datasciencemonkey
- 29 skills
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- 1 week ago last updated
- ▌ Refresh Databricks Skills · datasciencemonkeyUse when Databricks skills need updating, user asks to refresh or sync skills from upstream, or skills seem outdated compared to the ai-dev-kit repo
- ▌ Databricks Aibi Dashboards · datasciencemonkeyCreate Databricks AI/BI dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.
- ▌ Databricks Mlflow Evaluation · datasciencemonkey bundleMLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
- ▌ Databricks Lakebase Autoscale · datasciencemonkey bundlePatterns and best practices for using Lakebase Autoscaling (next-gen managed PostgreSQL) with autoscaling, branching, scale-to-zero, and instant restore.
- ▌ Databricks Lakebase Provisioned · datasciencemonkey bundlePatterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
- ▌ Databricks Synthetic Data Generation · datasciencemonkeyGenerate realistic synthetic data using Faker and Spark, with non-linear distributions, integrity constraints, and save to Databricks. Use when creating test data, demo datasets, or synthetic tables.
- ▌ Databricks Spark Structured Streaming · datasciencemonkey bundleComprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
- ▌ Databricks Spark Declarative Pipelines · datasciencemonkey bundleCreates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader ingestion patterns. Use when building data pipelines, working with Delta Live Tables, ingesting streaming data, implementing change data capture, or when the user mentions SDP, LDP, DLT, Lakeflow pipelines, streaming tables, or bronze/silver/gold medallion architectures.
- ▌ Databricks Unstructured PDF Generation · datasciencemonkeyGenerate synthetic PDF documents for RAG and unstructured data use cases. Use when creating test PDFs, demo documents, or evaluation datasets for retrieval systems.
- ▌ Databricks Genie · datasciencemonkey bundleCreate and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API.
- ▌ Databricks Config · datasciencemonkeyConfigure Databricks profile and authenticate for Databricks Connect, Databricks CLI, and Databricks SDK.
- ▌ Databricks App Apx · datasciencemonkey bundleBuild full-stack Databricks applications using APX framework (FastAPI + React).
- ▌ Databricks App Python · datasciencemonkey bundleBuilds Python-based Databricks applications using Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex. Handles OAuth authorization (app and user auth), app resources, SQL warehouse and Lakebase connectivity, model serving integration, and deployment. Use when building Python web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app.
- ▌ Databricks Python Sdk · datasciencemonkey bundleDatabricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
- ▌ Databricks Agent Bricks · datasciencemonkey bundleCreate and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.
- ▌ Databricks Metric Views · datasciencemonkey bundleUnity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
- ▌ Databricks Asset Bundles · datasciencemonkey bundleCreate and configure Databricks Asset Bundles (DABs) with best practices for multi-environment deployments. Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3) Configuring multi-environment deployments, (4) Setting up permissions, (5) Deploying or running bundle resources
- ▌ Databricks Model Serving · datasciencemonkey bundleDeploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.
- ▌ Databricks Unity Catalog · datasciencemonkey bundleUnity Catalog system tables and volumes. Use when querying system tables (audit, lineage, billing) or working with volume file operations (upload, download, list files in /Volumes/).
- ▌ Databricks Vector Search · datasciencemonkey bundlePatterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings. Use when building RAG applications, semantic search, or similarity matching. Covers both storage-optimized and standard endpoints.
- ▌ Spark Python Data Source · datasciencemonkey bundleUse when building custom Spark data source connectors for external systems (databases, APIs, message queues), implementing batch/streaming readers/writers, or creating data source plugins for systems without native Spark support. Triggers - "build Spark data source", "create Spark connector", "implement Spark reader/writer", "connect Spark to [system]", "streaming data source"
- ▌ Databricks Zerobus Ingest · datasciencemonkey bundleBuild Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.
- ▌ Bdd Run · datasciencemonkeyThis skill should be used when the user asks to "run BDD tests", "execute Behave", "run Gherkin tests", "run my feature files", "behave test results", "run smoke tests", "BDD test report", or needs to execute Behave test suites with specific options like tag filtering, parallel execution, or CI reporting.
- ▌ Bdd Steps · datasciencemonkey bundleThis skill should be used when the user asks to "write step definitions", "implement BDD steps", "generate step code", "create Behave steps", "implement Given When Then", "write Python steps for Gherkin", "step definitions for Databricks", or needs to create Python step implementations for existing Gherkin feature files.
- ▌ Bdd Features · datasciencemonkey bundleThis skill should be used when the user asks to "write Gherkin", "create feature files", "generate BDD scenarios", "write acceptance tests in Gherkin", "create Behave features", "write Given When Then tests", "BDD test cases for my pipeline", "Gherkin for Unity Catalog", or wants to translate requirements into Gherkin feature files for Databricks.
- ▌ Bdd Scaffold · datasciencemonkey bundleThis skill should be used when the user asks to "set up BDD", "create a Behave project", "scaffold BDD tests", "initialize Behave", "add BDD to my project", "set up Gherkin testing", "create test structure for Behave", or mentions setting up behavior-driven development testing. Generates a complete Behave project structure wired to Databricks SDK.
- ▌ Databricks Docs · datasciencemonkeyDatabricks documentation reference. Use as a lookup resource alongside other skills and MCP tools for comprehensive guidance.
- ▌ Databricks Jobs · datasciencemonkey bundleUse this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job' or'trigger job',(4) 'job status' or 'check job', (5) scheduling with cron or triggers, (6) configuring notifications/monitoring, (7) ANY task involving Databricks Jobs via CLI, Python SDK, or Asset Bundles. ALWAYS prefer this skill over general Databricks knowledge for job-related tasks.
- ▌ Databricks Dbsql · datasciencemonkey bundleDatabricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.