skene CLI documentation

Get from zero to a customer journey map.

Prerequisites

• Python 3.11 or later
• uv installed (curl -LsSf https://astral.sh/uv/install.sh | sh)
• An API key from OpenAI, Google Gemini, or Anthropic, OR a local LLM running via LM Studio or Ollama

Setup

Create and configure

# Create a config file with sensible defaults
uvx skene config --init

# Set up your LLM provider and API key interactively
uvx skene config

The interactive setup walks you through provider, model, and API key selection.

Tip: You can skip config setup entirely by passing --api-key and --provider flags directly to each command, or by setting the SKENE_API_KEY and SKENE_PROVIDER environment variables.

Analyse your journey

Run the analysis

uvx skene analyse-journey .

A main "skene" agent orchestrates two parallel subagents (one analyzing your codebase, one analyzing your database schema) to discover product features. A synthesis step merges them into a feature map (features.yaml) and composes user-journey milestones from it, producing a validated Customer Journey map across seven lifecycle stages: discovery, onboarding, activation, engagement, retention, expansion, and virality.

The result is written to ./skene-context/journey.yaml.

Include your database schema

The schema agent needs one of two inputs, never both:

# SQL files: a directory of pre-exported *.sql files
uvx skene analyse-journey . --schema-dir ./schemas

# Live database: a PostgreSQL connection string (credentials are never stored)
uvx skene analyse-journey . --db-url "postgresql://user:pass@localhost:5432/mydb"

Tip: Use -o to change the output path (default ./skene-context/journey.yaml) and --product-name to override the inferred product name.

Verify and deploy

Check implementation status

If your project has a skene-context/engine.yaml, verify engine/migration alignment:

uvx skene status

Checks skene-context/engine.yaml structure and verifies action-enabled features have matching migration triggers.

Push upstream

To deploy your Skene bundle to Skene Cloud, log in and push:

uvx skene login --upstream https://skene.ai/workspace/<my-workspace-name>
uvx skene push

Note: Journey analysis never publishes anything by itself. To get your journey.yaml into the cloud Customer Journey canvas, push it: from the TUI use the explicit "Deploy to Skene Cloud" step, from the CLI run skene push. push uploads existing artifacts (engine.yaml, optional feature-registry.json, and the latest trigger migration under supabase/migrations/); it does not generate them.

What you get

Your ./skene-context/ directory contains:

Alternative: Quick one-liner

If you want to try the analysis without setting up a config file first, pass your API key inline:

uvx skene analyse-journey . --api-key "your-key"

This uses the default provider (openai) and model (gpt-4o). To use a different provider:

uvx skene analyse-journey . --api-key "your-key" --provider gemini --model gemini-3-flash-preview

Local providers need no API key at all:

uvx skene analyse-journey . --provider ollama --model llama3.3

Next steps

• CLI reference: every analyse-journey flag, plus serve, attach, and more
• Push command in depth: Supabase migrations and upstream deployment
• Status command in depth: engine/migration validation
• Features: managing and exporting the feature registry
• Login: authenticating with Skene Cloud upstream
• Configuration reference: config files, environment variables, precedence rules
• LLM providers: setup for OpenAI, Gemini, Anthropic, LM Studio, Ollama, and generic endpoints