AI Analysis
Final verdict: SAFE
The aiqclib package has been assessed with minimal risks across all categories. It does not engage in network calls, shell executions, obfuscations, or credential harvesting, suggesting it is safe to use.
- No network calls
- No shell execution
- No obfuscation
- No credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Low (3.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
3 documentation file(s) (e.g. clean_api_rst.py)Detailed PyPI description (14203 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
117 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: outlook.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with aiqclib
Create a Python-based mini-application called 'ModelEvaluator' which will serve as a comprehensive tool for evaluating machine learning models using the 'aiqclib' package. The application should allow users to upload their datasets, select from a variety of pre-built models available in 'aiqclib', train these models on their data, and then evaluate the performance of each model using various metrics provided by 'aiqclib'. Additionally, the application should generate visual reports summarizing the evaluation results, making it easier for users to compare different models. Step-by-Step Instructions: 1. Start by setting up a user-friendly interface where users can upload their datasets (CSV format). 2. Integrate 'aiqclib' to provide a selection of pre-configured machine learning models (e.g., logistic regression, decision trees, random forests). 3. Implement a feature within 'ModelEvaluator' that splits the uploaded dataset into training and testing sets automatically. 4. Use 'aiqclib' to train each selected model on the training set. 5. Evaluate each trained model using multiple performance metrics supported by 'aiqclib', such as accuracy, precision, recall, F1-score, etc. 6. Generate visual reports that include graphs comparing the performance of different models across various metrics. 7. Ensure the application saves the evaluation reports in both PDF and HTML formats for easy sharing and documentation. Suggested Features: - Allow users to specify custom parameters for each model during training. - Include functionality to handle both classification and regression tasks. - Provide options for cross-validation to ensure robust model evaluation. - Offer recommendations based on the evaluation results to help users choose the best model for their specific use case. How 'aiqclib' is Utilized: - For simplifying the process of model building and evaluation, 'aiqclib' provides streamlined functions for training and assessing models. Users can leverage these functions to quickly implement and test different machine learning algorithms without worrying about the underlying complexities. - The package also includes utilities for generating detailed performance reports, which will be crucial for creating the visual summaries in 'ModelEvaluator'. Your task is to design and develop 'ModelEvaluator', ensuring it is intuitive, efficient, and makes full use of 'aiqclib' to deliver valuable insights to its users.