Overview

A CLI toolkit for analyzing codebases through the lens of Product-Led Growth (PLG): mapping the customer journey encoded in your code and database schema.

What skene does

• Maps the customer journey: analyse-journey uses parallel code and schema agents to produce a journey.yaml of your product's user lifecycle
• Maintains a feature registry: persistent tracking of growth features across analysis runs with merge-update semantics
• Pushes the Skene bundle upstream: uploads files from the configured output directory plus the latest trigger migration to Skene Cloud
• Validates engine/migration alignment: status checks action-enabled engine features against generated SQL artifacts
• Runs as a client/server system: a backend server (skene serve) owns the analysis engine and exposes an HTTP API; the CLI and TUI are clients, and every run is persisted as a session trace
• Supports multiple LLM providers: OpenAI, Gemini, Anthropic, LM Studio, Ollama, and any OpenAI-compatible endpoint

Core workflow

# 1. Create a config file
uvx skene config --init

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

# 3. Analyse your codebase and schema into a journey.yaml
uvx skene analyse-journey .

# 4. Login to Skene Cloud
uvx skene login

# 5. Push artifacts upstream
uvx skene push

Key concepts

Journey (journey.yaml): The only output of the analyse-journey command, written to skene-context/journey.yaml. A validated YAML map of your product's user lifecycle: parallel code and schema agents each propose candidate milestones, a deterministic merge reconciles them, and a classification pass places them across seven canonical lifecycle stages.

Engine model (skene-context/engine.yaml): A YAML model that captures subjects and features, including optional action definitions for trigger/runtime behavior.

Feature registry (feature-registry.json): A persistent registry of growth features that tracks features across analysis runs. Features are marked active or archived, linked to engine feature keys, and annotated with growth pillars (onboarding, engagement, retention).

Skene API key: A single key from Skene Cloud that manages all tokens required to use LLM models and authorizes pushing engine artifacts upstream. One key replaces per-provider API keys for LLM usage and enables cloud push. Get your key at https://www.skene.ai/workspace/apikeys

Documentation

Getting started

• Installation: Install via uvx, pip, or from source
• Quickstart: End-to-end walkthrough

Guides

Create

• Push: Pushing engine + trigger artifacts upstream

Manage

• Login: Authenticating with Skene Cloud upstream
• Status: Checking engine/migration implementation status
• Features: Managing and exporting the feature registry
• LLM providers: Configuring OpenAI, Gemini, Claude, local LLMs
• Configuration: Config files, env vars, and priority

Integrations

• MCP server: Using skene with AI assistants

Reference

• CLI reference: All commands and flags
• HTTP API: The skene serve REST + SSE API, domain model, and event stream
• Python API: CodebaseExplorer, journey models, schemas

Help

• Troubleshooting: LM Studio, Ollama, common errors

Hosted product

• Skene Cloud: Dashboard, Supabase connection, schema analysis, feature deploy, and logs (no CLI required for the full in-browser flow)