🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell, obfuscation, and credential risks. However, the missing maintainer's author name and the apparent newness or inactivity of the account raise some suspicion, warranting further investigation.

Missing maintainer's author name
Account appears new or inactive

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author name is missing and the account seems new or inactive, which raises some concerns but does not definitively indicate malicious intent.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository blackwell-systems/agent-lsp appears legitimate

⚠ 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 agent-lsp

Create a code analysis and auto-completion tool using the 'agent-lsp' package. Your application should serve as a Language Server Protocol (LSP) client that integrates with various programming languages supported by 'agent-lsp'. This tool will enable developers to analyze their code in real-time, receive suggestions for improvements, and get auto-completion hints as they type.

Step 1: Set up your development environment. Ensure you have Python installed and create a virtual environment. Install 'agent-lsp' and any other necessary dependencies like a web framework for the user interface (e.g., Flask).

Step 2: Design the user interface. The UI should allow users to input or upload code files. It should also display analysis results, errors, warnings, and suggestions in a clear, user-friendly manner.

Step 3: Implement the backend logic using 'agent-lsp'. Use its capabilities to analyze the uploaded code. Ensure your application supports at least three different programming languages (choose from those supported by 'agent-lsp').

Step 4: Integrate auto-completion functionality. When a user types a part of a function or variable name, your application should suggest possible completions based on the context.

Step 5: Add error detection and correction suggestions. Your application should be able to detect common errors in the code and provide suggestions on how to fix them.

Step 6: Test your application thoroughly. Make sure it works correctly with all supported languages and provides accurate analysis and suggestions.

Features:
- Real-time code analysis and feedback
- Auto-completion suggestions for functions, variables, etc.
- Error detection and correction suggestions
- Support for multiple programming languages
- User-friendly web interface for interacting with the tool

Utilize 'agent-lsp' to handle the core functionalities such as parsing, analyzing, and suggesting improvements for the code. Leverage its built-in tools and support for multiple languages to ensure your application is versatile and powerful.