Generate VeADK Agent architectures and code from user requirements.
npx skills add https://github.com/huangrt01/CS-Notes --skill veadk-huangrt01
Please help me install this Agent Skill. Skill: VeADK 技能集合 Source: https://github.com/huangrt01/CS-Notes/tree/main/.trae/openclaw-skills/veadk-skills Command: npx skills add https://github.com/huangrt01/CS-Notes --skill veadk-huangrt01
根据用户的功能需求，自动生成与 VeADK 相关的 Agent 架构与代码，并可将现有 Langchain/Langgraph 代码或 Dify 工作流转换为 VeADK 兼容实现。
Describe your VeADK use case and I will generate a root_agent with supporting code scaffolding.
High-intent search queries and answers about installing and using this skill.
Converting Langchain code to a VeADK Agent involves transforming existing Langgraph or Langchain scripts into VeADK compatible component implementations using established converter guidelines and architectural references.
Turning a Dify workflow into VeADK components is supported by translating the workflow logic into a component-based VeADK architecture, ensuring the output aligns with VeADK tool specifications and knowledge base structures.
Generating an Agent architecture from requirements uses a step-by-step process: analyzing functional needs, refining the prompt, and coding the root_agent with supporting scaffolding to match VeADK tool specifications.
VeADK supports code scaffolding for new agents by saving generated architectures into a standardized package structure, producing files like agent_name/agent.py and __init__.py through dedicated script tools.
There are specific guidelines for VeADK code generation and prompt refinement located in the references directory, utilizing analyze.md, refine_prompt.md, and coding.md documents to shape outputs and ensure architectural consistency.
Limitations when converting Langgraph workflows to VeADK include the strict necessity to follow converter guidelines to maintain structural consistency, as deviating from reference documents may break the generated agent's tool integration.
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