Looker Demo Creator (demo-create)

From zero to production Looker demo in minutes. Automated, deterministic orchestrator for creating end-to-end Looker demos, synthetic BigQuery datasets, 3NF Snowflake LookML semantic models, 100% test-verified dashboards, Conversational Analytics (CA) Agents published to Gemini Enterprise (GE), and Embedded Analytics portals.

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Overview

demo-create unifies the entire full-stack Looker demo creation lifecycle into a single automated pipeline:

1. Pre-flight & Environment Audit (pre-check): Verifies active GCP/ADC accounts, installs/patches global MCP tools (data-designer, bigquery, knowledge-catalog), checks Looker authentication (OAuth / API keys), and organizes agent skills into intent-based subfolders.
2. Dataset Decision & Synthesis: Automatically checks for existing BigQuery datasets, enables data augmentation or green-field relational schema generation, and validates referential integrity.
3. BigQuery Loading: Creates datasets and partitioned/clustered tables in BigQuery.
4. LookML Generation & Direct Code-Mode Deployment: Autogenerates production-ready views, explores, and executive dashboards, provisioning and deploying them directly to the Looker instance using lkr code-mode without requiring an MCP server.
5. Conversational Analytics & Gemini Enterprise: Provisions Looker CA AI Agents, extracts dashboard queries into 1:1 Golden Queries, and publishes to Gemini Enterprise.
6. Embedded Portal Scaffolding: Clones and configures a clean, dedicated looker-embed-demo workspace for external client demos.

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What Gets Automated: Production Deliverables Inventory

Instead of spending days or weeks stitching together synthetic data scripts, debugging LookML joins, hand-crafting dashboard tiles, writing validation queries, and plumbing AI agent endpoints, demo-create produces a complete, production-grade enterprise demo in minutes:

Production Asset	What Gets Automated	Exact Output Format
BigQuery Data Warehouse	3NF relational schema synthesis, realistic engineering distributions, PK/FK referential integrity, and batch Parquet upload	Clean BigQuery dataset with partitioned/clustered tables
LookML 3NF Semantic Model	Explore Base View selection, Chasm Trap elimination with Native Derived Table (NDT) rollups joined one_to_one, role-playing diamond joins, and field metadata (label:, description:, value_format_name:, drill_fields:)	Complete views/*.view.lkml, explores/*.explore.lkml, and models/*.model.lkml
Executive Tabbed Dashboard	Executive tabbed report architecture, single-value KPI banners, dual-axis timelines, advanced_vis_config rounded geometry, cross-filtering, and popovers	Production dashboards/*.dashboard.lookml deployed via API
LookML Performance Optimization	Static suggestions: [...] on low-cardinality dims, suggestable: no on unique IDs/text, model datagroup caching, BigQuery partition pruning filters, and raw foreign key hiding	Production-hardened LookML avoiding database query spikes
Pre-Deployment QA Audit	Dev branch push, LookML project validator, 100% test execution of all dashboard queries via Looker API, and bounded self-healing (max 3 iterations)	100% HTTP 200 OK query pass certificate before production release
Conversational Analytics (CA) Agent	Auto-generated domain persona and query rules, extraction of dashboard tiles into 1:1 Looker 4.0 Golden Queries with expanded_share_url grounding	Live AI Agent in Looker with natural language chat UI
Gemini Enterprise (GE) Integration	Automated registration and one-click publishing to connected Gemini Enterprise apps via Looker internal API	Natural language querying across enterprise Gemini apps in minutes
White-Labeled Embed Portal (Optional)	Scaffolding of React/Vite application (looker-embed-demo), .env configuration (VITE_CHAT_AGENT_ID), and CSS brand design tokens	Complete web application ready to run (npm run dev)

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⚡ Spotlight: From Raw Data to Gemini Enterprise in Minutes

A flagship capability of demo-create is bridging the gap between raw data synthesis and cross-organizational enterprise AI in minutes:

graph LR
    Tiles["Dashboard Query Tiles"] --> Ground["Extract & Ground Golden Queries<br/>(expanded_share_url)"]
    Ground --> LinkAgent["Link to Looker CA Agent<br/>(PATCH /api/4.0/agents/{id})"]
    LinkAgent --> PublishGE["One-Click Publish to GE<br/>(POST /api/4.0/internal/agents/{id}/publish)"]
    PublishGE --> Chat["Natural Language Querying<br/>in Gemini Enterprise Apps"]

1. Deterministic Dashboard-to-Query Extraction: Every query tile from your generated executive dashboard is inspected and translated into an active Looker query.
2. Looker 4.0 Golden Query Grounding: Base queries are created via POST /api/4.0/queries to obtain deterministic expanded_share_url permalinks, then registered as 1:1 Golden Queries (POST /api/4.0/golden_queries).
3. Agent Linking: Golden queries are bound to the Conversational Analytics Agent (PATCH /api/4.0/agents/{agent_id}), establishing high-precision semantic grounding.
4. One-Click Gemini Enterprise Publishing: The agent is published directly to connected Gemini Enterprise apps via POST /api/4.0/internal/agents/{agent_id}/publish.

