steven_li
8a12c30141
新增内部Task系统，包括验证、反馈门控机制，实现自动质量验证 (通过率>=0.75)和用户反馈闭环(satisfied/revise/abandon)。 实现Agent Team v1协调器，支持sequence/parallel/dag执行策略， sub-agent复用主AgentLoop，每个run使用独立memory snapshot。 建立Skill学习pipeline，包含draft/审核/发布/回滚完整生命周期， 通过Task验证通过且用户满意才生成学习候选。 重构目录结构，移除third_party依赖，建立统一engine内核， 所有agent共享运行时基础组件。 更新ContextBuilder清理provider消息字段，增强SkillContext版本管理， 集成TaskExecutionPlanner和TaskSkillResolver实现技能解析机制。
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""Runtime skill resolver。
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这层负责回答一个运行时问题：
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“这一次调用，哪些 skill 要被激活，并以什么形式注入上下文？”
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第一版保持保守，只综合三类来源：
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1. `always` skills
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不在这里做复杂的语义匹配或自动推荐。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from beaver.engine.context import SkillContext
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from beaver.skills.catalog.loader import SkillsLoader
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from beaver.skills.catalog.utils import strip_frontmatter
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@dataclass(slots=True)
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class ResolvedSkillSet:
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"""一次运行最终解析出的 skills 结果。"""
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activated_skills: list[SkillContext] = field(default_factory=list)
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class RuntimeSkillResolver:
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"""把 profile/request 转成当前轮次真正激活的 skill 集合。"""
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def __init__(self, loader: SkillsLoader) -> None:
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self.loader = loader
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def resolve(
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self,
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) -> ResolvedSkillSet:
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selected: list[str] = []
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for name in self.loader.get_always_skills():
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if name not in selected:
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selected.append(name)
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activated_skills: list[SkillContext] = []
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for name in selected:
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record = self.loader.get_skill_record(name)
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raw_content = self.loader.load_published_skill(name)
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content = strip_frontmatter(raw_content).strip() if raw_content else ""
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if not content:
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continue
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activated_skills.append(
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SkillContext(
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name=name,
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content=content,
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version=record.version if record is not None else "legacy",
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content_hash=(record.content_hash if record is not None and record.content_hash else ""),
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activation_reason="always_skill",
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tool_hints=list(record.tool_hints) if record is not None else [],
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)
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)
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return ResolvedSkillSet(activated_skills=activated_skills)
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