🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell execution sanitization, obfuscation, and credential harvesting. However, the metadata risk and low activity indicators raise suspicion, potentially pointing towards a new and less established package.

Metadata risk score of 4 out of 10
Low activity indicators

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: The presence of shell execution suggests the package may execute commands on the system, which could be risky if not properly sanitized or intended for malicious use.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with low activity indicators which may suggest potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

t an error for us. proc = subprocess.run( cmd, capture_output=True, text=True

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sarao.ac.za>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "landmanbester" 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 africalim

Create a radio astronomy data analysis tool using the 'africalim' Python package. This tool will serve as a mini-application designed for astronomers and researchers to analyze and visualize radio astronomy data collected from various telescopes across Africa. The application should be user-friendly, allowing users to upload their own datasets or use preloaded sample datasets provided by the package.

Key Features:
1. Data Import: Allow users to import their own CSV or FITS formatted radio astronomy datasets.
2. Data Cleaning: Implement basic data cleaning functionalities such as handling missing values and removing noise.
3. Analysis Tools: Provide tools for spectral analysis, including frequency and time domain analysis, using functions available in the 'africalim' package.
4. Visualization: Enable users to visualize their data through customizable plots, including spectral lines, power spectra, and waterfall diagrams.
5. Export Results: Users should be able to export their analyzed data and visualizations in common formats like PNG, PDF, or CSV.
6. Documentation: Include comprehensive documentation on how to use the tool effectively, detailing each feature and its functionality.

How to Utilize 'africalim':
The 'africalim' package provides essential tools for processing and analyzing radio astronomy data. It includes functions for reading different types of astronomical data files, performing complex mathematical operations on these data sets, and generating high-quality visual representations of the data. Your task is to integrate these functionalities into a cohesive application that not only simplifies the process of data analysis but also enhances the understanding of radio astronomy phenomena.