🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to network and shell command usage, though no direct evidence of malicious intent is present. The lack of a Git repository and the maintainer's limited history are concerning but not definitive.

moderate network risk
potential misuse of shell commands
unverified maintainer and missing repository

Per-check LLM notes

Network: The package makes network calls which could be related to fetching images or other resources, but without more context, there is some concern about unauthorized data retrieval.
Shell: Executing shell commands like 'nvidia-smi' and 'nvcc --version' might be legitimate for GPU-related operations, but it increases the risk of unexpected behavior or potential misuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and the git repository is not found, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (4717 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

14 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

age_types()] r = requests.head(data[key][0][0]) if r.headers["content-type"] i
image = Image.open(BytesIO(requests.get(img).content)) width, height = image.size
ssword } response = requests.post(base_url + url_prefix + '/login', headers=headers, json=data
token } response = requests.post(base_url + url_prefix + '/eval_adapter/' + str(card_id), hea

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

" try: result = subprocess.run(["nvidia-smi"], capture_output=True, text=True) if
try: result = subprocess.run(["nvcc", "--version"], capture_output=True, text=True)

✓ 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "CERTH" 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 aicard-eval

Develop a mini-application named 'CardEvaluator' that leverages the 'aicard-eval' Python package to evaluate the performance of different types of playing cards in a virtual card game environment. This application will simulate various card games, such as Poker or Bridge, and use 'aicard-eval' to assess the strength and strategic value of each card based on its rank, suit, and the context of the game. Here are the key steps and features for your project:

1. **Setup**: Install the necessary Python packages including 'aicard-eval'. Ensure you have a virtual environment set up for your project.
2. **Card Simulation**: Create a class or function that simulates a deck of cards, allowing for shuffling, dealing, and tracking of individual cards within the game state.
3. **Game Rules Integration**: Implement basic rules of the chosen card game(s). For instance, if choosing Poker, include logic for hand rankings like Royal Flush, Straight Flush, etc.
4. **Evaluation Engine**: Use 'aicard-eval' to develop an evaluation engine that assigns scores to cards based on their potential impact in the game. This could involve evaluating cards individually or in combinations.
5. **User Interface**: Design a simple command-line interface where users can interact with the game. Users should be able to see their current hand, make moves, and receive feedback on the quality of their decisions based on 'aicard-eval' evaluations.
6. **AI Opponent**: Incorporate an AI opponent that uses the evaluation results from 'aicard-eval' to make strategic decisions, enhancing the realism and challenge of the game.
7. **Scoring System**: Develop a scoring system that tracks user progress throughout multiple rounds or games, rewarding players for making optimal decisions according to 'aicard-eval'.
8. **Testing & Optimization**: Test the application thoroughly to ensure all features work correctly. Optimize the performance of the evaluation engine for real-time gameplay.
9. **Documentation**: Write comprehensive documentation explaining how to install and run the application, along with examples of how 'aicard-eval' enhances gameplay.

This project not only provides an engaging way to explore the capabilities of 'aicard-eval' but also offers insights into game development principles and the integration of machine learning-based evaluation systems.