🤖 AI Analysis

Final verdict: SUSPICIOUS

The package contains shell commands that can execute system-level operations without clear justification, raising suspicion of potential misuse.

Detection of potentially harmful shell commands
Low maintainer effort and potential anonymity

Per-check LLM notes

Network: No network calls detected, which is not inherently risky but unusual if the package should interact with cloud services.
Shell: Detection of shell commands like 'sudo reboot' and 'sudo shutdown now' suggests potential for system-level control which could be used maliciously without proper justification.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting the package does not engage in secret or credential theft.
Metadata: The package shows signs of low maintainer effort and potential anonymity, raising concerns but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

rt_network() subprocess.run(["sudo", "reboot"]) elif cmd=="SWN" and csl.c
. """ try: subprocess.check_call( ['ping', '-c', '1', host], stdout
"OKREB") subprocess.run(["sudo", "reboot"]) elif message == "SHT":
s=False) subprocess.run(["sudo", "shutdown", "now"]) else:
end("OKREB") subprocess.run(["sudo", "reboot"]) #shutdown elif
tness=False) subprocess.run(["sudo", "shutdown", "now"]) #Brightness

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: preciz.si>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 TPC-CloudScale

Your task is to develop a cloud-based performance monitoring tool using the Python package 'TPC-CloudScale'. This tool will help users assess the performance of their applications hosted on various cloud environments. Here’s a detailed breakdown of what your application should achieve:

1. **Setup and Configuration**: Start by setting up your development environment with Python and installing the necessary dependencies including 'TPC-CloudScale'. Ensure that you have access to a cloud provider's API for authenticating and accessing their services.

2. **User Interface**: Create a simple yet intuitive user interface where users can input details about their cloud-hosted applications such as the cloud provider, application name, and specific metrics they wish to monitor (e.g., CPU usage, memory usage, network traffic).

3. **Data Collection**: Utilize 'TPC-CloudScale' to collect real-time data from the specified cloud applications. Your application should be able to gather performance metrics at regular intervals and store them in a local database for analysis.

4. **Performance Analysis**: Implement functionality within your application to analyze the collected data. Use 'TPC-CloudScale' to process this data and generate insights such as peak performance times, trends over time, and potential bottlenecks.

5. **Visualization**: Provide visual representations of the analyzed data through graphs and charts. Users should be able to see trends, compare different metrics, and identify performance issues more easily.

6. **Alert System**: Set up an alert system that notifies users via email or SMS when certain thresholds are breached (e.g., high CPU usage, low memory availability). Use 'TPC-CloudScale' to trigger these alerts based on predefined conditions.

7. **Report Generation**: Allow users to generate detailed reports summarizing the performance of their applications over a given period. Reports should include key metrics, visualizations, and recommendations for improving performance.

8. **Security Measures**: Ensure that all user data and credentials are handled securely. Use best practices for securing API keys and other sensitive information.

**Suggested Features**:
- Integration with multiple cloud providers (AWS, Azure, Google Cloud)
- Customizable alert thresholds
- Historical data comparison
- Support for multiple languages (English, Spanish, French)
- Real-time dashboard for monitoring performance

Utilize 'TPC-CloudScale' to its fullest extent by leveraging its capabilities for performance data collection, analysis, and reporting. Make sure your application is scalable and can handle large volumes of data efficiently.