AI Analysis
Final verdict: SUSPICIOUS
The package aikosh v1.1.0 is assessed as suspicious due to its maintainer's limited activity and lack of a public GitHub repository, raising concerns about potential supply-chain risks.
- Maintainer has only one package listed on PyPI
- No associated GitHub repository found
Per-check LLM notes
- Metadata: The maintainer has only one package and no associated GitHub repository, which may indicate a less experienced or potentially suspicious maintainer.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (13301 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
Classifier: Typing :: Typed69 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
score 1.5
Found 1 network call pattern(s)
-> httpx.Client: return httpx.Client( **httpx_client_kwargs( follow_redirec
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "AIKosh SDK contributors" 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 aikosh
Your task is to develop a comprehensive mini-application using the 'aikosh' Python package, which serves as a powerful interface for accessing datasets, models, and other resources from the AIKosh platform. This application will be designed to facilitate the process of data analysis and machine learning model deployment for users who wish to leverage AIKosh's extensive offerings without diving into complex setup procedures. ### Project Scope: 1. **User Interface**: Design a simple yet effective command-line interface (CLI) for interacting with the application. Users should be able to easily navigate through different functionalities without requiring advanced technical knowledge. 2. **Dataset Management**: Implement functionality that allows users to search, download, and manage datasets available on AIKosh. Users should be able to filter datasets based on various criteria such as tags, popularity, and type. 3. **Model Deployment**: Enable users to select pre-trained models from AIKosh, upload their own custom datasets, and deploy these models for prediction tasks. The application should support common file formats and provide options for adjusting model parameters. 4. **Integration with Other Tools**: Provide integration points for popular data science tools like Jupyter Notebooks or Google Colab, allowing seamless transition from data exploration to model deployment. 5. **Documentation and Support**: Ensure that the application comes with detailed documentation and support channels, making it accessible to both beginners and experienced developers. ### Core Features Utilizing 'aikosh': - **Search and Filter Datasets**: Use 'aikosh' to query and filter datasets based on user preferences. Display results in a user-friendly manner and allow direct downloads from within the CLI. - **Model Selection and Customization**: Leverage 'aikosh' to list available models, allowing users to choose models suited for their specific tasks. Provide options for fine-tuning model parameters directly through the CLI. - **Data Upload and Model Training**: Allow users to upload their datasets via the CLI and use 'aikosh' to train selected models on these datasets. Offer real-time progress tracking and error handling. - **Prediction Services**: Once a model is trained, enable users to make predictions using the model. Results should be displayed clearly and saved automatically if required. - **Export and Share Models**: Provide functionality to export trained models in formats compatible with various platforms and share them with others. ### Additional Considerations: - Ensure the application is robust against errors and includes comprehensive error handling mechanisms. - Optimize performance to handle large datasets and complex models efficiently. - Consider adding a feature for version control, allowing users to track changes in their datasets and models over time.