🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has no apparent immediate risks such as network calls or shell execution. However, its recently created repository and lack of community engagement raise concerns about potential low effort or malicious intent.

Recently created repository
Lack of community engagement

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command execution from the package.
Metadata: The repository's recent creation and lack of community engagement suggest potential low effort or malicious intent.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T10:12:46Z)

Repository created very recently: 5 day(s) ago (2026-06-01T10:12:46Z)
Repository has zero stars and zero forks

⚠ 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 adxlite

Create a mini-application called 'DataInsight' that leverages the 'adxlite' package to manage and query a local SQLite database using Kusto Query Language (KQL). This application will serve as a simple data analysis tool for users who need to perform complex queries on their datasets without the overhead of setting up a full-fledged database system. Here are the steps and features to include:

1. **Setup Database**: Initialize the 'adxlite' database with a few predefined tables such as 'sales', 'customers', 'products', etc., each containing sample data.
2. **Data Entry**: Implement a feature that allows users to input new records into these tables via a command-line interface or a simple GUI.
3. **Query Interface**: Provide a user-friendly way for users to write KQL queries against the database. This could be through a command-line prompt or a graphical interface where users can see results in real-time.
4. **Visualization**: Integrate basic visualization capabilities so that users can plot query results as graphs or charts directly within the application.
5. **Export Results**: Allow users to export query results in CSV or Excel formats for further analysis outside the application.
6. **Security & Backup**: Include options for basic security measures like password protection and automatic backups of the database.
7. **Documentation & Help**: Ensure the application comes with comprehensive documentation and a help section explaining how to use KQL effectively.

This project aims to showcase the flexibility and power of using 'adxlite' for local data management and querying, making it accessible even to those who are not familiar with traditional SQL.