🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low individual risks but raises concerns due to the maintainer's lack of experience, indicated by having only one package and no associated GitHub repository.

Metadata risk score is moderately high (3/10) due to the maintainer's limited presence.
No direct security threats were identified, but the context around the maintainer's activity is questionable.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external services.
Shell: No shell execution detected, indicating the package likely does not execute 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 and lacks a GitHub repository, which could indicate a less experienced or potentially suspicious actor.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Franz Ehrlich" 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 SleepHarmonizer

Create a comprehensive sleep analysis app called 'DreamScape' using the Python package 'SleepHarmonizer'. This app will serve as both a plugin and standalone tool for users to input their sleep data and receive personalized recommendations on how to improve their sleep quality. The app should include the following features:

1. **Data Input**: Users should be able to enter their daily sleep duration, quality (e.g., light sleep, deep sleep, REM), and any external factors affecting their sleep such as caffeine intake, exercise, and stress levels.
2. **Analysis**: Utilize 'SleepHarmonizer' to process and analyze the entered data. This includes calculating average sleep quality over time, identifying patterns, and correlating external factors with sleep quality.
3. **Recommendations**: Based on the analysis, provide tailored advice to users on how they can enhance their sleep habits. For example, suggest earlier bedtimes if deep sleep is consistently low, or recommend reducing caffeine intake if it’s negatively impacting sleep quality.
4. **Visualization**: Implement graphs and charts to visually represent the user's sleep patterns and improvements over time.
5. **Integration**: Allow users to export their sleep data in various formats (CSV, PDF) for sharing with healthcare providers or personal records.

To achieve these functionalities, you'll need to integrate 'SleepHarmonizer' into your app to handle the backend processing of sleep data. Ensure the app is user-friendly and accessible, making it easy for individuals to track and improve their sleep health.