🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, and obfuscation but shows signs of potential low maintenance and supply-chain manipulation.

Low metadata health suggesting low maintenance effort
Potential risk of supply-chain attack due to low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its intended functionality.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintenance and could potentially be a low-effort attempt at supply-chain manipulation.

🔬 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 4.0

2 maintainer concern(s) found

Author "Agent Control Team" 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 agent-control-evaluator-budget

Create a fully-functional mini-application called 'BudgetBot' using Python that leverages the 'agent-control-evaluator-budget' package to manage and optimize costs associated with running large language models (LLMs). This application should serve as a personal finance tool for developers who frequently use LLMs and need to keep track of their spending on these services. Here’s what your application should do:

1. **User Registration & Authentication**: Implement a simple registration and login system where users can create accounts to save and manage their budget information securely.
2. **Budget Setting**: Allow users to set monthly budgets for their LLM usage, including options to specify different budgets for different types of tasks (e.g., chat, document generation, etc.).
3. **Cost Tracking**: Integrate the 'agent-control-evaluator-budget' package to automatically track the cumulative cost and token usage for each user's LLM interactions. This should include real-time updates and historical data.
4. **Notifications & Alerts**: Notify users via email or SMS when they are approaching their budget limits or have exceeded them.
5. **Analytics Dashboard**: Provide a dashboard where users can view their spending trends over time, compare actual spending against their set budgets, and receive recommendations on how to reduce costs.
6. **API Integration**: Offer an API that other applications can use to integrate BudgetBot’s budget management capabilities into their workflows.
7. **Security Measures**: Ensure all user data is encrypted both at rest and in transit. Use secure authentication methods like OAuth or JWT tokens for user sessions.
8. **Multi-Language Support**: While initially focusing on English, consider adding support for other languages to cater to a global audience.

Utilize the 'agent-control-evaluator-budget' package to handle the backend logic for cost and token tracking, ensuring that your application can scale efficiently even as more users join. This package will be crucial in providing accurate and up-to-date cost estimates, which can then be displayed in the analytics dashboard and used to trigger notifications. Your goal is to create a tool that not only helps developers stay within their financial limits but also encourages more efficient use of LLMs through informed decision-making.