🤖 AI Analysis

Final verdict: SUSPICIOUS

The package Sloe v1.2.0 exhibits low risks in terms of network calls, shell executions, obfuscation, and credential harvesting. However, its low maintenance and poor metadata quality raise concerns about potential supply-chain risks.

Low metadata quality
Low maintenance

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
Metadata: The package shows low maintenance and metadata quality, raising some suspicion but not enough to conclusively identify it as malicious.

🔬 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

No author email provided

✓ 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 Sloe

Create a financial analysis tool called 'BillAnalyzer' using the Python package 'Sloe'. This tool should enable users to upload their billing statements (in PDF format) and receive a detailed analysis of their expenses. The application will leverage 'Sloe' to extract key information from the uploaded PDFs, such as dates, amounts, and descriptions of transactions. Here’s a detailed breakdown of the project steps and features:

1. **Project Setup**: Set up your development environment with Python and install necessary packages including 'Sloe', 'Pandas' for data manipulation, and 'Flask' for building the web interface.
2. **PDF Extraction**: Use 'Sloe' to read and parse the PDF files. Implement functionality to handle various formats and layouts of billing statements.
3. **Data Cleaning**: Clean the extracted data to ensure accuracy. This includes removing unnecessary characters, correcting misread digits, and standardizing date formats.
4. **Data Analysis**: Apply statistical methods to analyze the cleaned data. Identify trends over time, categorize expenses (e.g., groceries, utilities), and calculate total spending per category.
5. **Visualization**: Create visual representations of the analyzed data using libraries like 'Matplotlib' or 'Seaborn'. Display charts and graphs on the web interface.
6. **User Interface**: Build a user-friendly web interface where users can upload their bills, view the analysis results, and download reports.
7. **Security Considerations**: Ensure all user data is handled securely. Implement encryption for data at rest and in transit, and provide clear guidelines on data privacy.
8. **Testing & Deployment**: Thoroughly test the application for bugs and performance issues. Deploy it on a cloud platform like AWS or Heroku.

Features to include:
- Support for multiple languages for international users.
- Integration with popular email services for automatic bill fetching.
- Alerts for unusual spending patterns.
- Historical data comparison for tracking long-term financial behavior.

The goal is to create a comprehensive financial management tool that leverages 'Sloe' for robust PDF parsing capabilities, making it easier for individuals to manage and understand their expenses.