Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
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databricks/databricks-agent-skills3542026年10月19日 更新
免费在 GitHub 公开
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.
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查看安装方法包含的文件（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（原文）
安装前可以查看交给 AI 代理的指令内容。
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
- Unity Gateway overview
- Unity Gateway release notes
- Unity Gateway developer documentation
- Model service API reference
- MCP service API reference
- Model provider service API reference
- Query model services
- Open Responses specification
- Open Responses provider behavior on Databricks
- Query model provider services
- Model service requirements and grants
- Model provider service governance
- Routing and fallbacks
- Rate limits
- Register an external MCP server
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databricks/databricks-agent-skills3542026年10月19日 更新
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.
中文简介正在准备中。当前显示原文说明。
databricks/databricks-agent-skills3542026年10月19日 更新
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.
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databricks/databricks-agent-skills3542026年10月19日 更新
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.
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databricks/databricks-agent-skills3542026年10月19日 更新
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.
中文简介正在准备中。当前显示原文说明。
databricks/databricks-agent-skills3542026年10月19日 更新