feat(beaver): 完成Task Team功能v1实现，重构后端架构支持统一内核
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。
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286
app-instance/backend/beaver/tasks/skill_resolver.py
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286
app-instance/backend/beaver/tasks/skill_resolver.py
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"""Resolve Task team nodes to pinned skills for generic sub-agents."""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field, replace
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from typing import Any
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from beaver.coordinator.models import AgentDescriptor, ExecutionGraph, ExecutionNode
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from beaver.engine.providers import ProviderBundle
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from beaver.skills.assembler.embedding_retriever import SkillEmbeddingRetriever
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from beaver.skills.catalog.loader import SkillsLoader
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from beaver.skills.drafts import DraftService
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from beaver.skills.learning import MissingSkillSynthesizer
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from beaver.tasks.models import TaskRecord
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@dataclass(slots=True)
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class SkillResolutionReport:
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node_id: str
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skill_query: str
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required_capabilities: list[str] = field(default_factory=list)
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selected_skill_names: list[str] = field(default_factory=list)
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generated_skill_draft_id: str | None = None
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generated_skill_name: str | None = None
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ephemeral_used: bool = False
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reason: str = ""
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def to_dict(self) -> dict[str, Any]:
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return {
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"node_id": self.node_id,
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"skill_query": self.skill_query,
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"required_capabilities": list(self.required_capabilities),
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"selected_skill_names": list(self.selected_skill_names),
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"generated_skill_draft_id": self.generated_skill_draft_id,
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"generated_skill_name": self.generated_skill_name,
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"ephemeral_used": self.ephemeral_used,
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"reason": self.reason,
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}
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class TaskSkillResolver:
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"""Pins published or draft-only skills onto generic team nodes."""
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def __init__(
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self,
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*,
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skills_loader: SkillsLoader,
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draft_service: DraftService,
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retriever: SkillEmbeddingRetriever | None = None,
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missing_skill_synthesizer: MissingSkillSynthesizer | None = None,
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) -> None:
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self.skills_loader = skills_loader
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self.draft_service = draft_service
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self.retriever = retriever or SkillEmbeddingRetriever()
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self.missing_skill_synthesizer = missing_skill_synthesizer or MissingSkillSynthesizer()
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async def resolve_graph(
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self,
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graph: ExecutionGraph,
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*,
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task: TaskRecord,
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user_message: str,
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attempt_index: int,
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provider_bundle: ProviderBundle,
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) -> tuple[ExecutionGraph, list[SkillResolutionReport]]:
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resolved_nodes: list[ExecutionNode] = []
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reports: list[SkillResolutionReport] = []
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for node in graph.nodes:
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resolved, report = await self.resolve_node(
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node,
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task=task,
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user_message=user_message,
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attempt_index=attempt_index,
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provider_bundle=provider_bundle,
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)
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resolved_nodes.append(resolved)
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reports.append(report)
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return ExecutionGraph(strategy=graph.strategy, nodes=resolved_nodes), reports
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async def resolve_node(
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self,
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node: ExecutionNode,
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*,
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task: TaskRecord,
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user_message: str,
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attempt_index: int,
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provider_bundle: ProviderBundle,
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) -> tuple[ExecutionNode, SkillResolutionReport]:
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skill_query = str(node.agent.metadata.get("skill_query") or node.task or node.node_id).strip()
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required_capabilities = [
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str(item).strip()
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for item in node.agent.metadata.get("required_capabilities", [])
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if str(item).strip()
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]
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selected = await self._select_published_skills(
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query="\n".join(
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part
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for part in [
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skill_query,
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node.task,
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" ".join(required_capabilities),
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task.goal,
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user_message,
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]
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if part
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),
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provider_bundle=provider_bundle,
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)
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if selected:
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pinned = _merge_names(node.inherited_pinned_skills, selected)
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resolved = self._generic_node(
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node,
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pinned_skill_names=pinned,
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metadata={
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**node.agent.metadata,
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"skill_query": skill_query,
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"required_capabilities": required_capabilities,
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"selected_skill_names": selected,
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"ephemeral_skill_names": [],
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},
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)
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return resolved, SkillResolutionReport(
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node_id=node.node_id,
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skill_query=skill_query,
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required_capabilities=required_capabilities,
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selected_skill_names=selected,
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ephemeral_used=False,
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reason="matched published skill",
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)
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missing = await self.missing_skill_synthesizer.synthesize(
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task=task,
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user_message=user_message,
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attempt_index=attempt_index,
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node_id=node.node_id,
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node_task=node.task,
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skill_query=skill_query,
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required_capabilities=required_capabilities,
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provider_bundle=provider_bundle,
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draft_service=self.draft_service,
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)
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resolved = self._generic_node(
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node,
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pinned_skill_names=list(node.inherited_pinned_skills),
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pinned_skill_contexts=[*node.inherited_pinned_skill_contexts, missing.skill_context],
