🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some unusual behaviors, particularly concerning metadata and network risks, which raise suspicion but do not conclusively point to malicious intent.

Suspiciously low activity and engagement with a very short email domain
Unusual network calls to localhost

Per-check LLM notes

Network: The network calls to localhost might be intended for local development or testing purposes, but without further context, it could indicate unusual behavior.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
Metadata: Suspiciously low activity and engagement, combined with a very short email domain.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

4 test file(s) detected (e.g. test_decorated.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6824 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

Type checker (mypy / pyright / pytype) referenced in project
39 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 4 commits in mariolpantunes/ai-game-framework
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

def get_url(): conn = http.client.HTTPConnection("127.0.0.1", 8766) conn.request("GET", "/")
ef get_file(): conn = http.client.HTTPConnection("127.0.0.1", 8766) conn.request("GET", "/framework/n
ef get_maps(): conn = http.client.HTTPConnection("127.0.0.1", 8766) conn.request("GET", "/api/maps")

✓ 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 score 3.0

Suspicious email domain flags: Very short email domain: ua.pt

Very short email domain: ua.pt

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks
Single contributor with only 4 commit(s) — possibly throwaway account

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Mário Antunes" 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-game-framework

Create a mini-game platform where users can train and test their AI agents in different game environments. The platform should include at least three distinct game types (e.g., racing, puzzle, and strategy), each requiring unique AI strategies to succeed. Utilize the 'ai-game-framework' package to streamline the process of setting up these game environments and managing AI training and testing sessions. Here’s a detailed plan on how to proceed:

1. **Setup Project Environment**: Initialize your Python environment with necessary packages including 'ai-game-framework'. Ensure you have a virtual environment set up for dependency management.
2. **Game Environment Design**: Use 'ai-game-framework' to define the core mechanics of each game type. For instance, for the racing game, set up tracks, obstacles, and scoring systems. Similarly, design puzzles and strategic elements for the other two games.
3. **AI Agent Development**: Implement basic AI agent classes using 'ai-game-framework'. These agents will interact with the game environments based on predefined rules or learning algorithms. Consider starting with simple rule-based agents before moving onto more complex machine learning models.
4. **Training and Testing Interface**: Develop a user-friendly interface where users can select which AI agents to train and in which game environment. This interface should allow for real-time observation of training progress and performance metrics.
5. **Evaluation and Comparison**: Integrate functionality within 'ai-game-framework' to automatically evaluate trained AI agents against each other and human players. Provide visualizations and statistics to help users understand the strengths and weaknesses of different AI approaches.
6. **Customization and Expansion**: Allow users to customize aspects of both the AI agents and the game environments through 'ai-game-framework'. This could include adjusting difficulty levels, adding new game modes, or even creating entirely new games based on similar principles.

By following these steps, you'll create a comprehensive mini-game platform that not only showcases the capabilities of 'ai-game-framework' but also offers educational value for those interested in AI and game development.