AI Genie Factory

by Marvin Nahmias & Javier Hauss — Alpura, Mexico City Premiering at DATA+AI Summit 2026

mexmarv.github.io/ai-genie-factory

Built on @databricks Genie Code Agent Mode and Unity Catalog.

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What Is This?

The AI Genie Factory is a methodology for building enterprise Databricks applications at scale using AI code generation that always adheres to your architecture.

The core insight: AI doesn't generate architecture — it generates code. Architecture has to be defined first, then enforced as constraints.

This repository contains two types of constraints:

• AGENTS.md — lean always-on guardrails (platform rules, stack, error handling, logging). Loaded once into Genie Code's instructions file. ~6,000 chars.
• skills/ — domain skills loaded on demand by Genie Code Agent mode, or @mentioned by name. Each skill is focused, contextual, and doesn't bloat every generation.

At Alpura, this system produced 90+ apps across Finance, Sales, Operations, and Marketing — most in days, some in hours.

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The Factory Methodology

Platform (Lakehouse)
  └── Governance (Unity Catalog)
        └── Semantic Layer (Central KPIs, Gold Tables)
              └── Factory (AGENTS.md + Skills)
                    └── Genie Code Agent Mode
                          └── Apps / Pipelines / Notebooks

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Repository Structure

ai-genie-factory/
│
├── AGENTS.md                          ← Lean always-on constraints (~8,300 chars)
├── GLOBAL_RULES.md                    ← Platform law (never override)
├── STACK.md                           ← Technology choices (never override)
├── LICENSE                            ← MIT
├── build_agents.py                    ← Assembles AGENTS.md from core modules only
├── deploy.sh                          ← Deploys instructions + skills via Databricks CLI
│
├── modules/                           ← Source files compiled into AGENTS.md
│   ├── error_handling.md              ← try/except contracts, custom exceptions
│   ├── logging.md                     ← Structured logging standard (_logger.py)
│   └── skill_index.md                 ← Always-loaded routing table — which skill covers what
│
├── skills/                            ← Agent Skills standard: one folder per skill
│   ├── ui-ux-patterns/SKILL.md          ← @ui-ux-patterns — dark/light tokens, typography, KPI cards, charts (Apps)
│   ├── databricks-app/SKILL.md          ← @databricks-app — app architecture, app.yaml, deployment, debug
│   ├── databricks-dashboard/SKILL.md    ← @databricks-dashboard — AI/BI datasets, widgets, filters, Vega-Lite waterfall
│   ├── databricks-dashboard-colors/SKILL.md ← @databricks-dashboard-colors — Lakeview dark/light theme + palette
│   ├── dlt-pipeline/SKILL.md            ← @dlt-pipeline — Bronze/Silver/Gold, CDC, SCD2, streaming
│   ├── data-access/SKILL.md             ← @data-access — Statement Execution, Unity Catalog, DataAccessError
│   └── testing-scaffold/SKILL.md        ← @testing-scaffold — pytest and mocked layer tests
│
├── templates/
│   ├── PROMPT_TEMPLATE.md             ← Exact prompt for every Genie Code session
│   └── APP_TEMPLATE.md                ← Blank APP spec — fill this out per app
│
├── docs/
│   └── index.html                     ← GitHub Pages landing page
│
└── apps/
    ├── nyc_taxi_explorer/
    │   └── APP.md                     ← Zero-permission starter: samples.nyctaxi.trips
    └── dbu_spend_app/
        └── APP.md                     ← Working example: DBU Spend Monitor

---

AGENTS.md vs Skills — When to Use Each

	AGENTS.md	Skills
When loaded	Every Genie Code session	Only when relevant to the request
Purpose	Non-negotiable platform guardrails	Domain knowledge, patterns, design systems
Size target	< 6,000 chars	As large as needed
Contents	Global rules, stack, error handling, logging, skill index	Charts, pipelines, testing, UX/UI design
How to invoke	Automatic	Automatic (Agent mode) or @skill-name

Rule of thumb: if it applies to every single generation, it goes in AGENTS.md. If it applies to a specific type of work, it's a skill.

