🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential typosquatting and incomplete metadata, which raises concerns about its legitimacy and maintenance.

Potential typosquatting targeting 'rq'
Incomplete metadata suggesting low maintainer activity

Per-check LLM notes

Network: Network calls may be legitimate if the package requires external services; however, without context, there's a risk of data exfiltration.
Shell: No shell execution patterns detected, suggesting low risk of direct system command abuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and incomplete metadata, which could indicate potential malicious intent or negligence.
⚠ Typosquatting target: rq

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

}" response = requests.get(url_with_token, timeout=60) data = response.

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: rq

"IQL" is 2 edit(s) from "rq"

✓ Registered Email Domain

Email domain looks legitimate: iqmo.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 IQL

Your task is to create a command-line utility called 'QueryMaster' using the Python package 'IQL'. This tool will allow users to query and manipulate data from various sources in a flexible and powerful way. The utility should support basic CRUD operations (Create, Read, Update, Delete) on a local SQLite database, as well as more advanced querying capabilities provided by the 'IQL' package.

Key Features:
1. **User-friendly CLI Interface**: Provide clear commands and help messages for all functionalities.
2. **Data Source Support**: Initially support SQLite databases but allow for easy extension to other data sources like CSV files or remote databases.
3. **Basic CRUD Operations**: Implement Create, Read, Update, and Delete operations on records within the database.
4. **Advanced Querying with IQL**: Utilize the 'IQL' package to allow users to write complex queries using its syntax. Users should be able to filter, sort, group, and aggregate data easily.
5. **Data Visualization**: Integrate a simple visualization feature to display query results graphically. This could be as simple as generating a bar chart of the query results.
6. **Configuration File**: Allow users to configure default settings such as database connection strings through a configuration file.
7. **Error Handling and Logging**: Ensure robust error handling and logging of actions taken by the user and any errors encountered during execution.

Steps to Build the Application:
1. Set up a new Python project and install necessary dependencies including 'IQL' and any required database connectors.
2. Design the schema for the SQLite database and initialize it with sample data if needed.
3. Develop the CLI interface using Python's argparse or similar library, ensuring each command is clearly documented.
4. Implement the basic CRUD operations directly using SQL queries or ORM (Object-Relational Mapping) techniques.
5. Integrate the 'IQL' package to parse and execute user-written queries. Test the package thoroughly to ensure it supports all required operations.
6. Add the data visualization feature, perhaps using matplotlib or seaborn for plotting.
7. Create a configuration file parser that reads settings such as database connection strings.
8. Implement comprehensive error handling and logging mechanisms.
9. Test the application extensively, focusing on edge cases and potential failure points.
10. Document the application thoroughly, providing examples of how to use each feature and troubleshoot common issues.

By following these steps and incorporating the key features listed above, you'll have built a versatile and powerful data querying utility that leverages the unique capabilities of the 'IQL' package.