🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct malicious activities such as network or shell risks. However, the low maintainer activity and poor metadata quality raise concerns about its trustworthiness.

Low maintainer activity
Poor metadata quality

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 signs of executing system commands from within the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (2683 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

89 type-annotated function signatures detected in source

○ 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: dhsit.co.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aidatapilot

Create a Python-based mini-application called 'DataSanitizer' that leverages the 'aidatapilot' package to streamline the process of validating and cleaning datasets. This application should serve as a user-friendly tool for data scientists and analysts who need to quickly ensure their datasets are clean and ready for analysis.

### Project Overview:
- **Name**: DataSanitizer
- **Purpose**: To automate the process of data validation and cleaning, ensuring datasets are error-free and consistent before further analysis.
- **Target Audience**: Data scientists, analysts, and anyone working with datasets.

### Core Features:
1. **Data Importation** - Allow users to upload CSV files containing datasets.
2. **Automatic Validation** - Use 'aidatapilot' to automatically check the dataset for common errors such as missing values, incorrect data types, inconsistencies, etc.
3. **Interactive Cleaning** - Provide an interface where users can manually correct issues flagged by the validation process, including setting specific rules for data cleaning.
4. **Automated Cleaning** - Implement 'aidatapilot' functionalities to automatically correct common data issues based on predefined rules.
5. **Export Cleaned Data** - Enable users to export the cleaned dataset back into a CSV file.
6. **Customizable Rules** - Allow users to define their own rules for data validation and cleaning.
7. **Progress Tracking** - Display progress and status updates during the validation and cleaning processes.
8. **Documentation & Help** - Include comprehensive documentation and a help section explaining how to use each feature of the application.

### Utilizing 'aidatapilot':
- **Data Validation**: Integrate 'aidatapilot' to perform initial checks on the uploaded dataset, identifying potential issues such as missing values, inconsistent formats, and type mismatches.
- **Cleaning Operations**: Leverage 'aidatapilot' to automatically apply corrections based on detected issues, allowing for customizable rules to be applied by the user.
- **User Interface Integration**: Ensure that 'aidatapilot' integrates seamlessly within the application's UI, providing real-time feedback and options for manual adjustments.

### Deliverables:
- A fully functional Python application named 'DataSanitizer'.
- Comprehensive documentation detailing setup, usage, and customization options.
- Example datasets to demonstrate the application's capabilities.
- User guides and FAQs to assist new users.

This project aims to create a robust yet simple-to-use tool that enhances the efficiency of data preprocessing, making it easier for professionals to focus on analysis rather than data preparation.