feat(skill-evolution): add ReviewWorkerConfig to SkillEvolutionConfig

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

offline-backend-20260512/backend/packages/harness/deerflow/config/skill_evolution_config.py

@@ -1,30 +1,39 @@
-from pydantic import BaseModel, Field
-
-
-class CuratorConfig(BaseModel):
-    """Configuration for the background skill curator."""
-
-    # 默认关闭:curator 仅在用户显式开启时才运行(配合 SkillEvolutionConfig.enabled
-    # 总开关,二者均为安全默认 False —— 未显式 opt-in 则整套技能进化不动作)。
-    enabled: bool = Field(default=False, description="Whether the curator runs automatically.")
-    interval_hours: int = Field(default=168, description="Hours between curator runs (default: 7 days).")
-    stale_after_days: int = Field(default=30, description="Days of inactivity before a skill is marked stale.")
-    archive_after_days: int = Field(default=90, description="Days of inactivity before a stale skill is archived.")
-    # LLM 评审整体超时:agent.invoke 内部可能多轮调 LLM,模型层的单次 request_timeout
-    # 兜不住整体;超过此秒数则中止本次评审,curator 正常收尾并释放运行锁。
-    review_timeout_seconds: int = Field(default=600, description="Overall timeout (seconds) for the LLM review pass.")
-    # curator LLM 评审使用的模型名(对应 config.yaml models[].name);为空则用主模型。
-    model_name: str | None = Field(default=None, description="Model name for the curator LLM review pass. Defaults to the primary chat model.")
-
-
-class SkillEvolutionConfig(BaseModel):
-    """Configuration for agent-managed skill evolution."""
-
-    enabled: bool = Field(
-        default=False,
-        description="Whether the agent can create and modify skills under skills/custom.",
-    )
-    curator: CuratorConfig = Field(
-        default_factory=CuratorConfig,
-        description="Configuration for the background skill curator.",
-    )
+from pydantic import BaseModel, Field
+
+
+class ReviewWorkerConfig(BaseModel):
+    """Configuration for the post-turn background skill review worker."""
+
+    enabled: bool = Field(default=False, description="Whether the review worker is active.")
+    min_tool_calls: int = Field(default=3, description="Minimum tool calls in a turn to trigger review.")
+    max_messages: int = Field(default=60, description="Max messages passed to the review LLM.")
+    model_name: str | None = Field(default=None, description="Model to use for review; defaults to primary model.")
+    timeout_seconds: int = Field(default=120, description="Overall timeout for a single review run.")
+
+
+class CuratorConfig(BaseModel):
+    """Configuration for the background skill curator."""
+
+    enabled: bool = Field(default=False, description="Whether the curator runs automatically.")
+    interval_hours: int = Field(default=168, description="Hours between curator runs (default: 7 days).")
+    stale_after_days: int = Field(default=30, description="Days of inactivity before a skill is marked stale.")
+    archive_after_days: int = Field(default=90, description="Days of inactivity before a stale skill is archived.")
+    review_timeout_seconds: int = Field(default=600, description="Overall timeout (seconds) for the LLM review pass.")
+    model_name: str | None = Field(default=None, description="Model name for the curator LLM review pass. Defaults to the primary chat model.")
+
+
+class SkillEvolutionConfig(BaseModel):
+    """Configuration for agent-managed skill evolution."""
+
+    enabled: bool = Field(
+        default=False,
+        description="Whether the agent can create and modify skills under skills/custom.",
+    )
+    curator: CuratorConfig = Field(
+        default_factory=CuratorConfig,
+        description="Configuration for the background skill curator.",
+    )
+    review_worker: ReviewWorkerConfig = Field(
+        default_factory=ReviewWorkerConfig,
+        description="Configuration for the post-turn background skill review worker.",
+    )

offline-backend-20260512/backend/tests/test_skill_review_worker.py

@@ -0,0 +1,19 @@
+from deerflow.config.skill_evolution_config import SkillEvolutionConfig, ReviewWorkerConfig
+
+
+def test_review_worker_config_defaults():
+    cfg = SkillEvolutionConfig()
+    assert cfg.review_worker.enabled is False
+    assert cfg.review_worker.min_tool_calls == 3
+    assert cfg.review_worker.timeout_seconds == 120
+    assert cfg.review_worker.model_name is None
+
+
+def test_review_worker_config_from_dict():
+    cfg = SkillEvolutionConfig.model_validate({
+        "enabled": True,
+        "review_worker": {"enabled": True, "min_tool_calls": 5, "model_name": "my-model"},
+    })
+    assert cfg.review_worker.enabled is True
+    assert cfg.review_worker.min_tool_calls == 5
+    assert cfg.review_worker.model_name == "my-model"