Within minutes of starting the flow, non-technical users and executives can query the entire domain dataset in natural language directly within Gemini Enterprise.

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📄 Inspect a Real Deliverable

Curious what the final output looks like? Inspect a real deliverable produced by a completed run:

👉 View Canonical Delivery Report: IoT Trucking Fleet Analytics

Key highlights from the report:

• BigQuery Summary: 6 relational tables, 21,675 rows across dim_vehicles, fct_trips, fct_sensor_telemetry, etc.
• LookML Architecture: 3NF ERD with Native Derived Table (vehicle_metrics_ndt) eliminating Chasm Traps.
• Quality Audit: 19 / 19 (100%) dashboard tile queries tested with HTTP 200 OK before production release.
• Live AI Agent: 7 pre-seeded Golden Queries and verified Gemini Enterprise publish state.

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Getting Started: Two Ways to Build

🚀 Mode 1: AI Agent Pair-Programmer (Primary Hero Flow)

Run interactively with your AI coding assistant (Jetski, Claude Code, or AgentAPI) using the looker-demo-orchestrator skill.

Step 1: Install Persistent CLI Tools

On any fresh machine, bootstrap the environment globally in seconds using uv:

uv tool install looker-demo-cli

This installs demo-create, looker-demo-cli, and lkr (lkr-dev-cli) into an isolated, persistent environment available across all terminal sessions.

Step 2: Run Pre-Flight Audit & Auto-Fix

Immediately run pre-check --fix to configure MCP servers, check dependencies, and sync agent skills:

demo-create pre-check --fix

Step 3: Configure Authentication (If Blocked)

1. Google Cloud & Application Default Credentials (ADC):

   gcloud auth login
   gcloud auth application-default login
   gcloud config set project <PROJECT_ID>
   

2. Looker Authentication (lkr auth login): Run the interactive Looker OAuth login:

   lkr auth login
   

   (Or ephemerally: uvx --from "lkr-dev-cli[codemode]" lkr-dev-cli auth login)

Step 4: Launch the AI Demo Creation Flow

Once authenticated, instruct your AI assistant in chat:

"Create an end-to-end Looker demo for IoT Fleet Analytics (or SaaS ARR, Retail, Fintech)."

The AI agent orchestrates the entire workflow interactively:

• Interactive Schema Co-Design: Collaborate with the agent on ERD diagrams, field definitions, and micro-sample data previews before generating full scale.
• Subagent Hub-and-Spoke Execution: The parent orchestrator delegates execution to specialized subagents:
  • data-engineer (Batch synthesis & BQ load)
  • lookml-modeler (Front-door semantic modeling & 3NF triage)
  • lookml-snowflake-modeler (3NF modeling, NDT rollups & diamond joins)
  • lookml-dashboard-designer (Pixel-perfect executive tabbed dashboards)
  • lookml-performance-optimizer (Google Cloud Looker performance best practices)
  • lookml-qa-validator (Dev push, validation & max 3 query self-healing)
  • ca-agent-provisioner (CA agent & golden queries; conditional on user confirmation)
  • embed-portal-engineer (Vite embed portal; conditional on user confirmation)

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⚙️ Gemini Enterprise (GE) Integration & Automated Provisioning

When deploying Conversational Analytics (CA) Agents to publish into Gemini Enterprise (GE), demo-create automatically verifies and configures Looker GE settings:

1. Automated Inspection (GET /api/4.0/gemini_enablement):
   • Inspects active Looker Gemini enablement settings.
   • If already configured, prompts to publish directly to the configured app or reconfigure.
2. Automated GCP Discovery & Configuration (PATCH /api/4.0/gemini_enablement):
   • If unconfigured, scans the GCP project for active Discovery Engine apps across standard regions (global, us, eu).
   • Updates Looker GE settings sending the full enablement payload with ai_ge_publish_enabled: true.
3. Automated Looker Service Account IAM Role:
   • Automatically grants the Looker Service Account (ai_ge_service_account_email) the Discovery Engine Admin (roles/discoveryengine.admin) role via gcloud projects add-iam-policy-binding.
4. Standalone CLI Inspection & Management:

   # Check Looker GE status
   demo-create ge status
   
   # Configure Looker GE settings interactively
   demo-create ge configure --gcp-project <PROJECT_ID>
   

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💻 Mode 2: Standalone CLI (Headless Engine)

Execute demo-create directly from your terminal or CI/CD pipeline:

demo-create run --project=retail_analytics --scope=internal

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Alternative Installation Methods

Run Ephemerally with uvx (Zero-Install Alternative)

You can also execute the CLI on-demand in an ephemeral cache without pre-installing:

# Run pre-flight audit and auto-fix MCP / skills
uvx looker-demo-cli pre-check --fix

# Run end-to-end interactive demo creator
uvx looker-demo-cli run --project=retail_analytics --scope=internal

Workspace Virtual Environment & Script Runner

To eliminate missing dependency errors across agent scratch scripts or data synthesis pipelines:

# Initialize a local .venv with all demo packages pre-installed:
demo-create env init
source .venv/bin/activate

# Execute ad-hoc scratch scripts using the CLI's bundled Python environment:
demo-create run-script scratch/generate_data.py

# Run one-off Python commands in the demo environment:
demo-create python -c "import pandas, pyarrow, google.cloud.bigquery; print('Ready!')"

