alloy-runtime-types

v0.2.107 safe
3.0
Low Risk

Type definitions and DTOs for Alloy Runtime

🤖 AI Analysis

Final verdict: SAFE

The package alloy-runtime-types v0.2.107 presents minimal risks as indicated by low scores across all categories except metadata, where there is some concern due to missing maintainer details and lack of a GitHub repository.

  • No network calls or shell executions detected.
  • Incomplete maintainer information and lack of a GitHub repository.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on runtime types and not likely to require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands which is expected for a typical library focused on type checking.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package has no associated GitHub repository and the maintainer's information is incomplete.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (883 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

  • 80 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 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 alloy-runtime-types 
Your task is to develop a simple yet powerful data transformation tool using the 'alloy-runtime-types' Python package. This tool will allow users to define complex data models and then transform raw input data into structured outputs based on these models. Here’s a step-by-step guide on how to create this application:

1. **Project Setup**: Start by setting up your Python environment. Make sure you have Python installed along with virtualenv for managing dependencies. Create a new virtual environment and install 'alloy-runtime-types'. Also, include other necessary packages like FastAPI for the web framework.
2. **Define Data Models**: Use the type definitions and DTOs provided by 'alloy-runtime-types' to define various data models. These models could represent different entities such as User, Product, Order, etc. Each model should include fields and constraints as per the business requirements.
3. **Data Transformation Logic**: Implement functions that take raw data (in JSON format) as input and map it to the defined data models. Ensure that these transformations handle validation according to the constraints specified in the models.
4. **Web API Integration**: Build a simple RESTful API using FastAPI. This API should expose endpoints for uploading raw data, transforming data according to the selected model, and retrieving transformed data.
5. **User Interface**: Although not mandatory, consider developing a basic UI using a frontend framework like React or Vue.js to make the tool more user-friendly. This UI should allow users to select data models, upload files, and view results.
6. **Testing & Documentation**: Write comprehensive tests for your data transformation logic and API endpoints. Ensure all functionalities are well-documented, including how to use the API and the structure of the DTOs.

Suggested Features:
- Support for multiple data models.
- Real-time validation feedback during data transformation.
- Ability to customize data models via a configuration file.
- Detailed logs for each data transformation process.
- Secure authentication for accessing the API endpoints.

By following these steps and incorporating the suggested features, you'll create a versatile data transformation tool that leverages the power of 'alloy-runtime-types' to simplify complex data handling tasks.

💬 Discussion Feed

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