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 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…