🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any direct security risks such as network calls, shell execution, obfuscation, or credential harvesting. However, there are some concerns regarding low maintainer activity and poor metadata quality.

Low risk for network calls, shell execution, obfuscation, and credential harvesting.
Metadata quality and maintainer activity are below optimal.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution detected, reducing the risk of arbitrary command execution or system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which may indicate potential risks.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ 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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 Refrax

Develop a real-time data transformation and visualization tool using the Python package 'Refrax' along with libraries such as Matplotlib and Streamlit. This application will take live sensor data (simulated or from a mock API) and apply complex transformations using Refrax's chainable optics on JAX PyTrees to process and visualize the data in real-time.

Step-by-Step Instructions:
1. Set up your development environment with Python, Refrax, Matplotlib, and Streamlit installed.
2. Create a Streamlit app that fetches simulated sensor data (or use a mock API for real-time data).
3. Use Refrax to define and chain together optics that transform the incoming PyTree data into meaningful metrics for analysis and visualization.
4. Implement real-time data processing within the Streamlit app using these defined optics.
5. Visualize the transformed data using Matplotlib in Streamlit.
6. Add interactive elements to your Streamlit app to allow users to select different types of transformations or visualizations.
7. Ensure your application is well-documented and includes comments explaining how Refrax is being utilized in each step.

Suggested Features:
- Support for multiple types of sensors (temperature, humidity, etc.)
- Ability to add custom optics through a user-friendly interface
- Real-time graph updates with smooth transitions
- Option to export visualized data as images or CSV files

How 'Refrax' is Utilized:
- Refrax's chainable optics will be used to define how raw sensor data is transformed into useful metrics. For example, you might chain together optics to first extract temperature readings, then calculate averages over certain time intervals, and finally normalize these values for visualization purposes.