assignment-evaluator

v1.0.0 suspicious
4.0
Medium Risk

AI-powered assignment grading tool — auto-generates rubrics and grades student PDF submissions using Google Gemini.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in terms of network, shell, obfuscation, and credential handling. However, the lack of a Git repository and maintainer inactivity raise concerns about its legitimacy and could indicate potential supply-chain issues.

  • No direct network, shell, obfuscation, or credential risks detected.
  • Suspicious metadata due to missing Git repository and inactive maintainer.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is suspicious due to the non-existent Git repository and the maintainer's inactivity.

📦 Package Quality Overall: Low (2.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8047 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

No author email provided

✓ 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

  • Only one version has ever been released — brand new package
  • Author "Nitesh Kumar" 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 assignment-evaluator 
Create a fully-functional mini-app called 'AutoGrader' that leverages the 'assignment-evaluator' package to automate the grading process of student assignments submitted as PDFs. The app should include the following functionalities:

1. **User Authentication**: Implement a simple login system where teachers can create accounts to access their grading dashboard.
2. **Assignment Upload**: Allow teachers to upload assignment documents (PDF format) which will serve as the basis for grading.
3. **Student Submission Handling**: Students can submit their completed assignments via the app, ensuring these submissions are also in PDF format.
4. **Automatic Rubric Generation**: Utilize the 'assignment-evaluator' package to automatically generate grading rubrics based on the uploaded assignment document.
5. **Automated Grading**: Automatically grade each student submission against the generated rubric using the AI capabilities provided by the 'assignment-evaluator' package.
6. **Feedback Generation**: Generate detailed feedback for each submission, highlighting strengths and areas for improvement.
7. **Grading Dashboard**: Teachers should have access to a dashboard displaying all submissions, grades, and feedback. They should also be able to manually adjust grades if necessary.
8. **Notifications**: Send automated notifications to students once their grades and feedback are available.
9. **Data Privacy Compliance**: Ensure all data handling complies with GDPR or other relevant data protection regulations.

Use Flask or Django for backend development, React or Angular for frontend, and PostgreSQL for database management. Integrate the 'assignment-evaluator' package effectively to ensure seamless grading and feedback generation processes.

💬 Discussion Feed

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