🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all categories with no network calls, shell executions, obfuscations, or credential harvesting attempts. The only minor concern is the metadata risk due to the maintainer having only one package.

Low network and shell execution risks
No signs of obfuscation or credential harvesting
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 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 may indicate a new or less active account, but no other suspicious elements were found.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

4 test file(s) detected (e.g. migration_dev_test.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3853 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

4 type-annotated function signatures (partial)

○ 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-data-migration-utils

Create a data migration tool using Python that leverages the 'aind-data-migration-utils' package to efficiently move data between different storage systems. This tool will be particularly useful for researchers who need to transfer large datasets between local file systems and cloud storage services like AWS S3 or Google Cloud Storage. Your project should include the following components:

1. **Data Source Selection**: Allow users to select the source of their data. This could be a local directory, an AWS S3 bucket, or a Google Cloud Storage bucket.
2. **Data Destination Configuration**: Similarly, allow users to specify where they want to migrate their data. This could be another local directory, a different AWS S3 bucket, or a Google Cloud Storage bucket.
3. **Data Validation**: Implement functionality to validate the integrity of the data being migrated. This includes checking for file corruption, missing files, and ensuring that all files have been transferred successfully.
4. **Progress Tracking**: Provide real-time progress tracking so users can see how much data has been transferred and estimate remaining time.
5. **Error Handling and Recovery**: Ensure robust error handling to deal with network issues, authentication failures, etc., and implement mechanisms for resuming interrupted transfers.
6. **User Interface**: Develop a simple and intuitive command-line interface (CLI) for interacting with the tool.
7. **Logging**: Maintain detailed logs of all migrations performed, including timestamps, source/destination details, and any errors encountered.
8. **Security**: Address security concerns by securely managing credentials and encrypting sensitive data during transit.

To achieve these goals, you will extensively utilize the 'aind-data-migration-utils' package. Specifically, use its functions for data validation, progress monitoring, and secure data transfer. Additionally, integrate it with AWS SDK (boto3) and Google Cloud Storage Client libraries for handling cloud storage interactions. Your final deliverable should be a fully functional CLI tool that simplifies the process of migrating large datasets across different storage systems.