databricks-solutions/vibe-coding-workshop-template

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# Vibe Coding Workshop Template > **Build end-to-end data products on Databricks with AI-assisted development** This template is a complete **data product builder** powered by vibe coding. Start from a raw customer schema or existing data, and build your way through the full Databricks stack — Databricks Apps with Lakebase, medallion architecture (Bronze, Silver, Gold), semantic layer, Genie Spaces, ML pipelines, and GenAI agents — all guided by 77 accelerator skills, a standalone GenAI agent course, and your AI coding assistant. --- ## What is Vibe Coding? **Vibe Coding** is an AI-assisted development approach where you collaborate with AI tools (like Cursor, GitHub Copilot, Claude Code, Windsurf, or similar) to rapidly build, iterate, and deploy production-quality data products. Instead of writing every line from scratch, you describe what you want and let the AI handle the implementation — guided by structured agent skills that encode best practices. --- ## Quick Start > **Workshop participants:** See [PRE-REQUISITES.md](PRE-REQUISITES.md) for the full setup checklist (workspace access, CLI, IDE, and authentication). ### Prerequisites | Tool | Required | Installation | |------|----------|-------------| | **Databricks Workspace** | Yes | Access to a Databricks workspace with Unity Catalog | | **Databricks CLI >= 0.295.0** | Yes | `curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh \| sh` | | **Node.js v22+** | Yes (Path A) | [nodejs.org](https://nodejs.org/) — required by AppKit | | **AI-Powered IDE** | Yes | [Cursor](https://cursor.com), [Windsurf](https://windsurf.com), VS Code + Copilot, or similar | | **Python 3.10+** | Yes | [python.org](https://www.python.org/downloads/) | | **Git** | Yes | [git-scm.com](https://git-scm.com/) | ### Clone the Template ```bash git clone https://github.com/databricks-solutions/vibe-coding-workshop-template.git my-project cd my-project ``` ### Choose Your Starting Point #### Path A: Build and Deploy a Databricks App Build a full-stack TypeScript app on Databricks AppKit, guided by 10 agent skills: 1. Read the workshop guide: [apps_lakebase/Instructions.md](apps_lakebase/Instructions.md) 2. Open your AI coding assistant and prompt: ``` I want to build a Databricks App. Read @apps_lakebase/skills/01-appkit-scaffold/SKILL.md and scaffold a new AppKit project. ``` 3. Follow the branch-aware workflow in Instructions.md: - **Phase 1:** Scaffold + build UI from a PRD, test locally (uses `01-appkit-scaffold` and `02-appkit-build` skills) - **Phase 2:** Deploy to Databricks Apps with mock data (uses `03-appkit-deploy` skill) - **Phase 3:** Setup Lakebase bundle resources (uses `04-appkit-plugin-add` and `apps_lakebase/prompts/03-setup-lakebase.md`) - **Phase 4:** Wire Lakebase backend locally (uses `04-appkit-plugin-add` and `05-appkit-lakebase-wiring` skills) - **Phase 4b (optional):** Wire Model Serving / Agent endpoint (uses `04-appkit-plugin-add` and `06-appkit-serving-wiring` skills) - **Phase 4c (optional):** Wire AppKit to a separate Agent App with OBO proxying (uses `06d-appkit-agent-app-proxy`) - **Phase 4d (optional):** Add persistent chat history and feedback (uses `07-appkit-chat-history` and `08-appkit-feedback`) - **Phase 5:** Deploy + E2E test with Lakebase (uses `03-appkit-deploy` skill) #### Path B: Build an End-to-End Data Pipeline Take a raw schema CSV through the full medallion architecture to production AI agents — one prompt per stage: 1. Drop your schema CSV into `data_product_accelerator/context/` 2. Open your AI coding assistant and prompt: ``` I have a customer schema at @data_product_accelerator/context/Wanderbricks_Schema.csv. Please design the Gold layer using @data_product_accelerator/skills/gold/00-gold-layer-design/SKILL.md ``` 3. Follow the [9-stage pipeline guide](data_product_accelerator/QUICKSTART.md) — one prompt per stage, one new conversation per stage. > **Both paths work together.