このプロンプトをコピーして、使用中の AI アシスタントに送ってください。
Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/databricks-solutions/vibe-coding-workshop-template.リポジトリの README を読み込んでいます…
収集済み skill 93 件中 40 件を表示しています。
Lakebase Change Data Feed (CDF) — stream INSERT/UPDATE/DELETE from Lakebase Postgres via the WAL (wal2delta) into Unity Catalog lb_<table>_history Delta tables. Use for the sync_from_lakebase step when enable_lakebase_cdf is true: prerequisites (Postgres 17…
原文の言語: 英語
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…
原文の言語: 英語
Session-start behavioral manifest for running this workshop inside Databricks Genie Code. Read FIRST when the client is Genie Code (the coding agent embedded in the Databricks workspace) so you begin knowing how it behaves — surface/page tool-scoping, the…
原文の言語: 英語
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…
原文の言語: 英語
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.,…
原文の言語: 英語
Owns the end-to-end state, gating, pathway-applicability, and retrospective contract for the `Instructions.md` / `example/<use_case>/WALKTHROUGH.md` vibecoding workshops. Every workshop prompt invokes this skill at the top (`enter`) and bottom (`exit`) so the…
原文の言語: 英語
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…
原文の言語: 英語
Use when any agent or downstream skill needs Unity Catalog schemas and managed volumes (knowledge_sources, agent_outputs, memory tables, benchmark tables, etc.) provisioned idempotently. Foundation Step 0 — runs before MLflow tracing setup and before any…
原文の言語: 英語
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…
原文の言語: 英語
Build high-fidelity custom charts inside Databricks AI/BI (Lakeview) dashboards using Vega-Lite custom visualizations (Public Preview). Covers the documented contract — the `databricks_query` data source, Fields-section Name references, theme-aware signals…
原文の言語: 英語
Build full-page, high-fidelity React "custom pages" inside a Databricks AI/BI (Lakeview) dashboard — the mechanism behind pixel-perfect, custom-app-like dashboards (composite KPI cards, in-cell sparklines, custom filter chips, status pills, email-preview…
原文の言語: 英語
Scaffold new Databricks AppKit applications using the Databricks CLI and Agent Skills. Creates blank or plugin-enabled AppKit projects (Lakebase, Analytics, Genie, Files). Use when asked to create a Databricks app, scaffold an AppKit project, bootstrap a new…
原文の言語: 英語
Build full-stack UI and backend features on a Databricks AppKit project from a PRD or feature spec. Covers SQL query design, type generation, React frontend with AppKit UI components, backend plugin wiring, and distinctive visual design. Use when asked to…
原文の言語: 英語
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,…
原文の言語: 英語
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,…
原文の言語: 英語
Wire a Lakebase PostgreSQL backend into an existing AppKit project. Covers database schema design from a PRD, idempotent DDL, Express API routes with mock fallback, React data hooks, and local testing. PRD-independent patterns that apply to any AppKit +…
原文の言語: 英語
Contract and guardrails for authoring the Genie Ontology in Databricks Discover — domains, subdomains, Pages, and a Question→Metric View Routing Page. Use when modeling a Discover domain, drafting Pages for measures, or writing a routing Page for a Genie…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.…
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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.…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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,…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
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…
原文の言語: 英語
README を読み込めませんでした。リポジトリの Skill 一覧は下で引き続き確認できます。