AI Analysis
The package has moderate risks due to its network activities and low maintainer activity, raising concerns about its legitimacy and security.
- network risk due to external HTTP requests
- low maintainer activity and poor metadata quality
Per-check LLM notes
- Network: The network call patterns suggest the package might be making HTTP requests to an external endpoint, which could be for legitimate purposes like API calls but also indicates potential for data exfiltration.
- Shell: No shell execution patterns were detected in the provided code snippet.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns but not conclusive 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
Detailed PyPI description (5379 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
11 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Found 2 network call pattern(s)
y}", } req = urllib.request.Request( self.endpoint, data=json.dutime.time() with urllib.request.urlopen(req) as response: result = json.load
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
构建一个名为 'ArkCommander' 的小型应用程序,该程序利用 'ark-utils-cli' 包来增强与火山引擎的交互体验。该应用程序应提供一个用户友好的界面,允许用户通过命令行执行一系列任务,包括但不限于资源管理、项目创建以及配置更新等。此外,它还应该支持脚本化工作流,使用户能够自动化其日常任务。 ### 功能要求 1. **资源管理**:用户应当能够查看、创建、删除及更新火山引擎中的资源(例如云服务器、数据库实例等)。 2. **项目创建**:简化项目创建流程,提供预设模板或自定义选项,快速启动新项目。 3. **配置更新**:允许用户直接在命令行中更新项目的配置信息,无需手动修改文件。 4. **脚本支持**:支持编写脚本以执行复杂的操作序列,提高效率。 5. **日志记录**:所有操作应有详细的日志记录功能,方便追踪和调试。 ### 使用 'ark-utils-cli' 包的方式 - 利用 'ark-utils-cli' 提供的命令行接口,实现对火山引擎服务的操作。 - 对于每一个功能模块,如资源管理、项目创建等,都需调用相应的 'ark-utils-cli' 命令或API。 - 在开发过程中,确保遵循 'ark-utils-cli' 的文档规范,以保证兼容性和稳定性。 ### 开发步骤 1. 安装必要的依赖,包括 'ark-utils-cli' 包。 2. 设计应用的基本架构,包括命令行解析器、主控制台以及各功能模块。 3. 实现基础功能,如资源管理和项目创建。 4. 进一步开发高级特性,如配置更新和脚本支持。 5. 测试所有功能,确保正确性和用户体验。 6. 编写详细的文档,指导用户如何使用 'ArkCommander' 应用程序。 此项目旨在展示 'ark-utils-cli' 包的强大功能,并鼓励开发者探索更多可能的应用场景。
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