diff --git a/offline-backend-20260512/backend/packages/harness/deerflow/skills/review_worker.py b/offline-backend-20260512/backend/packages/harness/deerflow/skills/review_worker.py
index 2b7ba663..99e886ea 100644
--- a/offline-backend-20260512/backend/packages/harness/deerflow/skills/review_worker.py
+++ b/offline-backend-20260512/backend/packages/harness/deerflow/skills/review_worker.py
@@ -108,17 +108,28 @@ _REVIEW_SYSTEM_PROMPT = textwrap.dedent("""\
     - The frontmatter block must start with `---` on its own line and end with `---`.
     - Do NOT add extra keys like "tags", "category", "type", etc.
 
+    ## Avoiding duplicate-creation errors
+    Before calling skill_manage with action="create":
+    1. Always call skill_list first.
+    2. If a skill with the same name already exists, use action="patch" instead.
+       Creating over an existing skill will raise an error.
+    3. If you get a tool error, read the message carefully and fix the issue
+       (wrong name, bad frontmatter, duplicate, etc.) before retrying.
+
     Be conservative. Fewer high-quality skills beat many noisy ones.
 """)
 
 
 async def _run_review(thread_id: str, messages: list[BaseMessage], app_config: "AppConfig") -> None:
-    """Core review logic: spin up a minimal React agent and invoke it."""
-    from langgraph.prebuilt import create_react_agent
+    """Core review logic: run a minimal lead agent with full middleware support.
 
-    from deerflow.models.factory import create_chat_model
-    from deerflow.tools.builtins.skill_tools import skill_list_tool
-    from deerflow.tools.skill_manage_tool import skill_manage_tool
+    Reuses make_lead_agent so that ToolErrorHandlingMiddleware, LLMErrorHandlingMiddleware,
+    and the rest of the middleware chain handle all error cases uniformly — no custom
+    handle_tool_errors hack needed.
+    """
+    from langchain_core.runnables import RunnableConfig
+
+    from deerflow.agents.lead_agent.agent import make_lead_agent
 
     evolution_cfg = app_config.skill_evolution
     timeout = evolution_cfg.review_worker.timeout_seconds
@@ -126,26 +137,36 @@ async def _run_review(thread_id: str, messages: list[BaseMessage], app_config: "
     model_name = evolution_cfg.review_worker.model_name
 
     conversation_text = _format_messages_for_review(messages, max_messages)
-
-    try:
-        model = create_chat_model(model_name)
-    except Exception:
-        logger.warning("skill_review_worker: could not load model %r, aborting review", model_name)
-        return
-
-    tools = [skill_list_tool, skill_manage_tool]
-    agent = create_react_agent(model, tools, prompt=_REVIEW_SYSTEM_PROMPT)
-
     user_message = (
-        "Review this conversation and apply any skill changes you deem appropriate.\n\n"
+        _REVIEW_SYSTEM_PROMPT
+        + "\n\nReview this conversation and apply any skill changes you deem appropriate.\n\n"
         f"<conversation>\n{conversation_text}\n</conversation>"
     )
 
+    # Use a synthetic thread id so this review run is isolated from the source thread.
+    review_thread_id = f"__skill-review__{thread_id}"
+
+    configurable: dict = {
+        "thread_id": review_thread_id,
+        "is_bootstrap": True,        # skip MemoryMiddleware injection
+        "is_scheduled_run": True,    # skip TitleMiddleware
+        "subagent_enabled": False,   # no task-delegation tool
+        "app_config": app_config,
+    }
+    if model_name:
+        configurable["model_name"] = model_name
+
+    run_config = RunnableConfig(configurable=configurable)
+    agent = make_lead_agent(run_config)
+
     try:
         await asyncio.wait_for(
             asyncio.get_event_loop().run_in_executor(
                 None,
-                lambda: agent.invoke({"messages": [("user", user_message)]}),
+                lambda: agent.invoke(
+                    {"messages": [("user", user_message)]},
+                    config=run_config,
+                ),
             ),
             timeout=timeout,
         )