amesa-core-dev

v0.31.0.dev1 suspicious
5.0
Medium Risk

the Amesa core functionality required to apply the Machine Teaching paradigm

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low direct security risks but has a metadata risk due to low maintenance effort and lack of author information, which raises concerns about its origin and reliability.

  • Metadata risk due to low maintenance effort and lack of author information.
  • Potential supply-chain attack concern given the unusual versioning and lack of clear licensing details.
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 of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
  • Metadata: The package shows low maintenance effort and lack of author information, raising some suspicion but not definitive signs of malice.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1344 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

Email domain looks legitimate: amesa.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amesa-core-dev 
Your task is to develop a mini-application named 'MachineTeachingHelper' using the Python package 'amesa-core-dev'. This application will serve as a tool for educators and trainers who wish to implement machine teaching methodologies in their curriculum or training programs. The goal of the application is to simplify the process of creating machine learning models through guided instruction and automated model generation based on provided datasets and user specifications.

### Features:
- **User Interface**: A simple, intuitive web-based UI allowing users to upload datasets, specify model types, and set training parameters.
- **Model Generation**: Utilize 'amesa-core-dev' to automatically generate ML models based on the uploaded dataset and specified parameters. The package's core functionality should handle the preprocessing, model training, and evaluation phases.
- **Visualization Tools**: Provide visual representations of model performance metrics such as accuracy, precision, recall, and F1 score post-training.
- **Documentation and Guidance**: Include detailed documentation within the application explaining each step of the machine teaching process, from data preparation to model deployment.

### Steps to Build the Application:
1. **Setup Environment**: Ensure you have Python installed along with Flask for the web framework. Install 'amesa-core-dev' via pip.
2. **Design User Interface**: Create HTML templates and CSS styles for the web interface using Bootstrap for responsiveness. Integrate JavaScript for dynamic interactions.
3. **Backend Development**:
   - Implement API endpoints using Flask to handle file uploads, parameter settings, and model generation requests.
   - Use 'amesa-core-dev' to process the uploaded datasets, train models according to user-specified parameters, and evaluate the models' performance.
4. **Model Visualization**: Develop functions to visualize the performance metrics of the generated models using libraries like Matplotlib or Seaborn.
5. **Testing and Documentation**: Thoroughly test the application for various edge cases and ensure all functionalities work as expected. Write comprehensive documentation explaining how to use the application effectively.

### How 'amesa-core-dev' is Utilized:
- **Data Preprocessing**: Use 'amesa-core-dev' to preprocess the uploaded datasets ensuring they are in the correct format for model training.
- **Model Training**: Leverage the package's core functionalities to train machine learning models based on the selected algorithm and parameters.
- **Evaluation and Reporting**: Apply 'amesa-core-dev' to evaluate the trained models and generate reports detailing the model's performance.

By completing this project, you'll gain hands-on experience with 'amesa-core-dev', understand its capabilities in simplifying the machine teaching process, and contribute to making machine learning more accessible to educators and trainers.

💬 Discussion Feed

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