AI Analysis
Final verdict: SAFE
The package exhibits minimal risk indicators with no signs of network, shell, or obfuscation risks. The metadata suggests a potentially new maintainer but does not raise significant concerns.
- No network calls detected
- Single package from maintainer
Per-check LLM notes
- Network: No network calls detected, which is not necessarily suspicious for an API client package if it's designed to make calls only when explicitly instructed.
- Shell: No shell execution patterns detected, which aligns with the expectation for a benign library focused on API interaction.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No secret harvesting patterns detected, indicating low risk of credential theft.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (3.6/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Docs" -> https://docs.intellegens.comDetailed PyPI description (4232 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
40 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
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
Email domain looks legitimate: intellegens.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 "Intellegens" 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 alchemite-apiclient
Create a Python-based mini-application that leverages the 'alchemite-apiclient' package to interact with Alchemite Analytics for predictive modeling. Your application should allow users to upload datasets, select appropriate models for analysis, and visualize predictions. Here’s a detailed step-by-step guide on how to build this application: 1. **Setup Environment**: Begin by setting up your Python environment. Install necessary packages including 'alchemite-apiclient', 'pandas', 'matplotlib', and 'seaborn'. 2. **User Interface Design**: Develop a simple command-line interface (CLI) where users can input dataset paths and model preferences. 3. **Data Loading & Preprocessing**: Implement functions to load data from CSV files into pandas DataFrames. Ensure preprocessing steps like handling missing values, normalization, and encoding categorical variables are included. 4. **Model Selection & Training**: Use 'alchemite-apiclient' to train models based on user input. Provide options for different types of predictive models available through the Alchemite API. 5. **Prediction & Visualization**: After training, use the selected model to make predictions on test data. Visualize these predictions alongside actual values using matplotlib or seaborn for comparison. 6. **Report Generation**: Finally, generate a report summarizing the model performance metrics such as accuracy, precision, recall, and F1 score. Suggested Features: - Support for multiple file formats for data import. - Advanced data preprocessing options like feature scaling and dimensionality reduction. - Integration with other APIs for more comprehensive analytics. - Interactive visualizations for better user engagement. How to Utilize 'alchemite-apiclient': - Use the 'alchemite-apiclient' package to authenticate and connect to the Alchemite Analytics platform. - Leverage its methods for uploading datasets, selecting models, and initiating training processes. - Retrieve prediction results and model details directly from the API calls made through 'alchemite-apiclient'. This mini-app will serve as a powerful tool for anyone looking to quickly apply predictive analytics to their datasets using Alchemite's capabilities.