🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe for use with no detected malicious activities or high-risk behaviors. However, there are some concerns regarding low maintainer activity and metadata quality.

No network calls or shell execution detected
Low maintainer activity and metadata quality noted

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 malicious shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintainer activity and metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (882 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 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

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 ai-sqlx

Create a command-line tool that allows users to interact with their databases using natural language commands instead of writing SQL queries manually. This tool will utilize the 'ai-sqlx' package to translate user-provided English instructions into corresponding SQL queries for execution. Here’s a step-by-step guide on how to develop this mini-application:

1. **Project Setup**: Start by setting up your Python environment. Install the necessary packages including 'ai-sqlx', 'mysql-connector-python', 'psycopg2', or 'sqlite3' depending on which database system you plan to support.

2. **User Interface Design**: Design a simple command-line interface (CLI) where users can input their natural language requests. Ensure the CLI is intuitive and easy to use, providing clear instructions on how to format their requests.

3. **Natural Language Processing Integration**: Utilize the 'ai-sqlx' package to handle the translation from natural language to SQL queries. Integrate this functionality so that when a user inputs a request, the tool can process it through 'ai-sqlx' and generate the appropriate SQL query.

4. **Database Connection Handling**: Implement code to connect to the chosen database (MySQL, PostgreSQL, SQLite). Your application should be able to establish a connection, execute the generated SQL query, and return the results back to the user.

5. **Error Handling**: Include robust error handling mechanisms to manage issues such as invalid input, connection failures, or errors during query execution. Provide meaningful feedback to the user when something goes wrong.

6. **Additional Features**: Consider adding extra functionalities like saving frequently used queries, allowing users to switch between different databases easily, or even supporting basic CRUD operations directly from the CLI without needing to write SQL.

7. **Testing**: Thoroughly test your application with various types of natural language inputs and ensure that it correctly translates these into SQL and retrieves accurate data from the database.

8. **Documentation**: Finally, document your application thoroughly, explaining how to install and run it, as well as any limitations or known issues. Make sure your documentation is accessible and easy to understand.