🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential obfuscation and metadata concerns. It lacks clear usage history and has minimal community engagement.

Obfuscation risk observed
Metadata risk due to repository's newness and limited activity

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires online services.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
Obfuscation: The obfuscation pattern observed may indicate an attempt to hide code or dependencies, which could be suspicious.
Credentials: No clear patterns of credential harvesting were detected.
Metadata: The repository's recent creation, low activity, and single contributor suggest potential risk.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_repair.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1809 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

6 type-annotated function signatures (partial)

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 2 commits in balswyan/arabic-repair
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

_unicode.""" pytest = __import__("pytest") try: from camel_tools.utils.normalize i

✓ 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 10.0

Git history flags: Repository created very recently: 3 day(s) ago (2026-06-04T06:59:21Z)

Repository created very recently: 3 day(s) ago (2026-06-04T06:59:21Z)
Repository has zero stars and zero forks
Very few commits: 2 total
Single contributor with only 2 commit(s) — possibly throwaway account

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Bandar AlSwyan" 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 arabic-repair

Create a mini-application called 'Arabic Text Savior' using Python and the 'arabic-repair' package. This tool aims to assist users in cleaning up Arabic text extracted from various sources such as scanned documents, PDFs, and legacy systems where the text might be visually corrupted due to OCR errors or other issues. Here's a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your Python environment and installing necessary packages including 'arabic-repair'. Ensure you have a working setup with all dependencies installed.

2. **User Interface**: Design a simple command-line interface (CLI) for users to interact with the application. The CLI should allow users to input the path of the file containing the problematic Arabic text.

3. **Text Extraction**: Implement functionality to read and extract text from different types of files (PDFs, images processed via OCR, etc.). Use libraries like PyPDF2 for PDFs and pytesseract for OCR text extraction.

4. **Text Analysis and Repair**: Utilize the 'arabic-repair' package to analyze and repair the extracted text. Integrate its core functionalities to detect common errors such as incorrect character encoding, missing or extra diacritical marks, and other visual corruption issues specific to Arabic script.

5. **Output Display and Saving**: After repairing the text, display it to the user through the CLI. Also, provide an option to save the cleaned-up text into a new file, ensuring the original text is preserved and the corrected version is easily accessible.

6. **Error Handling and Feedback**: Implement robust error handling to manage cases where the input file is not found or is unsupported. Additionally, include feedback mechanisms that inform users about the success of the repair process and any issues encountered.

7. **Testing and Validation**: Test the application thoroughly with various types of corrupted Arabic text samples to ensure reliability and effectiveness. Validate the repaired text against known correct versions to measure accuracy.

8. **Documentation and Deployment**: Write comprehensive documentation for the application, explaining how to use it and how to troubleshoot common issues. Consider deploying the application as a standalone executable or as a web-based service for broader accessibility.

By following these steps, you'll create a powerful yet user-friendly tool that significantly improves the quality of Arabic text extracted from problematic sources.