🤖 AI Analysis

Final verdict: SAFE

The package shows very low risk indicators with no network calls, shell executions, obfuscations, or credential risks detected. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest malicious intent.

No network calls detected
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other suspicious flags were raised.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

16 test file(s) detected (e.g. __init__.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1159 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

314 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 2.0

1 maintainer concern(s) found

Author "Allen Institute for Neural Dynamics" 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-metadata-upgrader

Create a mini-application called 'MetadataMigrator' that leverages the 'aind-metadata-upgrader' package to streamline the process of upgrading metadata for various datasets stored in different formats. This application will serve as a tool for data scientists and researchers who frequently work with large datasets and need to ensure their metadata is up-to-date and compatible with newer versions of their tools and systems.

The application should have the following core functionalities:
1. **Dataset Import**: Allow users to import datasets from various sources (CSV, JSON, SQL databases). The application should support multiple file formats and database types.
2. **Metadata Extraction**: Automatically extract metadata from imported datasets, including column names, data types, and any existing metadata annotations.
3. **Upgrade Rules Configuration**: Provide a user-friendly interface where users can define upgrade rules based on the latest schema requirements. These rules could include renaming columns, changing data types, or adding/removing specific metadata fields.
4. **Validation**: Before applying any upgrades, validate the current metadata against the defined upgrade rules to ensure compatibility and prevent data corruption.
5. **Automatic Upgrade**: Apply the defined upgrade rules automatically, updating the metadata in the dataset accordingly.
6. **Export Options**: After upgrading, allow users to export the updated dataset back into its original format or into a new desired format.
7. **Logging and Reporting**: Maintain a log of all actions performed during the upgrade process, including successes, failures, and warnings. Provide a summary report at the end of each session detailing the changes made.

To utilize the 'aind-metadata-upgrader' package effectively, follow these steps within your application:
- Use 'aind-metadata-upgrader' to handle the core logic of identifying discrepancies between current and target metadata schemas.
- Integrate its upgrade functions to apply the necessary transformations to the dataset's metadata.
- Leverage any additional utilities provided by the package for enhanced validation and logging capabilities.

Ensure the application is modular, allowing for easy integration of new dataset formats and upgrade rules in the future. Additionally, focus on making the UI intuitive and accessible, ensuring that non-technical users can also benefit from the tool.