AI Analysis
Final verdict: SUSPICIOUS
The PipeGraphPy package has a low risk score due to the absence of network calls, shell executions, obfuscations, and credential risks. However, the metadata risk raises suspicion, warranting further investigation.
- Metadata risk identified
- Lack of package description
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The package shows some red flags but lacks clear evidence of malicious intent.
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
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "liujm" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with PipeGraphPy
Develop a data processing pipeline mini-application using the 'PipeGraphPy' library. This application will serve as a robust tool for handling complex data transformations and analyses, leveraging the core algorithmic framework provided by 'PipeGraphPy'. Your goal is to create a versatile pipeline that can ingest raw data from various sources, apply a series of customizable transformations, and output the processed data into a desired format. Here are the steps and features to include in your project: 1. **Data Ingestion**: Design the pipeline to accept input data from multiple sources such as CSV files, JSON files, or even real-time streaming data. Utilize 'PipeGraphPy' to define nodes in the graph that represent different stages of data ingestion. 2. **Transformation Nodes**: Implement several transformation nodes within the pipeline. These could include data cleaning (removing null values), normalization, feature extraction, or any other relevant transformations based on the nature of the data. Each node should be modular and reusable across different pipelines. 3. **Customizability**: Allow users to add, remove, or modify nodes in the pipeline through a simple configuration file or command-line interface. This flexibility ensures that the pipeline can adapt to different use cases without requiring extensive code changes. 4. **Output Configuration**: Provide options for configuring the output of the pipeline. Users should be able to specify whether they want the processed data exported as a CSV, JSON, or even uploaded to a database. Use 'PipeGraphPy' to define the final nodes in the graph that handle these outputs. 5. **Visualization**: Include a feature that visualizes the current state of the pipeline graph. This helps users understand the flow of data and identify potential bottlenecks or areas for optimization. 6. **Performance Metrics**: Integrate performance metrics tracking within the pipeline. This could include measuring the time taken for each node to process data, the amount of data processed, and any errors encountered during execution. Use 'PipeGraphPy' to monitor these metrics throughout the pipeline. 7. **Documentation and Examples**: Provide comprehensive documentation explaining how to set up and use the pipeline, along with example configurations and scenarios to demonstrate its capabilities. By following these guidelines, you'll create a powerful yet flexible data processing tool that showcases the capabilities of 'PipeGraphPy'.