Analyze Command

The analyze command scans a codebase to detect its technology stack, identify existing growth features, surface growth opportunities, and flag revenue leakage -- all powered by an LLM of your choice.

Prerequisites

Before running analyze, you need one of the following:

An API key configured for a cloud LLM provider (OpenAI, Gemini, Anthropic). See configuration for setup instructions.
A local LLM server running (LM Studio, Ollama). No API key required.

Basic usage

Analyze the current directory:

uvx skene analyze .

Analyze a specific project path:

uvx skene analyze ./my-project

Save the manifest to a custom location:

uvx skene analyze . -o ./output/manifest.json

If no API key is configured and you are not using a local provider, the command displays a sample growth analysis preview instead of running a full LLM-powered analysis.

Flag reference

Flag	Short	Description
`PATH`		Path to codebase (default: `.`, must be an existing directory)
`--output PATH`	`-o`	Output path for `growth-manifest.json`
`--api-key TEXT`		API key for LLM provider (or `SKENE_API_KEY` env var)
`--provider TEXT`	`-p`	LLM provider: `openai`, `gemini`, `anthropic`/`claude`, `lmstudio`, `ollama`, `generic` (aliases: `openai-compatible`, `openai_compatible`)
`--model TEXT`	`-m`	Model name (e.g., `gpt-4o`, `gemini-3-flash-preview`, `claude-sonnet-4-5`)
`--base-url TEXT`		Base URL for OpenAI-compatible endpoint (required for `generic` provider; also `SKENE_BASE_URL` env var)
`--product-docs`		Generate `product-docs.md` with user-facing feature documentation (creates v2.0 manifest)
`--features`		Only analyze growth features and update `feature-registry.json` (skips opportunities and revenue leakage)
`--exclude TEXT`	`-e`	Folder names to exclude from analysis (repeatable). Also configurable in `.skene.config` as `exclude_folders`.
`--verbose`	`-v`	Enable verbose output
`--debug`		Log all LLM input/output to `.skene/debug/`
`--no-fallback`		Disable model fallback on rate limits. Retries the same model with exponential backoff instead of switching to a cheaper model.

Output files

By default, output files are saved to ./skene-context/. You can override this with the -o flag. If the -o path points to a directory or has no file extension, the tool appends growth-manifest.json automatically.

growth-manifest.json

The primary output. Contains the full analysis results as structured JSON:

Field	Description
`version`	Schema version (`"1.0"` or `"2.0"` with `--product-docs`)
`project_name`	Name of the analyzed project
`description`	Brief project description
`tech_stack`	Detected stack: `framework`, `language`, `database`, `auth`, `deployment`, `package_manager`, `services`
`industry`	Inferred industry vertical with `primary`, `secondary` tags, `confidence` score, and `evidence`
`current_growth_features`	Features with growth potential, including file paths, detected intent, confidence scores, and growth suggestions
`growth_opportunities`	Missing features that could drive growth, with priority levels
`revenue_leakage`	Revenue leakage issues with impact assessment and recommendations
`generated_at`	ISO 8601 timestamp of when the manifest was generated

feature-registry.json

A persistent registry of growth features maintained across analysis runs. Each analysis merges new features into the existing registry: new features are added with first_seen_at, matched features are updated with last_seen_at and marked active, and unmatched features are marked archived. The registry also maps features to growth loops via loop_ids and annotates them with growth pillars.

See the features guide for registry structure and export options.

growth-template.json

A custom PLG growth template generated alongside the manifest. Contains lifecycle stages tailored to your project's business type and industry. This template is used by the plan command to generate actionable growth plans.

product-docs.md (only with --product-docs)

A Markdown file containing user-facing product documentation generated from your codebase. See the Product docs mode section below.

Product docs mode

The --product-docs flag enables an extended analysis that generates user-facing documentation from your codebase:

uvx skene analyze . --product-docs

When enabled, the command:

Collects product overview information (tagline, value proposition, target audience)
Identifies user-facing features with descriptions, usage examples, and categories
Generates product-docs.md in the output directory
Produces a v2.0 manifest (extends the standard manifest with product_overview and features fields)

The v2.0 manifest is a superset of v1.0 -- all standard fields remain present.

Features-only mode

The --features flag runs a lightweight analysis that only updates the feature registry:

uvx skene analyze . --features

This mode:

Runs the growth features analyzer only (skips opportunities and revenue leakage)
Loads existing growth loops from skene-context/growth-loops/
Maps loops to features and updates feature-registry.json
Enriches the manifest with loop_ids and growth_pillars

This is faster than a full analysis and useful when you want to refresh the feature registry after building new growth loops.

Excluding folders

Use --exclude (or -e) to skip folders during analysis. This is useful for large repositories where you want to ignore generated code, vendored dependencies, or test fixtures.

The flag can be used multiple times:

uvx skene analyze . --exclude node_modules --exclude dist --exclude .next

You can also set exclusions permanently in your config file. In .skene.config:

exclude_folders = ["node_modules", "dist", ".next", "vendor", "__pycache__"]

CLI exclusions and config exclusions are merged (deduplicated). CLI flags do not override config -- they add to the list.

Debug mode

The --debug flag logs all LLM input and output to .skene/debug/:

uvx skene analyze . --debug

This is useful for:

Troubleshooting unexpected analysis results
Inspecting the prompts sent to the LLM
Verifying that the LLM is receiving the right context

You can also enable debug mode permanently in your config file:

debug = true

Or via environment variable:

export SKENE_DEBUG=true

Provider-specific examples

OpenAI

uvx skene analyze . --provider openai --api-key "sk-..." --model gpt-4o

Google Gemini

uvx skene analyze . --provider gemini --api-key "AI..." --model gemini-3-flash-preview

Anthropic

uvx skene analyze . --provider anthropic --api-key "sk-ant-..." --model claude-sonnet-4-5

Ollama (local)

# Start Ollama first, then:
uvx skene analyze . --provider ollama --model llama3.3

No API key required. The default model for Ollama is llama3.3.

LM Studio (local)

# Start LM Studio server first, then:
uvx skene analyze . --provider lmstudio

No API key required.

Generic / OpenAI-compatible

For any OpenAI-compatible API endpoint:

uvx skene analyze . \
  --provider generic \
  --base-url "http://localhost:8080/v1" \
  --model my-model

The --base-url flag is required when using the generic or openai-compatible provider. You can also set it via the SKENE_BASE_URL environment variable.

What happens without an API key

If no API key is configured and you are not using a local provider (lmstudio, ollama, generic), the command falls back to a sample report -- a preview of the analysis output structure using representative data. This lets you see what a full analysis looks like before committing to an API key.

To run the full analysis, provide an API key via any of these methods:

--api-key flag
SKENE_API_KEY environment variable
api_key field in .skene.config or ~/.config/skene/config

Next steps

Plan -- Generate a growth plan from your manifest using the Council of Growth Engineers
Features -- Export and manage the feature registry
Configuration -- Set up persistent config so you do not need to pass flags every time

skene CLI documentation