🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is high due to the new repository with unusual commit patterns and a suspicious maintainer profile, raising concerns about potential supply-chain compromise.

High metadata risk due to unusual repository activity
Suspicious maintainer profile

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository is new with unusual commit patterns and the maintainer has a suspicious profile.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 4 day(s) ago (2026-06-02T18:21:45Z)

Repository created very recently: 4 day(s) ago (2026-06-02T18:21:45Z)
Repository has zero stars and zero forks
All 34 commits happened within 24 hours

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentmagnet-mcp

Create a multi-store product comparison app using the 'agentmagnet-mcp' Python package. This app will allow users to input a product name or category and receive real-time comparisons from over 40 online stores across 30+ countries and 52 languages. Additionally, it will offer microtransactions and affiliate commission tracking for each transaction facilitated through the app.

Steps to create the app:
1. Set up a virtual environment and install the 'agentmagnet-mcp' package.
2. Design a simple user interface where users can enter product details or select a product category.
3. Integrate 'agentmagnet-mcp' to fetch product data from various stores based on user input.
4. Display the fetched information in a user-friendly manner, including prices, store names, and product descriptions.
5. Implement a feature that allows users to purchase products directly from the app via microtransactions supported by 'agentmagnet-mcp'.
6. Track affiliate commissions and display them to the user after a purchase is made.
7. Add functionality to propagate agent referrals, ensuring that the app can support a network of agents promoting sales.
8. Ensure the app supports multiple languages as per the 'agentmagnet-mcp' capabilities.

Suggested Features:
- Advanced search filters allowing users to refine their searches based on price range, store preferences, etc.
- A favorites section where users can save products they are interested in for future reference.
- Notifications for price drops on saved items.
- Integration with popular payment gateways for secure transactions.
- User profiles to track purchases, commissions earned, and referral activities.

The 'agentmagnet-mcp' package will be crucial in fetching product data from different sources, handling micropayments, and managing affiliate commissions. It simplifies the process of integrating commerce functionalities into the app, making it easier to focus on enhancing the user experience.