世界上有一种技术，叫AI的技能包 —— 它让AI从"什么都会"变成"什么都会得很好"
Skills Framework，就像AI的技能树系统。普通AI就像一个什么都会一点但什么都不精通的"万金油"，而有了Skills Framework的AI，就像专业工程师，每个技能都经过专门训练，做得又快又好。
Skills Framework是AI技能管理系统，它让AI能够：
Skills Framework采用分层技能架构：
通过技能注册中心，AI可以动态发现和调用合适的技能：
# 技能注册和发现
class SkillRegistry:
def __init__(self):
self.skills = {}
self.skill_index = {}
def register_skill(self, skill_name, skill_func, metadata):
"""注册技能"""
self.skills[skill_name] = {
"function": skill_func,
"metadata": metadata,
"version": "1.0.0"
}
# 建立技能索引
for tag in metadata.get("tags", []):
if tag not in self.skill_index:
self.skill_index[tag] = []
self.skill_index[tag].append(skill_name)
def discover_skills(self, query):
"""发现相关技能"""
relevant_skills = []
# 基于标签搜索
for tag, skills in self.skill_index.items():
if query.lower() in tag.lower():
relevant_skills.extend(skills)
# 基于技能名称搜索
for skill_name, skill_info in self.skills.items():
if query.lower() in skill_name.lower():
relevant_skills.append(skill_name)
return list(set(relevant_skills))
在OpenClaw中，Skills Framework可以让Agent按需加载技能：
# OpenClaw技能开发
from openclaw import Agent, Skill
# 定义React开发技能
class ReactDevelopmentSkill(Skill):
def __init__(self):
super().__init__(
name="react_development",
description="React组件开发",
tags=["frontend", "react", "components"]
)
def create_component(self, component_name, props, style):
"""创建React组件"""
component_code = f"""
function {component_name}({', '.join(props.keys())}) {{
return (
{self._render_content(props)}
);
}}
"""
return component_code
def _render_content(self, props):
"""渲染组件内容"""
# 根据props生成对应的内容
pass
# 定义数据分析技能
class DataAnalysisSkill(Skill):
def __init__(self):
super().__init__(
name="data_analysis",
description="数据分析处理",
tags=["data", "analysis", "pandas"]
)
def analyze_dataframe(self, df, analysis_type):
"""分析数据框"""
if analysis_type == "describe":
return df.describe()
elif analysis_type == "correlation":
return df.corr()
else:
return "不支持的分析类型"
# 创建Agent并注册技能
agent = Agent(name="FullStackDeveloper")
agent.register_skill(ReactDevelopmentSkill())
agent.register_skill(DataAnalysisSkill())
Skills Framework可以智能组合技能，完成复杂任务：
# 完整的技能管理系统
class SkillFramework:
def __init__(self):
self.skills = {}
self.skill_chains = {}
self.performance_metrics = {}
def add_skill(self, skill):
"""添加技能"""
self.skills[skill.name] = skill
print(f"技能已添加: {skill.name}")
def create_skill_chain(self, chain_name, skill_sequence):
"""创建技能链"""
self.skill_chains[chain_name] = skill_sequence
print(f"技能链已创建: {chain_name}")
def execute_chain(self, chain_name, input_data):
"""执行技能链"""
if chain_name not in self.skill_chains:
raise ValueError(f"技能链不存在: {chain_name}")
result = input_data
skill_sequence = self.skill_chains[chain_name]
for skill_name in skill_sequence:
if skill_name not in self.skills:
raise ValueError(f"技能不存在: {skill_name}")
skill = self.skills[skill_name]
result = skill.execute(result)
# 记录性能指标
self._record_performance(skill_name, result)
return result
def _record_performance(self, skill_name, result):
"""记录技能性能"""
if skill_name not in self.performance_metrics:
self.performance_metrics[skill_name] = []
# 简单的性能指标
execution_time = time.time() - start_time
self.performance_metrics[skill_name].append({
"execution_time": execution_time,
"result_size": len(str(result)),
"timestamp": time.time()
})
# 使用示例
framework = SkillFramework()
# 添加技能
framework.add_skill(ReactDevelopmentSkill())
framework.add_skill(DataAnalysisSkill())
# 创建技能链
framework.create_skill_chain("ecommerce_workflow", [
"react_development",
"data_analysis",
"api_integration"
])
# 执行技能链
result = framework.execute_chain("ecommerce_workflow", user_requirements)
# 技能优化系统
class SkillOptimizer:
def __init__(self, skill_framework):
self.framework = skill_framework
self.optimization_rules = {}
def add_optimization_rule(self, skill_name, rule_func):
"""添加优化规则"""
self.optimization_rules[skill_name] = rule_func
def optimize_skill(self, skill_name):
"""优化技能"""
if skill_name not in self.framework.skills:
raise ValueError(f"技能不存在: {skill_name}")
skill = self.framework.skills[skill_name]
# 应用优化规则
if skill_name in self.optimization_rules:
optimized_skill = self.optimization_rules[skill_name](skill)
self.framework.skills[skill_name] = optimized_skill
print(f"技能已优化: {skill_name}")
return skill
def get_skill_performance(self, skill_name):
"""获取技能性能报告"""
if skill_name not in self.framework.performance_metrics:
return {"error": "无性能数据"}
metrics = self.framework.performance_metrics[skill_name]
avg_time = sum(m["execution_time"] for m in metrics) / len(metrics)
total_executions = len(metrics)
return {
"skill_name": skill_name,
"average_execution_time": avg_time,
"total_executions": total_executions,
"optimization_suggestions": self._generate_suggestions(skill_name, metrics)
}
def _generate_suggestions(self, skill_name, metrics):
"""生成优化建议"""
suggestions = []
if avg_time > 1.0: # 如果执行时间超过1秒
suggestions.append("考虑缓存结果")
if len(metrics) > 1000: # 如果执行次数过多
suggestions.append("考虑异步处理")
return suggestions