Do I need to @mention a skill? No, not normally. Genie Code Agent mode reads each skill's description frontmatter and loads it automatically when your prompt matches — "build me a dashboard" alone is enough to pull in @databricks-dashboard and @databricks-dashboard-colors. There is no third "always force this skill" setting for skills — the only thing that is truly loaded on every single request, with zero matching required, is AGENTS.md/the workspace instructions file. That's why modules/skill_index.md exists: a short, always-loaded table naming every skill and when it should fire, so routing stays reliable even when a prompt is worded ambiguously. @mention is still there for when you want to force a specific skill regardless of wording.

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Constraint Priority

Priority	Source	Purpose
1	GLOBAL_RULES.md	Platform law — never overridden
2	STACK.md	Technology choices — never redefined per app
3	modules/error_handling.md	Error contracts — never overridden
4	modules/logging.md	Logging standard — never overridden
5	@data-access skill	WorkspaceClient Statement Execution, Unity Catalog, DataAccessError
6	@ui-ux-patterns skill	Design tokens, shadows, typography, charts, KPI cards (Apps)
7	@databricks-app skill	App file layers, app.yaml, OAuth M2M, deployment, debug checklist
8	@databricks-dashboard skill	AI/BI Lakeview dashboard SQL, widgets, filters, scheduling, Vega-Lite waterfall
9	@databricks-dashboard-colors skill	Lakeview dark/light theme tokens + visualization palette
10	@dlt-pipeline skill	Bronze/Silver/Gold, serverless DLT, CDC, SCD2, streaming
11	@testing-scaffold skill	pytest, mocked WorkspaceClient calls, pandas logic tests
12	APP.md	App-specific spec — only what's unique to this app

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Setup: One-Time Deployment

Prerequisites

• Databricks CLI v0.200+ installed and configured
• Run databricks auth login if you haven't set up a profile yet

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Step 1 — Deploy instructions + skills via deploy.sh

From the repo root, run:

# Deploy to your personal Genie Code folder (default)
./deploy.sh

# Deploy workspace-wide so all users get the skills (requires workspace admin)
./deploy.sh --workspace

# Use a specific ~/.databrickscfg profile
./deploy.sh --profile my-profile

# Deploy to another user's personal folder (skip current-user lookup)
./deploy.sh --user someone@alpura.com

# Preview the exact commands without uploading anything
./deploy.sh --workspace --dry-run

The script uploads the exact paths documented by Databricks:

• Workspace instructions: AGENTS.md → /Workspace/.assistant_workspace_instructions.md
• Personal instructions: AGENTS.md → /Users/<user>/.assistant_instructions.md
• Workspace skills: skills/<name>/SKILL.md → /Workspace/.assistant/skills/<name>/SKILL.md
• Personal skills: skills/<name>/SKILL.md → /Users/<user>/.assistant/skills/<name>/SKILL.md

What import-dir can't do: The Databricks CLI's import-dir only handles notebooks (.py, .sql, .ipynb). Plain .md skill files must be uploaded as raw files — deploy.sh handles this automatically using databricks workspace import --format RAW.

Redeploying after edits:

# After editing AGENTS.md or any skill, just re-run:
./deploy.sh

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Step 2 — Verify in Genie Code

1. Open Genie Code in Databricks (sparkle icon, top-right)
2. Click ⚙️ and open User instructions or Workspace instructions, depending on the deployment scope.
3. Switch to Agent mode and @mention any skill to confirm it's available:

   @ui-ux-patterns  @databricks-app  @databricks-dashboard  @databricks-dashboard-colors
   @dlt-pipeline  @data-access  @testing-scaffold
   

Genie Code picks up instructions on the next interaction. For a changed skill, start a new chat; hard-refresh the browser if its metadata remains stale.

Workspace instructions and skills guide Genie Code, but they are not a deterministic security boundary. Unity Catalog privileges, compute policies, service-principal permissions, and CI checks remain the enforcement layer. See Workspace deployment and enforcement.

