autogen-scavio

v0.1.1 suspicious
6.0
Medium Risk

AutoGen integration for Scavio Search API -- real-time Google, Amazon, Walmart, YouTube, Reddit, and TikTok search tools for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, obfuscation, and credential risks. However, the short-lived git repository and new maintainer account raise concerns about potential malicious intent.

  • Short-lived git repository
  • New maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The short-lived git repository and the new maintainer account suggest potential risk of malicious activity.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://scavio.dev/docs/autogen?utm_source=autogen_integrati
  • Detailed PyPI description (3185 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 47 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 4 commits in scavio-ai/autogen-scavio
  • 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 4 commit(s) — possibly throwaway account

  • Single contributor with only 4 commit(s) — possibly throwaway account
  • All 4 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Scavio" 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 autogen-scavio 
Create a Python-based mini-application named 'AI-SearchBot' that integrates the 'autogen-scavio' package to enable real-time search capabilities across multiple platforms including Google, Amazon, Walmart, YouTube, Reddit, and TikTok. This application will serve as an intelligent assistant for users to quickly find information and products from these sources using natural language queries. The application should include the following functionalities:

1. User Interface: Develop a simple command-line interface (CLI) for users to interact with the bot.
2. Query Processing: Implement natural language processing (NLP) to understand user queries and map them to appropriate search engines or platforms.
3. Search Execution: Utilize the 'autogen-scavio' package to execute searches on the specified platforms based on the parsed query.
4. Result Display: Present the top results from each platform in a structured format to the user, highlighting key details such as title, URL, and a brief description.
5. Advanced Features: Consider adding advanced features like filtering results by date, relevance, and popularity; allowing users to specify preferences for certain types of content (e.g., videos, articles); and integrating sentiment analysis to gauge public opinion on topics or products.
6. Error Handling: Ensure the application gracefully handles errors and provides meaningful feedback to users, such as when a query cannot be processed or a search engine is not supported.
7. Documentation: Provide clear documentation on how to install dependencies, run the application, and use its features effectively.

This project aims to demonstrate the power of integrating specialized APIs with AI to create versatile and user-friendly applications.

💬 Discussion Feed

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