airbyte-source-marketo

v2.0.1 safe
3.0
Low Risk

Source implementation for Marketo.

🤖 AI Analysis

Final verdict: SAFE

The package is developed by Airbyte, which shows minimal activity with just one package, but there are no other suspicious indicators. The description aligns with the expected functionality.

  • Author has only one package
  • No other suspicious metadata flags
Per-check LLM notes
  • Metadata: The author 'Airbyte' has only one package, suggesting it may be a new or less active account, but no other suspicious flags were found.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.airbyte.com/integrations/sources/marketo
  • Brief PyPI description (456 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

  • 26 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 14 unique contributor(s) across 100 commits in airbytehq/airbyte
  • Active community — 5 or more distinct contributors

🔬 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: airbyte.io

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository airbytehq/airbyte appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Airbyte" 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 airbyte-source-marketo 
Develop a fully-functional mini-application named 'MarketoDataExtractor' that leverages the 'airbyte-source-marketo' Python package to extract and analyze customer data from Marketo. This application will serve as a tool for marketers to gain insights into their customer behavior, lead generation, and campaign performance. The app should include the following functionalities:

1. **Authentication and Connection**: Implement a user-friendly interface to authenticate users with their Marketo credentials (client ID, client secret, instance URL). Ensure secure handling of these credentials.
2. **Data Extraction**: Use 'airbyte-source-marketo' to pull data from Marketo, focusing on key entities such as leads, campaigns, activities, and engagement metrics. Allow users to select specific data fields and time periods for extraction.
3. **Data Visualization**: Integrate a simple dashboard that visualizes extracted data using charts and graphs. Highlight trends over time, lead scores, campaign effectiveness, and other relevant metrics.
4. **Report Generation**: Enable users to generate comprehensive reports based on the extracted data. Reports should be downloadable in PDF and CSV formats.
5. **Custom Filters and Segmentation**: Provide advanced filtering options to allow users to segment data based on custom criteria such as lead source, activity type, or specific date ranges.
6. **Real-Time Monitoring**: Include real-time monitoring capabilities that notify users of significant changes or anomalies in data trends via email or push notifications.
7. **User Management**: Implement basic user management features allowing multiple users to access the application, each with their own set of permissions and data views.

To achieve these functionalities, utilize 'airbyte-source-marketo' for its robust integration with Marketo's API, ensuring efficient and reliable data extraction. Additionally, consider integrating other Python libraries like Pandas for data manipulation, Matplotlib or Plotly for visualization, and Flask or Django for the web framework.

💬 Discussion Feed

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