Using Skills With AnthropicVertex: A Code Example

Nov 27, 2025 by Alex Johnson 50 views

Using Skills with AnthropicVertex: A Comprehensive Guide

This article addresses a common challenge faced by developers using Anthropic's Claude models on Google Cloud Platform (GCP): how to effectively integrate skills using the AnthropicVertex class. We'll break down the problem, analyze the error message, and provide a step-by-step solution with code examples. This guide aims to provide a clear understanding of how to leverage AnthropicVertex for utilizing skills within your Claude-powered applications.

Understanding the Issue: Integrating Skills with AnthropicVertex

The core question revolves around utilizing skills with Anthropic models deployed on GCP via the AnthropicVertex class. The user encountered a 400 error, specifically an invalid_request_error, when attempting to incorporate skills into their code. The error message indicates an issue with the anthropic-beta header, suggesting that the values provided (code-execution-2025-08-25, skills-2025-10-02) are not recognized or are incorrectly formatted. This type of error often arises from using outdated or incorrect headers when interacting with beta features or specific versions of an API. The user's code snippet provides valuable context, showcasing their attempt to initialize the AnthropicVertex client and utilize beta features related to code execution and skills.

To effectively address this issue, it's crucial to understand the role of headers in API requests, particularly when dealing with beta functionalities. Headers act as metadata, providing additional information about the request to the server. In this case, the anthropic-beta header is intended to enable access to experimental or pre-release features. However, the error message suggests that the specified values are either invalid or no longer supported. Therefore, it's imperative to consult the official Anthropic documentation for the correct header values and usage guidelines. Furthermore, it is important to double-check the versions of the libraries being used, as outdated packages might contain deprecated features or incompatible API calls. The remainder of this article will delve into a detailed solution, outlining the necessary steps to resolve this error and successfully integrate skills with AnthropicVertex.

Decoding the Error Message

Let's dissect the error message to gain a deeper understanding of the problem:

Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'Unexpected value(s) `code-execution-2025-08-25`, `skills-2025-10-02` for the `anthropic-beta` header. Please consult our documentation at docs.anthropic.com or try again without the header.'}, 'request_id': 'req_vrtx_011CVY5XUKtwCCt4fRuBQ2xp'}

The key takeaway is the phrase "Unexpected value(s) code-execution-2025-08-25, skills-2025-10-02 for the anthropic-beta header." This clearly indicates that the values being passed in the anthropic-beta header are not recognized by the Anthropic API. This could be due to several reasons, including incorrect values, outdated feature flags, or changes in the API. The message explicitly advises consulting the official Anthropic documentation, which is the first and most crucial step in troubleshooting this issue. By referring to the documentation, we can verify the correct header values, the supported features, and any specific requirements for using skills with Anthropic models.

The 400 error code signifies a "Bad Request," meaning the server couldn't process the request due to a client error. In this context, the client error stems from the incorrect header values. Understanding the HTTP status codes is crucial for debugging API interactions, as they provide valuable insights into the nature of the problem. The request_id is also useful for tracking and debugging purposes, especially when contacting Anthropic support for assistance. This unique identifier allows Anthropic to pinpoint the specific request that failed and investigate the issue in more detail. In the next section, we'll explore the solution, which involves verifying the header values and adjusting the code accordingly, ensuring it aligns with the current Anthropic API specifications.

Step-by-Step Solution: Implementing Skills with AnthropicVertex

Based on the error message and the context provided, here’s a step-by-step solution to successfully use skills with AnthropicVertex:

Consult the Official Anthropic Documentation: This is the most critical step. Visit Anthropic's official documentation{target=_blank} to find the latest information on using skills, the correct header values for beta features, and any specific requirements for AnthropicVertex. Pay close attention to any recent updates or changes to the API.
Verify the anthropic-beta Header: The error message points directly to this header. Check the documentation for the correct values to use for accessing skills. It's possible that the values code-execution-2025-08-25 and skills-2025-10-02 are outdated or no longer valid. Ensure that you are using the correct feature flags as specified in the documentation. If the documentation suggests removing the header, try that as well.
Update the Anthropic Library: Ensure you are using the latest version of the anthropic Python library. Outdated libraries might contain bugs or incompatible code. You can update the library using pip:
```
pip install --upgrade anthropic
```
Keeping your libraries up-to-date is essential for accessing the latest features and bug fixes.

