AI Analysis
The package shows moderate risk due to network interactions and potential issues with metadata quality and maintenance history.
- Moderate network risk due to communication with external services.
- Low metadata quality and recent change in maintainer.
Per-check LLM notes
- Network: The POST request suggests the package is designed to communicate with an external service, likely for legitimate purposes like API calls or notifications.
- Shell: No shell execution patterns detected, indicating no immediate risk related to unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The presence of non-secure links and a new maintainer with low metadata quality suggests some risk, but there's no clear indication of malicious intent.
Package Quality Overall: Low (3.4/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Documentation URL: "Documentation" -> https://github.com/qy527145/anotiflowDetailed PyPI description (9981 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
109 type-annotated function signatures detected in source
Single-author or unverifiable project
1 unique contributor(s) across 14 commits in qy527145/anotiflowSingle author with few commits — possibly a personal or throwaway project
Heuristic Checks
Found 1 network call pattern(s)
try: resp = requests.post(self._signed_url(), json=payload, timeout=10) da
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: icloud.com>
Found 2 suspicious link(s) on the package page
Non-HTTPS external link: http://127.0.0.1:8765Non-HTTPS external link: http://127.0.0.1:8765/trigger/
Repository qy527145/anotiflow appears legitimate
2 maintainer concern(s) found
Author "wmymz" 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
构建一个名为 'NotificationScheduler' 的小型应用程序,该应用利用了Python包'anotiflow'的核心功能。这个应用旨在帮助用户设置和管理任务调度,并在任务执行前后发送通知。以下是构建此应用的步骤和建议功能列表。 1. **项目概述**:创建一个Web界面,允许用户定义各种任务(如定时发送邮件、运行脚本等),并为每个任务配置预设或自定义的通知方式。 2. **核心功能**: - **任务调度**:使用'anotiflow'中的触发器模块来设定定时任务或基于特定条件的任务。 - **通知机制**:通过'anotiflow'的行为插件模块,支持多种通知渠道(如电子邮件、短信、Slack消息等)。 - **事件总线集成**:利用'anotiflow'的事件总线特性,确保所有任务状态更新和通知发送都能实时同步。 3. **额外功能**: - **远程控制**:开发一个简单的远程SDK,允许管理员从其他服务中调用任务管理和通知操作。 - **Web控制台**:提供一个易于使用的Web界面,展示所有任务的状态和历史记录,以及用户能够直观地添加、编辑或删除任务。 4. **实现步骤**: - 初始化项目环境,安装必要的库,包括'anotiflow'。 - 设计数据库模型以存储任务信息及其相关通知设置。 - 实现任务调度逻辑,确保任务可以按预定的时间或条件触发。 - 集成通知系统,保证任务执行前后能够正确地向用户发送通知。 - 开发Web界面,使其具有良好的用户体验,同时提供API接口以便于未来的扩展和自动化。 5. **测试与部署**:完成上述步骤后,进行全面的功能测试以确保所有部分正常工作。最后,将应用部署到云服务器上,供用户访问。 通过以上步骤,你将能够创建一个强大的任务调度和通知系统,它不仅能够满足基本需求,还提供了高度可定制性和灵活性。
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