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/tests/unit/test_skill_learning_pipeline.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from beaver.memory.runs import RunMemoryStore
+from beaver.memory.skills import SkillLearningCandidate, SkillLearningStore
+from beaver.skills.drafts import DraftService
+from beaver.skills.learning import EvidenceSelector, SkillDraftSynthesizer, SkillLearningPipelineService, SkillLearningService
+from beaver.skills.publisher import SkillPublisher
+from beaver.skills.reviews import ReviewService
+from beaver.skills.specs import SkillReviewState, SkillSpecStore
+
+
+def _pipeline(tmp_path: Path) -> SkillLearningPipelineService:
+    spec_store = SkillSpecStore(tmp_path)
+    run_store = RunMemoryStore(tmp_path / "memory" / "runs")
+    learning_store = SkillLearningStore(tmp_path / "memory" / "skills")
+    draft_service = DraftService(spec_store)
+    learning_service = SkillLearningService(
+        run_store=run_store,
+        learning_store=learning_store,
+        draft_service=draft_service,
+        evidence_selector=EvidenceSelector(run_store),
+        synthesizer=SkillDraftSynthesizer(),
+    )
+    learning_store.record_learning_candidate(
+        SkillLearningCandidate(
+            candidate_id="candidate-1",
+            kind="retire_skill",
+            source_run_ids=["run-1"],
+            source_session_ids=["session-1"],
+            related_skill_names=["old-skill"],
+            reason="not useful",
+            evidence={"skill_version": "v0001"},
+        )
+    )
+    return SkillLearningPipelineService(
+        learning_store=learning_store,
+        learning_service=learning_service,
+        draft_service=draft_service,
+        review_service=ReviewService(spec_store),
+        publisher=SkillPublisher(spec_store),
+    )
+
+
+def test_pipeline_lists_candidates_and_moves_draft_through_review(tmp_path: Path) -> None:
+    pipeline = _pipeline(tmp_path)
+    draft = pipeline.draft_service.create_new_skill_draft(
+        skill_name="new-skill",
+        proposed_content="# New Skill\n\nDo the thing.",
+        proposed_frontmatter={"description": "test skill"},
+        created_by="test",
+        reason="test",
+    )
+
+    review = pipeline.submit_review(draft.skill_name, draft.draft_id, requested_by="tester")
+    approved = pipeline.approve(draft.skill_name, draft.draft_id, reviewer="tester")
+    safety = pipeline.check_safety(draft.skill_name, draft.draft_id)
+    version = pipeline.publish(draft.skill_name, draft.draft_id, publisher="tester")
+
+    assert pipeline.list_candidates()[0].candidate_id == "candidate-1"
+    assert review.status == SkillReviewState.IN_REVIEW.value
+    assert approved.status == SkillReviewState.APPROVED.value
+    assert safety.passed is True
+    assert version.skill_name == "new-skill"
+    assert pipeline.get_draft(draft.skill_name, draft.draft_id).status == SkillReviewState.PUBLISHED.value
+
+
+def test_pipeline_reject_blocks_publish(tmp_path: Path) -> None:
+    pipeline = _pipeline(tmp_path)
+    draft = pipeline.draft_service.create_new_skill_draft(
+        skill_name="blocked-skill",
+        proposed_content="# Blocked\n\nNo publish.",
+        proposed_frontmatter={"description": "blocked"},
+        created_by="test",
+        reason="test",
+    )
+
+    pipeline.reject(draft.skill_name, draft.draft_id, reviewer="tester")
+
+    with pytest.raises(ValueError, match="approved"):
+        pipeline.publish(draft.skill_name, draft.draft_id, publisher="tester")