🤖 AI Analysis

Final verdict: SAFE

The LLMBillingKit package exhibits minimal risks across various categories, with no indications of malicious behavior. However, its low maintenance status and metadata quality suggest potential issues that could arise from lack of updates or support.

Low maintenance and metadata quality
No network calls, shell execution, obfuscation, or credential harvesting detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API interactions.
Shell: No shell execution detected, indicating the package 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 sensitive information.
Metadata: The package shows low maintenance and metadata quality, but there are no clear signs 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

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 LLMBillingKit

Create a comprehensive financial tracking tool called 'LLMExpenseTracker' that leverages the Python package 'LLMBillingKit'. This tool aims to help developers and businesses monitor their expenses related to using Large Language Model (LLM) APIs, such as those from Anthropic or Azure Cognitive Services. The application should allow users to input details about their LLM API calls, including the model used, the number of tokens processed, and any other relevant metrics. Using LLMBillingKit, the app will automatically calculate the cost based on the provider's pricing model and track the net margin after applying any discounts or credits.

Key Features:
- User Authentication: Allow users to sign up and log in securely.
- API Call Logging: Enable users to log details of each API call, including timestamp, model used, token count, and any custom notes.
- Cost Calculation: Utilize LLMBillingKit to compute costs based on the specific pricing model of the LLM service provider.
- Discount Management: Provide a feature for users to manage any discounts or credits they receive, which will adjust the calculated costs accordingly.
- Reporting: Offer detailed reports and visualizations of total spending, cost trends over time, and net margins.
- Integration: Allow seamless integration with popular cloud services and APIs for automatic data collection.

Step-by-Step Guide:
1. Set up the basic structure of the application, including user authentication and database setup.
2. Integrate LLMBillingKit into your project to handle the cost calculation logic.
3. Develop a form-based interface for logging API calls, ensuring all necessary fields are included.
4. Implement the cost calculation feature, using LLMBillingKit to accurately reflect the cost based on the logged data.
5. Add functionality for managing discounts and credits, allowing users to apply these adjustments to their cost calculations.
6. Create a reporting dashboard that displays key financial metrics, leveraging visual tools like charts and graphs.
7. Test the application thoroughly, focusing on both functionality and performance.
8. Deploy the application to a production environment, ensuring it is secure and scalable.