🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with low risks across multiple categories. However, the metadata risk score is slightly elevated due to the maintainer's limited package history and the unavailability of the repository.

Network risk is acceptable given the nature of the package
No evidence of malicious activities such as shell execution, obfuscation, or credential harvesting

Per-check LLM notes

Network: Network calls indicate the package performs HTTP requests which may be part of its intended functionality.
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 repository is not found and the maintainer has a single package, indicating potential lack of community support or new account.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 32 test file(s) found

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

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7079 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

563 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

evice flow async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.pos
erval = 2 async with httpx.AsyncClient(timeout=30.0) as client: for _ in range(max_atte
try: async with httpx.AsyncClient(timeout=timeout) as client: await client.pos
try: async with httpx.AsyncClient(timeout=timeout) as client: response = await
) async with httpx.AsyncClient(timeout=self.timeout) as client: if method == "G
) async with httpx.AsyncClient(timeout=self.timeout) as client: resp = await cl

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Applied Labs" 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 applied-cli

Create a command-line tool named 'AI-Agent' using the 'applied-cli' Python package. This tool will serve as a bridge between support agents and Applied Labs' AI systems, enabling them to manage tickets, monitor system health, and perform diagnostics efficiently from the command line. Here are the key functionalities you need to implement:

1. **Ticket Management**: Agents should be able to create, view, update, and close support tickets directly from the CLI.
2. **System Monitoring**: Provide real-time monitoring of the AI system’s health status, including CPU usage, memory consumption, and network latency.
3. **Diagnostic Tools**: Implement diagnostic commands that help in identifying issues within the AI infrastructure, such as checking service availability, database connection status, and log file analysis.
4. **User Authentication**: Ensure secure access control with user authentication mechanisms supported by 'applied-cli'.
5. **Configuration Management**: Allow users to configure settings like API keys, server endpoints, and notification preferences through the CLI.
6. **Help and Documentation**: Integrate comprehensive help and documentation accessible via the CLI, guiding users on how to use each command effectively.

For each feature, utilize the 'applied-cli' package to interact with the Applied Labs AI systems. Your task is to design and implement this tool, ensuring it is user-friendly, efficient, and integrates seamlessly with the existing AI ecosystem provided by 'applied-cli'.