🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, obfuscation, and credential handling. However, potential shell execution and the package's new and possibly inactive status raise concerns, warranting further scrutiny.

Potential shell execution requires further investigation.
Signs of a new and potentially inactive package.

Per-check LLM notes

Network: No network calls detected, which is generally low risk.
Shell: Shell execution detected might be for local script execution but requires further investigation to confirm legitimate use.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
Metadata: The package shows signs of being new and potentially inactive, which raises some suspicion but not conclusive evidence of malice.

🔬 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)

ir / "src") result = subprocess.run( [ sys.executable,

✓ 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 dogusariturk/HEACalculator appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 HEACalculator

Create a mini-application called 'HEAFormationPredictor' that leverages the HEACalculator Python package to predict the likelihood of solid solution formation in High Entropy Alloys (HEAs). This application should allow users to input alloy compositions and receive predictions on whether these alloys will form solid solutions based on the calculated phenomenological parameters.

Steps to develop the application:
1. Set up a virtual environment and install the necessary packages including HEACalculator.
2. Design a user-friendly command-line interface (CLI) that guides users through the process of entering alloy compositions.
3. Implement functionality within the application to use HEACalculator to compute the phenomenological parameters from the user-provided alloy compositions.
4. Display the results to the user in an understandable format, indicating the predicted outcome regarding solid solution formation.
5. Include error handling to manage incorrect inputs and provide informative feedback to the user.
6. Document the code thoroughly and create a README file that explains how to set up and run the application, as well as any assumptions made during the development process.

Suggested Features:
- Allow users to save their alloy composition data and results for future reference.
- Provide a feature to visualize the computed phenomenological parameters using matplotlib or a similar library.
- Enable batch processing where multiple alloy compositions can be analyzed at once.
- Incorporate a help menu that provides context about the significance of the computed parameters and their implications for solid solution formation.