This file is the universal entry point for any AI coding assistant — Cursor, Claude Code, Windsurf, Copilot, Codex, or any agent that reads
AGENTS.md.
This is a monorepo with four components: a Databricks AppKit workshop with Lakebase and agent-chat extensions, a standalone GenAI agent course, a Data Product Accelerator with 77 agent skills, and an Agentic Framework for building multi-agent systems.
vibe-coding-workshop-template/ <-- workspace root / agent CWD
├── AGENTS.md <-- THIS FILE (root navigator)
├── README.md <-- Human-readable project overview
├── QUICKSTART.md <-- Quick-start guide (two pathways)
├── PRE-REQUISITES.md <-- Workshop prerequisites checklist
├── env.example <-- Environment variable template
│
├── apps_lakebase/ <-- Component 1: Databricks AppKit Workshop
│ ├── Instructions.md # Comprehensive workshop guide
│ ├── prompts/ # Numbered prompt files for each workshop step
│ └── skills/ # 10 agent skills for the full app lifecycle
│ ├── 00-appkit-navigator/ # Entry-point navigator (read first)
│ ├── 01-appkit-scaffold/ # Scaffold new AppKit projects
│ ├── 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 backend (DDL, APIs, 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
│
├── genai-agents/ <-- Component 2: GenAI Agent Development Course
│ ├── 00-course-orchestrator/ # Entry-point navigator for the course
│ ├── PROMPT-GUIDE.md # Canonical Track A + AppKit 2-Apps prompt cookbook
│ ├── foundation/ # UC, MLflow, tracing, tools, AI Gateway, KA
│ ├── tracks/ # Track A custom agent on Databricks Apps
│ └── sdlc/ # Prompt registry, evals, deployment, monitoring
│
├── agentic-framework/ <-- Component 3: Multi-Agent Build Framework
│ ├── agents/ # Agent prompts (PRD analyzer, skill scaffolder, etc.)
│ └── skills/
│ └── foundation-model-agent-loop/
│ └── SKILL.md # Tool-calling loop with Foundation Models
│
├── presentations/ <-- Workshop slide deck (Marp, HTML, PDF, PPTX)
│
├── data_product_accelerator/ <-- Component 4: 77 Agent Skills for Data Products
│ ├── AGENTS.md # *** DETAILED SKILL ROUTING TABLE ***
│ ├── QUICKSTART.md # One-prompt-per-stage guide
│ ├── README.md # Accelerator overview
│ ├── context/ # Customer schema CSV inputs
│ ├── skills/ # 77 skills across 12 domains
│ └── docs/ # Framework design documentation
│
├── gold_layer_design/ <-- GENERATED by Gold Design skill
├── src/ <-- GENERATED by implementation skills
├── plans/ <-- GENERATED by Planning skill
├── resources/ <-- GENERATED by Asset Bundle skills
└── databricks.yml <-- GENERATED by Asset Bundle skills
| Direction | Location | Examples |
|---|---|---|
| Read from (framework) | apps_lakebase/, agentic-framework/, data_product_accelerator/ |
Skills, instructions, agent prompts, docs |
| Write to (artifacts) | Repository root | gold_layer_design/, src/, plans/, resources/, databricks.yml |
Rule: Generated artifact paths (
gold_layer_design/,src/,plans/,resources/,databricks.yml) are relative to the repository root. Never create generated files insidedata_product_accelerator/,apps_lakebase/, oragentic-framework/.
MANDATORY: Before starting any task, match keywords below to find the right component. Read the linked file FIRST.
| Keywords | Action |
|---|---|
| "medallion architecture", "data product", "lakehouse", "data platform" | Read data_product_accelerator/AGENTS.md |
| "bronze layer", "test data", "Faker", "demo data", "source tables" | Read data_product_accelerator/AGENTS.md |
| "silver layer", "DLT", "expectations", "data quality", "DQX" | Read data_product_accelerator/AGENTS.md |
| "gold layer", "dimensional model", "ERD", "YAML schema", "merge scripts" | Read data_product_accelerator/AGENTS.md |
| "Genie Space", "semantic layer", "metric view", "TVF" | Read data_product_accelerator/AGENTS.md |
| "monitoring", "dashboard", "alert", "observability", "anomaly detection" | Read data_product_accelerator/AGENTS.md |
| "MLflow", "ML model", "training", "inference", "ML pipeline" | Read data_product_accelerator/AGENTS.md |
| "GenAI agent", "ResponsesAgent", "AI agent", "evaluation" | Read data_product_accelerator/AGENTS.md |
| "schema CSV", "bootstrap", "new project", "build data platform" | Read data_product_accelerator/AGENTS.md |
| "project plan", "architecture plan", "planning" | Read data_product_accelerator/AGENTS.md |
| "Asset Bundle", "DAB", "deploy pipeline", "job YAML" | Read data_product_accelerator/AGENTS.md |
| "naming", "tagging", "PII", "PK/FK", "constraints", "table properties" | Read data_product_accelerator/AGENTS.md |
The app is scaffolded at runtime via databricks apps init (not pre-built). The skills guide the full lifecycle.
