attune-gui

v0.8.0 suspicious
4.0
Medium Risk

Local dashboard for attune-rag / attune-help / attune-author. Server-rendered Jinja2 UI — ships clean via PyPI with no npm step.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to sparse author metadata and potential benign shell execution related to version control.

  • Sparse author metadata indicating potential unreliability
  • Shell execution patterns possibly related to version control operations
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: The shell execution pattern detected seems to be related to version control operations, likely benign but should be reviewed in context.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The author's information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Medium (6.4/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 17 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (14803 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Governance file: security.py
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 350 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 93 commits in Smart-AI-Memory/attune-gui
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: result = subprocess.run( # noqa: S603 ["git", "diff", "--stat", "HE
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: smartaimemory.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Smart-AI-Memory/attune-gui appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 attune-gui 
Create a simple yet functional knowledge management system using the 'attune-gui' Python package. This system will serve as a local dashboard to manage, search, and generate responses from a collection of documents. The application should allow users to upload documents, search through them, and request summaries or specific information based on user queries. Here’s a detailed plan for the project:

1. **Setup Environment**: Install Python and set up a virtual environment. Ensure you have the latest version of 'attune-gui' installed.
2. **Project Structure**: Organize your project into directories such as 'templates', 'static', 'data', and 'config'.
3. **User Interface**: Utilize Jinja2 templates provided by 'attune-gui' to create a clean and intuitive interface for document management. Design pages for uploading documents, viewing a library of uploaded files, searching through documents, and generating responses to user queries.
4. **Document Management**: Implement functionality to upload various types of documents (PDFs, Word Docs, etc.) and store them locally in the 'data' directory. Develop a feature to categorize and tag documents for better organization.
5. **Search Functionality**: Integrate a search bar where users can input keywords to find relevant documents. Enhance this feature by allowing users to filter results based on tags or categories.
6. **Query Response Generation**: Use 'attune-gui' capabilities to process user queries and return relevant sections of documents as answers. Implement a feature that allows users to request summaries of documents or specific information based on their queries.
7. **Security Measures**: Since this is a local application, focus on securing the stored documents and ensuring user data privacy. Consider implementing basic authentication for accessing the dashboard.
8. **Testing & Deployment**: Test all functionalities thoroughly to ensure everything works as expected. Since this is a local application, deployment involves setting up the application on a local server and making sure it runs smoothly.
9. **Documentation**: Write clear documentation explaining how to install, use, and extend the application. Include examples and best practices for managing and querying documents effectively.

This project aims to showcase the versatility of 'attune-gui' in building efficient, user-friendly dashboards for document management and information retrieval.

💬 Discussion Feed

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