autosynth

v0.1.1 suspicious
5.0
Medium Risk

Agentic synthetic-data generation framework inspired by Meta FAIR's Autodata / Agentic Self-Instruct.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low direct risk indicators such as network, shell, obfuscation, and credential risks but raises concerns due to the unusual commit pattern and limited maintainer history.

  • Unusual commit pattern
  • Limited maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's unusual commit pattern and the maintainer's limited history suggest potential risks.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 13 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 13 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10096 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 261 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 45 commits in Ahmad8864/autosynth
  • Single author but highly active (45 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 45 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ahmad Abdallah" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autosynth 
Develop a mini-application named 'SynthDataCraft' using the Python package 'autosynth'. SynthDataCraft aims to streamline the creation of synthetic datasets for various machine learning tasks, leveraging the agentic synthetic data generation capabilities provided by 'autosynth'. This tool will allow users to define a dataset schema, specify the desired number of records, and generate synthetic data that closely mimics real-world data characteristics. Here’s a detailed plan on how to develop SynthDataCraft:

1. **Project Setup**: Initialize a new Python project and install 'autosynth' along with any necessary dependencies. Ensure your environment supports Python 3.8 or higher.
2. **User Interface Design**: Create a simple CLI (Command Line Interface) for interacting with SynthDataCraft. Users should be able to input their dataset requirements through command-line arguments or configuration files.
3. **Schema Definition**: Allow users to define the schema of their synthetic dataset. This includes specifying column names, types (e.g., integer, string, date), and optional parameters such as minimum and maximum values, distribution types, etc.
4. **Data Generation**: Implement a feature within SynthDataCraft that uses 'autosynth' to generate synthetic data based on the defined schema. Pay special attention to ensuring the generated data reflects realistic distributions and relationships between columns where applicable.
5. **Output Options**: Provide options for outputting the generated data. Users should be able to choose between saving the data to a CSV file, a SQLite database, or exporting it directly into a Pandas DataFrame for further manipulation.
6. **Example Use Cases**: Include several example use cases within the documentation to demonstrate how SynthDataCraft can be used for different purposes, such as generating synthetic customer data for testing a recommendation system or creating fake transaction records for fraud detection model training.
7. **Testing & Validation**: Develop unit tests to ensure the correctness and reliability of the synthetic data generation process. Also, include validation steps to verify if the generated data adheres to the specified schema and distribution requirements.
8. **Documentation & README**: Write comprehensive documentation detailing how to install, configure, and use SynthDataCraft. Include a README file that serves as a quick start guide for new users.

By following these steps, you will create a powerful yet user-friendly tool that leverages the advanced synthetic data generation capabilities of 'autosynth', making it easier for developers and data scientists to work with synthetic data.

💬 Discussion Feed

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