ai_infrastructure_artifacts_repo

v0.5.0 suspicious
4.0
Medium Risk

Manager for AI infrastructure artifacts (e.g., skills, prompts) repositories

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, obfuscation, and credential handling. However, the detection of potential shell execution and the maintainer's lack of experience with GitHub repositories raise concerns.

  • Detection of potential shell execution
  • Maintainer has only one package and no associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Detection of shell execution suggests potential for executing arbitrary commands, which could be risky if not properly sanitized.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The maintainer has only one package and no associated GitHub repository, which may indicate a less experienced or potentially suspicious actor.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 31 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 31 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://ai-infrastructure-artifacts-repo.readthedocs.io
  • Detailed PyPI description (1379 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 208 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ) result = subprocess.run( cmd, env=env,
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Jesús Alonso Abad" 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 ai_infrastructure_artifacts_repo 
Create a mini-application named 'AI Prompt Organizer' that leverages the Python package 'ai_infrastructure_artifacts_repo' to manage and organize AI prompts effectively. This application will serve as a repository manager for AI-related artifacts such as prompts, skills, and other necessary components. Here are the key steps and features to implement:

1. **Setup Project Environment**: Begin by setting up a virtual environment for your Python project and installing the 'ai_infrastructure_artifacts_repo' package. Ensure you have all necessary dependencies installed.

2. **Define Repository Structure**: Use the 'ai_infrastructure_artifacts_repo' package to define a structured repository for storing AI prompts. Each prompt should be categorized under different folders based on their use-case (e.g., customer service, content generation).

3. **Prompt Management Features**:
   - **Add New Prompt**: Implement a feature to add new prompts into the repository. Users should be able to specify the category of the prompt and provide any additional metadata.
   - **Edit Existing Prompt**: Allow users to edit existing prompts within the repository. This includes updating the prompt text and modifying its metadata.
   - **Delete Prompt**: Provide functionality to delete prompts from the repository.

4. **Search and Filter**: Enable users to search for prompts based on keywords and filter them by category. Utilize the 'ai_infrastructure_artifacts_repo' package to efficiently query the repository.

5. **Version Control**: Integrate version control into the application so that every change made to a prompt is tracked. This will allow users to revert to previous versions if needed.

6. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. Commands should be intuitive and easy to remember (e.g., `add`, `edit`, `delete`, `search`).

7. **Documentation**: Write comprehensive documentation for the application, detailing how to install it, how to use each feature, and how to troubleshoot common issues. Include examples of how to integrate the application into larger AI workflows.

8. **Testing**: Ensure the application is thoroughly tested using unit tests and integration tests. Focus on verifying the correctness of operations like adding, editing, and deleting prompts.

9. **Deployment**: Prepare the application for deployment by packaging it as a standalone executable or containerized application. Consider deploying it on platforms like Docker or PyPI for broader accessibility.

By completing these steps, you'll create a robust and user-friendly tool for managing AI prompts, leveraging the capabilities of the 'ai_infrastructure_artifacts_repo' package.