🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of legitimate use but raises concerns due to its new upload status and the presence of hard-coded API keys.

Network risk due to potential exposure of API keys
Low trustworthiness due to new package and limited maintainer history

Per-check LLM notes

Network: The package appears to be making network requests with API keys, which could indicate legitimate API usage but also poses a risk if the API key is hardcoded and exposed.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is newly uploaded and the maintainer has limited history with PyPI, indicating potential unreliability.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

.cfg.api_key req = urllib.request.Request(url, data=data, headers=headers, method="POST")
try: with urllib.request.urlopen(req, timeout=self.cfg.timeout_s) as resp:

✓ 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 4.0

2 maintainer concern(s) found

Package is very new: uploaded 2 day(s) ago
Author "NIEA Team" 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 SkyalyticAI

Create a mini-application that leverages the 'SkyalyticAI' package to analyze and predict patterns within a set of EEG (Electroencephalogram) data. This application will serve as a tool for neuroscientists and researchers to better understand brain activity patterns and potentially diagnose neurological conditions early on. The app should include the following features:

1. Data Import: Users should be able to upload their EEG datasets, which can be in CSV format.
2. Preprocessing: Implement basic preprocessing steps such as filtering out noise, artifact removal, and normalization.
3. Pattern Recognition: Utilize the Neural Isomorphic Evolutionary Architecture provided by SkyalyticAI to identify distinct patterns within the EEG data.
4. Prediction Module: Develop a prediction module that uses the identified patterns to forecast future brain activity states.
5. Visualization: Provide visual representations of the EEG data, identified patterns, and predictions.
6. Reporting: Generate a report summarizing the analysis findings and prediction outcomes.

In utilizing the SkyalyticAI package, focus on leveraging its unique capabilities in neural architecture and evolutionary algorithms to enhance pattern recognition and predictive accuracy. Ensure the application is user-friendly and well-documented.