🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risks across all categories except for a moderate obfuscation risk. There's no evidence of malicious activity, and the obfuscation might serve legitimate purposes within the framework.

moderate obfuscation risk
low risks in other categories

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: The code shows signs of obfuscation which may indicate an attempt to hide functionality, but it could also be part of a complex internal logic.
Credentials: No clear patterns of credential harvesting are present.
Metadata: The maintainer has only one package, which could indicate a new or less active account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

return span def eval(self, x: float) -> float: # Evaluate the piecewise l
lensSetting = self.cfg.map.eval(self.fsmooth) self.afstatus.lensSetting = int(lensSe

✓ 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

Repository Idein/actfw-core appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Idein Inc." 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 actfw-core

Create a real-time activity recognition system using the Python package 'actfw-core'. This system will be designed to analyze sensor data from wearable devices to recognize various human activities such as walking, running, sitting, and standing. The goal is to develop a fully functional mini-application that can process live data streams and output recognized activities in real-time.

Step 1: Set up your development environment by installing Python and the 'actfw-core' package. Ensure you have the necessary dependencies installed to support real-time data processing.

Step 2: Design the architecture of your application. It should include modules for data acquisition, preprocessing, feature extraction, activity recognition, and result visualization.

Step 3: Implement the data acquisition module which interfaces with simulated or actual wearable devices to collect sensor data. Use 'actfw-core' to handle generic data processing tasks that are device-independent.

Step 4: Develop the preprocessing module to clean and normalize the raw sensor data. This module should leverage 'actfw-core' functionalities for efficient data manipulation.

Step 5: Create the feature extraction module to derive meaningful features from the preprocessed data. Utilize 'actfw-core' for any required mathematical operations or transformations.

Step 6: Build the activity recognition module using machine learning or deep learning models trained on labeled activity datasets. Integrate 'actfw-core' for model deployment and inference processes.

Step 7: Implement the result visualization module to display recognized activities in real-time. This could include graphical representations or simple text outputs.

Suggested Features:
- Real-time data streaming support
- User-friendly interface for monitoring activities
- Adjustable sensitivity levels for differentiating between similar activities
- Logging capabilities for storing historical activity data
- Compatibility with multiple types of wearable sensors

Utilization of 'actfw-core':
- For handling generic data processing tasks across different devices
- To provide robust and efficient computational methods for feature extraction
- For deploying and running machine learning models in real-time scenarios
- As a foundation for building scalable and maintainable applications