veadk-skills

Generate VeADK Agent code from user requirements and convert Langchain/Langgraph or Dify workflows.

417|87|Updated Nov 12, 2025

Search Tags:#automation #langchain #code-generation #workflow-conversion #agent-generation #veadk #dify

Ready to run

npx skills add https://github.com/bytedance/agentkit-samples --skill veadk-skills

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: veadk-skills
Source: https://github.com/bytedance/agentkit-samples/tree/main/skills/veadk-skills
Command: npx skills add https://github.com/bytedance/agentkit-samples --skill veadk-skills

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

本技能可以根据用户的功能需求，生成符合 VeADK 的 Agent 代码，或完成 VeADK 相关功能。

Core Features & Use Cases

通过分析用户需求，生成 VeADK Agent 架构与实现，并提供可重用的根 Agent（root_agent）及子智能体的示例。
将 Langchain/Langgraph、Dify 工作流等迁移为 VeADK Agent 代码，实现跨框架兼容。
提供参考文档与参考实现模板，方便后续扩展与代码复用。

Quick Start

Provide a VeADK Agent implementation based on the described user requirements.

Frequently Asked Questions about veadk-skills

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

FAQPage Schema

How do I convert Langchain or Dify workflows into VeADK compatible agents?▼

To convert Langchain or Dify workflows into VeADK agents, this skill translates your existing logic into a VeADK-compatible architecture featuring a root_agent and clear sub-agents. It ensures structured prompts and modular tooling for safe, reproducible code generation.

What is the best way to generate a VeADK agent architecture from user requirements?▼

Generating a VeADK agent architecture from requirements involves analyzing your needs to produce a root_agent and sub-agent implementations. This skill provides reusable examples, structured prompts, and reference documentation templates to enable modular tooling and reproducible code.

Can I migrate existing Langgraph logic to VeADK without losing workflow structure?▼

Migrating existing Langgraph logic to VeADK preserves your workflow structure by mapping it into a root_agent and sub-agent architecture. The skill applies reference documentation governance to ensure the translated agent code remains modular and reproducible.

Does VeADK code generation support structured prompts and modular tooling?▼

VeADK code generation supports structured prompts and modular tooling by applying reference documentation governance and reusable implementation templates. This ensures the generated root_agent and sub-agents are safe and reproducible.

Why do I need a root_agent and sub-agents when converting Dify workflows to VeADK?▼

A root_agent and sub-agents are needed when converting Dify workflows to VeADK to provide a clear, modular architecture. This structure enables safe, reproducible code generation and satisfies structured prompt requirements for your translated logic.

What are the limitations of converting existing logic into VeADK agents?▼

Converting existing logic into VeADK agents requires strict adherence to reference documentation governance to ensure safe, reproducible code generation. Without structured prompts and modular tooling, the translated Langchain or Dify workflows may lack the necessary architecture clarity.