🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential shell execution risks and signs of obfuscation, suggesting possible hidden malicious functionalities. The lack of a GitHub repository and single package from the maintainer also raise concerns about the project's legitimacy.

High obfuscation risk
Potential shell execution risks

Per-check LLM notes

Network: No network calls were detected, which is normal and does not indicate any immediate risk.
Shell: The use of os.system to execute shell commands may pose some risk if these commands are not properly sanitized or controlled, potentially leading to unintended side effects or vulnerabilities.
Obfuscation: The code shows signs of obfuscation through base64 and bz2 compression which may indicate an attempt to hide functionality.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The maintainer has only one package and lacks a GitHub repository, which may indicate a less established project.

📦 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 (5234 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

205 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 score 10.0

Found 5 obfuscation pattern(s)

eturn cp.loads(bz2.decompress(base64.b64decode(r))) return remote_fn # type: ignore def submi
MR Spectrum """ model.eval() with torch.no_grad(): all_predicts = []
ata["cells"] e, f, v = er.eval( extended_coords.reshape(1, -1), extended_ch
pkg_path = os.path.dirname(__import__(pkg).__file__) with tempfile.NamedTemporaryFile(suffix='
lf._keys[idx]) data = pickle.loads(datapoint_pickled) return data class FoldLMDBDatase

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

o_dir, exist_ok=True) os.system(f'cp -r {from_path} {to_dir}') return os.path.join(t
e link already exists os.system(get_ln_cmd(system_artifact.url, system_file)) templa
r, 'dw_model.pb') os.system(f'ln -sf {dw_model} {link_target}') # Modify the

✓ 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 2.0

1 maintainer concern(s) found

Author "weihong.xu" 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 ai2-kit

Create a simple sentiment analysis tool using the 'ai2-kit' Python package. This tool will allow users to input text and receive a sentiment score indicating whether the text is positive, negative, or neutral. The application should have a user-friendly interface where users can paste text into a text box and click a button to analyze the sentiment of the text. Upon clicking the analyze button, the tool should display the sentiment score and a brief description of the sentiment (e.g., 'Positive', 'Negative', 'Neutral').

### Features:
1. **Text Input Field**: A field where users can enter or paste their text.
2. **Analyze Button**: Clicking this button triggers the sentiment analysis process.
3. **Sentiment Score Display**: After analysis, show the sentiment score (a number between -1 and 1).
4. **Sentiment Description**: Provide a short description of the sentiment based on the score (Positive, Negative, Neutral).
5. **Error Handling**: If the input is not valid (e.g., empty), inform the user with a message.
6. **User Interface**: Design a clean and intuitive UI using a web framework like Flask or Django.

### Utilizing 'ai2-kit':
- Use 'ai2-kit' to perform the sentiment analysis. Ensure you install the package via pip before starting the development.
- The 'ai2-kit' package provides functions to analyze text sentiments. Refer to the package documentation to understand how to use these functions effectively.
- Integrate the sentiment analysis function into your application so that when the user inputs text and clicks the analyze button, the app calls the appropriate function from 'ai2-kit' to get the sentiment score and description.

### Steps to Develop:
1. Install necessary packages including 'ai2-kit'.
2. Set up your project structure (e.g., templates for HTML files if using Flask).
3. Create the main application file and define routes for displaying the form and processing the sentiment analysis request.
4. Implement the sentiment analysis functionality using 'ai2-kit'.
5. Test the application thoroughly to ensure it handles various types of input correctly and displays accurate sentiment scores.
6. Deploy the application to a hosting service of your choice.