apache-airflow-providers-facebook

v3.9.4 safe
3.0
Low Risk

Provider package apache-airflow-providers-facebook for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, primarily concerning metadata and licensing links, without any evidence of malicious activities such as network calls, shell executions, or credential harvesting.

  • Low network and shell execution risks
  • Potential metadata and licensing concerns
Per-check LLM notes
  • Network: No network call patterns detected, which aligns with the typical behavior of a library focused on providing connectors and operators for Airflow, not typically involving direct network calls.
  • Shell: No shell execution patterns detected, which is normal and expected.
  • Obfuscation: The observed pattern is likely for package path extension and not malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The package has a non-secure link and an author with limited information, suggesting potential issues but not strong indicators of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • 6 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-fac
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3510 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 3 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
✓ 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: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 apache-airflow-providers-facebook 
Create a social media analytics tool using Apache Airflow and the 'apache-airflow-providers-facebook' package. This tool will automate the process of collecting data from Facebook pages, analyzing the engagement metrics, and storing the results for future reference. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Project Setup**: Begin by setting up your development environment with Python, Apache Airflow, and the 'apache-airflow-providers-facebook' package. Ensure you have the necessary permissions and access tokens from Facebook to interact with their API.

2. **Data Collection DAG**: Develop a Directed Acyclic Graph (DAG) within Apache Airflow that fetches recent posts and associated metrics (such as likes, shares, comments) from a specified Facebook page. Use the 'apache-airflow-providers-facebook' package to authenticate and make requests to the Facebook Graph API. Schedule this DAG to run daily at midnight.

3. **Data Processing Task**: Implement a task within the DAG that processes the collected data. This task should calculate key metrics such as engagement rate, average comments per post, and sentiment analysis of comments. Utilize Python libraries like pandas for data manipulation and TextBlob for sentiment analysis.

4. **Data Storage**: Design a task to store the processed data into a database (e.g., PostgreSQL) for historical tracking and reporting purposes. Ensure the schema is optimized for querying and supports time-series analysis.

5. **Visualization Dashboard**: Create a simple dashboard using a web framework like Flask or Django that visualizes the stored data. The dashboard should allow users to view trends over time, compare different metrics, and filter by specific dates or post types.

6. **Alert System**: Add functionality to send email alerts when certain conditions are met, such as if the engagement rate drops below a predefined threshold. Use Apache Airflow’s email operator for sending notifications.

7. **Documentation & Deployment**: Write comprehensive documentation detailing setup instructions, DAG configurations, and usage guidelines. Prepare the project for deployment on a cloud platform like AWS or Google Cloud, ensuring all dependencies are properly managed.

By completing these steps, you'll have built a robust social media analytics tool that leverages the power of Apache Airflow and the 'apache-airflow-providers-facebook' package to streamline social media management tasks.

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