🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with minimal risks identified. It does not engage in network activities, shows no signs of obfuscation or credential theft, and its subprocess calls appear benign.

No network calls detected.
Subprocess execution is present but appears benign.
No obfuscation or credential harvesting detected.

Per-check LLM notes

Network: No network calls detected.
Shell: Subprocess execution is present but appears benign without evidence of malicious intent.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and the repository lacks community engagement.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

print(cmd) result = subprocess.run(cmd, stderr=subprocess.PIPE, text=True) if result
ile.seek(0) result = subprocess.run(self.running_cmd, stdin=self.input_file, stdout=self.output_

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Ujimatsu Chiya" 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 Sylveon

Create a mini-application named 'XCPC-OI-Prep' that helps students prepare for XCPC and OI contests by generating random problem sets based on specified criteria. The application will utilize the 'Sylveon' package to generate realistic test cases for these problems.

Step 1: Define the Problem Set Generator
- Users should be able to specify the number of problems they want to practice.
- They can also choose the difficulty level of the problems ranging from Easy, Medium, to Hard.
- Additionally, users can select specific topics or categories like Graph Theory, Dynamic Programming, etc., to focus their practice sessions.

Step 2: Implement Test Case Generation Using Sylveon
- Utilize the 'Sylveon' package to automatically generate test cases for each problem. Ensure that the test cases are diverse and cover various edge cases.
- The application should provide an option to save the generated problem set along with its test cases in a downloadable format such as PDF or ZIP file.

Step 3: Design User Interface
- Develop a simple yet intuitive web interface using Flask or Django.
- Include a form where users can input their preferences for the problem set generation.
- Display the generated problem set and allow users to download it.

Suggested Features:
- Integration with a code editor to allow users to write and test their solutions directly within the application.
- Option to share the generated problem set via social media or email.
- Statistics and analytics about user performance over time.
- Support for multiple programming languages.

How to Use Sylveon:
- Import the necessary modules from the 'Sylveon' package.
- Configure the parameters according to the user's input regarding difficulty and topic.
- Call the appropriate functions from 'Sylveon' to generate the test cases.
- Integrate these test cases into the problem descriptions for presentation and saving purposes.