🤖 AI Analysis

Final verdict: SAFE

The package shows very low risks across all categories checked. It does not engage in network calls, shell executions, or any form of obfuscation or credential harvesting, which are common indicators of malicious activity.

No network calls
No shell execution
No obfuscation patterns
No credential harvesting

Per-check LLM notes

Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity; the package may be designed to work offline or require manual configuration.
Shell: No shell execution detected, reducing risk of direct system compromise or command injection attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package, suggesting it may be new or less active, but no other red flags are present.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://docs.airbyte.com/integrations/sources/gcs
Brief PyPI description (494 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

23 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

14 unique contributor(s) across 100 commits in airbytehq/airbyte
Active community — 5 or more distinct contributors

🔬 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

Email domain looks legitimate: airbyte.io

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository airbytehq/airbyte appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Airbyte" 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 airbyte-source-gcs

Develop a data ingestion tool using the 'airbyte-source-gcs' Python package to streamline the process of transferring data from Google Cloud Storage (GCS) buckets into a local database or another cloud storage solution. Your goal is to create a user-friendly application that allows users to select a specific GCS bucket, specify the file types they wish to transfer, and then initiate the data transfer process. The application should also provide options to filter files based on metadata such as creation date, file size, and content type. Additionally, implement logging and error handling to ensure the reliability and traceability of the data transfer operations. Here are the steps and features you need to include:

1. **Setup and Configuration**: Begin by setting up a virtual environment and installing the 'airbyte-source-gcs' package along with other necessary dependencies such as 'google-cloud-storage', 'pandas', and 'sqlalchemy'. Configure your application to read API keys and other authentication details securely.
2. **User Interface**: Create a simple command-line interface (CLI) or a basic web interface where users can input their GCS bucket name and select the desired file types for transfer. Provide options to filter files based on metadata like creation date, file size, and content type.
3. **Data Ingestion Logic**: Utilize the 'airbyte-source-gcs' package to connect to the specified GCS bucket and retrieve the selected files. Implement logic to handle different file formats (CSV, JSON, etc.) and parse them appropriately before storing them in the destination (e.g., a PostgreSQL database).
4. **Error Handling and Logging**: Ensure that your application logs all actions taken during the data transfer process, including errors encountered and corrective measures taken. Use Python's logging module for this purpose.
5. **Testing and Documentation**: Write unit tests to verify that your application functions correctly under various scenarios. Also, document your code and provide clear instructions on how to install and run the application.
6. **Security Considerations**: Discuss how you will handle sensitive information such as API keys and ensure that they are not hard-coded in your scripts. Suggest best practices for securing your application against unauthorized access.