skene-growth 0.1.7b1

PLG analysis toolkit for codebases - analyze code, detect growth opportunities, generate documentation

skene-growth

PLG (Product-Led Growth) analysis toolkit for codebases. Analyze your code, detect growth opportunities, and generate documentation of your stack.

Quick Start

No installation required - just run with uvx:

#install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Analyze your codebase
uvx skene-growth analyze . --api-key "your-openai-api-key"

# Or set the API key as environment variable
export SKENE_API_KEY="your-openai-api-key"
uvx skene-growth analyze .

Get an OpenAI API key at: https://platform.openai.com/api-keys

What It Does

skene-growth scans your codebase and generates a growth manifest containing:

• Tech Stack Detection - Framework, language, database, auth, deployment
• Growth Hubs - Features with growth potential (signup flows, sharing, invites, billing)
• GTM Gaps - Missing features that could drive user acquisition and retention

With the --product-docs flag, it also collects:

• Product Overview - Tagline, value proposition, target audience
• Features - User-facing feature documentation with descriptions and examples
• Product Docs - Generates user-friendly product-docs.md file

After the manifest is created, skene-growth generates a custom growth template (JSON + Markdown) tailored to your business type using LLM analysis. The templates use examples in src/templates/ as reference but create custom lifecycle stages and keywords specific to your product.

Installation

Option 1: uvx (Recommended)

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Zero installation - runs instantly (requires API key):

uvx skene-growth analyze . --api-key "your-openai-api-key"
uvx skene-growth validate ./growth-manifest.json

Note: The analyze command requires an API key. By default, it uses OpenAI (get a key at https://platform.openai.com/api-keys). You can also use Gemini with --provider gemini, Anthropic with --provider anthropic, or local LLMs with --provider lmstudio or --provider ollama (experimental).

Option 2: pip install

pip install skene-growth

CLI Commands

analyze - Analyze a codebase

Requires an API key (set via --api-key, SKENE_API_KEY env var, or config file).

# Analyze current directory (uses OpenAI by default)
uvx skene-growth analyze . --api-key "your-openai-api-key"

# Using environment variable
export SKENE_API_KEY="your-openai-api-key"
uvx skene-growth analyze .

# Analyze specific path with custom output
uvx skene-growth analyze ./my-project -o manifest.json

# With verbose output
uvx skene-growth analyze . -v

# Use a specific model
uvx skene-growth analyze . --model gpt-4o

# Use Gemini instead of OpenAI
uvx skene-growth analyze . --provider gemini --api-key "your-gemini-api-key"

# Use Anthropic (Claude)
uvx skene-growth analyze . --provider anthropic --api-key "your-anthropic-api-key"

# Use LM Studio (local server)
uvx skene-growth analyze . --provider lmstudio --model "your-loaded-model"

# Use Ollama (local server) - Experimental
uvx skene-growth analyze . --provider ollama --model "llama2"

# Specify business type for custom growth template
uvx skene-growth analyze . --business-type "design-agency"
uvx skene-growth analyze . --business-type "b2b-saas"

# Generate product documentation (collects product overview and features)
uvx skene-growth analyze . --product-docs

Output:

• ./skene-context/growth-manifest.json (structured data)
• ./skene-context/growth-manifest.md (analysis summary)
• ./skene-context/growth-template.json (if --business-type specified)
• ./skene-context/growth-template.md (if --business-type specified)
• ./skene-context/product-docs.md (if --product-docs flag used)

Growth Templates: The system generates custom templates tailored to your business type, with lifecycle stages and keywords specific to your user journey. If no business type is specified, the LLM infers it from your codebase.

Flags:

• --product-docs: Generate user-friendly product documentation (collects product overview, features, and generates product-docs.md)
• --business-type: Specify business type for custom growth template

The --product-docs flag enables enhanced analysis mode which collects product overview and feature documentation, producing a v2.0 manifest with additional fields and a user-friendly product-docs.md file.

validate - Validate a manifest

uvx skene-growth validate ./growth-manifest.json

config - Manage configuration

# Show current configuration
uvx skene-growth config

# Create a config file in current directory
uvx skene-growth config --init

Configuration

skene-growth supports configuration files for storing defaults:

Configuration Files

Location	Purpose
./.skene-growth.toml	Project-level config (checked into repo)
~/.config/skene-growth/config.toml	User-level config (personal settings)

Sample Config File

# .skene-growth.toml

# API key for LLM provider (can also use SKENE_API_KEY env var)
# api_key = "your-api-key"

# LLM provider to use: "openai" (default), "gemini", "anthropic", "lmstudio", or "ollama" (experimental)
provider = "openai"

# Model to use (provider-specific defaults apply if not set)
# openai: gpt-4o-mini | gemini: gemini-2.0-flash | anthropic: claude-sonnet-4-20250514 | ollama: llama2
# model = "gpt-4o-mini"

# Default output directory
output_dir = "./skene-context"

# Enable verbose output
verbose = false

Configuration Priority

Settings are loaded in this order (later overrides earlier):

1. User config (~/.config/skene-growth/config.toml)
2. Project config (./.skene-growth.toml)
3. Environment variables (SKENE_API_KEY, SKENE_PROVIDER)
4. CLI arguments

Python API

CodebaseExplorer

Safe, sandboxed access to codebase files:

from skene_growth import CodebaseExplorer

explorer = CodebaseExplorer("/path/to/repo")

# Get directory tree
tree = await explorer.get_directory_tree(".", max_depth=3)

# Search for files
files = await explorer.search_files(".", "**/*.py")

# Read file contents
content = await explorer.read_file("src/main.py")

# Read multiple files
contents = await explorer.read_multiple_files(["src/a.py", "src/b.py"])

Analyzers

from pydantic import SecretStr
from skene_growth import ManifestAnalyzer, CodebaseExplorer
from skene_growth.llm import create_llm_client

