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dqx-patterns Databricks DQX framework patterns for advanced data quality validation with detailed failure insights and flexible qu…

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01-grain-definition Grain definition patterns for fact tables during the Gold layer design phase. Use when choosing grain types (transact…

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01-yaml-table-setup Patterns for creating Gold layer tables dynamically from YAML schema definitions at runtime. Use when managing 10+ Go…

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02-dimension-patterns Dimension design patterns for Gold layer modeling. Covers role-playing dimensions, degenerate dimensions, junk dimens…

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02-merge-patterns Provides production-grade patterns for Gold layer MERGE operations from Silver to Gold tables. Covers column mapping,…

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03-deduplication Delta MERGE deduplication pattern for Gold layer to prevent duplicate source key errors. Use when merging from Silver…

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03-fact-table-patterns Advanced fact table design patterns for Gold layer modeling. Covers measure additivity classification (additive, semi…

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04-conformed-dimensions Enterprise integration patterns for Gold layer dimensional models. Covers conformed dimensions, the enterprise data w…

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04-grain-validation Pre-merge grain validation patterns for Gold layer fact tables. Use when creating fact table merge scripts to validat…

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05-erd-diagrams Patterns for creating clean, professional Mermaid ERD diagrams for data modeling documentation. Use when documenting …

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05-schema-validation Runtime schema validation patterns for Gold layer merge scripts. Use when creating merge scripts to ensure DataFrame …

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06-table-documentation Comprehensive documentation standards for Gold layer tables including naming conventions, column descriptions, and me…

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07-design-validation Cross-validation of Gold layer design artifacts during the design phase. Use when validating that YAML schemas, ERDs,…

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adhoc-exploration-notebooks Create dual-format ad-hoc exploration notebooks for Databricks workspace (.py) and local Jupyter (.ipynb) with Databr…

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anomaly-detection Schema-level anomaly detection for Databricks Unity Catalog using the Data Quality API (Public Preview). Automaticall…

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bronze-layer-setup End-to-end Bronze layer creation for testing and demos. Creates table DDLs, generates fake data with Faker, copies fr…

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create-agent-skill Guides the creation of new Agent Skills following the AgentSkills.io specification. Covers required frontmatter (name…

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databricks-aibi-dashboards Production-grade patterns for Databricks AI/BI (Lakeview) dashboards. Prevents visualization errors, deployment failu…

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databricks-asset-bundles Standard patterns for Databricks Asset Bundles configuration files for serverless jobs, DLT pipelines, dashboards, al…

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databricks-autonomous-operations End-to-end autonomous deployment and operations skill. Deploys Databricks Asset Bundles, runs jobs and pipelines, pol…

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databricks-expert-agent Transforms the assistant into a Senior Databricks Solutions Architect Agent that designs, implements, and reviews pro…

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databricks-python-imports Patterns for sharing code between Databricks notebooks using pure Python files and standard imports. Enables code reu…

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databricks-table-properties Provides standard TBLPROPERTIES and metadata patterns for Unity Catalog Delta tables across Bronze, Silver, and Gold …

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databricks-table-valued-functions End-to-end guide for planning, creating, deploying, and validating Table-Valued Functions (TVFs) in Databricks optimi…

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deployment-automation Automated deployment workflows for GenAI agents - MLflow deployment job trigger, dataset linking, evaluation-then-pro…

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dlt-expectations-patterns Spark Declarative Pipeline (SDP, formerly DLT) expectations patterns for data quality with Unity Catalog Delta table …

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documentation-organization Enforces documentation organization standards and provides comprehensive framework documentation authoring for Databr…

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faker-data-generation Generate synthetic data with Faker for Bronze layer testing with configurable data corruption. Use when creating test…

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foundation-model-agent-loop Canonical pattern for building an agentic tool-calling loop using a Databricks-hosted Foundation Model. Use when crea…

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genai-agents-setup End-to-end orchestrator for implementing production-grade Databricks GenAI agents using MLflow 3.0 ResponsesAgent, Ge…

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genie-benchmark-evaluator Evaluates Genie Space responses against benchmarks using 8 scorers in mlflow.genai.evaluate(). Layer 1: quality judge…

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genie-benchmark-generator

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genie-metadata-optimizer Optimizes Genie Space metadata using L1 (ASI-grounded introspection), L2 (GEPA), or L3 (multi-objective Pareto). Maps…

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genie-optimization-applier Applies metadata change proposals to Genie Space via dual persistence (API + repo files). Six control levers in prior…

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genie-optimization-orchestrator Orchestrates the Genie Space optimization loop by routing to 4 workers on demand. Maintains session state in MLflow e…

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genie-space-export-import-api Comprehensive patterns for Databricks Genie Space Export/Import API - JSON schema, serialization format, and programm…

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genie-space-patterns Patterns for setting up Databricks Genie Spaces with comprehensive agent instructions, data assets, and benchmark que…

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gold-layer-design End-to-end orchestrator for designing complete Gold layer schemas with ERDs, YAML files, lineage tracking, and compre…

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gold-layer-setup End-to-end orchestrator for implementing Gold layer tables, merge scripts, FK constraints, and Asset Bundle jobs from…

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lakebase-memory-patterns Lakebase memory patterns for stateful agents - CheckpointSaver for short-term conversation continuity, DatabricksStor…

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lakehouse-monitoring-comprehensive Comprehensive guide for Databricks Lakehouse Monitoring (Data Profiling) with quick-start workflow (2 hours), fill-in…

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metric-views-patterns Standard patterns for creating Databricks Metric Views with semantic metadata for Genie and AI/BI. Use when creating …

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ml-pipeline-setup MLflow and ML Model patterns for Databricks including experiment creation, model training, batch inference, and Unity…

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mlflow-genai-evaluation MLflow 3 GenAI evaluation patterns with LLM judges, MemAlign judge alignment, GEPA prompt optimization, custom scorer…

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mlflow-genai-foundation Core MLflow 3.0 GenAI patterns for Databricks agents - model signatures, tracing fundamentals, evaluation basics, pro…

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multi-agent-genie-orchestration Multi-agent orchestration patterns with Genie Spaces for structured data access. Includes Conversation API integratio…

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naming-tagging-standards Enforces enterprise naming conventions (snake_case, table prefixes, approved abbreviations), dual-purpose COMMENT for…

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observability-setup End-to-end orchestrator for setting up Databricks observability including Lakehouse Monitoring, Anomaly Detection, AI…

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production-monitoring Continuous production monitoring for GenAI agents - registered scorers with sampling, on-demand assess(), scorer life…

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project-planning Create multi-phase project plans for Databricks data platform solutions with Agent Domain Framework and Agent Layer A…

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