databrickslabs

Use after merging CoDA PRs, before a release, or when asked whether coda-main still works. Drives coda-main through an authenticated Chrome session and CoDA's JSON terminal API, runs the structured scripts/verify_coda_live.py smoke test, and returns evidence…

Create 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.

Use Databricks built-in AI Functions (ai_classify, 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…

Create Databricks AI/BI dashboards. Use when creating, updating, or deploying Lakeview dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.

Builds Databricks applications. Prefers AppKit (TypeScript + React SDK) for new apps; falls back to Python frameworks (Dash, Streamlit, Gradio, Flask, FastAPI, Reflex) when Python is required. Handles OAuth authorization, app resources, SQL warehouse and…

Create and configure Declarative Automation Bundles (formerly Asset Bundles) with best practices for multi-environment deployments (CICD). Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3)…

Manage Databricks workspace connections: check current workspace, switch profiles, list available workspaces, or authenticate to a new workspace. Use when the user mentions "switch workspace", "which workspace", "current profile", "databrickscfg", "connect to…

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.

Guide the user through creating or updating a pipeline for a source connector — read the docs, build a pipeline spec interactively, and run create_pipeline or update_pipeline.

Generate public-facing documentation for a connector targeted at end users.

Set up authentication for a source connector — generate connector spec, collect credentials interactively, and validate auth.

Run the authenticate script to collect credentials from the user via a browser form.

Generate the connector spec YAML file defining connection parameters and external options allowlist.

Single step only: audit a completed connector — implementation, testing & simulator validation, artifacts, security smells, cross-doc consistency — and produce a scored markdown review report. Read-mostly; does not modify connector code.

Generate and run an auth verification test to confirm that collected credentials are valid.

Single step only: implement the connector in Python when the API doc already exists. Do NOT use for full connector creation — use the create-connector agent instead.

Compute calculated (derived) channels in Impulse — new time-series signals built from existing channels and materialized at the same per-sample grain. Use when the user wants to "add a calculated channel", derive/persist a signal (e.g. "speed in km/h", "power…

Write the Impulse report configuration (`ImpulseConfig`) — the JSON/dict that points Impulse at its silver input tables, the gold output location, and tunes the solver. Use when the user asks to "configure an Impulse report", set the source/sink tables,…

Understand and prepare the data Impulse reads and writes. Use when the user asks "what tables does Impulse need", how to land / ingest measurement data into the silver layer, what the gold-layer output looks like, how fact and dimension tables join, or how to…

Build and run an Impulse reporting pipeline that persists events and aggregations to the gold-layer star schema. Use when the user wants to "build an Impulse report", compute histograms/statistics across all recordings and write them to Delta tables for a…

Compute results over channels in Impulse, optionally scoped to an event. Use when the user wants a "histogram" or "2D histogram / heatmap" of a signal, duration- or distance-weighted binning, per-event descriptive statistics (min/max/mean/median), or channel…

Run ad-hoc Impulse analysis in a notebook — evaluate TSAL expressions directly through the query engine and get a Spark or pandas DataFrame back, with no gold-layer write and no reporting setup. Use when the user wants to "explore signals interactively", "get…

Entry point for the Impulse framework — the Databricks Labs library for analyzing large-scale time-series measurement data (automotive testing, industrial IoT sensor recordings) on Spark and Delta. Use when the user mentions "Impulse", "TSAL",…

Write TSAL (Time Series Analytics Language) expressions in Impulse — select measurement channels by metadata tags, derive virtual signals with arithmetic, express conditions as time windows, detect edges, resample, integrate, and build histograms. Use when…

Use when the user asks to deploy, ship, release, or push OntoBricks to Databricks. Wraps the Databricks Asset Bundle deploy for the FastAPI app and the MCP server, with the bootstrap-perms safety net described in README.md.

Use after any code change (feature, fix, refactor, review fixup) to update /changelogs/YYYY-MM-DD.log and run the test suite. Mandatory post-change routine — see .cursorrules.

Use when the user asks for a code review, asks to "review the code", or requests review of a feature/PR/branch. Runs the OntoBricks review checklist defined in .cursorrules.

Use when the user asks to "refactor", restructure, clean up, simplify, deduplicate, extract, or reorganize code. Enforces the Martin Fowler discipline defined in src/.coding_rules.md and .cursor/08-testing-and-deployment.mdc.

Use when the user adds, changes, or refactors an LLM agent under src/agents/ — or anything that goes through Foundation Model API or an MLflow-traced LLM call. Mandatory under CNS §3.5 and .cursor/12-ai-feature-lifecycle.mdc. Walks the SPEC → dataset →…

Use when adding a new subpackage under back/core/, back/objects/, or agents/ — e.g. a new graph DB engine, W3C parser, industry importer, reasoning module, or domain class. Enforces the checklist defined in .cursor/07-project-conventions.mdc.

Run DQX validation end-to-end — read an input table or path, apply checks, and write valid and quarantined rows to output locations — in a single call. Use when the user asks for "apply and save", "quality-check a table and split the output", "DQX on a whole…

Validate a PySpark DataFrame or Delta table against a set of DQX quality rules using DQEngine. Use when the user asks to "run data quality checks", "apply DQX rules to a DataFrame/table", "split valid and invalid rows", "quarantine bad records", or "integrate…

Create DQX quality rules (checks) for a PySpark DataFrame or Delta table. Use when the user asks to "add a DQX check", "define a data quality rule", "validate that column X is not null / unique / in a set", or wants checks expressed in YAML/JSON for storage.…

Profile a DataFrame or table and generate DQX quality rule candidates with summary statistics. Use when the user asks to "profile a table", "generate DQX rules from data", "suggest data quality checks", "bootstrap a checks.yml", or "generate DLT…

Load and save DQX checks (quality rules) to a file, workspace path, Unity Catalog volume, Delta table, Lakebase, or the DQX installation folder. Use when the user asks to "load DQX checks from YAML", "save checks to a Delta table", "read checks from a…

Use this skill for metadata-driven Databricks pipelines built with SDP-META (the framework in this repo; formerly DLT-META, package databricks-labs-sdp-meta, CLI `databricks labs sdp-meta`). Triggers: (1) 'sdp-meta', 'sdp meta', 'dlt-meta', 'dlt meta', or…

DAX to SQL/UC-metric-view translation patterns. Use when converting Power BI DAX measures to SQL aggregations or metric view YAML measure expressions. Covers CALCULATE, FILTER, DIVIDE, SWITCH, ALLSELECTED, time intelligence, SELECTEDVALUE, and common…

Migrate a Databricks workspace's existing IP access list (ACL) into a context-based ingress (CBI) account network policy, as-is, using the dbx-migrate-ip-acls CLI. Use when the user wants to convert / migrate an existing IP access list to a network policy,…