🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no signs of direct malicious activity such as network calls or shell execution. However, the sparse metadata and untraceable repository raise concerns about its origin and reliability.

Metadata risk score of 5 out of 10
Repository and author details are not available

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package does not engage in secret or credential theft activities.
Metadata: The repository is not found, and the author details are sparse, suggesting potential unreliability.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

3 test file(s) detected (e.g. test_basic.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6766 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

50 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 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

Email domain looks legitimate: cruxia.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 agm-r

Create a Python-based mini-application named 'BeliefRevisionTool' that leverages the 'agm-r' package to demonstrate the principles of belief revision according to the AGM theory. Your application should allow users to input a set of beliefs (represented as propositions or statements) and then revise these beliefs based on new information provided by the user. The application should support the following operations defined by the AGM theory:

1. Expansion: Add a new belief to the existing set of beliefs without changing any other beliefs.
2. Contraction: Remove a belief from the existing set while maintaining consistency among the remaining beliefs.
3. Revision: Replace an old belief with a new one, ensuring the resulting set of beliefs remains consistent.

The application should provide a user-friendly interface where users can input their beliefs and see the results of each operation in a clear, understandable format. Additionally, include error handling to manage cases where the operations might not be possible due to inconsistencies in the belief sets.

Use the 'agm-r' package to implement the core functionalities related to belief revision. Ensure your code is well-documented and includes comments explaining how the functions from 'agm-r' are utilized in each step of the process. Finally, write test cases to validate the correctness of your application's logic.