🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of connecting to external services, which could be legitimate but needs closer scrutiny. Additionally, the author's limited history with PyPI adds a layer of uncertainty.

Network calls to external services
Single-package author history

Per-check LLM notes

Network: The observed network call patterns indicate the package may be attempting to connect to external services, possibly for schema registry interactions. This could be legitimate but requires further investigation.
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 author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

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

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://www.agora.my-working.com/plugins/
Detailed PyPI description (3684 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 5.0

Partial type annotation coverage

274 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 8 commits in thanhtham010891/agora-etl-plugins
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

try: with socket.create_connection((host, port), timeout=1.0): return e
9}) monkeypatch.setattr("urllib.request.urlopen", _fake_urlopen) client = ConfluentSchemaRegist
) monkeypatch.setattr("urllib.request.urlopen", _fake_urlopen) client = ConfluentSchemaRegist

✓ 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

Repository thanhtham010891/agora-etl-plugins appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Tham Tra" 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 agora-etl-plugins

Create a real-time data processing mini-app that integrates with various databases and message queues using the 'agora-etl-plugins' package. Your app will serve as a bridge between different data sources, enabling efficient data extraction, transformation, and loading (ETL) operations. Here’s a step-by-step guide on how to build it:

1. **Project Setup**: Start by setting up your Python environment. Install the necessary packages including 'agora-etl-plugins', 'Kafka', 'PostgreSQL', 'Redis', and any other dependencies required for connecting to these services.
2. **Data Extraction**: Use the 'Kafka' plugin from 'agora-etl-plugins' to subscribe to a specific topic where new data entries are published in real-time. Implement functionality to extract this streaming data efficiently.
3. **Transformation Logic**: After extracting the data, implement transformation logic within your app. This could include cleaning the data, converting data types, or applying business rules to enrich the data before it is loaded into another system.
4. **Loading Data**: Utilize the 'PostgreSQL' and 'Redis' plugins provided by 'agora-etl-plugins' to load transformed data into both a relational database (PostgreSQL) and a key-value store (Redis). Ensure that data integrity and consistency are maintained during this process.
5. **Scheduling and Coordination**: Employ the 'cron scheduling' and 'distributed coordination' functionalities of 'agora-etl-plugins' to ensure that your ETL processes run at specified intervals and manage concurrency issues effectively across multiple instances if needed.
6. **Monitoring and Alerts**: Implement monitoring and alerting mechanisms to track the performance of your ETL jobs and receive notifications in case of failures or anomalies.
7. **User Interface**: Optionally, develop a simple web-based UI to visualize the data being processed and managed by your app, allowing users to interact with the data stored in PostgreSQL and Redis.

**Features to Consider**:
- Real-time data ingestion from Kafka.
- Efficient data transformation with support for custom scripts or functions.
- Seamless integration with PostgreSQL for structured data storage and Redis for caching.
- Scheduled execution of ETL jobs using cron-like functionality.
- Distributed coordination to handle multi-instance scenarios.
- Monitoring tools to keep track of job status and performance metrics.
- A user-friendly interface for data visualization and management.

This project will demonstrate the power and flexibility of 'agora-etl-plugins' in handling complex data workflows and integrating diverse data systems.