atlas-python

v0.9.1 suspicious
4.0
Medium Risk

Python bindings for the ATLAS array store

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell execution, obfuscation, and credential handling. However, the metadata risk score is moderately high due to the maintainer's new or inactive account and lack of detailed author information.

  • Low risk in network, shell execution, obfuscation, and credential handling.
  • Moderate risk in metadata due to new/inactive maintainer account and missing author details.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands which reduces risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and lacks a full author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_smoke.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11017 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

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

Limited contributor diversity

  • 1 unique contributor(s) across 49 commits in maris-development/atlas
  • Single author but highly active (49 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository maris-development/atlas appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 atlas-python 
Create a data visualization tool called 'DataVizPro' using Python and the 'atlas-python' package. This tool will allow users to upload CSV files containing various datasets and visualize them in different ways. Here’s a detailed breakdown of what the application should do:

1. **User Interface**: Develop a simple yet intuitive GUI using a library like PyQt or Tkinter. The interface should have options for file uploads, selection of columns for visualization, and choosing between different types of plots.
2. **CSV File Upload**: Implement functionality that allows users to upload CSV files. Ensure the application checks if the uploaded file is indeed a CSV and handles common errors gracefully.
3. **Data Preprocessing**: Utilize the 'atlas-python' package to efficiently manage and preprocess the uploaded dataset. This includes handling missing values, converting data types as needed, and performing any necessary transformations to prepare the data for visualization.
4. **Visualization Options**: Provide several visualization options such as line charts, bar graphs, scatter plots, and histograms. Each plot type should have customizable parameters such as colors, labels, and titles.
5. **Interactive Features**: Add interactive elements to your visualizations. For example, enable zooming, panning, and tooltips that display more information when hovering over data points.
6. **Export Functionality**: Allow users to export their visualizations as image files (PNG, JPEG) or as interactive HTML files using Plotly or Bokeh.
7. **Documentation and Help**: Include a help section within the application that explains how to use each feature and provides examples of CSV files and their corresponding visualizations.

To utilize the 'atlas-python' package, focus on its ability to efficiently handle large arrays of data. Use it for loading the CSV data into memory, preprocessing steps like filtering and aggregation, and potentially for generating the actual plots if it offers plotting functionalities. Remember to document how you integrate 'atlas-python' into your workflow and why it was chosen over other libraries.

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