🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network activity, which may or may not be benign, and lacks basic metadata such as an author name and a GitHub repository link, indicating potential low maintainer effort or malintent.

moderate network risk
lack of author information
absence of GitHub repository link

Per-check LLM notes

Network: The use of network calls could be legitimate if the package is designed to interact with external services, but it requires further investigation to ensure it's not used for unauthorized data transfer.
Shell: No shell execution patterns detected, which is normal and expected unless the package's functionality explicitly involves running system commands.
Metadata: The package shows signs of low maintainer effort and could be suspicious due to the lack of an author name and a GitHub repository link.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

6 type-annotated function signatures (partial)

○ 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 1.5

Found 1 network call pattern(s)

try: with httpx.Client(timeout=3.0) as c: c.post(

✓ 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 agentpulse-astrik

Create a mini-application called 'AgentPulse Dashboard' which leverages the 'agentpulse-astrik' package to monitor and analyze the performance of various agentic AI systems within an organization. This dashboard will provide real-time insights into the health, efficiency, and effectiveness of these AI agents through an intuitive web interface. Here are the key steps and features to implement:

1. **Setup Environment**: Ensure you have Python installed along with Flask or a similar lightweight web framework. Install 'agentpulse-astrik' via pip.
2. **Data Collection**: Use 'agentpulse-astrik' to collect data from different AI agents about their operations, errors, and interactions. This includes metrics such as response times, success rates, and error logs.
3. **Real-Time Monitoring**: Implement real-time monitoring capabilities using WebSocket technology. Display live updates on the dashboard regarding the status of each AI agent.
4. **Performance Analysis**: Utilize 'agentpulse-astrik' to perform deep analysis on collected data. Identify trends, anomalies, and potential issues affecting the performance of AI agents.
5. **User Interface**: Design a user-friendly dashboard with charts, graphs, and tables to visualize the analyzed data. Include options to filter and sort information based on specific criteria.
6. **Alert System**: Integrate an alert system that notifies users when critical thresholds are breached or significant changes occur in the performance metrics of AI agents.
7. **Customization Options**: Allow users to customize alerts and visualization preferences according to their needs.
8. **Documentation**: Provide comprehensive documentation detailing how to set up and use the 'AgentPulse Dashboard', including integration instructions with existing AI systems.

By following these steps and utilizing the 'agentpulse-astrik' package effectively, you'll create a powerful tool for enhancing the management and oversight of agentic AI systems.