🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low individual risk factors but raises suspicion due to potential typosquatting targeting 'scrapy' and incomplete metadata, suggesting it may be a supply-chain attack or a poorly maintained package.

Potential typosquatting targeting 'scrapy'
Incomplete and lacking description in metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and possibly a new maintainer, but lacks clear indicators of malicious intent.
⚠ Typosquatting target: scrapy

🔬 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 score 3.0

Possible typosquat of: scrapy

"DuraPy" is 2 edit(s) from "scrapy"

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Simon Stordal Amundgård" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with DuraPy

Your task is to develop a simple yet powerful engineering tool using the DuraPy package, which is designed to support multi-discipline engineering tasks. This tool will primarily focus on material durability analysis and stress-strain curve visualization for various materials under different loading conditions. The application should allow users to input basic parameters such as material type, loading conditions, and environmental factors, then calculate and display the expected durability and stress-strain behavior of the material.

**Step-by-Step Instructions:**
1. **Setup**: Begin by installing the necessary packages including DuraPy. Ensure you have Python installed and create a virtual environment for your project.
2. **Input Interface**: Design a user-friendly interface where users can select a material from a predefined list, input loading conditions (such as tension, compression, shear), and specify environmental conditions (temperature, humidity).
3. **Calculation Engine**: Utilize DuraPy’s core functionalities to process the inputs and perform the calculations required to determine the material’s durability and generate stress-strain curves.
4. **Visualization**: Implement a feature that visualizes the results, showing both numerical data and graphical representations of the stress-strain curves.
5. **Output Display**: Present the results back to the user in a clear, understandable format, possibly including recommendations based on the calculated durability and stress-strain characteristics.
6. **Documentation**: Write comprehensive documentation for your tool, explaining how to use it, what inputs are accepted, and how to interpret the outputs.

**Suggested Features**:
- Ability to save/load projects for future reference.
- Support for multiple units of measurement.
- Integration with external databases for more extensive material properties.
- Advanced options for users to customize the environmental conditions and loading scenarios.

Ensure that your application demonstrates the versatility and power of DuraPy in handling complex engineering calculations and providing valuable insights into material behavior under various conditions.