AI Analysis
Final verdict: SAFE
The package has minimal risks with no evidence of malicious activities. The main concern is the telemetry data sent to an external server, but this does not conclusively point towards malicious intent.
- Moderate network risk due to telemetry data collection
- Low risk in all other categories
Per-check LLM notes
- Network: The package sends telemetry data to an external server, which could be for legitimate purposes like usage analytics but may also indicate data collection without user consent.
- Shell: No shell execution patterns were detected, indicating low risk for direct system command injection.
- Obfuscation: The obfuscation appears to be related to common PyTorch code patterns for evaluation and inference, likely not malicious.
- Credentials: No secret harvesting patterns detected.
- Metadata: The maintainer has a single package and lacks PyPI classifiers, indicating low effort or a new/inactive account.
Package Quality Overall: Low (3.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
2 test file(s) detected (e.g. test_in_ia.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (5632 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
35 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)
load: dict): try: requests.post( "https://api.ai-critic.dev/telemetry",
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
orch": self.model.eval() with torch.no_grad(): X_tensor
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 4.0
2 maintainer concern(s) found
Author "Luiz Filipe Seabra" 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 ai-critic
Create a Python-based mini-application called 'ModelEvaluator' that leverages the 'ai-critic' package to evaluate the performance of various machine learning models on a given dataset. This tool will serve as an educational resource and a practical utility for data scientists and machine learning enthusiasts. Step 1: Setup the Project - Initialize a new Python virtual environment. - Install necessary packages including 'ai-critic', 'scikit-learn', and 'pandas'. - Ensure 'ai-critic' is properly configured and ready to use. Step 2: Data Preparation - Integrate functionality to load datasets from CSV files or URLs. - Provide options to preprocess the data (e.g., handling missing values, encoding categorical variables). Step 3: Model Evaluation - Implement support for multiple machine learning models (e.g., Logistic Regression, Decision Trees, Random Forests). - Use 'ai-critic' to create a graph-based evaluation framework where nodes represent different stages of the model evaluation process (data preprocessing, model training, prediction, scoring). - Allow users to customize evaluation metrics and criteria within 'ai-critic'. Step 4: Visualization - Develop visualizations to compare the performance of different models based on user-defined metrics. - Utilize libraries like Matplotlib or Seaborn to display graphs and charts. Suggested Features: - A user-friendly command-line interface for easy interaction. - An option to save and load previous evaluations for comparison. - Integration with Jupyter Notebooks for interactive analysis. - Detailed documentation explaining each step of the evaluation process and how 'ai-critic' contributes to it.