agentscope使用相关 ”调用工具前，请使用提供的工具（view_text_file）查看相关技能文件。“

”调用工具前，请使用提供的工具（view_text_file）查看相关技能文件。“

njsgcs

599人浏览 · 2026-01-30 13:31:55

njsgcs · 2026-01-30 13:31:55 发布

agentscope-ai/agentscope | DeepWiki

AgentScope

来个使用skill然后可以在agentscope studio里可以对话的示例代码，ollama服务

可在 AgentScope Studio 中对话的示例代码：


# -*- coding: utf-8 -*-
"""The main entry point of the agent skill example."""
import asyncio
import os

from agentscope.agent import ReActAgent
from agentscope.formatter import DashScopeChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.message import Msg
from agentscope.model import DashScopeChatModel
from agentscope.tool import (
    Toolkit,
    execute_shell_command,
    execute_python_code,
    view_text_file,
)


async def main() -> None:
    """The main entry point for the ReAct agent example."""
    toolkit = Toolkit()

    # To use agent skills, your agent must be equipped with text file viewing
    # tools.
    toolkit.register_tool_function(execute_shell_command)
    toolkit.register_tool_function(execute_python_code)
    toolkit.register_tool_function(view_text_file)

    # Register the agent skill
    toolkit.register_agent_skill(
        "./skill/analyzing-agentscope-library",
    )

    agent = ReActAgent(
        name="Friday",
        sys_prompt="""You are a helpful assistant named Friday.

# IMPORTANT
- Don't make any assumptions. All your knowledge about AgentScope library must come from your equipped skills.
""",  # noqa: E501
        model=DashScopeChatModel(
            api_key=os.environ.get("DASHSCOPE_API_KEY"),
            model_name="qwen3-max",
            enable_thinking=False,
            stream=True,
        ),
        formatter=DashScopeChatFormatter(),
        toolkit=toolkit,
        memory=InMemoryMemory(),
    )

    # First, let's take a look at the agent's system prompt
    print("\033[1;32mAgent System Prompt:\033[0m")
    print(agent.sys_prompt)
    print("\n")

    print(
        "\033[1;32mResponse to Question 'What skills do you have?':\033[0m",
    )
    # We prepare two questions
    await agent(
        Msg("user", "What skills do you have?", "user"),
    )

    print(
        "\n\033[1;32mResponse to Question 'How to create my own tool function "
        "for the agent in agentscope?':\033[0m",
    )
    # The second question
    await agent(
        Msg(
            "user",
            "How to custom tool function for the agent in agentscope?",
            "user",
        ),
    )


asyncio.run(main())

使用说明：

在项目根目录下创建 skill/analyzing-agentscope-library/ 文件夹
在其中放置 SKILL.md 文件，内容参考官方示例中的结构

Ollama 配置：

确保 Ollama 服务正在运行（默认端口 11434）
将 model_name 替换为你本地已下载的模型名称

在 AgentScope Studio 中运行：

将此代码保存为 .py 文件
在 Studio 中加载并运行
Agent 会根据 Skill 定义自动调用相应工具来回答问题完整示例代码结构可参考：https://github.com/agentscope-ai/agentscope/tree/main/examples/functionality/agent_skill
注意：Skill 的核心是 SKILL.md 文件，其中的 YAML 元数据（name/description）会被自动加载到系统提示词中，而具体内容需要通过 view_text_file 工具在运行时按需读取。