🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits medium risk due to network and shell execution risks, despite showing no signs of obfuscation or credential harvesting. The metadata suggests low maintainer activity, raising concerns about its legitimacy.

Moderate network risk due to communication with unknown URLs.
High shell risk from executing external commands.
Low maintainer effort indicated by package metadata.

Per-check LLM notes

Network: The network calls to unknown URLs could indicate external communication which might be unexpected and potentially risky.
Shell: Executing shell commands like fetching remote URLs from Git can pose risks if not properly controlled, potentially leading to unintended actions on the user's system.
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 effort and new/inactive account, which could indicate potential risk.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1536 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

28 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)

try: sessions_req = urllib.request.Request( url=urllib.parse.urljoin(relay_url + "
ST", ) with urllib.request.urlopen(sessions_req) as res: session_data = js
try: pair_req = urllib.request.Request( url=urllib.parse.urljoin(relay_url + "
ST", ) with urllib.request.urlopen(pair_req) as res: pair_data = json.load

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

one try: proc = subprocess.run( ["git", "remote", "get-url", "origin"],
d") try: proc = subprocess.run( argv, cwd=cwd, captu
20) try: proc = subprocess.run( command, cwd=cwd, sh
try: proc = subprocess.run( ["git", "remote", "get-url", "origin"],
cwd=cwd, shell=True, capture_output=True, text=True,

✓ 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 agentrix-cli

Develop a command-line tool named 'CodeAssistant' using the 'agentrix-cli' Python package. This tool aims to streamline the process of managing coding tasks such as linting, testing, and code generation. Here’s a detailed step-by-step guide on how to implement this project:

1. **Project Setup**: Start by setting up your Python environment. Install 'agentrix-cli' and any other necessary dependencies. Ensure your development environment is ready for both local and remote execution.

2. **Command Line Interface (CLI) Design**: Design a user-friendly CLI that allows users to interact with 'CodeAssistant'. Implement commands such as 'lint', 'test', and 'generate' which will delegate tasks to agents managed by 'agentrix-cli'.

3. **Linting Functionality**: Integrate linting capabilities into 'CodeAssistant'. When the 'lint' command is issued, 'CodeAssistant' should use 'agentrix-cli' to run a linting agent headlessly. The results should then be displayed in the terminal, indicating any issues found in the codebase.

4. **Testing Functionality**: Add support for automated testing. When the 'test' command is invoked, 'CodeAssistant' should launch a testing agent through 'agentrix-cli'. It should execute the tests and output the results directly in the CLI interface.

5. **Code Generation**: Implement a feature where 'CodeAssistant' can generate boilerplate code based on user input. For instance, it could create basic classes, functions, or files according to specified parameters. Use 'agentrix-cli' to run code generation agents that handle this task.

6. **Remote Execution Support**: One of the key features of 'agentrix-cli' is its ability to run agents remotely. Your application should allow users to specify a remote server where 'CodeAssistant' can execute tasks. This could be useful for distributed development environments.

7. **Logging and Reporting**: Incorporate logging functionality that captures the output from each task executed by 'CodeAssistant'. Provide options for users to save these logs locally or send them to a remote server for analysis.

8. **User Interface Customization**: Allow users to customize the CLI appearance and behavior. They should be able to set preferences such as default linting tools, testing frameworks, and code generation templates.

9. **Documentation and Help**: Finally, ensure your application comes with comprehensive documentation and inline help commands. Users should be able to get started quickly and find answers to common questions without leaving the CLI.

By following these steps, you will have developed a powerful and versatile tool that leverages the capabilities of 'agentrix-cli' to enhance coding efficiency and quality.