🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network and shell activities, but its recent creation and lack of community engagement suggest potential concerns.

Metadata risk due to new package with no community engagement
No direct malicious activities detected

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions 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 is newly created with no community engagement, which raises some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.0/10)

○ 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 (8073 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

13 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 11 commits in rodneigk2/ai-execution-protocol
Single author with few commits — possibly a personal or throwaway project

🔬 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 score 5.0

Git history flags: Repository created very recently: 3 day(s) ago (2026-06-04T00:34:36Z)

Repository created very recently: 3 day(s) ago (2026-06-04T00:34:36Z)
Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Package is very new: uploaded 3 day(s) ago
Author "AI Execution Protocol" 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-execution-protocol

Create a mini-application called 'SafeAIExecutor' that leverages the 'ai-execution-protocol' package to ensure safe and controlled execution of AI tasks. This application will serve as a sandbox environment where users can submit AI tasks (such as generating text, image processing, etc.) while ensuring these tasks adhere to predefined safety and ethical guidelines. Here’s a step-by-step guide on how to develop this application:

1. **Setup Project**: Initialize a new Python project and install the 'ai-execution-protocol' package.
2. **Define Safety Rules**: Implement a set of rules using the 'ai-execution-protocol' to validate inputs and outputs of AI tasks. These rules should include checks for inappropriate content, excessive resource usage, and adherence to ethical guidelines.
3. **Task Submission Interface**: Develop a simple command-line interface (CLI) or web interface where users can submit their AI tasks. Each task submission must pass through the safety rules defined earlier.
4. **Execution Engine**: Utilize the 'ai-execution-protocol' to execute submitted tasks only if they meet all safety criteria. If a task fails any rule, it should not be executed and the user should receive feedback on why.
5. **Result Delivery**: Once a task passes all validations and is executed successfully, deliver the results back to the user via the same interface they used for submission.
6. **Logging & Reporting**: Implement logging functionality to record each task submission, its status (passed/failed), and reasons for failure if applicable. Provide a reporting feature to allow administrators to review these logs.
7. **Enhancements**: Consider adding features like user authentication, task prioritization based on urgency or importance, and integration with popular AI services like OpenAI’s API for more diverse task executions.

By following these steps, you'll create a robust, secure, and ethical mini-application for executing AI tasks, demonstrating the power and versatility of the 'ai-execution-protocol' package.