AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate network risk due to its attempt to communicate externally, and a somewhat high metadata risk because it's new and lacks community engagement. These factors suggest potential risks that require further investigation.
- Moderate network risk due to external communication
- High metadata risk due to package novelty and low engagement
Per-check LLM notes
- Network: The package attempts to communicate with an external server, which could be for legitimate purposes like status checks or updates, but also raises concerns about potential unauthorized data transfer.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is relatively new with low engagement and a single release, raising some suspicion.
Package Quality Overall: Low (4.8/10)
○ 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: "Documentation" -> https://github.com/pizenkov13-boop/AxiomCore/blob/main/READMDetailed PyPI description (12482 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project46 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 40 commits in pizenkov13-boop/AxiomCoreSingle author but highly active (40 commits)
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
on try: requests.get(f"{self.base_url}/api/status", timeout=1) except:try: requests.post(f"{self.base_url}/api/update", json=data, timeout=1)
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: axiomcore.dev
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://makeapullrequest.com
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "AxiomCore Team" 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 axiomcore-ml
Your task is to create a simple yet powerful mini-application using the 'axiomcore-ml' package. This package is designed to facilitate autonomous machine learning optimization without the need for cloud services or expensive GPU farms, making it ideal for developers looking to integrate advanced ML capabilities into their projects locally. Your application will be a predictive maintenance tool for industrial machinery, which predicts potential failures based on historical sensor data.
### Step-by-Step Instructions:
1. **Data Collection**: Gather or simulate historical sensor data from industrial machinery. This data should include various sensor readings such as temperature, vibration, pressure, etc., alongside timestamps and labels indicating whether a failure occurred ('failure' or 'no failure').
2. **Preprocessing**: Clean and preprocess the data to ensure it's suitable for training a machine learning model. This might involve handling missing values, normalizing the data, and splitting it into training and testing sets.
3. **Model Training**: Utilize 'axiomcore-ml' to train a predictive model. Since 'axiomcore-ml' is designed for autonomous optimization, you'll leverage its ability to automatically tune hyperparameters and select the best model architecture without manual intervention.
4. **Evaluation**: Evaluate the performance of your trained model using appropriate metrics such as accuracy, precision, recall, and F1-score.
5. **Deployment**: Create a simple interface (command-line or GUI) where users can input real-time sensor data, and the application predicts the likelihood of a machinery failure based on the trained model.
### Suggested Features:
- **Real-Time Prediction**: Allow users to input current sensor data and receive immediate predictions about potential failures.
- **Historical Analysis**: Provide insights into past predictions versus actual outcomes to help users understand the reliability of the model.
- **User-Friendly Interface**: Ensure the application is easy to use, either through a command-line interface or a graphical user interface.
- **Detailed Reporting**: Offer detailed reports on model performance and predictions.
- **Customizable Model Tuning**: Allow users to customize certain aspects of the model tuning process if they wish to explore different configurations.
### Utilization of 'axiomcore-ml':
- Use 'axiomcore-ml' to automate the model selection and hyperparameter tuning processes. This ensures that the model is optimized for predicting machinery failures without requiring extensive machine learning expertise.
- Leverage the package's local execution capabilities to run the application on a standard desktop or laptop computer without needing cloud resources or specialized hardware.
Your goal is to create a robust, efficient, and accessible predictive maintenance tool that showcases the power and ease-of-use of 'axiomcore-ml'.
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