autonometrics

v0.9.0a1 suspicious
5.0
Medium Risk

An instrument for quantifying structural self-determination across systems.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to low repository activity and limited maintainer history, raising concerns about its origin and maintenance.

  • Low repository activity
  • Limited maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, indicating no direct system command risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The low activity in the repository and the maintainer's limited history suggest potential risk, but no clear indicators of malicious intent.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 27 test file(s) found

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

Some documentation present

  • Detailed PyPI description (51182 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 261 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 74 commits in bugerchip/Autonometrics
  • Single author but highly active (74 commits)

🔬 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 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "bugerchip" 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 autonometrics 
Create a Python-based mini-app called 'SystemAnalyzer' which leverages the 'autonometrics' package to analyze and quantify the structural self-determination of various systems. This app will serve as a tool for researchers and engineers to better understand the inherent complexity and autonomy within different system structures, ranging from biological networks to social organizations.

Step-by-Step Instructions:
1. Begin by installing the necessary packages including 'autonometrics'.
2. Design a user-friendly interface where users can input details about their system, such as nodes, connections, and any specific parameters relevant to the type of system being analyzed.
3. Implement functionality using 'autonometrics' to process the input data and calculate measures of structural self-determination. These measures could include metrics like resilience, adaptability, and feedback loop strength.
4. Develop a visualization component that graphically represents the system's structure and highlights key findings from the analysis.
5. Include a reporting feature that generates a detailed report summarizing the analysis results, providing insights into the system's characteristics and potential areas for improvement.
6. Ensure the app supports multiple types of systems by allowing users to select the domain or context of their system (e.g., biological, social, technological).
7. Add documentation and examples to guide new users on how to effectively utilize SystemAnalyzer.

Suggested Features:
- Customizable analysis settings based on the specific needs of different system types.
- Integration with external data sources for importing system descriptions.
- Real-time feedback during analysis to help users adjust their inputs if needed.
- Comparative analysis tools allowing users to assess multiple systems side-by-side.
- Export options for sharing or archiving analysis results.

How 'autonometrics' is Utilized:
'Autonometrics' provides the core algorithms and methodologies for quantifying structural self-determination. Users will interact with the app through its interface, but behind the scenes, the app will use 'autonometrics' functions to perform the actual analysis. For example, after receiving system data from the user, the app will call specific methods from 'autonometrics' to compute the desired metrics. The results returned by these methods will then be processed further by the app to generate visualizations and reports.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!