2448 Python API 78bd98af

Python API

Programmatic access to skene-growth's codebase analysis, manifest generation, and documentation tools.

Quick example

import asyncio
from pathlib import Path
from pydantic import SecretStr
from skene_growth import CodebaseExplorer, ManifestAnalyzer
from skene_growth.llm import create_llm_client

async def main():
    codebase = CodebaseExplorer(Path("/path/to/repo"))
    llm = create_llm_client(
        provider="openai",
        api_key=SecretStr("your-api-key"),
        model="gpt-4o",
    )

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

    manifest = result.data["output"]
    print(manifest["tech_stack"])
    print(manifest["current_growth_features"])

asyncio.run(main())

CodebaseExplorer

Safe, sandboxed access to codebase files. Automatically excludes common build/cache directories.

from pathlib import Path
from skene_growth import CodebaseExplorer, DEFAULT_EXCLUDE_FOLDERS

# Create with default exclusions
explorer = CodebaseExplorer(Path("/path/to/repo"))

# Create with custom exclusions (merged with defaults)
explorer = CodebaseExplorer(
    Path("/path/to/repo"),
    exclude_folders=["tests", "vendor", "migrations"]
)

Methods

Related

• build_directory_tree — Standalone function for building directory trees
• DEFAULT_EXCLUDE_FOLDERS — List of default excluded folder names

Analyzers

ManifestAnalyzer

Runs a full codebase analysis and produces a growth manifest.

from skene_growth import ManifestAnalyzer

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

manifest = result.data["output"]

TechStackAnalyzer

Detects the technology stack of a codebase.

from skene_growth import TechStackAnalyzer

analyzer = TechStackAnalyzer()
result = await analyzer.run(codebase=codebase, llm=llm)
tech_stack = result.data["output"]

GrowthFeaturesAnalyzer

Identifies existing growth features in a codebase.

from skene_growth import GrowthFeaturesAnalyzer

analyzer = GrowthFeaturesAnalyzer()
result = await analyzer.run(codebase=codebase, llm=llm)
features = result.data["output"]

Configuration

from skene_growth import Config, load_config

# Load config from files + env vars
config = load_config()

# Access properties
config.api_key       # str | None
config.provider      # str (default: "openai")
config.model         # str (auto-determined if not set)
config.output_dir    # str (default: "./skene-context")
config.verbose       # bool (default: False)
config.debug         # bool (default: False)
config.exclude_folders  # list[str] (default: [])
config.base_url      # str | None

# Get/set arbitrary keys
config.get("api_key", default=None)
config.set("provider", "gemini")

LLM Client

from pydantic import SecretStr
from skene_growth.llm import create_llm_client, LLMClient

client: LLMClient = create_llm_client(
    provider="openai",          # openai, gemini, anthropic, ollama, lmstudio, generic
    api_key=SecretStr("key"),
    model="gpt-4o",
    base_url=None,              # Required for generic provider
    debug=False,                # Log LLM I/O to .skene-growth/debug/
)

Manifest schemas

All schemas are Pydantic v2 models. See Manifest schema reference for full field details.

from skene_growth import (
    GrowthManifest,     # v1.0 manifest
    DocsManifest,       # v2.0 manifest (extends GrowthManifest)
    TechStack,
    GrowthFeature,
    GrowthOpportunity,
    IndustryInfo,
    ProductOverview,    # v2.0 only
    Feature,            # v2.0 only
)

GrowthManifest fields

DocsManifest additional fields

Documentation generation

from skene_growth import DocsGenerator, GrowthManifest

manifest = GrowthManifest.model_validate_json(open("growth-manifest.json").read())

generator = DocsGenerator()
context_doc = generator.generate_context_doc(manifest)
product_doc = generator.generate_product_docs(manifest)

The PSEOBuilder class generates programmatic SEO content from manifests.

Strategy framework

The analysis pipeline is built on a composable strategy framework:

from skene_growth.strategies import (
    AnalysisStrategy,    # Base strategy class
    AnalysisResult,      # Result container with data + metadata
    AnalysisMetadata,    # Timing, token usage, step info
    AnalysisContext,     # Shared context between steps
    MultiStepStrategy,   # Chains multiple steps together
)

from skene_growth.strategies.steps import (
    AnalysisStep,        # Base step class
    SelectFilesStep,     # Select relevant files for analysis
    ReadFilesStep,       # Read file contents
    AnalyzeStep,         # Send to LLM for analysis
    GenerateStep,        # Generate structured output
)

These classes are primarily used internally by the analyzers but can be composed for custom analysis pipelines.

Planner

from skene_growth.planner import Planner

The Planner class generates growth plans from manifests and templates. It is used internally by the plan CLI command.