🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with a low risk score due to its expected network behavior and lack of shell execution patterns. The maintainer's single package suggests they might be new or less active but does not necessarily imply malicious intent.

Network calls are expected for an HTTP client.
No shell execution patterns were detected.

Per-check LLM notes

Network: The package performs network calls to download Parquet files and communicates over HTTP, which is expected behavior for a client that handles asynchronous HTTP requests.
Shell: No shell execution patterns were detected.
Metadata: The maintainer has only one package, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

1 documentation file(s) (e.g. conf.py)
Detailed PyPI description (7291 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

83 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

12 unique contributor(s) across 100 commits in maximdanilchenko/aiochclient
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

) response = requests.get("https://url_to_download_parquet_file") await cl
:: python async with aiohttp.ClientSession() as s: client = ChClient(s, compress_response=T

✓ 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

Repository maximdanilchenko/aiochclient appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Danilchenko Maksim" 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 aiochclient

Your task is to create a simple yet powerful data ingestion and query tool using Python's 'aiochclient' package. This tool will serve as a bridge between your local system and a ClickHouse database, enabling real-time data analysis and visualization. Here’s a step-by-step guide on how to develop this tool:

1. **Project Setup**: Start by setting up your development environment. Ensure you have Python 3.10+ installed along with aiochclient. Use pip to install the aiochclient package.

2. **Database Connection**: Utilize aiochclient to establish a connection to a ClickHouse server. Make sure to handle connection errors gracefully and include reconnection logic in case of network issues.

3. **Data Ingestion**: Implement a feature where users can upload CSV files directly into the ClickHouse database via your tool. Ensure that the CSV file format is validated before ingestion and that data types are correctly mapped to ClickHouse columns.

4. **Query Interface**: Develop an interactive query interface that allows users to input SQL-like queries to fetch data from ClickHouse. The tool should support basic SQL operations like SELECT, INSERT, UPDATE, and DELETE.

5. **Real-Time Data Visualization**: Integrate a simple real-time data visualization component that can plot charts based on the fetched data. Libraries such as matplotlib or plotly can be used for this purpose.

6. **Security Measures**: Implement security measures such as encryption for data transfer and secure authentication methods when connecting to the ClickHouse server.

7. **User Interface**: Design a user-friendly interface (CLI or GUI) for ease of use. A CLI interface might suffice initially, but consider a GUI for more complex operations and better user experience.

8. **Documentation**: Write comprehensive documentation detailing how to set up the tool, connect to ClickHouse, ingest data, and perform queries. Include examples and best practices.

9. **Testing**: Thoroughly test your application to ensure it works as expected under various conditions. Pay special attention to error handling and performance optimization.

10. **Deployment**: Prepare your application for deployment. Consider packaging it as a Docker container for easy distribution and setup.

By following these steps, you'll create a robust and versatile tool that leverages the power of aiochclient for efficient data management and analysis.