avoidance-cascade

v0.1.0 suspicious
5.0
Medium Risk

Python implementation of avoidance cascade detection for ternary agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of malicious activity such as network calls, shell execution, or obfuscation. However, its recent creation and lack of supporting metadata raise concerns about potential supply-chain risks.

  • Newly created package
  • Limited activity and no associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell executions detected, indicating no immediate risk of command injection or system manipulation.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package appears to be newly created with limited activity and no associated GitHub repository, raising some suspicion.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (3859 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

  • 37 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

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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "SuperInstance" 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 avoidance-cascade 
Develop a Python-based social network analysis tool called 'CascadeAnalyzer' that leverages the 'avoidance-cascade' package to detect avoidance cascades within networks of ternary agents. This tool will be particularly useful for sociologists, psychologists, and network scientists interested in understanding complex human interactions where agents can either avoid, engage, or be neutral towards each other. The tool should allow users to input or upload network data in various formats (such as CSV, JSON, or even directly from a database), and then analyze these networks to identify potential avoidance cascades.

### Key Features:
1. **Network Data Import**: Allow users to import network data in multiple formats, including but not limited to CSV and JSON files, and directly from databases like MySQL or PostgreSQL.
2. **Visualization**: Implement a feature that visualizes the network graphically, highlighting nodes (agents) and edges (interactions) between them. Use libraries such as NetworkX and Matplotlib for visualization.
3. **Avoidance Cascade Detection**: Utilize the 'avoidance-cascade' package to detect avoidance cascades within the network. The tool should be able to identify which agents are initiating avoidance cascades and how these cascades spread through the network.
4. **Interactive Analysis**: Provide an interactive interface where users can select specific nodes or groups of nodes to focus on for more detailed analysis. Users should be able to manipulate the visualization to explore different scenarios.
5. **Report Generation**: Automatically generate detailed reports summarizing the findings from the analysis, including key metrics such as the number of avoidance cascades detected, the impact of each cascade, and recommendations based on the analysis.
6. **User Interface**: Develop a user-friendly GUI using a framework like PyQt or Streamlit to make the tool accessible to users who may not have extensive programming knowledge.

### Implementation Steps:
1. Set up the development environment with necessary Python packages, including 'avoidance-cascade', NetworkX, Matplotlib, and any chosen GUI framework.
2. Design the data import functionality to support multiple input formats.
3. Integrate the 'avoidance-cascade' package into the tool to enable cascade detection.
4. Create visualization functions to display the network and highlight detected cascades.
5. Develop the interactive analysis component to allow users to explore different aspects of the network.
6. Implement report generation capabilities to provide comprehensive insights from the analysis.
7. Build the user interface to ensure ease of use and accessibility for all users.
8. Test the tool thoroughly with various datasets to ensure accuracy and reliability.
9. Document the project and prepare a user guide for end-users.

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