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 75005616..aebc67c4 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
@@ -123,15 +123,26 @@ _REVIEW_SYSTEM_PROMPT = textwrap.dedent("""\
 
 
 async def _run_review(thread_id: str, messages: list[BaseMessage], app_config: "AppConfig") -> None:
-    """Core review logic: run a minimal lead agent with full middleware support.
+    """Core review logic: create_agent with full middleware chain, no 'silent creation' constraint.
 
-    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.
+    Uses create_agent (the deerflow agent factory) directly so we can:
+    - Reuse the full middleware chain (ToolErrorHandlingMiddleware, etc.)
+    - Reuse the full tool set
+    - But pass app_config with skill_evolution.enabled=False to strip the
+      "Never create skills silently without user confirmation" constraint
+      from the system prompt.
+    Review instructions are injected via PromptPrefixMiddleware through
+    additional_kwargs["prompt_prefix"] on the HumanMessage.
     """
-    from langchain_core.runnables import RunnableConfig
+    from langchain.agents import create_agent
+    from langchain_core.messages import HumanMessage
 
-    from deerflow.agents.lead_agent.agent import make_lead_agent
+    from deerflow.agents.lead_agent.prompt import apply_prompt_template
+    from deerflow.agents.middlewares.prompt_prefix_middleware import PromptPrefixMiddleware
+    from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares
+    from deerflow.agents.thread_state import ThreadState
+    from deerflow.models import create_chat_model
+    from deerflow.tools.tools import get_available_tools
 
     evolution_cfg = app_config.skill_evolution
     timeout = evolution_cfg.review_worker.timeout_seconds
@@ -139,36 +150,53 @@ 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)
-    user_message = (
-        _REVIEW_SYSTEM_PROMPT
-        + "\n\nReview this conversation and apply any skill changes you deem appropriate.\n\n"
-        f"<conversation>\n{conversation_text}\n</conversation>"
+
+    # Disable skill_evolution so the system prompt does NOT include
+    # "Never create skills silently without user confirmation".
+    # Also prevents SkillReviewMiddleware from registering (no recursive reviews).
+    review_app_config = app_config.model_copy(deep=True)
+    review_app_config.skill_evolution.enabled = False
+
+    try:
+        model = create_chat_model(name=model_name, app_config=review_app_config)
+    except Exception:
+        logger.warning("skill_review_worker: could not load model %r, aborting review", model_name)
+        return
+
+    tools = get_available_tools(app_config=review_app_config, subagent_enabled=False)
+    middlewares = build_lead_runtime_middlewares(app_config=review_app_config)
+    middlewares.append(PromptPrefixMiddleware())
+
+    system_prompt = apply_prompt_template(
+        subagent_enabled=False,
+        max_concurrent_subagents=0,
+        app_config=review_app_config,
+        memory_injection_enabled=False,
     )
 
-    # Use a synthetic thread id so this review run is isolated from the source thread.
-    review_thread_id = f"__skill-review__{thread_id}"
+    agent = create_agent(
+        model=model,
+        tools=tools,
+        middleware=middlewares,
+        system_prompt=system_prompt,
+        state_schema=ThreadState,
+    )
 
-    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)
+    # Review instructions arrive as prompt_prefix: PromptPrefixMiddleware prepends
+    # them before every model call without persisting them to state.
+    first_message = HumanMessage(
+        content=(
+            "Review this conversation and apply any skill changes you deem appropriate.\n\n"
+            f"<conversation>\n{conversation_text}\n</conversation>"
+        ),
+        additional_kwargs={"prompt_prefix": _REVIEW_SYSTEM_PROMPT},
+    )
 
     try:
         result = await asyncio.wait_for(
             asyncio.get_event_loop().run_in_executor(
                 None,
-                lambda: agent.invoke(
-                    {"messages": [("user", user_message)]},
-                    config=run_config,
-                ),
+                lambda: agent.invoke({"messages": [first_message]}),
             ),
             timeout=timeout,
         )
@@ -178,7 +206,7 @@ async def _run_review(thread_id: str, messages: list[BaseMessage], app_config: "
         for msg in reversed(final_msgs):
             content = getattr(msg, "content", None)
             if content and isinstance(content, str) and content.strip():
-                final_text = content.strip()[:300]
+                final_text = content.strip()[:1000]
                 break
         logger.info(
             "skill_review_worker: review completed for thread %s — agent said: %s",