modify-agent

Modify agent code, tools, and configuration for Databricks OpenAI Agents SDK workflows.

Updated Mar 15, 2026
One-click install
npx skills add https://github.com/sumitsaxena-git/databricks-app --skill modify-agent-sumitsaxena-git
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: modify-agent
Source: https://github.com/sumitsaxena-git/databricks-app/tree/main/agent-openai-agents-sdk/.claude/skills/modify-agent
Command: npx skills add https://github.com/sumitsaxena-git/databricks-app --skill modify-agent-sumitsaxena-git

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams evolve their AI agents by guiding modifications to code, tools, and configurations, enabling rapid iteration and safer upgrades.

Core Features & Use Cases

  • Modify agent behavior and instructions to fit new requirements.
  • Add or adjust tools and MCP servers to enhance capabilities.
  • Validate changes against existing workflows and deployment patterns.

Quick Start

Provide updated agent configuration and code changes based on user requests.

Frequently Asked Questions about modify-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I modify OpenAI agent behavior and instructions on Databricks?

To modify OpenAI agent behavior on Databricks, you apply explicit changes to the agent.py file and databricks.yml configuration, allowing you to safely update agent instructions and characteristics for new requirements.

How can I add MCP servers and tools to a Databricks OpenAI Agents SDK workflow?

You can add MCP servers and tools to a Databricks OpenAI Agents SDK workflow by requesting tool integration adjustments, which applies safe and auditable modifications directly to your agent code and configuration.

What is the best way to adjust model configuration for existing Databricks agents?

The best way to adjust model configuration for Databricks agents is through explicit databricks.yml modifications, ensuring changes are validated against existing workflows and deployment patterns across your environments.

Can I use this approach to validate agent code changes against existing Databricks deployment patterns?

Yes, you can validate agent code changes against existing Databricks deployment patterns, ensuring that modifications to agent characteristics and tools are safe and compatible with your current workflows.

When do I need to update agent.py for Databricks OpenAI Agents SDK workflows?

You need to update agent.py when users request explicit modifications to agent characteristics, require new tool integration, or need to adjust model instructions across Databricks environments.