Running from Local Source or Git (Development)

# Run directly from local source directory:
uvx --from . demo-create pre-check --fix

# Run directly from Git:
uvx --from git+https://github.com/lkrdev/looker-demo-cli.git demo-create pre-check --fix

# Local Editable Installation:
cd ~/looker-demo-cli
demo-create env init
source .venv/bin/activate
uv pip install -e .

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Commands & Usage

1. Environment & Skill Audit (pre-check)

# Run visual audit of GCP credentials, MCP tools, and intent skills
demo-create pre-check

# Automatically install missing MCP configs and symlink skills into intent subfolders
demo-create pre-check --fix

# Emit raw JSON report for programmatic agent consumption
demo-create pre-check --json

2. End-to-End Demo Creation (run)

# Interactive wizard
demo-create run --project=retail_insights --scope=internal

# Non-interactive / Agent Mode
demo-create run \
  --project=logistics_analytics \
  --scope=external \
  --gcp-project=my-analytics-project \
  --gcp-account=user@example.com \
  --agent-mode

3. Modular Pipeline Commands

You can run any phase of the pipeline independently. State is automatically persisted in .demo-state.json across executions:

Data Synthesis & Ingestion (demo-create data)

# Synthesize local Parquet files
demo-create data generate --domain retail --scale medium --output-dir scratch/parquet

# Upload Parquet tables to BigQuery
demo-create data upload --parquet-dir scratch/parquet --project my-gcp-project --dataset retail_analytics

# Inspect existing BigQuery tables and schema
demo-create data inspect --project my-gcp-project --dataset retail_analytics

Semantic Modeling & Deployment (demo-create lookml)

# Generate LookML from an existing BigQuery dataset with Knowledge Catalog / Dataplex introspection
demo-create lookml model --project retail_analytics --dataset retail_analytics --connection bigquery_connection

# Generate LookML from local Parquet files
demo-create lookml model --project retail_analytics --parquet-dir scratch/parquet --connection bigquery_connection

# Deploy staged LookML files to dev workspace, run query tests, and release to production
demo-create lookml deploy --project retail_analytics --lookml-dir lookml/

Conversational Analytics & Golden Queries (demo-create agent)

# Provision CA Agent, extract dashboard Golden Queries, and publish to Gemini Enterprise
demo-create agent create --model retail_analytics --explore orders --dashboard-id retail_analytics::executive_overview --publish-ge

# Extract and link Golden Queries from dashboard files to an existing agent
demo-create agent golden-queries --agent-id 1042 --dashboard-id retail_analytics::executive_overview

# Publish agent to Gemini Enterprise
demo-create agent publish --agent-id 1042

Standalone Embed Portal Scaffolding (demo-create embed)

# Scaffold React/Vite portal with .env configured for Looker, dashboard, and CA chat
demo-create embed scaffold --project retail_analytics --dashboard-id 1042 --agent-id 1042 --brand-name "Retail Insights"

Gemini Enterprise Management (demo-create ge)

# Check Looker Gemini enablement and GE status
demo-create ge status

# Discover GCP GE instances, configure Looker, and grant IAM roles
demo-create ge configure --instance-id my-ge-app --location us

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Intent-Based Skill Organization

When you run demo-create pre-check --fix, skills are automatically pulled from remote repositories and organized into ~/.gemini/config/skills/:

~/.gemini/config/skills/
├── data-design/
│   ├── data-designer/
│   ├── data-designer-architect/
│   ├── data-designer-engineer/
│   ├── data-designer-evaluator/
│   └── vertex-ai/
├── lookml/
│   ├── lkr-code-mode/
│   ├── repo-lookml/
│   ├── lookml-model/
│   ├── lookml-explore/
│   ├── lookml-view/
│   ├── lookml-dashboard/
│   ├── lookml-dashboard-to-query/
│   └── embed-themes/
└── embed-portal/
    ├── looker-demo-orchestrator/
    ├── setup-embed-demo/
    ├── customize-frontend/
    ├── customize-frontend-branding/
    ├── customize-frontend-theme/
    └── sso-embed/

This guarantees that any Jetski agent in any directory can discover and execute Looker demo workflows.