🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators with no network calls, shell executions, or obfuscations detected. Although there are concerns about low activity and poor metadata quality, these do not strongly suggest malicious intent.

No network calls
No shell executions
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API access.
Shell: No shell execution detected, indicating no direct system command execution within the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (17573 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

139 type-annotated function signatures detected in source

○ 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

Email domain looks legitimate: dkl.digital>

✓ 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 airflow-toolkit

Create a mini-application using the 'airflow-toolkit' package that automates the process of monitoring and managing data pipelines for a fictional e-commerce company. This application will help the company ensure that its critical data processes run smoothly and efficiently.

The application should include the following features:
1. **Data Ingestion**: Automate the process of ingesting sales data from various sources such as CSV files, databases, and APIs into a central data warehouse.
2. **Data Validation**: Implement a validation task to check the integrity of the ingested data, ensuring no discrepancies exist between the source and target datasets.
3. **Error Handling**: Set up robust error handling mechanisms to log errors and automatically retry failed tasks after a specified interval.
4. **Notifications**: Configure notifications to alert stakeholders via email or Slack when critical tasks fail or complete successfully.
5. **Scheduling**: Schedule these tasks to run at specific intervals (e.g., daily, hourly) based on the company's operational needs.
6. **Visualization**: Integrate a simple dashboard within the application to visualize key performance indicators (KPIs) related to data pipeline health and efficiency.

To achieve these goals, you will utilize the 'airflow-toolkit' package, which provides a set of operators, hooks, and utilities tailored for Apache Airflow 3. Specifically, leverage the package's operators for data ingestion, validation, and error handling; its hooks for connecting to external data sources; and its utilities for scheduling and notifications. Additionally, explore the package's documentation and examples to discover any other functionalities that could enhance your application's capabilities.