arxivflow

v0.3.0 safe
4.0
Medium Risk

Automate arXiv paper tracking with LLM-powered metadata extraction and Google Sheets sync.

🤖 AI Analysis

Final verdict: SAFE

The package has low risks across most categories, with only metadata showing some concern due to the unavailability of the repository and the maintainer's sole package.

  • Low network risk
  • No shell execution detected
  • No obfuscation patterns
  • Safe handling of credentials
  • Metadata raises minor concerns
Per-check LLM notes
  • Network: Network calls are expected for fetching data from APIs like arXiv, but should be monitored for unusual endpoints or excessive traffic.
  • Shell: No shell execution patterns detected, indicating low risk of direct system command execution.
  • 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 maintainer has only one package and the repository is not found, raising some concerns but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

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

Some documentation present

  • Detailed PyPI description (7228 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

  • 25 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 score 9.0

Found 6 network call pattern(s)

  • ze, ) async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, follow_redirects=True) as new_clien
  • else: async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, follow_redirects=True) as new_clien
  • _API.") self.client = httpx.AsyncClient(timeout=request_timeout, follow_redirects=True) self
  • pages[start]) async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as client: r
  • "1001.0001")) async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as client: r
  • .7\ncontent") async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as client: p
✓ 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

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 "Zhijie Zhao" 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 arxivflow 
Create a Python-based mini-application called 'ArxivTrack' that leverages the 'arxivflow' package to automate the process of tracking and organizing academic papers from arXiv.org. Your application should include the following core functionalities:

1. **Paper Subscription**: Users should be able to subscribe to specific topics or keywords. The app will automatically fetch new papers matching these criteria as they are published on arXiv.
2. **Metadata Extraction**: Utilize the LLM-powered feature within 'arxivflow' to extract key metadata such as title, authors, abstract, and publication date from each paper.
3. **Google Sheets Integration**: Sync the extracted metadata into a Google Sheets document, which serves as a centralized repository for all tracked papers.
4. **Notifications**: Implement a notification system that alerts users via email when new papers matching their subscriptions are added to the Google Sheets document.
5. **Search Functionality**: Allow users to search through the papers stored in the Google Sheets document using various filters such as author name, title keywords, or publication date.
6. **User Interface**: Develop a simple web interface using Flask, where users can manage their subscriptions, view the synced papers, and perform searches.

The 'arxivflow' package will be used primarily for fetching new papers based on user subscriptions, extracting metadata, and synchronizing data with Google Sheets. Additionally, explore integrating the package's capabilities to enhance user interaction and data management within your application.

💬 Discussion Feed

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