🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks for shell execution, obfuscation, and credential harvesting. However, the network risk is moderate due to the use of aiohttp, and the metadata risk is elevated because the maintainer is new and there's limited community engagement.

Moderate network risk due to HTTP request capabilities
Elevated metadata risk due to new maintainer and lack of community engagement

Per-check LLM notes

Network: The use of aiohttp suggests the package is intended to perform HTTP requests, which may be legitimate depending on its purpose.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer seems new and the repository lacks community engagement, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

Test runner config found: conftest.py
Test runner config found: conftest.py
16 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (10669 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Type checker (mypy / pyright / pytype) referenced in project
153 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 58 commits in darkstussy/aiochlite
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

kip("aiohttp") async with aiohttp.ClientSession() as session, _client(clickhouse_config, session=session) as

✓ 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 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "darkstussy" 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 aiochlite

Create a real-time data analytics dashboard using Python and the 'aiochlite' package. This dashboard will allow users to query and visualize live data from a ClickHouse database in near real-time. Here are the key steps and features for this project:

1. **Setup**: Begin by installing 'aiochlite' and setting up a ClickHouse server instance. Ensure you have a dataset to work with, such as stock market data, sensor readings, or any time-series data.
2. **Data Ingestion**: Design a script to continuously ingest new data into your ClickHouse database at regular intervals. Use aiochlite to handle the asynchronous communication efficiently.
3. **Real-Time Querying**: Develop a function that allows querying the latest data from the database using aiochlite. This function should support various types of queries, including aggregations and filtering based on timestamps.
4. **Visualization**: Integrate a visualization library like Plotly or Matplotlib to display the queried data in real-time. Update the visualizations periodically as new data comes in.
5. **User Interface**: Create a simple web interface using Flask or a similar framework to interact with the dashboard. Users should be able to select different datasets and apply filters to see customized visualizations.
6. **Error Handling and Logging**: Implement robust error handling and logging mechanisms to ensure the system remains stable and informative even under unexpected conditions.
7. **Performance Optimization**: Since aiochlite is built on asyncio, focus on optimizing the performance of your application by leveraging asynchronous programming techniques.

This project aims to demonstrate the power of aiochlite in handling real-time data processing and visualization tasks, showcasing its efficiency and ease of use.