🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential shell execution risks that could be exploited for malicious purposes, despite showing no signs of network calls, obfuscation, or credential harvesting.

Detected shell execution patterns suggest potential unauthorized git operations.
No other significant security risks identified.

Per-check LLM notes

Network: No network calls detected.
Shell: Detected shell execution patterns suggest potential unauthorized git operations which could be used for malicious purposes.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package does not engage in suspicious activities related to secret or credential handling.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (18568 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
Type checker (mypy / pyright / pytype) referenced in project
512 type-annotated function signatures detected in source

○ 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 score 10.0

Found 6 shell execution pattern(s)

".git").exists(): subprocess.run( ["git", "init", "-b", "main"],
ue, ) subprocess.run( ["git", "add", "."], cwd=ro
ue, ) subprocess.run( ["git", "commit", "-m", "Initialize knowled
global_hook: result = subprocess.run( ["git", "config", "--global", "core.hooksPath"]
=True, exist_ok=True) subprocess.run( ["git", "config", "--global", "core.hooksPath",
h / ".git").exists(): subprocess.run( ["git", "init", "-b", "main"], cwd=

✓ 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 2.0

1 maintainer concern(s) found

Author "Edgar Parenti" 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 ambara

Create a collaborative code review tool using the 'ambara' Python package. This tool will facilitate a streamlined process for developers to share their code snippets, receive feedback, and improve their coding practices collaboratively. Here are the key steps and features of your project:

1. **Project Setup**: Begin by setting up a virtual environment and installing the 'ambara' package along with other necessary dependencies such as Flask for web development.
2. **User Authentication**: Implement user authentication so that only registered users can submit and review code snippets. Use Flask-Security for easy integration.
3. **Code Snippet Submission**: Allow users to upload code snippets through a simple form. Ensure that the snippets are stored securely and can be retrieved easily.
4. **Code Review Process**: Utilize the 'ambara' package to generate detailed context around each uploaded code snippet. This context should include relevant documentation links, similar code examples, and potential improvements based on best practices.
5. **Feedback System**: Enable users to provide feedback on the submitted code snippets. The feedback should be linked to specific parts of the code, allowing for precise comments and suggestions.
6. **Discussion Threads**: Integrate a discussion thread feature where users can discuss the code snippet and its review process. This will foster a community-driven improvement culture.
7. **Analytics Dashboard**: Provide an analytics dashboard for administrators to monitor the usage of the platform, track user engagement, and identify trends in the types of code being reviewed and the feedback provided.
8. **Testing and Deployment**: Thoroughly test your application to ensure all features work as expected. Once satisfied, deploy your application to a cloud service provider like Heroku or AWS.

In summary, this project aims to leverage the 'ambara' package to enhance the code review process by providing rich context and facilitating collaborative feedback, making it easier for developers to learn and improve together.