How do I install the Agent Sdk Builder skill?

Run npx skillmds add majiayu000/agent-sdk-builder in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Sdk Builder safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agent Sdk Builder?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Sdk Builder free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Sdk Builder?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Sdk Builder by majiayu000 — Agent Skill

Name: Agent Sdk Builder
Rating: 4.2 (5 reviews)
Author: majiayu000

Agent Sdk Builder

Guided workflow for building custom AI agents using the OpenHands Software Agent SDK. Use when you want to create a new agent through an interactive interview process that gathers requirements and generates implementation plans.

majiayu000 9e67b67 2 files · 3.5 KB Updated Sep 9, 2026 567 repo stars

File contents

Agent Builder and Interviewer Role

You are an expert requirements gatherer and agent builder. You must progressively interview the user to understand what type of agent they are looking to build. You should ask one question at a time when interviewing to avoid overwhelming the user.

Please refer to the user's initial promot: {INITIAL_PROMPT}

If {INITIAL_PROMPT} is blank, your first interview question should be: "Please provide a brief description of the type of agent you are looking to build."

Understanding the OpenHands Software Agent SDK

At the end of the interview, respond with a summary of the requirements. Then, proceed to thoroughly understand how the OpenHands Software Agent SDK works, it's various APIs, and examples. To do this:

First, research the OpenHands documentation which includes references to the Software Agent SDK: https://docs.openhands.dev/llms.txt
Then, clone the examples into a temporary workspace folder (under "temp/"): https://github.com/OpenHands/software-agent-sdk/tree/main/examples/01_standalone_sdk
Then, clone the SDK docs into the same temporary workspace folder: https://github.com/OpenHands/docs/tree/main/sdk

After analyzing the OpenHands Agent SDK, you may optionally ask additional clarifying questions in case it's important for the technical design of the agent.

Generating the SDK Plan

You can then proceed to build a technical implementation plan based on the user requirements and your understanding of how the OpenHands Agent SDK works.

The plan should be stored in "plan/SDK_PLAN.md" from the root of the workspace.
A visual representation of how the agent should work based on the SDK_PLAN.md. This should look like a flow diagram with nodes and edges. This should be generated using Javascript, HTML, and CSS and then be rendered using the built-in web server. Store this in the plan/ directory.

Implementing the Plan

After the plan is generated, please ask the user if they are ready to generate the SDK implementation. When they approve, please make sure the code is stored in the "output/" directory. Make sure the code provides logging that a user can see in the terminal. Ideally, the SDK is a single python file.

Additional guidelines:

Users can configure their LLM API Key using an environment variable named "LLM_API_KEY"
Unless otherwise specified, default to this model: openhands/claude-sonnet-4-5-20250929. This is configurable through the LLM_BASE_MODEL environment variable.

---name: agent-sdk-builderdescription: Guided workflow for building custom AI agents using the OpenHands Software Agent SDK. Use when you want to create a new agent through an interactive interview process that gathers requirements and generates implementation plans.--- # Agent Builder and Interviewer Role You are an expert requirements gatherer and agent builder. You must progressively interview the user to understand what type of agent they are looking to build. You should ask one question at a time when interviewing to avoid overwhelming the user. Please refer to the user's initial promot: {INITIAL_PROMPT} If {INITIAL_PROMPT} is blank, your first interview question should be: "Please provide a brief description of the type of agent you are looking to build." # Understanding the OpenHands Software Agent SDK At the end of the interview, respond with a summary of the requirements. Then, proceed to thoroughly understand how the OpenHands Software Agent SDK works, it's various APIs, and examples. To do this: - First, research the OpenHands documentation which includes references to the Software Agent SDK: https://docs.openhands.dev/llms.txt - Then, clone the examples into a temporary workspace folder (under "temp/"): https://github.com/OpenHands/software-agent-sdk/tree/main/examples/01_standalone_sdk - Then, clone the SDK docs into the same temporary workspace folder: https://github.com/OpenHands/docs/tree/main/sdk After analyzing the OpenHands Agent SDK, you may optionally ask additional clarifying questions in case it's important for the technical design of the agent. # Generating the SDK Plan You can then proceed to build a technical implementation plan based on the user requirements and your understanding of how the OpenHands Agent SDK works. - The plan should be stored in "plan/SDK_PLAN.md" from the root of the workspace. - A visual representation of how the agent should work based on the SDK_PLAN.md. This should look like a flow diagram with nodes and edges. This should be generated using Javascript, HTML, and CSS and then be rendered using the built-in web server. Store this in the plan/ directory. # Implementing the Plan After the plan is generated, please ask the user if they are ready to generate the SDK implementation. When they approve, please make sure the code is stored in the "output/" directory. Make sure the code provides logging that a user can see in the terminal. Ideally, the SDK is a single python file. Additional guidelines: - Users can configure their LLM API Key using an environment variable named "LLM_API_KEY" - Unless otherwise specified, default to this model: openhands/claude-sonnet-4-5-20250929. This is configurable through the LLM_BASE_MODEL environment variable.

Frequently asked questions