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SkillsMP는 databricks-solutions/vibe-coding-workshop-template에서 88개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 88개 중 40개를 표시합니다.
End-to-end orchestrator for designing complete Gold layer schemas with ERDs, YAML files, lineage tracking, and comprehensive business documentation. Guides users through dimensional modeling, ERD creation (master/domain/summary based on table count), YAML…
원문 언어: 영어
Cross-validation of Gold layer design artifacts during the design phase. Use when validating that YAML schemas, ERDs, lineage CSVs, and PK/FK references are internally consistent before handing off to implementation. Catches design-time inconsistencies (e.g.,…
원문 언어: 영어
Active, source-bounded alignment of a Gold dimensional design to Databricks Industry Vibe Data Models (and other canonical industry reference models — TM Forum SID, ARTS, ACORD, HL7, BIAN). Use when the customer operates in a recognizable vertical (retail,…
원문 언어: 영어
Intelligent skill navigation system with tiered loading and orchestrator-first routing for context-efficient agent operation. Routes tasks to the correct domain skill based on keyword detection with orchestrator priority. Each skill uses progressive…
원문 언어: 영어
Deploy a Databricks AppKit application to Databricks Apps. Covers config validation, build verification, deployment, UI verification, error diagnosis with fix loop, and workspace app limit handling. Use when asked to deploy an AppKit app, push to production,…
원문 언어: 영어
Add plugins to an existing Databricks AppKit project. Covers Lakebase (PostgreSQL), Analytics (SQL queries + dashboards), Genie (natural language AI/BI), Files (UC Volumes), and Serving (Model Serving / Agent endpoints). Guides through plugin registration,…
원문 언어: 영어
Enables agent self-reflection and learning from mistakes through systematic skill updates. Prioritizes updating existing skills over creating new ones - always searches existing skills first, creates new skills only when justified. Includes upstream source…
원문 언어: 영어
Systematic skill freshness auditing with verification anchors, volatility classification, staleness detection, and upstream lineage tracking across every Agent Skill in the repository (data_product_accelerator/, genai-agents/, apps_lakebase/). Fetches…
원문 언어: 영어
End-to-end Bronze layer creation for testing and demos. Creates table DDLs, generates fake data with Faker, copies from existing sources, and configures Asset Bundle jobs. Covers Unity Catalog compliance, Change Data Feed, automatic liquid clustering, and…
원문 언어: 영어
Generate synthetic data with Faker for Bronze layer testing with configurable data corruption. Use when creating test data for data quality validation, testing DLT expectations, or simulating production-like datasets. Supports realistic data generation with…
원문 언어: 영어
End-to-end autonomous deployment and operations skill. Deploys Databricks Asset Bundles, runs jobs and pipelines, polls for completion, diagnoses failures, applies fixes, redeploys, and verifies — all without human intervention. Also serves as SDK/CLI/REST…
원문 언어: 영어
Patterns for sharing code between Databricks notebooks using pure Python files and standard imports. Covers Asset Bundle path setup (rsplit canonical pattern), notebook-to-module conversion, import patterns vs %run magic commands, job submission context vs…
원문 언어: 영어
Provides standard TBLPROPERTIES and metadata patterns for Unity Catalog Delta tables across Bronze, Silver, and Gold medallion layers. Ensures governance compliance, performance optimization, and proper metadata tagging for all table creation operations.…
원문 언어: 영어
Enforces enterprise naming conventions (snake_case, table prefixes, approved abbreviations), dual-purpose COMMENT formats for tables/columns/TVFs/metric views/dashboards/Genie Spaces, and config-aware tagging standards. Scans context/ for customer tagging…
원문 언어: 영어
Provides schema management patterns for Databricks Asset Bundles with Unity Catalog. Enables programmatic schema creation and configuration for medallion architecture layers. Covers CREATE SCHEMA IF NOT EXISTS patterns, DLT pipeline schema configuration,…
원문 언어: 영어
Unity Catalog Primary Key and Foreign Key constraint patterns for proper relational modeling in Databricks. Use when implementing star schema dimensional models with PK/FK relationships in Gold layer tables. Covers surrogate keys as PRIMARY KEYS (not business…
원문 언어: 영어
End-to-end orchestrator for implementing Gold layer tables, merge scripts, FK constraints, and Asset Bundle jobs from YAML schema definitions. Guides users through Silver contract validation, YAML-driven table creation, Silver-to-Gold MERGE operations (SCD…
원문 언어: 영어
Patterns for creating Gold layer tables dynamically from YAML schema definitions at runtime. Use when managing 10+ Gold layer tables across multiple domains, when schema evolves frequently, or when you want to avoid embedded SQL DDL strings in Python.…
원문 언어: 영어
MLflow and ML Model patterns for Databricks including experiment creation, model training, batch inference, and Unity Catalog integration. Use when implementing ML pipelines, training models with Feature Store, or deploying batch inference jobs. Includes 19…
원문 언어: 영어
Comprehensive guide for Databricks Lakehouse Monitoring (Data Profiling) with quick-start workflow (2 hours), fill-in-the-blank requirements template, concrete fact/dimension monitor examples, and complete deployment patterns. Uses the new Data Quality API…
