alexandria-project

v0.2.0 suspicious
4.0
Medium Risk

Unified CatPhan phantom analysis library - combining best features from multiple implementations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell executions, obfuscation, and credential harvesting. However, its metadata quality and low maintainer activity raise concerns about its reliability and long-term support.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating the package does not execute system commands without user interaction.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risk.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://dunirkdynamo.github.io/alexandria-project
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (8779 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 17 commits in DunirkDynamo/alexandria-project
  • Two distinct contributors found

🔬 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

Email domain looks legitimate: example.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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with alexandria-project 
Create a mini-application called 'PhantomAnalyzer' using the Python package 'alexandria-project'. This application will serve as a tool for medical imaging researchers to analyze CT scans of CatPhan phantoms, which are used to calibrate and evaluate computed tomography scanners. The application should be user-friendly, allowing users to upload CT scan images of CatPhan phantoms, select specific analysis modules, and receive detailed reports on the quality and accuracy of their imaging equipment.

Key Features:
1. User Interface: Develop a simple GUI using Tkinter that allows users to easily navigate through the application.
2. Image Upload: Users should be able to upload DICOM files of their CatPhan phantom scans directly into the application.
3. Analysis Modules: Implement various analysis modules available in 'alexandria-project', such as uniformity, low-contrast detectability, high-contrast spatial resolution, noise, and CT dose index.
4. Report Generation: Automatically generate comprehensive PDF reports based on the analysis results, including visual representations like graphs and charts.
5. Customization Options: Allow users to customize certain parameters within each analysis module according to their specific needs.
6. Data Visualization: Provide real-time visualization of the analysis process and final results within the GUI.
7. Error Handling: Ensure robust error handling to guide users through potential issues and provide meaningful feedback.

Utilizing 'alexandria-project':
- Use 'alexandria-project' to handle the core analysis tasks, leveraging its unified approach to CatPhan phantom analysis.
- Integrate 'alexandria-project' functions to read DICOM files, perform the necessary image processing and analysis, and extract key metrics.
- Utilize 'alexandria-project' modules for generating detailed analysis reports.
- Explore additional functionalities provided by 'alexandria-project' to enhance the application's capabilities.

💬 Discussion Feed

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