🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscation techniques detected. While there are minor concerns regarding metadata, these do not suggest any malicious intent.

No network calls detected
No shell execution patterns found
Lack of maintainer information and GitHub repository link

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires online resources.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some minor red flags such as lack of maintainer information and a GitHub repository link, but no clear signs 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 7.0

Some documentation present

2 documentation file(s) (e.g. conf.py)
Brief PyPI description (651 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

38 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 aind-data-schema

Create a mini-application called 'DataValidator' that leverages the 'aind-data-schema' Python package to validate JSON files against predefined schemas. The application should have a user-friendly command-line interface (CLI) and support multiple schema definitions stored in a configuration file. Here are the key requirements and features for the DataValidator application:

1. **Schema Management**: Users should be able to define their own schemas using the provided schema definitions in 'aind-data-schema'. These schemas will be stored in a configuration file (e.g., `schemas.json`) which the application reads during initialization.
2. **CLI Interface**: Develop a CLI that allows users to specify a JSON file and the corresponding schema to validate against. The CLI should provide feedback on whether the JSON file conforms to the specified schema, including detailed error messages if validation fails.
3. **Batch Validation**: Extend the functionality to allow batch validation of multiple JSON files against a single schema. This feature would be useful for validating large datasets.
4. **Custom Error Reporting**: Implement custom error reporting that not only indicates where the validation failed but also suggests possible corrections based on the schema definition.
5. **Integration with Other Tools**: Consider integrating the DataValidator into other workflows, such as CI/CD pipelines or as part of a larger data processing toolchain. For example, it could be invoked as a pre-commit hook in version control systems to ensure all committed JSON files are valid according to the defined schemas.
6. **Documentation and Examples**: Provide comprehensive documentation and examples on how to use the application, including how to create and manage schemas, and how to integrate it into existing workflows.

The 'aind-data-schema' package will be used primarily for defining the schema structure and performing the actual validation of JSON files. Your task is to design and implement the 'DataValidator' application, ensuring it is robust, efficient, and easy to use for developers working with structured JSON data.