AI Analysis
Final verdict: SAFE
The package appears to serve legitimate purposes with low risks associated. While there are some signs of obfuscation and potential issues with metadata quality, these do not conclusively indicate malicious behavior.
- Low shell and credential risks
- Moderate network and obfuscation risks
- Metadata quality concerns
Per-check LLM notes
- Network: The use of aiohttp for network requests is common and suggests legitimate HTTP interactions, but further review of the URLs accessed is recommended.
- Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
- Obfuscation: The use of base64 decoding suggests some level of obfuscation, but it could be legitimate for data handling purposes.
- Credentials: No clear patterns indicating credential harvesting were found.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns but not conclusive evidence of malicious intent.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
try: async with aiohttp.ClientSession(headers=self.headers) as session: async with
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
try: content = base64.b64decode(data["content"]).decode("utf-8", errors="replace")
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
score 3.0
Suspicious email domain flags: Very short email domain: qq.com>
Very short email domain: qq.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
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 ErisPulse-GitHubParser
构建一个名为"GitHub Insights"的小型应用程序,该应用利用Python的'ErisPulse-GitHubParser'包来提供强大的GitHub仓库分析和可视化功能。这个应用将帮助用户快速了解和展示GitHub项目的详细信息,包括但不限于仓库、Issues和Pull Requests。 ### 应用程序功能概述 1. **仓库信息解析**:通过输入GitHub仓库的URL,应用程序能够解析出仓库的基本信息(如名称、描述、创建时间、最后一次更新时间等)以及贡献者列表。 2. **Issues与Pull Requests管理**:展示特定仓库中的所有Issues和Pull Requests,包括它们的状态、作者、创建时间和最后更新时间等。 3. **富文本处理与README预览**:利用'ErisPulse-GitHubParser'的富文本处理能力,为用户提供README文件的预览,同时支持富文本格式到纯文本的转换,以便于在不支持富文本的环境中也能查看内容。 4. **数据可视化**:使用图表形式展示仓库中Issues和Pull Requests的数量趋势、状态分布等。 5. **交互式搜索**:允许用户通过关键字搜索Issues和Pull Requests的内容。 6. **导出功能**:提供导出解析后的仓库信息、Issues和Pull Requests为CSV或JSON格式的功能。 ### 利用'ErisPulse-GitHubParser'的方式 - **初始化连接**:首先,使用'ErisPulse-GitHubParser'初始化对指定GitHub仓库的访问。 - **获取并解析信息**:调用相应的方法从GitHub仓库获取基本信息、Issues和Pull Requests,并使用'ErisPulse-GitHubParser'提供的API进行解析。 - **富文本处理**:对于README文件,利用'ErisPulse-GitHubParser'的富文本处理功能来生成易于阅读的预览版本。 - **数据准备**:将解析后的数据准备好用于后续的数据可视化和交互式搜索操作。 - **集成第三方库**:考虑集成matplotlib或seaborn等库来进行数据可视化。 ### 开发步骤 1. 安装必要的Python库,包括'ErisPulse-GitHubParser'。 2. 编写代码以实现上述功能。 3. 使用Flask或其他Web框架创建一个简单的前端界面。 4. 测试整个应用程序的功能性和用户体验。 5. 发布应用程序至云服务提供商(如Heroku、AWS等),使用户可以在线访问。 通过这一项目,开发者不仅能深入理解如何使用'ErisPulse-GitHubParser'进行GitHub仓库信息的解析和处理,还能掌握基本的Web开发技能,包括前后端分离的思想。