altimate-dataminion

v0.1.2 suspicious
4.0
Medium Risk

Internal package. Use this at your own risk, support not guaranteed

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious intent, but the metadata risk raises some concern due to the maintainer having only one package.

  • Metadata risk is elevated due to the maintainer having only one package.
  • The package description warns users about lack of support, suggesting it might be experimental or abandoned.
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which reduces the risk of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 110 type-annotated function signatures detected in source
○ 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: altimate.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Altimate Inc" 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 altimate-dataminion 
Create a data analysis mini-application using the Python package 'altimate-dataminion'. This application will serve as a tool for users to import datasets, perform basic statistical analyses, visualize data, and export results. Given that 'altimate-dataminion' is an internal package, it is assumed to offer functionalities such as data loading, manipulation, statistical computation, and visualization utilities.

### Application Features:
- **Data Import**: Allow users to upload CSV or Excel files.
- **Basic Statistics**: Calculate mean, median, mode, standard deviation, variance, and correlation coefficients for numerical columns.
- **Visualization**: Generate plots such as histograms, scatter plots, and line charts based on user selections.
- **Export Results**: Enable users to save the analysis results (both data and visualizations) into a new CSV file or an image format.

### Steps to Build the Application:
1. **Setup Environment**: Ensure 'altimate-dataminion' is installed in your Python environment. If not, you may need to install it via pip or another method specified by the package documentation.
2. **User Interface Design**: Develop a simple web interface using Flask or Django where users can upload their datasets.
3. **Data Processing**: Utilize 'altimate-dataminion' functions to load the uploaded dataset and perform necessary data cleaning or preprocessing steps.
4. **Statistical Analysis**: Implement methods to calculate the mentioned statistics using 'altimate-dataminion'. Display these results on the web interface.
5. **Data Visualization**: Integrate 'altimate-dataminion' visualization capabilities to create interactive graphs. Users should be able to choose which variables to plot and what type of chart they prefer.
6. **Result Export**: Provide options for users to download the analyzed data and visualizations in formats like CSV or PNG.
7. **Testing & Documentation**: Thoroughly test the application with different datasets and document each feature clearly.

### Utilizing 'altimate-dataminion':
- Use 'altimate-dataminion.load_data()' for importing datasets.
- Apply 'altimate-dataminion.stats()' for computing statistics.
- Leverage 'altimate-dataminion.plot()' for generating visualizations.
- Consider 'altimate-dataminion.export()' for saving outputs.

This project aims to demonstrate the versatility and utility of 'altimate-dataminion' while providing a practical tool for data enthusiasts.

💬 Discussion Feed

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