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。

app-instance/backend/beaver/skills/learning/synthesizer.py

@@ -0,0 +1,118 @@
+"""LLM-backed draft synthesis for skill learning."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from beaver.engine.providers.base import LLMProvider
+from beaver.skills.learning.evidence import EvidencePacket
+from beaver.memory.skills.models import SkillLearningCandidate
+
+
+class SkillDraftSynthesizer:
+    async def synthesize_revision(
+        self,
+        candidate: SkillLearningCandidate,
+        evidence_packet: EvidencePacket,
+        provider: LLMProvider,
+        model: str,
+    ) -> dict[str, Any]:
+        return await self._synthesize(candidate, evidence_packet, provider, model, "revise")
+
+    async def synthesize_new_skill(
+        self,
+        candidate: SkillLearningCandidate,
+        evidence_packet: EvidencePacket,
+        provider: LLMProvider,
+        model: str,
+    ) -> dict[str, Any]:
+        return await self._synthesize(candidate, evidence_packet, provider, model, "new")
+
+    async def synthesize_merge(
+        self,
+        candidate: SkillLearningCandidate,
+        evidence_packet: EvidencePacket,
+        provider: LLMProvider,
+        model: str,
+    ) -> dict[str, Any]:
+        return await self._synthesize(candidate, evidence_packet, provider, model, "merge")
+
+    async def _synthesize(
+        self,
+        candidate: SkillLearningCandidate,
+        evidence_packet: EvidencePacket,
+        provider: LLMProvider,
+        model: str,
+        action: str,
+    ) -> dict[str, Any]:
+        prompt = self._build_prompt(candidate, evidence_packet, action)
+        response = await provider.chat(
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "You synthesize Beaver skill drafts from execution evidence. "
+                        "Return only JSON with keys: frontmatter, content, change_reason."
+                    ),
+                },
+                {"role": "user", "content": prompt},
+            ],
+            tools=None,
+            model=model,
+            max_tokens=1500,
+            temperature=0,
+        )
+        payload = self._parse_payload(response.content or "")
+        if payload:
+            return payload
+        return self._fallback_payload(candidate, evidence_packet, action)
+
+    @staticmethod
+    def _build_prompt(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> str:
+        return (
+            f"Action: {action}\n"
+            f"Candidate kind: {candidate.kind}\n"
+            f"Reason: {candidate.reason}\n"
+            f"Related skills: {candidate.related_skill_names}\n"
+            f"Task summaries:\n- " + "\n- ".join(evidence_packet.task_summaries)
+            + "\n\nSession excerpts:\n" + "\n\n".join(evidence_packet.session_excerpts)
+            + "\n\nReturn JSON only."
+        )
+
+    @staticmethod
+    def _parse_payload(content: str) -> dict[str, Any]:
+        cleaned = content.strip()
+        if cleaned.startswith("```"):
+            lines = cleaned.splitlines()
+            if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
+                cleaned = "\n".join(lines[1:-1]).strip()
+        try:
+            payload = json.loads(cleaned)
+        except json.JSONDecodeError:
+            return {}
+        if not isinstance(payload, dict):
+            return {}
+        frontmatter = payload.get("frontmatter")
+        content_value = payload.get("content")
+        if not isinstance(frontmatter, dict) or not isinstance(content_value, str):
+            return {}
+        return {
+            "frontmatter": frontmatter,
+            "content": content_value.strip(),
+            "change_reason": str(payload.get("change_reason") or ""),
+        }
+
+    @staticmethod
+    def _fallback_payload(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> dict[str, Any]:
+        related = candidate.related_skill_names[0] if candidate.related_skill_names else "generated-skill"
+        title = related.replace("_", "-")
+        content = "\n".join(f"- {item}" for item in evidence_packet.task_summaries[:5]) or "- No evidence captured."
+        return {
+            "frontmatter": {
+                "description": candidate.reason or f"Auto-generated {action} draft for {title}.",
+                "tools": [],
+            },
+            "content": f"# {title}\n\n## Evidence\n\n{content}\n",
+            "change_reason": candidate.reason or f"Fallback {action} synthesis.",
+        }