🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning signs, particularly regarding shell execution and metadata quality, which suggest potential risks that need further scrutiny.

Shell risk detected
Low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: Shell execution detected may be for legitimate purposes like interacting with MongoDB, but requires further investigation to ensure it's not being used maliciously.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
Metadata: The package shows signs of low activity and metadata quality, which could indicate potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

, ) process = subprocess.Popen( mongo_command_txt, stdout=subprocess.PIPE, stde
) process = subprocess.Popen( mongo_command_txt, stdout=subprocess.PIPE,

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "tlibs313" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Python-DataEngine

Create a data migration tool named 'DataMigrator' using the Python-DataEngine package. This tool will facilitate the seamless transition of data from one SQL database to another, supporting various SQL dialects such as MySQL, PostgreSQL, and SQLite. The application should allow users to specify source and target databases, select tables to migrate, and provide options for data transformation during the migration process. Here are the key steps and features to include in your project:

1. **Setup**: Install Python-DataEngine and any necessary database drivers.
2. **Configuration**: Develop a user-friendly configuration interface where users can input connection details for both the source and target databases.
3. **Table Selection**: Implement functionality that allows users to choose which tables they want to migrate.
4. **Transformation Rules**: Provide options for applying transformation rules to the data being migrated, such as renaming columns, changing data types, or filtering records.
5. **Execution**: Design the migration process to run efficiently, ensuring that data integrity is maintained throughout.
6. **Logging & Reporting**: Include logging capabilities to track the migration process and generate reports on the success and failure of each table migration.
7. **Error Handling**: Ensure robust error handling mechanisms are in place to manage exceptions gracefully and provide meaningful feedback to users.

Utilize Python-DataEngine's core functionalities to manage the data transition process, leveraging its ability to handle different SQL dialects and perform complex data manipulations efficiently.