AI Analysis
Final verdict: SAFE
The package autoai-libs v3.0.12 appears safe with no indications of malicious activities such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to the author's single package history.
- No network calls detected
- Single package by author
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell executions detected, indicating no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (2.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (287 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
score 3.0
Suspicious email domain flags: Very short email domain: pl.ibm.com>
Very short email domain: pl.ibm.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 "IBM" 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 autoai-libs
Create a predictive maintenance tool using the 'autoai-libs' Python package. This tool will analyze sensor data from industrial machinery to predict potential failures before they occur, thus reducing downtime and maintenance costs. Here’s a step-by-step guide on how to build this tool: 1. **Data Collection**: Start by collecting historical sensor data from industrial machinery. This data should include various sensor readings over time as well as information about any known failures. 2. **Data Preprocessing**: Use 'autoai-libs' to preprocess the collected data. This includes handling missing values, scaling features, and encoding categorical variables. The goal is to prepare the data for model training. 3. **Feature Engineering**: Apply feature engineering techniques to extract meaningful features from the raw sensor data. 'autoai-libs' provides tools to automate and standardize these processes. 4. **Model Training**: Utilize 'autoai-libs' to train a predictive model on the preprocessed and engineered data. Experiment with different models available within the package to find the best one for predicting machinery failures. 5. **Model Evaluation**: Evaluate the trained model's performance using appropriate metrics such as accuracy, precision, recall, and F1 score. Ensure that the model performs well on both training and validation datasets. 6. **Deployment**: Once satisfied with the model's performance, deploy it into a real-time monitoring system where it can receive live sensor data and predict potential failures. 7. **User Interface**: Develop a simple web interface using Flask or Django to visualize predictions and alert users when a machine is predicted to fail soon. 8. **Documentation**: Write comprehensive documentation detailing how each part of the tool works, including setup instructions, data preprocessing steps, and model deployment procedures. Suggested Features: - Real-time data ingestion and processing - Automated alerts via email or SMS when a failure is predicted - Historical performance tracking of the predictive model - User-friendly dashboard for visualizing predictions and historical data - Integration with existing industrial IoT platforms The 'autoai-libs' package will be utilized throughout the project for its powerful data preprocessing and feature engineering capabilities, making the development process more efficient and the resulting models more robust.
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