aiq-quivers

v1.2.0 safe
2.0
Low Risk

Impact Automata on Quivers (AIQ) — An algebraic framework for modeling dynamics on complex systems

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across all assessed categories with no indications of malicious activities or supply-chain attacks.

  • Low network risk associated with benign file operations.
  • No signs of shell execution, obfuscation, or credential harvesting.
Per-check LLM notes
  • Network: The observed network calls seem to be related to downloading and extracting files, which is typical for packages that include datasets or additional resources.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Izainea/aiq-quivers#readme
  • Detailed PyPI description (3862 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 7 commits in Izainea/aiq-quivers
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • ra desde {_CORA_URL}...") urllib.request.urlretrieve(_CORA_URL, tgz_path) print("Extrayendo...")
  • rgando {url}...") urllib.request.urlretrieve(url, gz_path) print(f"Descomprimiend
✓ 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

Email domain looks legitimate: unal.edu.co>

✓ 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 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aiq-quivers 
Develop a Python-based application that leverages the 'aiq-quivers' package to simulate and visualize the impact of automata on quiver structures within complex systems. This mini-app should allow users to define their own quivers (directed graphs with additional vector space data at each vertex), initialize automata rules, and observe how these rules propagate through the system over time. The application should include the following features:

1. A user-friendly interface for defining quivers, including adding vertices and edges with associated vector spaces.
2. Predefined automata rules that can be applied to the quivers, such as simple diffusion models or more complex interaction patterns.
3. Visualization tools to graphically represent the initial state of the quiver and its evolution over time based on the applied automata rules.
4. An analysis module that provides insights into the dynamics observed, such as identifying stable states, cycles, or chaotic behavior.
5. The ability to save and load different quiver configurations and rule sets for future use or sharing with others.

The 'aiq-quivers' package will be utilized to handle the underlying mathematical operations and algorithms required for simulating the automata on the quivers. Users should be able to interactively modify parameters and see immediate results, making it an engaging tool for both educational purposes and research in complex systems dynamics.

💬 Discussion Feed

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