skelegen 0.1.1

Skelegen is a CLI tool that generates skeleton code for your projects

skelegen

Skelegen is a CLI tool that generates skeleton code for your projects. Describe what you want to build in natural language; it uses AI to define your project idea, data shapes (DTOs and custom types), and code structure, then writes a Python project with modules, Pydantic models, and function stubs ready for you to implement.

How it works

Skelegen runs a three-step, conversational flow powered by pydantic-ai and Google Gemini:

1. Project idea — You describe what you want to build. The tool asks follow-up questions until it has a clear title, description, goals, tech stack, and constraints.

2. DTOs and custom types — From that idea, it proposes Data Transfer Objects (request/response bodies, domain entities, configs) and custom types (enums, type aliases). You can confirm or ask for changes.

3. Code skeleton — It then proposes a module/script/function layout (packages, files, and function signatures) that use those DTOs and types. Again you confirm or refine.

After you approve each step, Skelegen generates a project directory:

• A models.py with Pydantic BaseModel DTOs and custom types (enums, Literal, etc.).
• Packages and scripts — one folder per module, one .py file per script, with function stubs that reference the generated models. Each function has a ... # TODO: implement body so you can fill in logic.

The result is a consistent, type-hinted skeleton you can extend rather than starting from a blank slate.

Requirements

• Python 3.12+
• Google API key — Set the GOOGLE_API_KEY environment variable (used for Gemini).

Installation

From the project root (or from PyPI once published):

pip install -e .

Or install in a virtual environment:

python -m venv .venv
.venv\Scripts\activate   # Windows
# source .venv/bin/activate  # macOS/Linux
pip install -e .

Usage

1. Set your API key:

   set GOOGLE_API_KEY=your_key_here
   

2. Run the CLI:

   skelegen
   

3. Follow the prompts:

   • Describe your project (e.g. “A REST API for orders and inventory with auth”).
   • Answer any clarifying questions until the tool shows a ProjectIdeaDefinition; confirm with y if it looks good.
   • Review the proposed DTOs and custom types; confirm or describe changes.
   • Review the proposed code skeleton (modules, scripts, functions); confirm or describe changes.

4. Skelegen creates a directory named after your project title (e.g. order_api) in the current working directory, containing:

   • models.py — all DTOs and custom types.
   • One subfolder per module (e.g. api/, domain/), each with an __init__.py and script files (e.g. orders.py, auth.py) with stubbed functions.

You can then open the generated project and implement the # TODO sections.

Example

Defining project idea, describe what you want to build...

> You: A small CLI to track daily habits with categories and optional notes.

> Agent: Do you want to persist data (e.g. file or DB) or keep it in memory for now?
> You: SQLite is fine.

> Agent: [outputs ProjectIdeaDefinition]
> Are you okay with this output? (y/n): y

Defining data transfer objects and custom types...
[outputs DtosCustomTypes]
> Are you okay with this output? (y/n): y

Defining code skeleton...
[outputs CodeSkeleton]
> Are you okay with this output? (y/n): y

Project generated successfully.

A new folder (e.g. habit_tracker) will appear with models.py and modules/scripts ready to implement.

License

MIT. See the project license file for details.