🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is flagged as suspicious due to its lack of maintainer history and a linked Git repository, despite having a relatively low risk profile in terms of network, shell, obfuscation, and credential risks.

No maintainer history
No linked Git repository

Per-check LLM notes

Network: The presence of network calls suggests the package interacts with an external API, which is common for CLI tools that integrate with services.
Shell: No shell execution patterns were detected, indicating no direct command execution risks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some concerning attributes such as no maintainer history and no linked Git repository, which raises suspicion.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://agent-wisdom.gitlab.io/agentry/
Detailed PyPI description (2956 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 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
Type checker (mypy / pyright / pytype) referenced in project
34 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 1.5

Found 1 network call pattern(s)

= self.token return httpx.AsyncClient(base_url=f"{self.base_url}/api/v4", headers=headers) @data

✓ 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 agentry-cli

Create a Python-based mini-application named 'AgentCraft' that leverages the 'agentry-cli' package to manage and customize AI agents for various tasks. This application should allow users to create, modify, and execute different types of AI agents using predefined templates and custom configurations. Here’s a detailed breakdown of what your application should include:

1. **User Interface**: Develop a simple yet intuitive command-line interface (CLI) for interacting with the application. Users should be able to navigate through menus and options without needing extensive technical knowledge.
2. **Agent Templates**: Preload the application with several agent templates, each designed for specific tasks such as customer service, data analysis, or content creation. Each template should come with default settings and instructions tailored to its purpose.
3. **Customization Options**: Allow users to tweak the settings of any loaded agent template. This includes modifying the agent’s philosophy, skill set, and input/output behavior based on user preferences or specific requirements.
4. **Execution Environment**: Implement functionality that enables users to run their customized agents directly from within the application. Ensure that the execution environment supports real-time interaction and feedback loops.
5. **Integration Capabilities**: Integrate 'agentry-cli' to facilitate the saving and loading of agent configurations. Users should be able to save their modifications to disk and load them later, allowing for persistent customization.
6. **Documentation and Help**: Provide comprehensive documentation and help sections accessible via the CLI. Include examples and explanations of how to use the application effectively, especially for new users unfamiliar with AI agents.
7. **Security Measures**: Incorporate basic security measures to protect user data and configurations stored within the application. For instance, encrypt sensitive information and ensure secure file handling practices.

To utilize the 'agentry-cli' package, you will need to integrate its functionalities into your application's backend logic. Specifically, leverage 'agentry-cli' to handle the loading, saving, and managing of agent configurations and templates. Ensure that your application seamlessly integrates these operations into the user workflow, making it easy for users to interact with AI agents using the provided CLI.