AI Analysis
The package appears to be safe with low risks across all categories except for metadata, which indicates low maintainer activity and poor metadata quality. However, there is no concrete evidence of malicious behavior.
- Low network, shell, obfuscation, and credential risks.
- Poor metadata quality and low maintainer activity.
Per-check LLM notes
- Network: The observed network pattern suggests legitimate HTTP/HTTPS request handling, likely for sending notifications.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there's no direct evidence of malicious intent.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Brief PyPI description (233 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
5 type-annotated function signatures (partial)
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β contributor count unavailable
Heuristic Checks
Found 1 network call pattern(s)
et=utf-8"} async with httpx.AsyncClient(proxy=AppConfig.proxy) as client: response = awa
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based mini-application that integrates with the 'automas-notification-serverchan' package to send notifications via ServerChan service. This application will serve as a versatile notification tool that users can utilize to receive alerts about various events or conditions from different sources. Hereβs a step-by-step guide on how to develop this application: 1. **Setup**: Begin by installing the necessary packages including 'automas-notification-serverchan'. Ensure your development environment is set up with Python. 2. **Configuration**: Design a configuration file where users can input their ServerChan API token and other settings needed for the application to function. 3. **Core Functionality**: Implement the core functionality of sending notifications through ServerChan using the 'automas-notification-serverchan' package. This should include methods to trigger notifications based on user-defined events. 4. **Event Triggers**: Develop a system within the application to monitor specific events or conditions that would trigger a notification. Examples could include monitoring stock prices, weather updates, or system health checks. 5. **User Interface**: Create a simple command-line interface (CLI) for users to interact with the application. This should allow them to view past notifications, configure event triggers, and manage their API tokens. 6. **Testing & Documentation**: Thoroughly test the application to ensure it functions correctly under various scenarios. Provide comprehensive documentation detailing installation, configuration, and usage instructions. Suggested Features: - Support for multiple API tokens for managing different accounts. - Customizable notification templates allowing users to personalize their messages. - Scheduled notifications for recurring events or reminders. - Logging mechanism to record all sent notifications and any errors encountered during execution.
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