Refactor the Code: Based on the documentation, adjust your code to use the correct header values and API calls. Here’s an example of how the code might look after applying the necessary changes (this is a hypothetical example, and you should adapt it based on the actual documentation):

import os
from anthropic import AnthropicVertex
from dotenv import load_dotenv

load_dotenv()

client = AnthropicVertex(
    region="global",
    project_id="xxx-xx",
# Removed the default_headers as per documentation (example)
)
# Ensure the environment variable is set
# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "d:/Python/2025/claude_skills/service-account.json"
MODEL = os.getenv("MODEL", "claude-sonnet-4-5@20250929")


def main():

    response = client.beta.messages.create(
        model=MODEL,
        max_tokens=4096,
# Removed betas parameter (example)
        container={
            "skills": [{
                "type": "anthropic",
                "skill_id": "pptx",
                "version": "latest"
            }]
        },
        messages=[{
            "role": "user",
            "content": "Create a presentation about renewable energy"
        }],
# Removed tools parameter (example)
    )

    print(response.content[0].text)


if __name__ == "__main__":
    main()

This refactored code snippet illustrates the potential changes, such as removing the default_headers and betas parameters based on the updated API specifications.

Verify Authentication: Double-check that your Google Cloud credentials are set up correctly. Ensure the GOOGLE_APPLICATION_CREDENTIALS environment variable points to the correct service account JSON file, and that the service account has the necessary permissions to access the Anthropic models.
Test the Code: After making the changes, thoroughly test your code to ensure it’s working as expected. If you still encounter issues, revisit the documentation and carefully review your code for any discrepancies.

By following these steps and referring to the official Anthropic documentation, you should be able to resolve the error and successfully use skills with AnthropicVertex. Remember, the documentation is your primary resource for accurate and up-to-date information about the API.

Best Practices for Working with AnthropicVertex and Skills

To ensure a smooth experience when working with AnthropicVertex and skills, consider these best practices:

Stay Updated with the Documentation: Anthropic's API and features are constantly evolving. Regularly review the official documentation for the latest updates, changes, and best practices. Proactive monitoring of the documentation can prevent unexpected issues and ensure you're leveraging the most current functionalities.
Use Environment Variables: Store sensitive information like API keys and project IDs in environment variables instead of hardcoding them in your code. This improves security and makes your code more portable. This practice ensures that sensitive credentials are not exposed in your codebase, enhancing security and simplifying deployment across different environments.
Implement Error Handling: Implement robust error handling in your code to gracefully handle exceptions and provide informative error messages. This will help you identify and resolve issues more quickly. Comprehensive error handling not only improves the stability of your application but also provides valuable insights for debugging and troubleshooting.
Test Thoroughly: Write unit tests and integration tests to ensure your code is working correctly. This is especially important when working with complex APIs and features like skills. Thorough testing is crucial for identifying potential bugs and ensuring the reliability of your application, especially when integrating with external services.
Leverage Logging: Use logging to track the execution of your code and capture important information, such as API requests and responses. This can be invaluable for debugging and monitoring your application. Effective logging provides a detailed audit trail of your application's behavior, making it easier to diagnose issues and optimize performance.
Monitor API Usage: Keep track of your API usage to avoid exceeding rate limits or quotas. Anthropic might have limits on the number of requests you can make per minute or per day. Monitoring API usage helps prevent disruptions and ensures you stay within the allocated resources, optimizing cost and performance.

Conclusion

Integrating skills with Anthropic's Claude models via AnthropicVertex offers powerful capabilities for building advanced applications. However, it's crucial to stay informed about API updates and follow best practices to avoid common pitfalls. By carefully consulting the official documentation, implementing robust error handling, and testing your code thoroughly, you can leverage the full potential of AnthropicVertex and create innovative solutions. Remember, the key to success lies in staying updated with the latest information and adapting your code accordingly. This guide has provided a comprehensive approach to troubleshooting the specific error encountered, along with general best practices for working with AnthropicVertex. By following these guidelines, you'll be well-equipped to build and deploy successful applications powered by Anthropic's cutting-edge AI models.

For further information and updates on Anthropic models and features, be sure to visit the official Anthropic Documentation{target=_blank}.