🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, such as network calls, shell execution, obfuscation, or credential harvesting. However, the maintainer's lack of other packages and PyPI classifiers suggests they may be new or inactive.

No network calls
No shell execution
No obfuscation
No credential harvesting
Low maintenance activity

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution detected, which is typical and does not indicate any suspicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and lacks PyPI classifiers, indicating low effort or new/inactive status.

🔬 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

Author "Laurent-Philippe Albou" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with abstractsemantics

Create a semantic web explorer tool using the Python package 'abstractsemantics'. This tool will enable users to explore and query a knowledge graph that represents entities and their relationships based on predefined predicates and types. The tool should include the following functionalities:

1. **Graph Visualization**: Implement a feature that visualizes the knowledge graph in a user-friendly interface. Users should be able to see nodes representing entities and edges representing the relationships between them.
2. **Querying Mechanism**: Allow users to query the graph using natural language inputs. The tool should parse these queries and return relevant results based on the registered predicates and types from 'abstractsemantics'.
3. **Custom Predicate Addition**: Provide an option for users to add custom predicates and types to the knowledge graph, enhancing its flexibility and adaptability.
4. **Export/Import Functionality**: Enable users to export their knowledge graphs in various formats (e.g., JSON, RDF) and import existing graphs to integrate into the current system.
5. **Security Features**: Ensure that the tool includes basic security measures such as user authentication and authorization to manage access to different parts of the knowledge graph.

The 'abstractsemantics' package plays a crucial role in this project by providing the framework for defining and managing the semantics of the entities and relationships within the knowledge graph. It allows developers to register new types and predicates easily, which can then be queried and manipulated through the tool's interface. Use 'abstractsemantics' to dynamically generate and update the graph structure based on user interactions and data inputs.