AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest a supply-chain attack.
- No network calls detected.
- No shell execution patterns detected.
- No obfuscation or credential harvesting detected.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity that would involve executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 3 test file(s) found
3 test file(s) detected (e.g. test_extract_lerobot_tasks_subset.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1467 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
187 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
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 2.0
1 maintainer concern(s) found
Author "JFG" 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 any4robot
Your task is to create a Python-based mini-application that leverages the 'any4robot' package to streamline the process of preparing datasets for VLA (Very Large Array) model training in robotics. This application will serve as a foundational tool for researchers and developers working on advanced robotic systems that require large-scale data processing and analysis. The application should include the following key features: 1. Data Ingestion: Implement a feature that allows users to upload multiple CSV files containing sensor data from robotic experiments. Ensure that the application can handle different file formats and sizes efficiently. 2. Data Preprocessing: Use 'any4robot' functionalities to clean and preprocess the ingested data. This includes handling missing values, normalizing data ranges, and converting raw sensor readings into meaningful features for model training. 3. Feature Engineering: Utilize 'any4robot' to automatically generate additional features from the raw data, which could improve the performance of the VLA models. For example, calculate velocities from position data or derive new variables that capture temporal trends. 4. Dataset Splitting: Implement functionality within your application to split the preprocessed dataset into training, validation, and test sets, ensuring a balanced distribution of classes if applicable. 5. Visualization: Provide visual analytics tools within the application to help users understand the structure and quality of their datasets before and after preprocessing. Use matplotlib or seaborn for plotting. 6. Export Options: Allow users to export the cleaned and processed datasets in various formats such as CSV, JSON, or directly save them into a database for further use in model training. To utilize the 'any4robot' package effectively, you'll need to familiarize yourself with its core functionalities related to data ingestion, preprocessing, and feature engineering. Make sure to document each step of your implementation clearly, explaining how 'any4robot' is integrated into the workflow and why certain choices were made in terms of data handling and processing.
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