🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

one else None ) req = urllib.request.Request(BASE + path, data=data, method=method, headers=heade
ders) try: resp = urllib.request.urlopen(req) return resp.status, resp.read(), dict(r
return async with httpx.AsyncClient(timeout=10.0) as client: resp = await client.get(f"{
text}]}, } async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.post(f"
ion__}") try: r = httpx.get(f"{server}/health", timeout=5) data = r.json()
["skill"] = skill r = httpx.get(f"{server}/agents/", params=params, timeout=5) except ht

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

out}", url, ] subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL,
rsion}") if tag: subprocess.run(["git", "add", str(pyproject), str(init_file)], cwd=root, ch
cwd=root, check=True) subprocess.run( ["git", "commit", "-m", f"chore: bump version t
check=True, ) subprocess.run(["git", "tag", f"v{new_version}"], cwd=root, check=True)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentgate.sh>

✓ 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 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 agentgatesh

Create a mini-application called 'AgentMarket' that leverages the 'agentgatesh' package to facilitate the deployment of AI agents and enable users to monetize their services through Stripe payments. The application should have a user-friendly interface and allow users to easily onboard their AI agents, set pricing, and manage payment settings. Additionally, it should support various communication protocols such as MCP, A2A, and UCP to ensure compatibility with different systems. Here are the key steps and features for building 'AgentMarket':

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary dependencies including the 'agentgatesh' package.

2. **User Authentication**: Implement a robust authentication system using OAuth or JWT tokens to secure user accounts and ensure only authorized users can manage their AI agents.

3. **AI Agent Onboarding**: Develop a form where users can input details about their AI agents, such as name, description, supported APIs, and deployment requirements. This information will be stored in a database and used to create instances of these agents on the 'agentgatesh' platform.

4. **Pricing Configuration**: Allow users to set prices for their AI services. Users should be able to specify pricing models like per call, subscription-based, or custom pricing tiers.

5. **Payment Integration**: Integrate Stripe to handle payments securely. Ensure that transactions are processed seamlessly and that users receive accurate invoices for services rendered.

6. **Communication Protocols**: Utilize 'agentgatesh' to support multiple communication protocols (MCP, A2A, UCP) so that AI agents can interact with diverse systems efficiently.

7. **Monitoring & Analytics**: Provide real-time monitoring tools to track performance metrics of deployed AI agents. Also, include analytics dashboards that display revenue generated from each agent.

8. **Testing & Deployment**: Thoroughly test the application to identify and fix any bugs before deploying it to production. Use Docker containers to make the deployment process smoother and more reliable.

9. **Documentation**: Write comprehensive documentation detailing how to use 'AgentMarket', including setup instructions, API documentation, and best practices for managing AI agents.

By following these steps and incorporating these features, you'll create a valuable tool for developers looking to monetize their AI projects while ensuring seamless integration and management of AI services.