-services.md. It contains the current resource-name conventions, JSON payload fields, required-input gate, and lifecycle commands.
Before any MCP-service operation, read references/mcp-services.md for the equivalent MCP-specific contract.
Before any model-provider-service operation, read references/model-provider-services.md.
Before setting up a spend budget or spend alert on Unity Gateway usage, read references/budgets.md.
Before any account-scoped operation (account groups as grant principals, account budgets), read references/account-access.md.

Do not fetch public documentation for fields already covered by these references. Consult the authoritative documentation only when a required field is absent from the reference or the user explicitly asks for the latest documentation. Do not assign a Beta or preview status unless the installed CLI help or current documentation explicitly does so.

Migration from Model Serving to Unity Gateway

When migrating from workspace-scoped Model Serving endpoints to Unity Catalog-scoped Unity Gateway services, read references/model-serving-migration.md.

Authoritative Documentation

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

databricks-agent-bricks

무료

Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks-ai-functions

무료

Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks-ai-runtime

무료

Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks-aibi-dashboards

무료

Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks-app-design

무료

Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks-apps

무료

Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

databricks/databricks-agent-skills3522026년 10월 18일 업데이트

databricks의 스킬 모두 보기

이 스킬의 문제 신고

설치

명령으로 설치

npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-unity-gateway

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/databricks/databricks-agent-skills/tree/main/skills/databricks-unity-gateway

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/databricks-unity-gateway/, Codex는 ~/.agents/skills/databricks-unity-gateway/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: databricks/databricks-agent-skills
스타: 352
포크: 93
카테고리: 개발
원문 언어: 영어
최종 업데이트: 2026년 10월 18일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.