astrocyte-neo4j

v0.15.0 safe
2.0
Low Risk

Neo4j GraphStore adapter for Astrocyte

🤖 AI Analysis

Final verdict: SAFE

The package astrocyte-neo4j v0.15.0 has minimal risks associated with it, with no indications of malicious activities or vulnerabilities.

  • No network or shell execution risks detected.
  • Low risk of obfuscation or credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that does not require external API access.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for a library focused on specific functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low engagement and poor metadata quality, but there are no clear signs of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

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

Some documentation present

  • Brief PyPI description (453 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

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 astrocyte-neo4j 
Create a social network analysis tool using Python's 'astrocyte-neo4j' package. This application will allow users to visualize and analyze connections within a social network dataset. Here’s a detailed plan for the project:

1. **Setup**: Install necessary packages including 'astrocyte-neo4j', 'networkx', and 'matplotlib'. Ensure Neo4j is running on your local machine or a cloud service.
2. **Data Import**: Design a simple user interface to upload CSV files containing social network data (e.g., users and their relationships). Use 'pandas' for data manipulation.
3. **Graph Construction**: Utilize 'astrocyte-neo4j' to create a graph database model of the social network. Define nodes as users and edges as relationships between them.
4. **Querying and Analysis**: Implement functions to query the graph database for various metrics such as degree centrality, betweenness centrality, and community detection. Use these queries to identify key influencers and communities within the network.
5. **Visualization**: Develop a feature to visualize the social network graph using 'networkx' and 'matplotlib'. Allow users to customize the visualization (e.g., node size based on influence).
6. **Reporting**: Provide an option to generate reports summarizing the findings from the analysis. Reports should include visualizations and key statistics about the network structure.
7. **User Interface**: Create a simple web-based interface using Flask to interact with the application. Users should be able to upload datasets, view analyses, and download reports.
8. **Testing and Documentation**: Thoroughly test the application to ensure it works correctly with different types of social network datasets. Document the setup process, usage instructions, and API documentation for future maintenance.

The 'astrocyte-neo4j' package is essential for managing the graph database operations, making it easier to handle large-scale social network data efficiently. By leveraging its capabilities, you'll be able to focus more on the analytical aspects rather than the underlying database management.

💬 Discussion Feed

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