AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, or obfuscation techniques observed. However, the metadata risk suggests a potential new maintainer, which slightly increases suspicion.
- No network calls
- No shell execution
- No obfuscation
- Single package by maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, suggesting a new or less active account which may warrant further investigation.
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
Email domain looks legitimate: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository thombashi/DataProperty appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Tsuyoshi Hombashi" 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 DataProperty
Create a mini-application called 'PropertyInspector' using the Python package 'DataProperty'. This application will serve as a tool for developers and data analysts to quickly inspect and understand the properties of various datasets. Here’s how you would build it step-by-step: 1. **Setup**: Begin by setting up your Python environment. Ensure you have Python installed and then install the 'DataProperty' package via pip. 2. **Core Functionality**: Use 'DataProperty' to extract key properties from different types of data inputs such as CSV files, JSON objects, and simple lists or dictionaries. These properties might include but are not limited to: data types, missing values, unique values, and statistical summaries. 3. **User Interface**: Develop a simple command-line interface (CLI) where users can input their dataset and choose which properties they want to inspect. The CLI should support both file-based inputs (e.g., uploading a CSV file) and direct data entry. 4. **Feature Enhancements**: - **Visualization**: Integrate basic visualization capabilities to show distributions of data visually. - **Interactive Mode**: Implement an interactive mode where users can query additional properties dynamically after initial inspection. - **Report Generation**: Allow users to generate a report summarizing the inspected properties in a PDF format. 5. **Testing & Validation**: Write unit tests to ensure each functionality works as expected. Validate the app with different types of datasets to ensure robustness. 6. **Documentation**: Provide comprehensive documentation on how to use 'PropertyInspector', including setup instructions, usage examples, and explanations of all functionalities. Throughout the development process, leverage 'DataProperty' for its core functionalities to streamline the extraction and presentation of data properties, making the tool both powerful and user-friendly.