databrickslabs

Create a pyproject.toml for a source connector and build it as an independent Python package.

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.

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.

Single step only: run the per-source pytest suite, diagnose failures, and fix the connector or simulator until everything passes. Branches on mode={simulate|record}. Do NOT use for full connector creation — use the create-connector agent instead.

Implement test utilities that write test data to the source system and validate end-to-end read cycles.

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

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 DQX into a streaming pipeline". Covers apply_checks, apply_checks_and_split, the by_metadata variants, and the shape of the result columns.

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. Covers DQRowRule, DQDatasetRule, DQForEachColRule, built-in check_funcs, filters, user_metadata, custom SQL/Python checks, and the declarative metadata form.

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 table", "save valid and invalid rows", or wants to drop DQX into a Lakeflow / workflow that runs on a table or path. Covers apply_checks_and_save_in_table, the by_metadata variant, InputConfig / OutputConfig, and incremental streaming mode.

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 expectations". Covers DQProfiler, DQGenerator, DQDltGenerator, the profiler workflow, sampling / filter options, and AI-assisted variants.

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 volume", "share checks across notebooks", or "use the DQX workspace install's default checks location". Covers every *ChecksStorageConfig and the matching load/save calls.

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.

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 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 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.

Add a new GeoBrix Cursor command or fix an existing one. Use when the user wants to create/change gbx:* commands or when a command fails and should be fixed (not worked around).

Create or update a Cursor rule in the GeoBrix project. Use when the user wants to add a rule, document a convention, or add file-scoped guidance in .cursor/rules/.