Explore AI Agent Skills & Claude Prompts

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.

dqx-apply-checks

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.

dqx-define-checks

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.

dqx-end-to-end

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.

schedule Updated 15 days ago

dqx-profile-and-generate

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.

adding-subpackage

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.

code-review

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.

changelog

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.

refactoring

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.

deploy

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.

ai-feature

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 → eval-harness → impl → re-eval sequence.

schedule Updated 21 days ago

research-write-api-of-source

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Research and document write/create APIs of a source system to enable write-back testing functionality.