🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of obfuscation and has low metadata quality, raising concerns about its legitimacy and purpose.

obfuscation risk of 5/10
low metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: The observed patterns may indicate an attempt to obfuscate code, but without additional context, it's hard to determine if this is malicious.
Credentials: No clear evidence of credential harvesting detected.
Metadata: Low activity and lack of detail suggest potential low quality or malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

'i+9' return str(eval(expr, {"i": i + 1})) # Evaluate the expression using the cur
return str(Field.idx_to_code(eval(expr, {"j": j}))) # Evaluate the expression using the curren

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Nicolas Bschor" 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 SheetMachine

Develop a Python-based mini-application named 'DataSheetMaster' that leverages the SheetMachine package to automate the creation of complex Excel (.xlsx) files from various data sources. The application should have the following core functionalities:

1. **Data Source Integration**: Allow users to input data from multiple sources such as CSV files, SQL databases, and JSON objects.
2. **Dynamic Data Manipulation**: Implement basic data transformation capabilities like filtering, sorting, and aggregation to tailor the dataset before it is converted into an Excel file.
3. **Customizable Excel Formatting**: Enable customization of the Excel file's appearance, including setting column widths, row heights, applying conditional formatting, and adding charts based on the data.
4. **Automated Report Generation**: Schedule and automate the generation of these Excel reports at specified intervals using Python's scheduling libraries.
5. **User Interface**: Provide a simple command-line interface (CLI) for users to interact with the application and configure settings without needing advanced programming knowledge.
6. **Error Handling and Logging**: Ensure robust error handling and logging mechanisms are in place to help diagnose and fix issues easily.

The SheetMachine package will be primarily used for converting the processed data into well-formatted Excel files. It will handle the complexities of Excel file creation, allowing you to focus on the data processing and user interaction aspects of the application.