AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of direct threats like network calls, shell execution, and obfuscation, but the metadata raises concerns with missing author information and an inactive maintainer, suggesting potential issues with accountability and maintenance.
- Absence of author name
- Inactive maintainer account
Per-check LLM notes
- Network: No network calls detected, which is normal for most utility packages.
- Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has some red flags such as an absent author name and a new/inactive maintainer account, but no clear signs of typosquatting or malicious intent.
Package Quality Overall: Low (4.6/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
2 test file(s) detected (e.g. __init__.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1862 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: TypedType checker (mypy / pyright / pytype) referenced in project7 type-annotated function signatures (partial)
○ 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
Email domain looks legitimate: ringlet.net>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 as-dict-rec
Create a utility application named 'ComplexStructSimplifier' using Python that simplifies complex data structures into dictionaries of primitive types for easier manipulation and analysis. This utility will take any Python object (e.g., lists, dictionaries, sets, tuples, custom objects) and convert it into a dictionary containing only basic types such as integers, floats, strings, booleans, and NoneType. The utility should handle nested structures gracefully, converting each level down to the simplest form possible.
### Core Functionality:
- **Input Handling**: Accept various inputs like lists, dictionaries, sets, tuples, and custom objects.
- **Conversion Logic**: Use the 'as-dict-rec' package to recursively convert complex structures into dictionaries with only primitive types.
- **Output Display**: Print the simplified dictionary in a readable format.
- **Error Handling**: Provide meaningful error messages if the input cannot be processed (e.g., unsupported types).
### Additional Features:
- **Command Line Interface (CLI)**: Allow users to run the utility from the command line, passing the object they want to simplify as an argument.
- **File Input/Output**: Support reading input from a file and writing the simplified output back to a file.
- **GUI Interface**: Develop a simple graphical user interface (using Tkinter or similar) where users can paste complex structures and see the simplified version.
- **Customizable Output**: Include options to customize how the output is displayed (e.g., pretty-printing).
- **Logging**: Implement logging to track when and how the utility was used, which could be useful for debugging and improving the tool over time.
### Steps to Build:
1. Install the necessary packages ('as-dict-rec', tkinter for GUI, argparse for CLI).
2. Define the main conversion function utilizing 'as-dict-rec' for the recursive conversion.
3. Create a CLI using argparse for command-line interaction.
4. Add functionality for file I/O within the CLI.
5. Develop a simple GUI using Tkinter that allows users to input complex structures and view the simplified result.
6. Implement logging for tracking usage and errors.
7. Test the utility thoroughly with various types of inputs to ensure robustness.
8. Document the code and provide instructions on how to use the utility.
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