🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscation techniques observed. The primary concern is the maintainer's limited history with other packages, but this alone does not suggest a supply-chain attack.

Low risk scores across all categories
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, which is expected and does not indicate any immediate risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The maintainer has only one package, suggesting it might be a new or less active account.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3552 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

24 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in bilbyai/air2neo
Two distinct contributors found

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository bilbyai/air2neo appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "David Lee" 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 air2neo

Develop a social network analysis tool that ingests user data from Airtable and stores it in a Neo4j graph database using the 'air2neo' package. This tool will enable users to analyze connections between individuals based on shared interests, mutual friends, and other relevant attributes. Here’s a detailed plan for building this mini-app:

1. **Setup Environment**: Ensure you have Python installed along with the necessary packages including 'air2neo', 'neo4j', and any additional libraries needed for data processing and visualization.

2. **Data Ingestion**: Use 'air2neo' to fetch user data from an Airtable base containing fields such as user ID, name, email, interests, and friend lists. Customize the ingestion process to map Airtable fields accurately to Neo4j nodes and relationships.

3. **Database Schema Design**: Define a schema for Neo4j that includes nodes representing users and their interests, and relationships indicating friendships and shared interests.

4. **Data Transformation & Validation**: Implement logic to clean and validate the data fetched from Airtable before storing it in Neo4j. Ensure data integrity and consistency.

5. **Querying & Analysis**: Write Cypher queries to explore the graph data. For example, find the shortest path between two users, identify clusters of users based on shared interests, or discover influencers within the network.

6. **Visualization**: Integrate a visualization component to display network graphs and insights derived from the queries. Tools like D3.js or Graphviz can be useful here.

7. **User Interface**: Develop a simple web interface using Flask or Django where users can input Airtable credentials, initiate data import, run predefined queries, and visualize results.

8. **Security Considerations**: Ensure sensitive information such as user emails are handled securely. Implement authentication for accessing the web interface and consider encrypting stored data.

9. **Documentation & Testing**: Provide comprehensive documentation on how to set up and use the tool. Include unit tests for the backend and integration tests for the entire pipeline to ensure reliability.

This project aims to showcase the power of combining Airtable for data collection and Neo4j for complex data analysis in a real-world application.