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 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 — required by AppKit
AI-Powered IDE	Yes	Cursor, Windsurf, VS Code + Copilot, or similar
Python 3.10+	Yes	python.org
Git	Yes	git-scm.com

Clone the Template

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:

Read the workshop guide: apps_lakebase/Instructions.md
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.

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:

Drop your schema CSV into data_product_accelerator/context/
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

Follow the 9-stage pipeline guide — 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 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. 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; 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

Configure a Databricks CLI profile to authenticate:

databricks auth login --host https://your-workspace.cloud.databricks.com

Verify it works:

databricks current-user me

To use a named profile (useful when working with multiple workspaces):

databricks auth login --host https://your-workspace.cloud.databricks.com --profile myprofile
databricks current-user me --profile myprofile

All skills and CLI commands accept a --profile flag to target a specific workspace.

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

npm install your-package

Consulting AppKit Docs

npx @databricks/appkit docs              # documentation index
npx @databricks/appkit docs "<query>"    # search for a specific topic

Troubleshooting

Check Databricks CLI

databricks --version          # Should be >= 0.295.0
databricks current-user me    # Verify authentication
databricks auth profiles      # List configured profiles

Authentication failed

databricks auth login --host https://your-workspace.cloud.databricks.com

Port 8000 in use

lsof -ti:8000 | xargs kill -9 2>/dev/null || true
npm run dev

View Deployed App Logs

databricks apps get <APP_NAME> --profile <PROFILE>

Local Testing (after scaffolding)

cd <your-app-directory>
npm run dev
# Open http://localhost:8000

Resources

PRE-REQUISITES.md — Workshop prerequisites checklist
Workshop Presentation — Marp slide deck (HTML, PDF, PPTX)
AppKit Workshop Guide — Databricks App workshop guide
GenAI Agent Prompt Guide — canonical Track A + AppKit 2-Apps course prompts
Data Product Accelerator QUICKSTART — 9-stage pipeline guide
Databricks AppKit Documentation — AppKit SDK reference
Databricks Apps Documentation
Databricks CLI Installation
Agent Skills (SKILL.md) Format
Unity Catalog
Delta Live Tables
Metric Views

Authoritative upstream skill registries

For canonical Databricks-platform skills (Apps, Lakebase, Model Serving, DABs, Pipelines, Core), see databricks/databricks-agent-skills. 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. The databricks-solutions/ai-dev-kit registry remains a parallel authoritative source for accelerator-specific medallion / semantic-layer / ML patterns.

Using This Template

This is a Git template repository. To use it:

Click "Use this template" on GitHub, or clone directly
Choose your starting point:
- Build a Databricks App: Follow the AppKit workshop guide — 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
- Build a GenAI agent: Start with genai-agents/00-course-orchestrator/SKILL.md and use 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
Iterate with your AI coding assistant — the agent skills handle the patterns

Ready to build? Let's go!

git clone https://github.com/databricks-solutions/vibe-coding-workshop-template.git my-project
cd my-project

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Vibe Coding Workshop Template

What is Vibe Coding?

Quick Start

Prerequisites

Clone the Template

Choose Your Starting Point

Path A: Build and Deploy a Databricks App

Path B: Build an End-to-End Data Pipeline

Project Structure

How It All Fits Together

Data Product Accelerator (77 Agent Skills)

Databricks AppKit Workshop (10 Agent Skills)

Workshop Skills

Local Development (after scaffolding)

GenAI Agent Development Course

Agentic Framework

How Deployment Works

Key Files in the Generated App

Authentication

Customizing Your App

Adding Backend Routes

Adding Plugins

Adding Dependencies

Consulting AppKit Docs

Troubleshooting

Check Databricks CLI

Authentication failed

Port 8000 in use

View Deployed App Logs

Local Testing (after scaffolding)

Resources

Authoritative upstream skill registries

Using This Template

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Vibe Coding Workshop Template

What is Vibe Coding?

Quick Start

Prerequisites

Clone the Template

Choose Your Starting Point

Path A: Build and Deploy a Databricks App

Path B: Build an End-to-End Data Pipeline

Project Structure

How It All Fits Together

Data Product Accelerator (77 Agent Skills)

Databricks AppKit Workshop (10 Agent Skills)

Workshop Skills

Local Development (after scaffolding)

GenAI Agent Development Course

Agentic Framework

How Deployment Works

Key Files in the Generated App

Authentication

Customizing Your App

Adding Backend Routes

Adding Plugins

Adding Dependencies

Consulting AppKit Docs

Troubleshooting

Check Databricks CLI

Authentication failed

Port 8000 in use

View Deployed App Logs

Local Testing (after scaffolding)

Resources

Authoritative upstream skill registries

Using This Template