🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, and obfuscation activities but shows potential signs of low maintenance efforts and lacks detailed maintainer history, raising suspicion.

Low metadata quality
Lack of maintainer history

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell executions detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and could be potentially suspicious due to the lack of maintainer history and a git repository.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 acplint

Create a Python-based command-line tool that leverages the 'acplint' package to validate AI agents according to the Agent Client Protocol (ACP) specifications. This tool will serve as a quality assurance mechanism for developers working on AI projects that involve ACP-compliant agents. Your task is to develop a robust application that not only validates agents but also provides insightful feedback to improve their compliance with the ACP standards. Here’s a step-by-step guide on how to approach this project:

1. **Project Setup**: Initialize a new Python project and install the necessary packages, including 'acplint'. Ensure you have the latest version of 'acplint' to take advantage of all its features.
2. **Command-Line Interface (CLI)**: Develop a CLI that allows users to input the path to their AI agent code. The tool should support both single-file and multi-file directory validation.
3. **Validation Logic**: Integrate 'acplint' into your application to perform the actual validation process. Make sure to handle different types of errors gracefully, providing clear error messages that help users understand what needs to be corrected in their agent code.
4. **Feedback Mechanism**: Implement a feature that generates detailed reports after the validation process. These reports should include suggestions for improvement and highlight areas where the agent fails to meet ACP specifications.
5. **Customization Options**: Allow users to customize certain aspects of the validation process, such as setting a specific ACP version to validate against or ignoring certain rules if they are deemed unnecessary for the user's context.
6. **Testing and Documentation**: Write comprehensive tests to ensure your application works as expected under various scenarios. Additionally, create detailed documentation that explains how to use the tool, including examples and best practices.
7. **Deployment**: Prepare your application for deployment. Consider packaging it as a standalone executable or a Docker container to make it easily accessible to other developers.

By following these steps, you'll create a valuable tool that helps ensure AI agents adhere to industry standards, making them more reliable and interoperable within ACP ecosystems.