| Keywords | Read This |
|---|---|
| "scaffold", "create app", "new app", "AppKit", "init app", "bootstrap app" | apps_lakebase/skills/01-appkit-scaffold/SKILL.md |
| "build app", "implement UI", "frontend", "backend", "PRD", "components" | apps_lakebase/skills/02-appkit-build/SKILL.md |
| "deploy app", "app.yaml", "Databricks Apps", "validate app" | apps_lakebase/skills/03-appkit-deploy/SKILL.md |
| "Lakebase", "PostgreSQL", "database tables", "wiring", "persistence" | apps_lakebase/skills/00-appkit-navigator/SKILL.md |
| "wire lakebase", "DDL", "CRUD API", "database schema design", "useLakebaseData", "mock fallback" | apps_lakebase/skills/05-appkit-lakebase-wiring/SKILL.md |
| "wire agent", "agent endpoint", "serving plugin", "model serving", "useServingStream" | apps_lakebase/skills/06-appkit-serving-wiring/SKILL.md |
| "agent app proxy", "two apps", "separate Agent App", "OBO proxy", "x-forwarded-access-token" | apps_lakebase/skills/06d-appkit-agent-app-proxy/SKILL.md |
| "chat history", "persistent chat", "conversation sidebar", "save messages" | apps_lakebase/skills/07-appkit-chat-history/SKILL.md |
| "feedback", "thumbs up", "thumbs down", "MLflow assessment", "rate response" | apps_lakebase/skills/08-appkit-feedback/SKILL.md |
| "add plugin", "analytics plugin", "genie plugin", "files plugin" | apps_lakebase/skills/04-appkit-plugin-add/SKILL.md |
| "workshop guide", "full lifecycle", "phases", "Instructions" | apps_lakebase/Instructions.md |
The standalone GenAI course is the current entry point for building production GenAI agents on Databricks. Use it for Track A custom Agent Apps, MLflow GenAI foundation, evaluation, deployment, monitoring, and the AppKit 2-Apps prompt guide.
| Keywords | Read This |
|---|---|
| "GenAI agent course", "agent course", "Track A", "custom agent app", "canonical 2-Apps" | genai-agents/00-course-orchestrator/SKILL.md |
| "prompt guide", "copy paste prompts", "AppKit agent app", "OBO proxy walkthrough" | genai-agents/PROMPT-GUIDE.md |
| "agent tracing", "MLflow GenAI", "evaluation datasets", "scorers", "prompt registry", "production monitoring" | genai-agents/00-course-orchestrator/SKILL.md |
| Keywords | Read This |
|---|---|
| "multi-agent", "Foundation Model", "tool-calling loop", "agent loop" | agentic-framework/skills/foundation-model-agent-loop/SKILL.md |
| "PRD", "product requirements", "analyze PRD" | agentic-framework/agents/prd-analyzer.md |
| "scaffold skill", "create skill", "SKILL.md template" | agentic-framework/agents/skill-scaffolder.md |
| "build tool", "Python tool", "agent tool" | agentic-framework/agents/tool-builder.md |
| "test agent", "agent behavior test" | agentic-framework/agents/agent-tester.md |
| "agent UI", "wire agent to UI", "frontend agent" | agentic-framework/agents/agent-ui-wiring-prompt.md |
| "multi-agent orchestrator", "orchestrator build" | agentic-framework/agents/multi-agent-build-prompt.md |
| "deploy agent", "agent deployment" | agentic-framework/agents/databricks-deployer.md |
For data product, medallion architecture, or Lakehouse tasks, the Data Product Accelerator provides a Design-First Pipeline with 77 agent skills. Read data_product_accelerator/AGENTS.md for the full routing table.
