AI Analysis
Final verdict: SAFE
The package appears to be safe with minimal risks identified. The primary concern is the use of base64 encoding, which may indicate some level of obfuscation, but there's no concrete evidence of malicious intent.
- Base64 encoding used, possibly for data obfuscation
- Single-package maintainer account, potentially new or less active
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: The use of base64 encoding suggests some form of data obfuscation, but it could be for legitimate purposes like handling binary data.
- Credentials: No clear patterns indicating credential harvesting were detected.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (3.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (5965 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
7.0
Partial type annotation coverage
Classifier: Typing :: Typed30 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
score 2.0
Found 1 obfuscation pattern(s)
parse_value=lambda v: base64.b64decode(v.encode("ascii")), ), ) JSONScalar = cast( GraphQL
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: users.noreply.github.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Nicholas Williams" 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 alchemyql
Your task is to develop a fully functional mini-application that allows users to manage a simple inventory system using the 'alchemyql' package, which integrates GraphQL with SQLAlchemy. This application will serve as a backend service for a potential e-commerce platform or a store management system. #### Application Overview: The application should allow users to perform CRUD (Create, Read, Update, Delete) operations on product data. Additionally, it should support searching and filtering of products based on specific criteria such as category, price range, etc. #### Core Features: 1. **Product Management**: Users should be able to add new products, update existing ones, delete products, and retrieve product information. 2. **Category Management**: Products can belong to different categories. The application should allow adding, updating, and deleting categories. 3. **Search & Filter**: Implement search functionality to find products by name, category, and price range. 4. **GraphQL API**: Utilize 'alchemyql' to expose a GraphQL API that supports all the above functionalities. 5. **Database Integration**: Use SQLAlchemy ORM to interact with a PostgreSQL database. Ensure that relationships between products and categories are correctly modeled. #### Detailed Steps: 1. **Setup Environment**: - Install necessary packages including 'alchemyql', 'sqlalchemy', 'graphene', and 'psycopg2'. - Configure your PostgreSQL database. 2. **Define Models**: - Create SQLAlchemy models for Product and Category. - Define relationships between Product and Category. 3. **Implement GraphQL Schema**: - Use 'alchemyql' to define a GraphQL schema that maps to your SQLAlchemy models. - Ensure that the schema supports all CRUD operations and search/filter queries. 4. **Build Resolvers**: - Implement resolvers for each field in the GraphQL schema. - Handle complex queries like filtering and sorting. 5. **Testing**: - Write tests to ensure all functionalities work as expected. - Test the GraphQL API using tools like GraphQL Playground or similar. 6. **Documentation**: - Provide comprehensive documentation on how to use the GraphQL API. - Include examples of queries and mutations. #### Additional Suggestions: - Consider adding pagination for large datasets. - Implement user authentication to restrict access to certain operations. - Add validation rules to ensure data integrity when creating or updating entries. - Explore integrating this backend with a frontend UI framework for a complete solution. This project not only helps you understand the power of GraphQL and SQLAlchemy but also provides practical experience in building robust backend services.