attractor-basin-cognition

v0.1.0 suspicious
6.0
Medium Risk

Attractor-basin primitives for cognitive signal routing, field dynamics, and host-neutral mental model substrates.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential usage but raises concerns due to the metadata risk associated with its repository's suspicious behavior.

  • Low risk in network, shell, obfuscation, and credential handling.
  • High metadata risk due to suspicious repository patterns.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires online resources.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository shows signs of being a throwaway account with suspicious commit patterns and low activity, indicating potential risk.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. test_field_engine.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2862 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

  • 34 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 bionicbutterfly13/attractor-basin-cognition
  • 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 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
  • All 3 commits happened within 24 hours
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Dr. Mani Saint Victor" 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 attractor-basin-cognition 
Create a Python-based mini-application that leverages the 'attractor-basin-cognition' package to simulate cognitive signal routing within a simplified neural network model. This application will allow users to input various types of signals (such as text, images, or audio snippets) and observe how these signals are routed through different cognitive 'attractor basins' based on their content and context. The goal is to demonstrate the dynamic nature of cognitive processing and the adaptability of neural networks in handling diverse inputs.

Step-by-Step Guide:
1. Set up the project environment including necessary libraries such as attractor-basin-cognition, numpy, matplotlib, and any others required for data manipulation and visualization.
2. Define a set of predefined cognitive 'attractor basins' using attractor-basin-cognition primitives. Each basin could represent a specific cognitive function like language processing, visual recognition, or auditory interpretation.
3. Implement a user interface that allows uploading of different types of input data (text, images, audio).
4. Develop a signal processing module that translates raw input into a format suitable for routing through the attractor basins.
5. Utilize attractor-basin-cognition to route the processed signals through appropriate cognitive functions based on their characteristics.
6. Visualize the routing process and outcomes using matplotlib or similar visualization tools.
7. Allow users to experiment with different input configurations and observe changes in routing patterns.

Suggested Features:
- Interactive visualization of signal flow through cognitive attractor basins.
- Adjustable parameters for influencing signal routing behavior.
- Comparative analysis tools for understanding differences between various input types.
- A feature to save and share simulation results.

How 'attractor-basin-cognition' is utilized:
- To define and manage the cognitive attractor basins that represent different cognitive functions.
- For routing signals through these basins based on their characteristics and context.
- In modeling the dynamics of cognitive processing and the adaptability of neural networks to new inputs.

💬 Discussion Feed

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