mirror of
https://github.com/csunny/DB-GPT.git
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ef83851b31
Co-authored-by: lusain <lusain1990@gmail.com> Co-authored-by: alan.cl <1165243776@qq.com> Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
"""
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Example: Using Claude-style SKILL files with DB-GPT agents.
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This demonstrates how to use the Claude SKILL mechanism
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where skills are defined in Markdown files.
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"""
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import asyncio
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import os
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from dbgpt.agent import AgentContext, AgentMemory, LLMConfig
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from dbgpt.agent.claude_skill import (
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ClaudeSkillAgent,
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get_registry,
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load_skills_from_dir,
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)
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from dbgpt.model.proxy.llms.siliconflow import SiliconFlowLLMClient
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async def main():
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"""Main function demonstrating Claude SKILL usage."""
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# Load skills from directory
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skill_dir = os.path.join(os.path.dirname(__file__), "../skills/claude")
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load_skills_from_dir(skill_dir, recursive=True)
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# List available skills
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registry = get_registry()
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print("Loaded skills:")
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for skill_metadata in registry.list_skills():
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print(f" - {skill_metadata.name}: {skill_metadata.description}")
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# Create LLM client
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llm_client = SiliconFlowLLMClient(
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model_alias=os.getenv(
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"SILICONFLOW_MODEL_VERSION", "Qwen/Qwen2.5-Coder-32B-Instruct"
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),
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)
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# Create agent context
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agent_memory = AgentMemory()
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agent_memory.gpts_memory.init(conv_id="claude_skill_test")
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context: AgentContext = AgentContext(
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conv_id="claude_skill_test",
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gpts_app_name="Claude Skill Agent",
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)
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# Create Claude Skill Agent
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agent = ClaudeSkillAgent()
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# Bind necessary components
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await (
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agent.bind(context)
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.bind(LLMConfig(llm_client=llm_client))
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.bind(agent_memory)
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.build()
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)
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print("\n" + "=" * 60)
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print("Claude Skill Agent Ready!")
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print("=" * 60)
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# Example interactions
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test_inputs = [
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"Can you explain how this code works?",
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"My code isn't working, can you help debug it?",
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"Write a function to sort a list",
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"Simplify this complex code for me",
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]
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print("\nTest inputs and skill detection:")
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print("-" * 60)
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for user_input in test_inputs:
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agent.detect_and_apply_skill(user_input)
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if agent.current_skill:
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print(f"Input: {user_input}")
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print(f"Matched Skill: {agent.current_skill.metadata.name}")
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print(f"Description: {agent.current_skill.metadata.description}")
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print(f"Instructions preview: {agent.current_skill.instructions[:100]}...")
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print()
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# Show available skills
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print("\nAvailable skills:")
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for skill_name in agent.get_available_skills():
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print(f" - {skill_name}")
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# Manual skill selection example
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print("\n" + "-" * 60)
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print("Manual skill selection:")
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agent.set_skill("explain-code")
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print(f"Current skill: {agent.current_skill.metadata.name}")
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agent.clear_skill()
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print(f"After clearing: {agent.current_skill}")
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if __name__ == "__main__":
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asyncio.run(main())
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