🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears legitimate based on its functionality and lack of malicious activities such as network calls or shell executions. However, the metadata risk score is elevated due to missing repository and sparse maintainer information.

No network calls or shell executions detected
Repository and maintainer information are sparse

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package is not involved in unauthorized data collection.
Metadata: The repository is not found and the maintainer information is sparse, raising concerns about the legitimacy and intent of the package.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

6 test file(s) detected (e.g. test_flux.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "documentation" -> https://github.com/ccossou/airsplorer/README.md
Detailed PyPI description (3431 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

4 type-annotated function signatures (partial)

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 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: cea.fr>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 airsplorer

Create a mini-application named 'AIR Explorer' using the Python package 'airsplorer'. This application will serve as a tool for researchers and scientists to analyze and manipulate Atmospheric Infrared Sounder (AIRS) data more efficiently. The primary goal of 'AIR Explorer' is to simplify the process of downloading, analyzing, and visualizing AIRS data, making it accessible even to those without extensive programming knowledge.

Step 1: Setup Environment
- Install necessary Python packages including 'airsplorer', 'pandas', 'matplotlib', and 'seaborn'.

Step 2: Data Downloading Module
- Implement a feature within 'AIR Explorer' that allows users to select specific dates and geographical regions to download AIRS data from the provided datasets.

Step 3: Data Analysis Module
- Utilize 'airsplorer' to perform basic statistical analyses on the downloaded data, such as calculating mean temperature, humidity levels, and other relevant atmospheric measurements.
- Provide options for filtering data based on certain criteria (e.g., temperature range, humidity percentage).

Step 4: Visualization Module
- Integrate 'matplotlib' and 'seaborn' for generating interactive plots and graphs based on the analyzed data.
- Users should be able to customize these visualizations, adjusting parameters like color schemes, plot types, and more.

Step 5: Reporting Module
- Develop a feature that compiles all the analysis results into a comprehensive report.
- The report should include key findings, visual representations of the data, and any significant patterns or anomalies identified during the analysis.

How 'airsplorer' is utilized:
- 'airsplorer' will be primarily used for handling and processing AIRS data, providing convenient functions for data retrieval, manipulation, and analysis. It simplifies complex tasks involved in working with AIRS data, allowing developers and end-users to focus more on interpreting the data rather than dealing with technical intricacies.