🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no evidence of malicious activity. The network calls appear legitimate for authentication purposes, and there are no signs of shell execution, obfuscation, or credential harvesting.

Low network risk
No shell execution detected
No obfuscation detected
No credential risk

Per-check LLM notes

Network: The network call is likely for authentication purposes and seems to be part of the package's intended functionality.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author 'Airbyte' has only one package on PyPI, which could indicate a new or less active account.

📦 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/box-files-text
Brief PyPI description (474 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

21 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 score 1.5

Found 1 network call pattern(s)

e: raise resp = requests.get(url, headers={"Authorization": f"Bearer {access_token}"})

✓ 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-box-data-extract

Create a mini-application called 'BoxTextExtractor' that leverages the 'airbyte-source-box-data-extract' Python package to extract text content from files stored in Box. This application should serve as a tool for users who need to quickly gather textual data from various file types within their Box accounts for analysis, indexing, or other purposes. Here are the steps and features you should include in your project:

1. **Setup**: Begin by installing the necessary packages including 'airbyte-source-box-data-extract'. Ensure that the user has the appropriate Box API credentials to authenticate and access their Box account.
2. **Authentication**: Implement a secure method for users to input their Box API keys. Use these keys to authenticate with the Box API and gain access to the user's Box account.
3. **File Selection**: Provide functionality for users to select specific folders or files from which they want to extract text. Users should be able to browse their Box directories and choose files based on criteria such as file type, date modified, etc.
4. **Text Extraction**: Utilize the 'airbyte-source-box-data-extract' package to extract text from selected files. The application should support a variety of file formats commonly found in Box, including PDFs, Word documents, Excel spreadsheets, and more.
5. **Output Options**: Allow users to choose how they want the extracted text to be outputted. Options could include saving the text to a local file, sending it to a cloud storage service like Google Drive, or integrating it into another application via APIs.
6. **Advanced Features**: Consider adding advanced features such as automatic language detection, sentiment analysis on the extracted text, or the ability to filter out certain types of text (e.g., headers, footers).
7. **User Interface**: Develop a simple yet effective user interface using a framework like Tkinter for desktop applications or Flask for web-based applications. The UI should guide users through the process of authenticating, selecting files, and choosing output options.
8. **Documentation and Help**: Include comprehensive documentation and help resources to assist users in understanding how to use the application effectively and troubleshoot any issues they might encounter.

This project aims to streamline the process of extracting text from Box files, making it easier for individuals and teams to work with digital content stored in Box.