databricks

Plugin databricks from marketplace ccode-personal-plugins in mrkhachaturov/ccode-personal-plugins. Commands read from the repo's manifest — 2 days ago.

Skills (36)

• databricks-ai-functions

  Use Databricks built-in AI Functions (ai_classify, ai_decide, 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 m

• databricks-ai-runtime

  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, choosing an accelerator type and Python environment, passing hyperparameters and secrets, conf

• databricks-aibi-dashboards

  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.

• databricks-app-design

  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: ch

• databricks-apps-python

  Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app

• databricks-apps

  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 (analy

• databricks-core

  Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or gen

• databricks-dabs

  Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.

• databricks-data-discovery

  Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP. MUST be invoked whenever the user asks to find or locate data ('what tables are in X', 'where does X live', 'which catalog/schema has Y'), answer a natural-language question about the data, or write a S

• databricks-dbsql

  Databricks 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", "|>", "geospa

• databricks-docs

  Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.

• databricks-execution-compute

  Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.

• databricks-genie-agents

  Create, manage, and query Databricks Genie Agents (formerly Genie Spaces): curated agents over Unity Catalog tables, Metric Views, and documents in UC volumes. Use when building, migrating, tuning, or asking a specific Agent, and for document Q&A or docs + data agents (replaces the legacy Knowledge

• databricks-iceberg

  Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External

• databricks-jobs

  Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation. For compute (serverless, performance mode, classic), also use databricks-serverless.

• databricks-lakebase

  Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

• databricks-lakeflow-connect-saas

  Lakeflow Connect ingestion from SaaS apps (Salesforce, Workday, ServiceNow, HubSpot, SharePoint, Google Drive, OneDrive, Jira, ads, audit logs, 70+ more) and Kafka or RabbitMQ into Unity Catalog. Use when finding a connector, creating its connection and pipeline, scheduling or debugging it.

• databricks-lakeflow-connect

  Lakeflow Connect managed ingestion into Unity Catalog from databases (SQL Server, PostgreSQL, MySQL, Oracle) by CDC or query-based. Use when creating, running or debugging these pipelines.

• databricks-metric-views

  Unity 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-ml-training

  Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC

• databricks-mlflow-evaluation

  MLflow GenAI evaluation and agent improvement on Databricks. Use when running mlflow.genai.evaluate(), building scorers or datasets, aligning judges with MemAlign, optimizing prompts, or monitoring production quality.

• databricks-mlflow-tracing

  MLflow tracing and observability on Databricks. Use when getting started with MLflow, instrumenting GenAI apps and agents, retrieving and analyzing traces, debugging chat sessions, or querying aggregate trace metrics on supported OSS servers.

• databricks-model-serving

  Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gat

• databricks-pipelines

  Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.

• databricks-python-sdk

  Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.

• databricks-sandbox

  Databricks Sandbox: on-demand serverless compute for running untrusted or agent-generated code and background tasks. Create/start/stop/delete sandboxes, run commands (synchronous execute_command_sync in all SDKs; streaming execute_command in the Python SDK only), and manage files through the Databri

• databricks-serverless-migration

  Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concre

• databricks-serverless

  Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invo

• databricks-setup-local

  Previews, provisions, or diagnoses a uv-managed local Python .venv with `databricks environments setup-local`. Use when the user wants to set up or fix one for Databricks Connect, cluster or serverless compute, `--job-task`, or a bundle target, or when setup-local fails.

• databricks-spark-structured-streaming

  Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, writing custom statef

• databricks-synthetic-data-gen

  Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use whe

• databricks-unity-catalog

  Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer \"w

• 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.servic

• databricks-unstructured-pdf-generation

  Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for a Genie Agent with an attached volume) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.

• databricks-vector-search

  Databricks AI Search (formerly Vector Search) endpoints and indexes for RAG and semantic search. Use when asked to: create Standard or Storage-Optimized endpoints and Delta Sync or Direct Access indexes with AISearchClient; query with ANN, hybrid, or full-text search and filters; sync, monitor, or m

• databricks-zerobus-ingest

  Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, teleme

Slash commands (2)

• /databricks:doctor

  Read-only Databricks health check: CLI, profiles, auth validity via one API call. Pass `full` to also check compute and recent job failures.

• /databricks:setup

  Set up Databricks CLI auth: install check, then an OAuth / PAT / service-principal profile (workspace or account-level), then verify.

Hooks (3)

• UserPromptSubmit

  python3 "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-router.py" || python "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-router.py" || true

• SessionStart

  python3 "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-context.py" || python "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-context.py" || true

• PostToolUse

  python3 "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-auth-helper.py" || python "${CLAUDE_PLUGIN_ROOT}/hooks/databricks-auth-helper.py" || true

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