🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all evaluated categories except for metadata, where there are some concerns regarding the author's details and account activity. However, these alone do not indicate a supply-chain attack.

Low network and shell execution risks
No signs of obfuscation or credential harvesting
Sparse author details and possibly inactive account

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external communication for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author's details are sparse, and the account seems new or inactive, raising some concerns.

🔬 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 score 3.0

Suspicious email domain flags: Very short email domain: fs.uni-lj.si>

Very short email domain: fs.uni-lj.si>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ladisk/FLife 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 FLife

Create a fatigue analysis mini-app using the Python package 'FLife'. This app will serve as a tool for engineers to perform vibration fatigue analysis on materials under various loading conditions. The goal is to predict the life expectancy of mechanical components based on spectral methods provided by the 'FLife' package.

Step 1: Define the User Interface
- Design a simple and intuitive UI where users can input material properties such as Young's modulus, yield strength, and Poisson's ratio.
- Allow users to upload or manually enter time series data representing the vibration spectrum.

Step 2: Implement Core Analysis Features
- Utilize the 'FLife' package to perform Rainflow counting on the input vibration data to determine the stress cycles.
- Calculate the fatigue damage using Miner's rule based on the S-N curve (Stress-Life curve) specific to the given material.

Step 3: Enhance with Additional Functionalities
- Include a feature to visualize the input vibration data and the calculated stress cycles.
- Provide a report generation option that summarizes the fatigue analysis results, including graphs and key metrics like estimated life expectancy.

Step 4: Ensure Robustness and Flexibility
- Make the app adaptable to different materials by allowing users to select from predefined material types or input custom S-N curves.
- Implement error handling to manage invalid inputs and provide meaningful feedback to users.

Utilize the 'FLife' package extensively throughout the development process to ensure accurate and reliable fatigue analysis capabilities.