smoosh 0.0.1

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smoosh: Software Module Outline & Organization Summary Helper

smoosh is a Python tool that helps developers understand and work with Python packages by generating LLM-optimized summaries of package structure, dependencies, and patterns. It creates compressed yet meaningful representations that can be effectively used in LLM prompts for package understanding and troubleshooting.

Features

• Intelligent Package Analysis: Parse and analyze Python packages using AST to understand structure, relationships, and patterns
• Smart Compression: Generate compact package representations while preserving essential information
• LLM-Optimized Output: Create summaries specifically formatted for effective use with Language Models
• Flexible Output Formats: Export summaries in JSON, YAML, Markdown, or custom LLM formats
• Command Line Interface: Easy-to-use CLI for quick package analysis

Installation

pip install smoosh

Quick Start

Analyze a Python package and generate a summary:

smoosh /path/to/package

Generate a focused API summary:

smoosh /path/to/package --focus api

Export to specific format:

smoosh /path/to/package --format json --output summary.json

Configuration

Create a smoosh.yaml in your project root:

analysis:
  exclude_patterns: ['tests/*', '**/__pycache__/*']
  max_depth: 3
  focus: ['api', 'structure', 'patterns']

compression:
  level: medium  # low, medium, high
  custom_patterns:
    df_ops: "standard pandas operations"
    api_call: "external service request/response"

output:
  format: json
  include_schema: true
  max_tokens: 1000

Key Components

Code Analyzer

• AST-based Python file parsing
• Function and class relationship mapping
• Dependency analysis
• Pattern detection

Compression Engine

• Intelligent type abbreviation
• Pattern reference system
• Call chain compression
• Reference deduplication

Summary Generator

• Multiple output format support
• Customizable summary types
• LLM-optimized formatting

CLI Options

Options:
  --focus TEXT          Analysis focus (api, structure, patterns)
  --format TEXT         Output format (json, yaml, markdown, llm)
  --output FILE        Output file path
  --compression-level  Compression level (low, medium, high)
  --to-clipboard       Copy output to clipboard
  --help              Show this message and exit

Example Output

package:
  name: "example_pkg"
  structure:
    modules: ["core", "utils", "api"]
    patterns:
      p1: "DataFrame processor"
      p2: "Data validation"

  api:
    core:
      - "process_data(df: DataFrame) -> DataFrame"
      - "validate_input(D[s, Any]) -> bool"
    utils:
      - "load_config(path: s) -> Config"

Development

1. Clone the repository:

git clone https://github.com/yourusername/smoosh.git
cd smoosh

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

3. Install development dependencies:

pip install -e ".[dev]"

4. Run tests:

pytest

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with Python's ast module for code analysis
• Inspired by the need for better LLM-based code understanding tools

Roadmap

Future developments may include:

• Snakemake pipeline analysis
• Error pattern detection
• IDE integration
• Documentation generation
• Learning path creation

Support

For questions and support, please open an issue in the GitHub repository.