🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential handling. However, its metadata quality and maintainer activity levels raise concerns, suggesting potential issues that warrant further scrutiny.

Metadata risk at 5/10 due to low maintainer activity and poor metadata quality
Normal network calls but further investigation recommended

Per-check LLM notes

Network: The observed network call patterns are likely normal for a client that makes HTTP requests, but further investigation into the URL and request types is recommended.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_client.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (10796 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

33 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 score 6.0

Found 4 network call pattern(s)

rip("/") self._http = httpx.AsyncClient(base_url=self._base_url, timeout=timeout) async def acl
-> None: self._http = httpx.AsyncClient( base_url=base_url.rstrip("/"), time
rip("/") self._http = httpx.Client(base_url=self._base_url, timeout=timeout) def close(sel
-> None: self._http = httpx.Client( base_url=base_url.rstrip("/"), time

✓ 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 agentshield-pythonv2

Create a fully functional mini-app called 'BudgetBuddy' that helps users manage their AI agent spending through the use of the 'agentshield-pythonv2' package. This app will serve as a personal finance tool tailored specifically for managing costs associated with running AI agents, such as chatbots, data processing services, etc. Here’s a detailed breakdown of what the application should do and how it will utilize the 'agentshield-pythonv2' package:

1. **User Authentication**: Implement a simple user authentication system where users can sign up and log in to their accounts. This ensures that each user's spending data is kept private.

2. **AI Agent Spend Tracking**: Integrate the 'agentshield-pythonv2' package to track the cost of running various AI agents. Users should be able to add new AI agents, specify their estimated hourly cost, and monitor the cumulative spend over time.

3. **Budget Setting & Alerts**: Allow users to set monthly budgets for their AI agent spending. When the budget is nearing depletion or has been exceeded, send out alerts via email or SMS.

4. **Spend Analysis**: Provide detailed reports on AI agent spending, including total monthly spend, average daily spend, and a breakdown by individual agents. Users should also be able to filter these reports based on date ranges.

5. **Cost Optimization Tips**: Based on the usage patterns of the AI agents, suggest ways to optimize spending. For example, if an AI agent is idle during off-peak hours, recommend scheduling its operation during peak times only.

6. **Integration with Payment Gateways**: Optionally, integrate the app with popular payment gateways (such as Stripe) so users can automatically top up their accounts when they run low on funds.

7. **Dashboard Interface**: Develop a clean, user-friendly dashboard where all the above functionalities can be accessed easily. Ensure that the UI/UX design is intuitive and accessible.

8. **Security Measures**: Implement necessary security measures to protect user data, especially financial information. Use HTTPS for secure data transmission and ensure that sensitive data is encrypted both at rest and in transit.

By following these steps and utilizing the 'agentshield-pythonv2' package effectively, you will create a powerful tool for anyone looking to manage and optimize their AI agent spending efficiently.