** Build your data pipeline first, then deploy a Databricks App on top of it — or start with the app and add data products incrementally. --- ## Project Structure ``` vibe-coding-workshop-template/ │ ├── README.md # This file ├── QUICKSTART.md # Quick-start guide with two pathways ├── AGENTS.md # AI assistant routing (universal entry point) ├── PRE-REQUISITES.md # Workshop prerequisites checklist ├── CONTRIBUTING.md # Contribution guidelines ├── LICENSE.md # License ├── SECURITY.md # Security policy ├── env.example # Environment variable template │ ├── apps_lakebase/ # Databricks AppKit Workshop (10 agent skills) │ ├── Instructions.md # Comprehensive workshop guide │ ├── prompts/ # Numbered prompt files for each workshop step │ └── skills/ # Agent skills for the full app lifecycle │ ├── 00-appkit-navigator/ # Entry-point navigator (read first) │ ├── 01-appkit-scaffold/ # Scaffold new AppKit projects (+ agent skills install) │ ├── 02-appkit-build/ # Build UI + backend from a PRD │ ├── 03-appkit-deploy/ # Deploy to Databricks Apps │ ├── 04-appkit-plugin-add/ # Add plugins (Lakebase, Analytics, Genie, Files, Serving) │ ├── 05-appkit-lakebase-wiring/ # Wire Lakebase DDL, API routes, frontend hooks │ ├── 06-appkit-serving-wiring/ # Wire Model Serving / Agent endpoint to frontend │ ├── 06d-appkit-agent-app-proxy/ # Wire AppKit frontend to a separate Agent App │ ├── 07-appkit-chat-history/ # Persist chat conversations in Lakebase │ └── 08-appkit-feedback/ # Add thumbs feedback linked to MLflow assessments │ ├── presentations/ # Workshop slide deck (Marp, HTML, PDF, PPTX) │ ├── genai-agents/ # GenAI Agent Development Course │ ├── 00-course-orchestrator/ # Current GenAI course navigator │ ├── PROMPT-GUIDE.md # Canonical Track A + AppKit 2-Apps prompts │ ├── foundation/ # UC resources, MLflow, tracing, tools, AI Gateway │ ├── tracks/ # Track A custom Agent Apps │ └── sdlc/ # Prompt registry, evals, deployment, monitoring │ ├── data_product_accelerator/ # 77 Agent Skills for End-to-End Data Products │ ├── AGENTS.md # Detailed skill routing table │ ├── QUICKSTART.md # One-prompt-per-stage guide (9 stages) │ ├── README.md # Accelerator overview │ ├── context/ # Schema CSV inputs (starting point) │ ├── skills/ # 77 skills across 12 domains │ │ ├── admin/ # Skill creation, auditing (4) │ │ ├── bronze/ # Bronze layer + Faker data (2) │ │ ├── common/ # Cross-cutting shared skills (8) │ │ ├── exploration/ # Ad-hoc notebooks (1) │ │ ├── genai-agents/ # GenAI course mirror, tracks, SDLC, capstone │ │ ├── gold/ # Gold design, implementation, workers (14) │ │ ├── ml/ # MLflow pipelines (1) │ │ ├── monitoring/ # Monitors, dashboards, alerts (5) │ │ ├── planning/ # Project planning (1) │ │ ├── semantic-layer/ # Metric Views, TVFs, Genie, optimization (10) │ │ ├── silver/ # DLT pipelines, DQ rules (3) │ │ └── skill-navigator/ # Master routing system (1) │ └── docs/ # Framework design documentation │ └── agentic-framework/ # Multi-Agent Build Framework ├── agents/ # Agent prompts for building multi-agent systems │ ├── prd-analyzer.md # Parse PRDs, map to agent capabilities │ ├── skill-scaffolder.md # Create new Agent Skills (SKILL.md) │ ├── tool-builder.md # Build runtime Python tools │ ├── agent-tester.md # Configure agent behavior tests │ ├── agent-ui-wiring-prompt.md # Guide agent-to-UI wiring │ ├── multi-agent-build-prompt.md # Orchestrator build with Foundation Models │ ├── databricks-deployer.md # Deployment guidance │ └── prd-template.md # PRD template └── skills/ └── foundation-model-agent-loop/ └── SKILL.md # Tool-calling loop with Foundation Models ``` --- ## How It All Fits Together This template supports a unified workflow from raw data to production data products: ``` Raw Schema CSV or Existing Data │ ├─► Gold Design — dimensional model, ERDs, YAML schemas ├─► Bronze Layer — source tables + test data (Faker) ├─► Silver Layer — DLT pipelines + data quality expectations ├─► Gold Layer — tables, MERGE scripts, FK constraints ├─► Semantic Layer — Metric Views, TVFs, Genie Spaces ├─► Observability — Lakehouse Monitors, AI/BI Dashboards, SQL Alerts ├─► ML Pipelines — MLflow experiments, training, inference ├─► GenAI Agents — ResponsesAgent, evaluation, deployment │ └─► Databricks App — AppKit (full-stack TypeScript), deployed on Databricks Apps ``` Each stage is driven by a single prompt to your AI coding assistant. The 77 agent skills in `data_product_accelerator/` encode production-tested patterns so you get governed, high-quality output at every step. --- ## Data Product Accelerator (77 Agent Skills) The `data_product_accelerator/` directory contains **77 agent skills** organized by domain that guide your AI assistant through the entire pipeline: | Domain | Skills | Focus | |--------|--------|-------| | **Gold** | 14 | Dimensional modeling, design workers, pipeline workers, ERDs, MERGE scripts | | **Semantic Layer** | 5 | Metric Views, TVFs, Genie Spaces, export/import API | | **GenAI Agents** | 31 | Course orchestrator, foundation, Track A/B/C, SDLC, capstone, simple agent scaffold | | **Common** | 8 | Asset Bundles, naming, constraints, imports | | **Monitoring** | 5 | Lakehouse Monitors, dashboards, SQL alerts | | **Admin** | 4 | Skill creation, auditing, docs | | **Silver** | 3 | DLT pipelines, expectations, DQX | | **Bronze** | 2 | Bronze tables, Faker data generation | | **ML** | 1 | MLflow pipelines | | **Planning** | 1 | Project planning | | **Exploration** | 1 | Ad-hoc notebooks | | **Skill Navigator** | 1 | Master routing system | See [data_product_accelerator/QUICKSTART.md](data_product_accelerator/QUICKSTART.md) for the step-by-step 9-stage guide. --- ## Databricks AppKit Workshop (10 Agent Skills) The `apps_lakebase/` directory contains **10 agent skills** and a comprehensive workshop guide for building full-stack TypeScript apps on [Databricks AppKit](https://databricks.github.io/appkit/). The app is **not pre-built** — it gets scaffolded at runtime via `databricks apps init` and built iteratively with your AI coding assistant. **What gets built:** - Full-stack TypeScript app (React + Tailwind CSS frontend, AppKit backend) - Optional SQL Warehouse integration for analytics queries - Lakebase (managed PostgreSQL) persistence (wired after the mock-data app is deployed) - Optional agent chat with either Model Serving or a separate Agent App proxy - Optional persistent chat history and MLflow-linked feedback - Deployed to Databricks Apps with hot reload for local dev ### Workshop Skills | Skill | Purpose | |-------|---------| | `00-appkit-navigator` | Entry-point navigator — routes tasks to the correct skill | | `01-appkit-scaffold` | Scaffold new AppKit projects with plugins (analytics, lakebase, genie, files) | | `02-appkit-build` | Build UI and backend from a PRD — components, queries, type generation | | `03-appkit-deploy` | Deploy to Databricks Apps, validate configuration | | `04-appkit-plugin-add` | Add plugins to an existing AppKit project | | `05-appkit-lakebase-wiring` | Wire Lakebase DDL, Express API routes, frontend hooks, mock fallback | | `06-appkit-serving-wiring` | Wire Model Serving / Agent endpoint to frontend (streaming, invoke) | | `06d-appkit-agent-app-proxy` | Wire an AppKit frontend to a separate Agent App with OBO forwarding | | `07-appkit-chat-history` | Persist agent chat conversations in Lakebase with session auth | | `08-appkit-feedback` | Add thumbs up/down feedback linked to MLflow assessments | ### Local Development (after scaffolding) | Service | URL | |---------|-----| | App + API | http://localhost:8000 | | Health Check | http://localhost:8000/health | Start the dev server from your scaffolded app directory with `npm run dev`. --- ## GenAI Agent Development Course The root `genai-agents/` directory is the current course entry point for production GenAI agents on Databricks. Start with `genai-agents/00-course-orchestrator/SKILL.md`, then use `genai-agents/PROMPT-GUIDE.md` for the canonical Track A + AppKit 2-Apps walkthrough. **Canonical flow:** ``` Foundation (UC, MLflow, tracing, tools) → Track A custom Agent App on Databricks Apps → AppKit frontend proxy with OBO (`06d`) → Chat history + feedback (`07`, `08`) → SDLC pipeline (prompt registry, evals, deployment, monitoring) ``` The canonical GenAI agent skills live in the root `genai-agents/` tree; stage 