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Step 3 — Fill out an APP spec

Copy templates/APP_TEMPLATE.md and fill in every field before prompting.

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Step 4 — Open Genie Code in Agent mode and prompt

Use templates/PROMPT_TEMPLATE.md as your prompt. Skills are loaded automatically, or @mention them:

@databricks-app @ui-ux-patterns build the sales dashboard per the spec below
@databricks-dashboard @databricks-dashboard-colors build a native Lakeview dashboard for daily revenue
@dlt-pipeline create the ingestion pipeline for the orders feed
@testing-scaffold add tests to the app I just built

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Rebuilding AGENTS.md

After editing GLOBAL_RULES.md, STACK.md, or modules/*.md:

python build_agents.py

Output:

✅  LEAN  AGENTS.md — 8,344 / 20,000 chars (41.7%)
   Target: keep under 12,000 chars (60%) — domain knowledge belongs in skills/

  Skills to deploy separately (copy to /Workspace/.assistant/skills/):
    @data-access
    @databricks-app
    @databricks-dashboard
    @databricks-dashboard-colors
    @dlt-pipeline
    @testing-scaffold
    @ui-ux-patterns

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Debugging a Generated App

1. FILE STRUCTURE
   □ data.py / logic.py / ui.py / app.py / _logger.py all present?
   □ If no → re-prompt with PROMPT_TEMPLATE.md, @mention missing skill

2. LAYER VIOLATIONS
   □ SQL outside data.py?  → @data-access, move it into the data layer
   □ WorkspaceClient created in ui.py? → move it into data.py
   □ Business logic in ui.py?          → move to logic.py
   □ toPandas() called anywhere?       → Apps use pandas; data.py returns pd.DataFrame

3. DATA ACCESS
   □ Using WorkspaceClient Statement Execution API in Databricks Apps?
   □ Warehouse ID comes from configuration rather than a hardcoded literal?
   □ Statement result types are normalized before logic/UI use?

4. TABLE NAMES
   □ All refs three-part (catalog.schema.table)?
   □ Hardcoded strings outside app.py config dict?

5. LOGGING
   □ Each file imports from _logger?
   □ Check Databricks Apps logs for ERROR lines first

6. ERROR HANDLING
   □ data.py raises DataAccessError on failure?
   □ ui.py catches exceptions and shows _error_figure()?

7. DESIGN SYSTEM
   □ Uses the shared dark or light theme tokens? → @ui-ux-patterns
   □ Plotly template follows the selected theme?
   □ No px.pie (use px.treemap), no #636efa (use #00bcd4)?

8. PIPELINE
   □ DLT tables named bronze_/silver_/gold_?  → @dlt-pipeline
   □ No spark.read() inside DLT notebooks?
   □ File paths use /Volumes/ not dbfs:/?
   □ New pipelines use serverless DLT?

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Example Apps

App	Table	Permissions needed
NYC Taxi Explorer	samples.nyctaxi.trips	None — works in every Unity Catalog workspace out of the box
DBU Spend Monitor	system.billing.usage	Requires system tables enabled by an admin

Start with NYC Taxi Explorer to verify the factory is wired up before building on your own data.

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Skill Reference

All skills are aligned with Databricks' official AI Dev Kit: github.com/databricks-solutions/ai-dev-kit

Key patterns enforced by this factory:

• Apps data access: WorkspaceClient + Statement Execution API, isolated in data.py
• App startup: no remote data calls at module import time
• app.yaml resources: valueFrom: sql-warehouse — never hardcode IDs
• DLT: serverless by default, dlt.read() not spark.read()
• Grants: SP applicationId UUID — never display name

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Contributing

1. Edit GLOBAL_RULES.md, STACK.md, or modules/*.md → run python3 build_agents.py
2. Edit skills in skills/<name>/SKILL.md → run ./deploy.sh to push changes
3. Never edit AGENTS.md directly — it's generated
4. Add new apps under apps/<app-name>/APP.md

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License

MIT — use freely, adapt to your stack.

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AI generates code. Not architecture. — Marvin Nahmias, DAIS 2026