원문 언어: 영어
Production-grade patterns for Databricks AI/BI (Lakeview) dashboards. Prevents visualization errors, deployment failures, and maintenance issues through widget-query alignment, number formatting, parameter configuration, monitoring table patterns, chart scale…
원문 언어: 영어
End-to-end orchestrator for building the Databricks semantic layer including Metric Views, Table-Valued Functions (TVFs), and Genie Spaces. Guides users through metric view creation, TVF development, Genie Space setup, and API-driven deployment. Orchestrates…
원문 언어: 영어
Standard patterns for creating Databricks Metric Views with semantic metadata for Genie and AI/BI. Use when creating metric views, troubleshooting metric view creation errors, validating schema references before deployment, implementing joins (including…
원문 언어: 영어
End-to-end guide for planning, creating, deploying, and validating Table-Valued Functions (TVFs) in Databricks optimized for Genie Space natural language queries. Use when creating TVFs for Genie Spaces, planning TVF requirements from business questions,…
원문 언어: 영어
Patterns for setting up Databricks Genie Spaces with comprehensive agent instructions, data assets, SQL expressions, and benchmark questions. Use when creating Genie Spaces, configuring agent behavior, selecting data assets, defining SQL expressions…
원문 언어: 영어
Comprehensive patterns for Databricks Genie Space Export/Import API - JSON schema, serialization format, and programmatic deployment. Use when programmatically creating, exporting, or importing Genie Spaces via REST API, troubleshooting API deployment errors,…
원문 언어: 영어
End-to-end orchestrator for creating Silver layer pipelines using Spark Declarative Pipelines (SDP, formerly DLT) with Delta table-based data quality rules, quarantine patterns, and monitoring views. Orchestrates mandatory dependencies on common skills…
원문 언어: 영어
Spark Declarative Pipeline (SDP, formerly DLT) expectations patterns for data quality with Unity Catalog Delta table storage. Use when implementing Silver layer SDP/DLT pipelines, creating portable data quality rules, or needing runtime-updateable…
원문 언어: 영어
Use when creating, syncing, or managing a Databricks Agent Bricks Knowledge Assistant (KA) as a document Q&A source for an agent. Covers source mode selection (UC Files vs Vector Search index), Python SDK lifecycle (create/update/sync/delete), REST API…
원문 언어: 영어
Use when you need to version, manage, or load prompts for any GenAI agent on Databricks. Covers how to register prompts in Unity Catalog, set up aliases for safe deployments, link prompts to traces, and load them in production apps — even if you just want…
원문 언어: 영어
Use when you need to create, manage, or load evaluation datasets for testing agent quality. Covers the MLflow GenAI data format, persisting benchmarks in Unity Catalog, merging records without duplicates, and validating data before evaluation — even if you…
원문 언어: 영어
Use when running mlflow.genai.evaluate() to test agent quality before deployment. Covers the predict_fn contract, answer-sheet mode for re-scoring existing outputs, threshold gates, retry wrappers, human feedback sessions, and conversation evaluation — even…
원문 언어: 영어
Use when wiring **end-user feedback** (thumbs up/down, ratings, free-form comments) from a deployed app back into MLflow as Assessments on the originating trace. Covers the canonical `mlflow.log_feedback(...)` pattern, trace-id vs `client_request_id`…
원문 언어: 영어
Use when you need to log an agent to MLflow, register it in Unity Catalog, or promote a new version as champion. Covers the full model lifecycle — even if you just want "log my agent and make it the production version." Also use when setting up…
원문 언어: 영어
Use when deploying an agent to production or setting up CI/CD automation. Covers Databricks Apps deployment, Asset Bundles, service principal permissions, and evaluate-then-promote pipelines — even if you just want "deploy my agent and set up a release gate."…
원문 언어: 영어
Use when prompt quality has plateaued on your evaluation dataset and you need to programmatically improve one or more registered prompts. Covers `mlflow.genai.optimize_prompts()` and `GepaPromptOptimizer`, `predict_fn` contracts, single and multi-prompt…
원문 언어: 영어
Default reusable MLflow workshop path for instruction-shaped prompt iteration. Hand-authored prompt revisions guided by failing-scorer rationales from the first scored eval, with full-dataset re-eval and alias-gated promotion. Use when Step 4 evaluation…
원문 언어: 영어
Standard patterns for Databricks Asset Bundles configuration files for serverless jobs, DLT pipelines, dashboards, alerts, apps, and workflows. Use when creating, configuring, or deploying DABs for infrastructure-as-code deployments. Covers mandatory…
원문 언어: 영어
Use when starting any new GenAI agent project on Databricks. Covers MLflow 3.x GenAI setup: install dependencies, enable autolog, configure connection pooling, understand model signatures, detect runtime environment. Foundation Step 1. Produces the configured…
원문 언어: 영어
Use when setting up MLflow experiments, tracing, or UC OTEL trace storage for a GenAI agent. Covers structured experiment paths, tracing decorators, manual spans, tags, connection pooling, and Unity Catalog OTEL storage for SQL-queryable trace retention.…
원문 언어: 영어