9 of the data product pipeline routes into the same agent-building patterns via `genai-agents/00-course-orchestrator/SKILL.md`. For Databricks-platform reference patterns (Knowledge Assistants, Genie Spaces, Model Serving, etc.) see the upstream registry [`databricks/databricks-agent-skills`](https://github.com/databricks/databricks-agent-skills); local skills here extend or specialize those for our workshop and accelerator use cases. --- ## Agentic Framework The `agentic-framework/` directory provides prompts and patterns for building **multi-agent systems** with Databricks Foundation Models. **Agent prompts** (in `agentic-framework/agents/`): | Agent | Purpose | |-------|---------| | **prd-analyzer** | Parse PRDs, map requirements to agent capabilities | | **skill-scaffolder** | Create new Agent Skills (SKILL.md) for any domain | | **tool-builder** | Build runtime Python tools for agents | | **agent-tester** | Configure tests for agent behavior | | **agent-ui-wiring-prompt** | Guide agent-to-UI integration | | **multi-agent-build-prompt** | Build multi-agent orchestrators with Foundation Models | | **databricks-deployer** | Deployment guidance for agents | | **prd-template** | PRD template for agent projects | **Foundation Model Agent Loop** (in `agentic-framework/skills/foundation-model-agent-loop/`): - Pattern for tool-calling loops with Databricks Foundation Models (e.g., `databricks-meta-llama-3-3-70b-instruct`) - Supports function calling without custom model deployment --- ## How Deployment Works After scaffolding your AppKit app, the workflow progresses from mock data to a wired backend, with optional branches for agent chat, a separate Agent App proxy, chat history, and feedback: ``` ┌─────────────────────────────────────────────────────────────────────┐ │ AppKit Workshop Lifecycle │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ Phase 1: SCAFFOLD + BUILD Phase 2: DEPLOY (mock data) │ │ ──────────────────────── ────────────────────────── │ │ databricks apps init npm run build │ │ Build UI from PRD databricks apps deploy --profile <P> │ │ npm run dev (localhost:8000) Verify at Databricks Apps URL │ │ │ │ Phase 3: SETUP LAKEBASE Phase 4: WIRE LAKEBASE │ │ ────────────────────── ───────────────────── │ │ Add Lakebase bundle config Add Lakebase plugin (skill 04) │ │ Configure endpoint + compute DDL, API routes, frontend (skill 05)│ │ Record host in state file Test locally with mock fallback │ │ │ │ Optional Agent Branches │ │ ─────────────────────── │ │ Skill 06: Model Serving / Agent endpoint │ │ Skill 06d: Separate Agent App proxy with OBO forwarding │ │ Skill 07: Persistent chat history │ │ Skill 08: User feedback linked to MLflow assessments │ │ │ │ Phase 5: DEPLOY + E2E TEST │ │ ──────────────────────── │ │ databricks apps deploy (with Lakebase config) │ │ Verify live data end-to-end │ │ │ └─────────────────────────────────────────────────────────────────────┘ ``` ### Key Files in the Generated App | File | Purpose | |------|---------| | `app.yaml` | Defines how Databricks starts your app | | `databricks.yml` | Databricks bundle configuration | | `server/server.ts` | AppKit backend entry point | | `client/src/` | React + Tailwind CSS frontend | | `package.json` | Node.js dependencies | See the `03-appkit-deploy` skill for the full deployment workflow. --- ## Authentication Authentication is a **client-specific, one-time setup** — the full guide lives in **[PRE-REQUISITES.md §11](PRE-REQUISITES.md)** (the single home for local auth): - **IDE/CLI client:** authenticate the Databricks CLI locally (PRE-REQUISITES §11). Verify with `databricks current-user me`; all skills and CLI commands accept a `--profile` flag to target a specific workspace (use `--profile myprofile` when juggling multiple workspaces). - **Genie Code client:** already authenticated to its host workspace — nothing to configure, and omit `--profile` from every command. --- ## Customizing Your App After scaffolding, your generated AppKit app is a full-stack TypeScript project. Customize it using standard AppKit patterns: ### Adding Backend Routes Edit `server/server.ts` in your generated app directory to add tRPC routes or custom endpoints. See the `02-appkit-build` skill for patterns. ### Adding Plugins Use the `04-appkit-plugin-add` skill to add capabilities: ``` Read @apps_lakebase/skills/04-appkit-plugin-add/SKILL.md and add the Lakebase plugin to my app. ``` Available plugins: `analytics`, `lakebase`, `genie`, `files`, `serving` ### Adding Dependencies ```bash npm install your-package ``` ### Consulting AppKit Docs ```bash npx @databricks/appkit docs # documentation index npx @databricks/appkit docs "<query>" # search for a specific topic ``` --- ## Troubleshooting ### Check Databricks CLI ```bash databricks --version # Should be >= 0.295.0 databricks current-user me # Verify authentication databricks auth profiles # List configured profiles ``` ### Authentication failed Re-run the IDE/CLI auth from **[PRE-REQUISITES.md §11](PRE-REQUISITES.md)** (does not apply to Genie Code — it is pre-authenticated). Then re-verify: ```bash databricks current-user me ``` ### Port 8000 in use ```bash lsof -ti:8000 | xargs kill -9 2>/dev/null || true npm run dev ``` ### View Deployed App Logs ```bash databricks apps get <APP_NAME> --profile <PROFILE> ``` ### Local Testing (after scaffolding) ```bash cd <your-app-directory> npm run dev # Open http://localhost:8000 ``` --- ## Resources - [PRE-REQUISITES.md](PRE-REQUISITES.md) — Workshop prerequisites checklist - [Workshop Presentation](presentations/README.md) — Marp slide deck (HTML, PDF, PPTX) - [AppKit Workshop Guide](apps_lakebase/Instructions.md) — Databricks App workshop guide - [GenAI Agent Prompt Guide](genai-agents/PROMPT-GUIDE.md) — canonical Track A + AppKit 2-Apps course prompts - [Data Product Accelerator QUICKSTART](data_product_accelerator/QUICKSTART.md) — 9-stage pipeline guide - [Databricks AppKit Documentation](https://databricks.github.io/appkit/) — AppKit SDK reference - [Databricks Apps Documentation](https://docs.databricks.com/dev-tools/databricks-apps/) - [Databricks CLI Installation](https://docs.databricks.com/dev-tools/cli/install.html) - [Agent Skills (SKILL.md) Format](https://agentskills.io) - [Unity Catalog](https://docs.databricks.com/unity-catalog/) - [Delta Live Tables](https://docs.databricks.com/dlt/) - [Metric Views](https://docs.databricks.com/metric-views/) ### Authoritative upstream skill registries For canonical Databricks-platform and accelerator skills (Apps, Lakebase, Model Serving, DABs, Pipelines, Core, Unity Catalog, Metric Views, Genie, MLflow evaluation, synthetic data, and more), see [`databricks/databricks-agent-skills`](https://github.com/databricks/databricks-agent-skills) — the single authoritative upstream skill registry. The local skills in this repo extend or specialize those for our workshop and accelerator use cases. Where a local skill genuinely derives from an upstream skill, its frontmatter records a structured `upstream_sources` entry that the freshness scanner audits for drift; lighter "see also" references appear as a `## See Also` footer in the skill body. --- ## Using This Template This is a **Git template repository**. To use it: 1. Click "Use this template" on GitHub, or clone directly 2. Choose your starting point: - **Build a Databricks App:** Follow the [AppKit workshop guide](apps_lakebase/Instructions.md) — scaffold, build, deploy, and wire Lakebase - **Build a data product:** Drop a schema CSV in `data_product_accelerator/context/` and follow the [9-stage guide](data_product_accelerator/QUICKSTART.md) - **Build a GenAI agent:** Start with [genai-agents/00-course-orchestrator/SKILL.md](genai-agents/00-course-orchestrator/SKILL.md) and use [genai-agents/PROMPT-GUIDE.md](genai-agents/PROMPT-GUIDE.md) for the AppKit 2-Apps walkthrough - **Build agents:** Use the prompts in `agentic-framework/agents/` to scaffold multi-agent systems 3. Iterate with your AI coding assistant — the agent skills handle the patterns --- <div align="center"> **Ready to build? Let's go!** ```bash git clone https://github.com/databricks-solutions/vibe-coding-workshop-template.git my-project cd my-project ``` </div>