# Initialize
codebase = CodebaseExplorer("/path/to/repo")
llm = create_llm_client(
    provider="openai",  # or "gemini", "anthropic", "lmstudio", or "ollama" (experimental)
    api_key=SecretStr("your-api-key"),
    model_name="gpt-4o-mini",  # or "gemini-2.0-flash" / "claude-sonnet-4-20250514" / local model
)

# Run analysis
analyzer = ManifestAnalyzer()
result = await analyzer.run(
    codebase=codebase,
    llm=llm,
    request="Analyze this codebase for growth opportunities",
)

# Access results (the manifest is in result.data["output"])
manifest = result.data["output"]
print(manifest["tech_stack"])
print(manifest["growth_hubs"])

Documentation Generator

from skene_growth import DocsGenerator, GrowthManifest

# Load manifest
manifest = GrowthManifest.parse_file("growth-manifest.json")

# Generate docs
generator = DocsGenerator()
context_doc = generator.generate_context_doc(manifest)
product_doc = generator.generate_product_docs(manifest)

Growth Manifest Schema

The growth-manifest.json output contains:

{
  "version": "1.0",
  "project_name": "my-app",
  "description": "A SaaS application",
  "tech_stack": {
    "framework": "Next.js",
    "language": "TypeScript",
    "database": "PostgreSQL",
    "auth": "NextAuth.js",
    "deployment": "Vercel"
  },
  "growth_hubs": [
    {
      "feature_name": "User Invites",
      "file_path": "src/components/InviteModal.tsx",
      "detected_intent": "referral",
      "confidence_score": 0.85,
      "growth_potential": ["viral_coefficient", "user_acquisition"]
    }
  ],
  "gtm_gaps": [
    {
      "feature_name": "Social Sharing",
      "description": "No social sharing for user content",
      "priority": "high"
    }
  ],
  "generated_at": "2024-01-15T10:30:00Z"
}

Product Docs Schema (v2.0)

When using --product-docs flag, the manifest includes additional fields:

{
  "version": "2.0",
  "project_name": "my-app",
  "description": "A SaaS application",
  "tech_stack": { ... },
  "growth_hubs": [ ... ],
  "gtm_gaps": [ ... ],
  "product_overview": {
    "tagline": "The easiest way to collaborate with your team",
    "value_proposition": "Simplify team collaboration with real-time editing and sharing.",
    "target_audience": "Remote teams and startups"
  },
  "features": [
    {
      "name": "Team Workspaces",
      "description": "Create dedicated spaces for your team to collaborate on projects.",
      "file_path": "src/features/workspaces/index.ts",
      "usage_example": "<WorkspaceCard workspace={workspace} />",
      "category": "Collaboration"
    }
  ],
  "generated_at": "2024-01-15T10:30:00Z"
}

Environment Variables

Variable	Description
SKENE_API_KEY	API key for LLM provider
SKENE_PROVIDER	LLM provider to use: openai (default), gemini, anthropic, lmstudio, or ollama (experimental)
LMSTUDIO_BASE_URL	LM Studio server URL (default: http://localhost:1234/v1)
OLLAMA_BASE_URL	Ollama server URL (default: http://localhost:11434/v1) - Experimental

Requirements

• Python 3.11+
• API key (required for analyze command, except local LLMs):
  • OpenAI (default): https://platform.openai.com/api-keys
  • Gemini: https://aistudio.google.com/apikey
  • Anthropic: https://platform.claude.com/settings/keys
  • LM Studio: No API key needed (runs locally at http://localhost:1234)
  • Ollama (experimental): No API key needed (runs locally at http://localhost:11434)

Troubleshooting

LM Studio: Context length error

If you see an error like:

Error code: 400 - {'error': 'The number of tokens to keep from the initial prompt is greater than the context length...'}

This means the model's context length is too small for the analysis. To fix:

1. In LM Studio, unload the current model
2. Go to Developer > Load
3. Click on Context Length: Model supports up to N tokens
4. Reload to apply changes

See: https://github.com/lmstudio-ai/lmstudio-bug-tracker/issues/237

LM Studio: Connection refused

If you see a connection error, ensure:

• LM Studio is running
• A model is loaded and ready
• The server is running on the default port (http://localhost:1234)

If using a different port or host, set the LMSTUDIO_BASE_URL environment variable:

export LMSTUDIO_BASE_URL="http://localhost:8080/v1"

Ollama: Connection refused (Experimental)

Note: Ollama support is experimental and has not been fully tested. Please report any issues.

If you see a connection error, ensure:

• Ollama is running (ollama serve)
• A model is pulled and available (ollama list to check)
• The server is running on the default port (http://localhost:11434)

If using a different port or host, set the OLLAMA_BASE_URL environment variable:

export OLLAMA_BASE_URL="http://localhost:8080/v1"

To get started with Ollama:

# Install Ollama (see https://ollama.com)
# Pull a model
ollama pull llama2

# Run the server (usually runs automatically)
ollama serve

MCP Server

skene-growth includes an MCP server for integration with AI assistants.

Add this to your AI assistant configuration file:

{
  "mcpServers": {
    "skene-growth": {
      "command": "uvx",
      "args": ["--from", "skene-growth[mcp]", "skene-growth-mcp"],
      "env": {
        "SKENE_API_KEY": "your-openai-api-key"
      }
    }
  }
}

See docs/mcp-server.md for more detailed instructions.

License

MIT