Databricks Labs
Official@databrickslabs
Labs projects to accelerate use cases on the Databricks Unified Analytics Platform
Agent Skills by Databricks Labs
Showing 58 vetted skills indexed across 2 GitHub repositories.
databrickslabsbdd-run
Execute Behave BDD test suites with tag filtering and JUnit/JSON reports.
databrickslabsdatabricks-docs
Retrieves official Databricks llms.txt index and product documentation for APIs and platform features.
databrickslabsusing-git-worktrees
Create isolated git worktrees with gitignore verification and baseline tests.
databrickslabsdatabricks-config
Automate secure Databricks authentication profile setup for Connect, CLI, and SDK.
databrickslabsdatabricks-spark-structured-streaming
Develop Spark Structured Streaming pipelines on Databricks with Kafka ingestion and Delta writes.
databrickslabstest-driven-development
Enforce red-green-refactor cycles with mandatory failing tests before implementation.
databrickslabsrefresh-databricks-skills
Synchronize Databricks skills from the upstream ai-dev-kit repository while preserving custom non-Databricks skills.
databrickslabssystematic-debugging
Investigate root causes through a structured 4-phase debugging workflow.
databrickslabsspark-python-data-source
Develops custom Apache Spark 4.0+ Python data source connectors for external systems.
databrickslabsdatabricks-model-serving
Deploy and manage MLflow models and AI agents on Databricks Model Serving endpoints.
databrickslabsdatabricks-dbsql
Implement Databricks SQL workflows with stored procedures and geospatial functions.
databrickslabsdatabricks-python-sdk
Configure Databricks Python SDK clients, clusters, and SQL queries.
databrickslabsusing-superpowers
Enforce mandatory skill invocation before coding agent actions on Databricks Apps.
databrickslabsdispatching-parallel-agents
Dispatch focused agents to investigate independent technical issues in parallel.
databrickslabsdatabricks-vector-search
Create and operate vector similarity search infrastructure on Databricks.
databrickslabsbdd-features
Generate standardized Gherkin BDD feature files for Databricks workloads.
databrickslabsdatabricks-genie
Create Databricks Genie Spaces for natural language SQL queries on Unity Catalog data.
databrickslabsdatabricks-synthetic-data-generation
Generate synthetic datasets with Faker and Spark, saving parquet to Databricks Unity Catalog volumes.
databrickslabsexecuting-plans
Execute pre-written implementation plans in batches with human review checkpoints.
databrickslabsfinishing-a-development-branch
Finalize development branches with guarded merge, pull request, retention, or discard options after mandatory test verification.
databrickslabsdatabricks-app-python
Build and deploy Python web apps on Databricks Apps with OAuth and resource integration.
databrickslabsdatabricks-app-apx
Automate full-stack Databricks app development with APX framework patterns.
databrickslabsdatabricks-zerobus-ingest
Ingest records directly into Databricks Delta tables via gRPC without a message bus.
databrickslabsdatabricks-jobs
Manage Databricks Jobs across Python SDK, CLI, and Asset Bundles.
Frequently Asked Questions About Databricks Labs
FAQPage SchemaWhat specific tasks can engineers perform using these resources?▼
Engineers can build custom Spark data connectors, manage Databricks Jobs, configure Lakeflow pipelines, and deploy Python-based applications. The framework supports end-to-end development, including authentication verification, CDC offset tracking, and unit testing with pytest.
Which technical personas benefit most from these capabilities?▼
Data engineers, platform architects, and backend developers working within the Databricks ecosystem benefit from these standardized patterns. These resources are designed for professionals tasked with building scalable ingestion pipelines, managing infrastructure-as-code, and enforcing rigorous test-driven development cycles.
What are the primary prerequisites for implementing these connectors?▼
Implementation requires a functional Databricks environment, configured authentication profiles, and familiarity with the LakeflowConnect interface. Developers must also manage dependencies via pyproject.toml and ensure access to Unity Catalog for volume and table operations.