data_product_accelerator/context/*.csv
→ (1) Gold Design — dimensional model, ERDs, YAML schemas
→ (2) Bronze — source tables + test data (Faker)
→ (3) Silver — DLT pipelines + data quality
→ (4) Gold Impl — tables, merges, constraints
→ (5) Planning — phase plans + manifest contracts
→ (6) Semantic Layer — Metric Views, TVFs, Genie Spaces
→ (7) Observability — monitors, dashboards, alerts
→ (8) ML — experiments, training, inference
→ (9) GenAI Agents — agents, evaluation, deployment
| Stage | Keywords | Skill Entry Point |
|---|---|---|
| 1 | "new project", "schema CSV", "Gold design", "ERD" | data_product_accelerator/skills/gold/00-gold-layer-design/SKILL.md |
| 2 | "Bronze", "test data", "Faker" | data_product_accelerator/skills/bronze/00-bronze-layer-setup/SKILL.md |
| 3 | "Silver", "DLT", "expectations" | data_product_accelerator/skills/silver/00-silver-layer-setup/SKILL.md |
| 4 | "Gold tables", "merge scripts" | data_product_accelerator/skills/gold/01-gold-layer-setup/SKILL.md |
| 5 | "project plan", "architecture plan" | data_product_accelerator/skills/planning/00-project-planning/SKILL.md |
| 6 | "metric view", "TVF", "Genie Space" | data_product_accelerator/skills/semantic-layer/00-semantic-layer-setup/SKILL.md |
| 7 | "monitoring", "dashboard", "alert" | data_product_accelerator/skills/monitoring/00-observability-setup/SKILL.md |
| 8 | "MLflow", "ML model", "training" | data_product_accelerator/skills/ml/00-ml-pipeline-setup/SKILL.md |
| 9 | "GenAI agent", "ResponsesAgent" | genai-agents/00-course-orchestrator/SKILL.md |
New project? Start at stage 1: place your schema CSV in data_product_accelerator/context/, then read data_product_accelerator/skills/gold/00-gold-layer-design/SKILL.md.
- Route first, act second. Match keywords in the tables above, then read the linked file before writing any code.
- Read the component-level AGENTS.md or README.md FIRST — each component owns its own detailed routing.
- Generated artifacts go at the repo root — never inside
data_product_accelerator/,apps_lakebase/, oragentic-framework/. - Framework content is read-only — skills, manifests, and server code are inputs; do not modify them unless explicitly asked.
- For data product tasks, always defer to
data_product_accelerator/AGENTS.md— it contains the full 55-skill routing table.
This framework is built on the open Agent Skills (SKILL.md) format and works with any AI coding assistant that can read files.
| IDE / Agent | How It Discovers This File | File Reference Syntax |
|---|---|---|
| Cursor | Auto-loads AGENTS.md |
@path/to/file |
| Claude Code | Reads AGENTS.md (or CLAUDE.md) at repo root |
Reference files by path |
| VS Code / Copilot | Reads AGENTS.md or .github/copilot-instructions.md |
#file:path/to/file |
| Windsurf | Reads AGENTS.md or .windsurfrules at repo root |
@path/to/file |
| Codex | Reads AGENTS.md at repo root |
Reference files by path |
| Other | Point the agent to this file manually | Paste file contents or path |
Databricks Agent Skills are installed project-level into .agents/skills/ (gitignored) for all IDEs:
git clone --depth 1 https://github.com/databricks/databricks-agent-skills .agents/skills/databricks-skillsThis follows the agentskills.io cross-agent standard and is discovered by Cursor, VS Code, Windsurf, Claude Code, and any compatible agent. See apps_lakebase/skills/01-appkit-scaffold/SKILL.md Step 1 for details.
I have a customer schema at @data_product_accelerator/context/booking_app_schema.csv.
Please design the Gold layer using @data_product_accelerator/skills/gold/00-gold-layer-design/SKILL.md
Scaffold a new Databricks AppKit app with analytics and Lakebase plugins.
Read @apps_lakebase/skills/01-appkit-scaffold/SKILL.md
Start the GenAI agent course using @genai-agents/00-course-orchestrator/SKILL.md.
Use @genai-agents/PROMPT-GUIDE.md when you want the canonical Track A + AppKit 2-Apps walkthrough.
Build a multi-agent orchestrator using Databricks Foundation Models.
Read @agentic-framework/skills/foundation-model-agent-loop/SKILL.md for the tool-calling pattern.
If your IDE doesn't support @ references, paste the file path or ask the agent to read it:
Read the file data_product_accelerator/AGENTS.md and follow its routing instructions.