Databricks
Official@databricks · United States of America
Helping data teams solve the world’s toughest problems using data and AI
Agent Skills by Databricks
Showing 29 vetted skills indexed across 3 GitHub repositories.
databricksdeploy-to-vercel
Deploy projects to Vercel and return a live preview URL.
databricksagent-browser
Automate browser navigation, form filling, screenshots, and testing for AI agents.
databricksseo-audit
Audit website SEO health and deliver a prioritized action plan.
databricksvercel-cli
Deploy and manage Vercel projects from the command line.
databricksauthor-recipes-and-cookbooks
Standardize DevHub recipe, cookbook, and example templates with consistent metadata.
databricksdatabricks-core
Guide Databricks CLI authentication, profile management, and bundle operations.
databricksshadcn
Validate SKILL.md frontmatter and detect resource directories in repositories.
databricksfrontend-design
Create production-grade frontend interfaces from design briefs across React, Vue, and HTML/CSS.
databricksbuilding-components
Create accessible, composable React UI components with structured documentation.
databricksresource-image-generator
Generate deterministic light/dark PNG placeholder pairs for DevHub resources.
databricksvercel-composition-patterns
Refactor React components into composable variants with a shared ComposerContext.
databricksmcp-builder
Implement MCP servers in TypeScript and Python with tool registration and schemas.
databricksdatabricks-model-serving
Manage Databricks Model Serving endpoints via CLI for LLMs and custom models.
databricksdatabricks-dabs
Automate creation, validation, and deployment of Databricks Asset Bundles.
databricksdatabricks
Operate Databricks CLI for authentication, profile selection, and asset bundle deployment.
databricksdatabricks-pipelines
Develop batch and streaming data pipelines on Databricks with Lakeflow Spark Declarative Pipelines.
databricksdatabricks-apps
Scaffold and deploy full-stack applications on Databricks with AppKit.
databricksdatabricks-jobs
Develop and deploy Databricks Lakeflow Jobs using Databricks Asset Bundles.
databricksdatabricks-lakebase
Manage Lakebase Postgres projects, branches, and endpoints via the Databricks CLI.
databricksrun-locally
Run and test a conversational agent backend locally with Python and Uvicorn.
databricksadd-tools
Configure Databricks agent access to external resources in databricks.yml.
databricksdeploy
Deploy and debug Databricks Apps using Databricks Asset Bundles.
databricksdiscover-tools
Discovers Databricks workspace resources including UC Functions, Tables, Vector Search Indexes, Genie Spaces and MCP servers, with optional catalog/schema filtering and JSON or Markdown output.
databricksmigrate-from-model-serving
Migrate MLflow ResponsesAgents from Databricks Model Serving to Databricks Apps.
Frequently Asked Questions About Databricks
FAQPage SchemaWhat specific tasks can I perform using these capabilities?▼
You can manage workspace resources, deploy full-stack applications, orchestrate batch and streaming data pipelines, and persist multi-turn conversation history. The system supports resource discovery, including tables, vector search indexes, and function management, alongside standardized deployment of asset bundles.
Which personas are the primary users of these technical capabilities?▼
Data engineers, backend developers, and full-stack engineers are the primary users. These capabilities are designed for technical teams building data-intensive applications, managing infrastructure-as-code for workspace assets, and developing frontend interfaces that interact with backend data services.
What are the prerequisites for running these deployments?▼
Deployment requires a configured environment with authenticated access to the workspace. Users must define project configurations in YAML files, manage environment variables for authentication, and ensure the local runtime environment supports the necessary dependencies for bundle operations and resource management.