arxivist

v0.1.4 suspicious
5.0
Medium Risk

Agent + CLI that fetches arXiv papers, maintains a knowledge base, implements paper solutions, and benchmarks them against the papers' claims.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to suspiciously low activity, suggesting potential misuse of a throwaway account.

  • Suspiciously low number of commits and contributors
  • Possibly using a throwaway account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspiciously low number of commits and contributors, indicating possible use of a throwaway account.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

Some documentation present

  • Detailed PyPI description (2450 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

  • 50 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in agentculture/arxivist
  • Single author with few commits — possibly a personal or throwaway project

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Single contributor with only 3 commit(s) — possibly throwaway account

  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AgentCulture" 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 arxivist 
Create a mini-application named 'PaperBenchmarker' using the Python package 'arxivist'. This application should enable researchers and enthusiasts to easily fetch, analyze, and benchmark research papers from arXiv based on their specific interests. Here’s a step-by-step guide to building this application:

1. **Setup**: Begin by installing the 'arxivist' package and setting up your development environment with Python.
2. **Fetching Papers**: Implement a feature within PaperBenchmarker that allows users to search for and download arXiv papers based on keywords, categories, or author names.
3. **Knowledge Base Integration**: Utilize 'arxivist' to maintain a local or cloud-based knowledge base where summaries, key points, and metadata of downloaded papers are stored for quick reference.
4. **Solution Implementation**: Develop a module that reads through each paper, extracts problem statements, and suggests or implements potential solutions using relevant tools and libraries.
5. **Benchmarking**: Implement a benchmarking tool that compares the implemented solutions against the claims made in the original papers, providing metrics such as accuracy, efficiency, and novelty.
6. **User Interface**: Design a simple yet effective CLI interface for users to interact with the application, including options for fetching papers, accessing the knowledge base, viewing solution implementations, and running benchmarks.
7. **Documentation**: Ensure comprehensive documentation is provided for both developers and end-users, detailing how to install, configure, and use the application effectively.

Suggested Features:
- Automatic categorization of papers into predefined domains.
- Integration with visualization tools for better understanding of benchmark results.
- Support for user-defined evaluation metrics beyond the default ones provided.
- Option to export analysis and benchmark data for further study or publication.

This project aims to streamline the process of researching and validating scientific findings, making it more accessible and efficient for everyone involved.

💬 Discussion Feed

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