AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network, shell execution, obfuscation, or credential harvesting activities observed. The metadata suggests a potentially new maintainer, but there are no other red flags.
- No network calls detected
- Single package by maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- 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 red flags are present.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3881 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
129 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 "Chris Larabee" 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 autodrive
Create a Python-based mini-application called 'AutoSheetSync' that leverages the 'autodrive' package to manage data synchronization between Google Sheets and a local SQLite database. This application will allow users to maintain a local copy of their Google Sheets data, facilitating offline access and enhanced local data manipulation capabilities. Here’s a detailed outline of the project requirements: 1. **User Authentication**: Implement OAuth2 authentication using 'autodrive' to securely connect to Google Drive and Google Sheets APIs. Ensure that users can authorize the application to access their Google accounts. 2. **Data Synchronization**: Develop functionality that automatically syncs data between a specified Google Sheet and a local SQLite database. Users should be able to specify which sheet they want to sync and define the sync frequency (e.g., every hour, daily). 3. **Data Manipulation**: Allow users to perform CRUD (Create, Read, Update, Delete) operations on the local SQLite database. These changes should be reflected back into the Google Sheet during the next sync operation. 4. **Conflict Resolution**: Implement a conflict resolution mechanism to handle scenarios where both the Google Sheet and the local SQLite database have been modified. For example, if a row is updated in both places, the application could choose the most recent update based on timestamps. 5. **User Interface**: Design a simple command-line interface (CLI) for users to interact with 'AutoSheetSync'. Commands should include options for initiating syncs manually, viewing sync status, and managing user settings. 6. **Error Handling and Logging**: Ensure the application logs errors and provides meaningful feedback to users when issues occur, such as network connectivity problems or API rate limits. 7. **Security Considerations**: Pay attention to security best practices, especially around handling OAuth tokens and sensitive data stored in the SQLite database. To utilize 'autodrive', you'll need to install it via pip and then use its methods to authenticate with Google APIs, read/write data from/to Google Sheets, and manage files/folders in Google Drive. The goal is to create a versatile tool that simplifies working with Google Sheets data while providing robust offline capabilities.
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