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metadata={
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**node.agent.metadata,
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"skill_query": skill_query,
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"required_capabilities": required_capabilities,
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"selected_skill_names": [],
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"generated_skill_draft_id": missing.draft.draft_id,
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"generated_skill_name": missing.draft.skill_name,
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"ephemeral_skill_names": [missing.skill_context.name],
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},
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)
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return resolved, SkillResolutionReport(
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node_id=node.node_id,
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skill_query=skill_query,
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required_capabilities=required_capabilities,
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generated_skill_draft_id=missing.draft.draft_id,
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generated_skill_name=missing.draft.skill_name,
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ephemeral_used=True,
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reason="generated draft-only skill for missing sub-agent guidance",
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)
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async def _select_published_skills(self, *, query: str, provider_bundle: ProviderBundle) -> list[str]:
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candidates = self.skills_loader.build_selection_candidates()
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if not candidates:
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return []
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candidates = await self.retriever.retrieve(
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query=query,
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candidates=candidates,
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top_k=8,
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api_key=provider_bundle.embedding_runtime.api_key if provider_bundle.embedding_runtime is not None else None,
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api_base=provider_bundle.embedding_runtime.api_base if provider_bundle.embedding_runtime is not None else None,
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model=provider_bundle.embedding_runtime.model if provider_bundle.embedding_runtime is not None else None,
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extra_headers=(
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provider_bundle.embedding_runtime.extra_headers
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if provider_bundle.embedding_runtime is not None
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else None
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),
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timeout_seconds=(
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provider_bundle.embedding_runtime.request_timeout_seconds
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if provider_bundle.embedding_runtime is not None
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else None
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),
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fallback_top_k=8,
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)
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if not candidates:
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return []
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provider = provider_bundle.auxiliary_provider or provider_bundle.main_provider
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runtime = provider_bundle.auxiliary_runtime or provider_bundle.main_runtime
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model = getattr(runtime, "model", None)
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candidate_names = {item["name"] for item in candidates}
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try:
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response = await provider.chat(
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messages=[
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{
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"role": "system",
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"content": (
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"Select published Beaver skills for one generic sub-agent node. "
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"Return only a JSON array of skill names. Do not invent names. "
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"If none of the candidates directly match the required guidance, return []."
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),
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},
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{
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"role": "user",
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"content": (
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f"Node skill query:\n{query}\n\n"
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f"Candidate skills:\n{self._render_candidates(candidates)}\n\n"
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"Return only JSON, for example: [\"skill-a\"] or []"
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),
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},
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],
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tools=None,
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model=model,
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max_tokens=512,
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temperature=0,
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)
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parsed = self._parse_names(response.content or "")
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except Exception:
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parsed = []
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selected: list[str] = []
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for name in parsed:
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if name in candidate_names and name not in selected:
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selected.append(name)
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return selected
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@staticmethod
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def _generic_node(
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node: ExecutionNode,
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*,
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pinned_skill_names: list[str],
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metadata: dict[str, Any],
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pinned_skill_contexts: list[Any] | None = None,
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) -> ExecutionNode:
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return replace(
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node,
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agent=AgentDescriptor(
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name=node.node_id,
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role="",
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system_prompt="",
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metadata={
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**metadata,
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"sub_agent_kind": "generic_skill_worker",
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},
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),
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inherited_pinned_skills=pinned_skill_names,
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inherited_pinned_skill_contexts=list(pinned_skill_contexts or node.inherited_pinned_skill_contexts),
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)
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@staticmethod
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def _render_candidates(candidates: list[dict[str, str]]) -> str:
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return "\n".join(f"- {item['name']}: {item['description']}" for item in candidates)
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@staticmethod
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def _parse_names(content: str) -> list[str]:
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cleaned = content.strip()
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if cleaned.startswith("```"):
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lines = cleaned.splitlines()
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if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
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cleaned = "\n".join(lines[1:-1]).strip()
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if cleaned.lower().startswith("json"):
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cleaned = cleaned[4:].strip()
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try:
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payload = json.loads(cleaned)
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except json.JSONDecodeError:
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return []
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if isinstance(payload, dict):
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for key in ("skills", "selected_skills", "selected"):
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value = payload.get(key)
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if isinstance(value, list):
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payload = value
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break
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if not isinstance(payload, list):
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return []
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return [str(item).strip() for item in payload if str(item).strip()]
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def _merge_names(parent: list[str], selected: list[str]) -> list[str]:
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result: list[str] = []
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for name in [*parent, *selected]:
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if name and name not in result:
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result.append(name)
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return result
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