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lakebase-cdf

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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…

원문 언어: 영어

2026-10-16

databricks-asset-bundles

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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

2026-10-16

genie-code-environment

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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…

원문 언어: 영어

2026-10-16

gold-layer-design

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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

2026-10-16

07-design-validation

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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

2026-10-16

vibecoding-state

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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…

원문 언어: 영어

2026-10-16

databricks-autonomous-operations

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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

2026-10-09

00-uc-resources-foundation

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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…

원문 언어: 영어

2026-10-07

databricks-aibi-dashboards

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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

2026-09-22

databricks-aibi-custom-viz-vega

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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…

원문 언어: 영어

2026-09-22

databricks-aibi-custom-pages-react

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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…

원문 언어: 영어

2026-09-22

01-appkit-scaffold

소프트웨어 개발자

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…

원문 언어: 영어

2026-09-21

02-appkit-build

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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…

원문 언어: 영어

2026-09-21

03-appkit-deploy

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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

2026-09-21

04-appkit-plugin-add

소프트웨어 개발자

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

2026-09-21

05-appkit-lakebase-wiring

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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 +…

원문 언어: 영어

2026-09-21

genie-discover-ontology

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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…

원문 언어: 영어

2026-09-21

metric-views-patterns

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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

2026-09-14

skill-freshness-audit

소프트웨어 품질 보증 분석가·테스터

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

2026-09-14

semantic-layer-setup

미분류

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

2026-09-14

genie-space-export-import-api

미분류

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

2026-09-14

skill-navigator

기타 컴퓨터 관련 직업

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

2026-09-14

08-industry-alignment

소프트웨어 개발자

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

2026-08-31

self-improvement

기타 컴퓨터 관련 직업

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

2026-08-31

bronze-layer-setup

소프트웨어 개발자

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

2026-08-31

faker-data-generation

데이터 과학자

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

2026-08-31

databricks-python-imports

소프트웨어 개발자

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

2026-08-31

databricks-table-properties

소프트웨어 개발자

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

2026-08-31

naming-tagging-standards

소프트웨어 개발자

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

2026-08-31

schema-management-patterns

데이터베이스 아키텍트

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

2026-08-31

unity-catalog-constraints

데이터베이스 아키텍트

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

2026-08-31

gold-layer-setup

소프트웨어 개발자

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

2026-08-31

01-yaml-table-setup

소프트웨어 개발자

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

2026-08-31

ml-pipeline-setup

데이터 과학자

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

2026-08-31

lakehouse-monitoring-comprehensive

데이터 과학자

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

2026-08-31

databricks-table-valued-functions

소프트웨어 개발자

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

2026-08-31

genie-space-patterns

소프트웨어 개발자

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

2026-08-31

silver-layer-setup

소프트웨어 개발자

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

2026-08-31

dlt-expectations-patterns

소프트웨어 개발자

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

2026-08-31

05-knowledge-assistant

소프트웨어 개발자

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

2026-08-31

수집된 skill 93개 중 40개를 표시합니다.