AI Analysis
Final verdict: SUSPICIOUS
The package exhibits minimal individual risks, but the lack of metadata and author information raises concerns about its origin and purpose.
- Low metadata quality
- Lack of author information
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating no direct system command risks.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activities aimed at stealing secrets or credentials.
- Metadata: The package shows low maintenance effort and lacks proper author information, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
44 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 6.0
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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with backend-toolbag
构建一个名为 'FastAPI Weather Station' 的小型应用，该应用将使用Python的 'backend-toolbag' 包来快速搭建后端服务。此项目旨在展示如何利用 'backend-toolbag' 包的核心功能来创建一个实用且高效的天气数据查询接口。 项目目标： 1. 创建一个简单的RESTful API，允许用户通过HTTP请求获取当前天气信息。 2. 实现对多个城市的天气数据支持，包括但不限于温度、湿度和风速等基本信息。 3. 利用 'backend-toolbag' 提供的快速脚手架功能简化后端服务的开发流程。 核心功能与实现步骤： 1. **环境搭建**：首先安装必要的依赖包，包括 'backend-toolbag' 和用于获取实时天气数据的第三方库（如 'requests' 或 'aiohttp'）。 2. **定义模型**：使用 'backend-toolbag' 中提供的工具定义表示天气信息的数据模型。 3. **创建API端点**：基于定义的数据模型，使用 'backend-toolbag' 搭建一个或多个API端点，以接收GET请求并返回指定城市当前的天气状况。 4. **集成外部API**：整合一个公共的天气API（例如OpenWeatherMap），通过调用其接口获取最新的天气数据，并将其转换为应用内部的数据格式。 5. **测试与部署**：完成API开发后，进行单元测试确保所有功能正常工作；然后选择合适的云服务平台（如阿里云）进行部署。 额外建议功能： - 添加文档自动生成功能，方便开发者和终端用户了解如何正确使用API。 - 引入日志记录机制，便于追踪错误和调试。 - 考虑加入缓存策略，减少对外部API的频繁调用，提高响应速度。 通过以上步骤，我们不仅能够充分利用 'backend-toolbag' 提供的便捷功能来加速开发过程，还能够学习到如何高效地设计和维护RESTful Web服务。
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