awescholar

v0.1.7 safe
4.0
Medium Risk

Automated scientific literature discovery and curation for Awesome lists

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all categories except for metadata, where it shows signs of low maintenance efforts. However, there are no clear indications of malicious activity.

  • Low network and shell risk
  • No obfuscation or credential harvesting
  • Metadata suggests low maintenance
Per-check LLM notes
  • Network: No network calls detected, which is typical and not suspicious.
  • Shell: Detection of shell execution may indicate the package runs CLI commands but does not necessarily imply malicious intent without further context.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The package shows low maintenance and effort signs, but lacks clear malicious indicators.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_cli.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 65 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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)

  • .CompletedProcess: return subprocess.run( [sys.executable, "-m", "awescholar.cli", *args],
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 awescholar 
Create a mini-application called 'SciLister' that leverages the 'awescholar' Python package to automate the creation and maintenance of curated lists of scientific papers for various research domains. This application will serve as a tool for researchers, students, and enthusiasts to discover and manage relevant scientific literature easily. Here’s a detailed plan on how to build it:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have Python 3.x installed along with pip. Install the 'awescholar' package using pip.

2. **Core Functionality**:
   - Implement a function to search for scientific papers based on keywords, authors, publication dates, and other metadata using 'awescholar'.
   - Develop a feature to filter and sort these papers based on relevance scores provided by 'awescholar'.
   - Integrate a user-friendly interface where users can input their preferences and receive a list of recommended papers.

3. **Features**:
   - **User Profiles**: Allow users to create profiles where they can save their search history, favorite papers, and notes.
   - **Notifications**: Set up a system that notifies users when new papers matching their interests are published.
   - **Custom Lists**: Enable users to create custom lists of papers categorized by topics or projects.
   - **Collaboration**: Add functionality for users to share their lists with others and collaborate on curating them.

4. **Integration with External Tools**:
   - Connect 'SciLister' with citation managers like Zotero or Mendeley to allow direct import/export of paper data.
   - Consider integrating with academic social networks like ResearchGate or Academia.edu for enhanced discovery and sharing capabilities.

5. **Deployment**:
   - Package 'SciLister' as a standalone application or web service.
   - Deploy the application on a platform like Heroku or AWS for easy access by users worldwide.

By following these steps, you’ll create a powerful yet accessible tool that streamlines the process of discovering and managing scientific literature, making it easier for anyone involved in research to stay informed and organized.

💬 Discussion Feed

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