AI写作：技能检索进度反馈与超时兜底

检索等待时 SkillStepsCard 显示 spinner；单技能检索 25s 超时避免卡死；前后端增加 skill-timing 调试日志。

Co-authored-by: Cursor <cursoragent@cursor.com>

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/nodes/researcher.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 import asyncio
 import logging
 import re
+import time
 import uuid
 from typing import List
 
@@ -29,6 +30,11 @@ _MAX_MAX_MATERIALS = 100
 _DEFAULT_KEYWORD_COUNT = 6
 _MAX_KEYWORD_COUNT = 10
 
+# 单个技能的 web 检索超时（秒）。技能检索是顺序循环、最多 5 个技能，
+# DuckDuckGo 等 provider 偶发限速/挂起会把整段"技能检索过程"拖到几十秒看起来卡死；
+# 超时即放弃该技能、emit 0 条结果继续下一个，保证时间线持续推进。
+_SKILL_SEARCH_TIMEOUT_SECONDS = 25
+
 
 def _resolve_keyword_count(raw: object) -> int:
     """把用户传入的检索关键词数量规整到 [1, _MAX_KEYWORD_COUNT]，非法值回退默认 6。"""
@@ -178,6 +184,10 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
 
     def _emit_skill_step(step: dict) -> None:
         skill_steps.append(step)
+        # 技能检索时机 trace：grep "[skill-timing]" 看每步 emit 的真实时刻，
+        # 与前端 [ai-writing:custom] 日志对齐，钉死是否实时推送。
+        logger.info("[skill-timing] t=%.3f emit skill_search_step type=%s skill=%s",
+                    time.monotonic(), step.get("type"), step.get("skill_name") or "")
         writer({"type": "skill_search_step", "agent_name": "素材收集专家", **step})
 
     if is_skill_search:
@@ -205,15 +215,25 @@ async def researcher_node(state: AIWritingState, config: RunnableConfig) -> dict
 
         for entry in selected:
             sk = entry["skill"]
-            # 用 LLM 把 user_query + skill.description 转成针对性关键词
+            skill_query = f"{user_query} {sk.description or ''}".strip() or intent
+            # invoke_skill 步骤带上实际检索词（截断防过长），让用户在每次长 await 之前
+            # 就看到"这一步在用技能 X 检索什么"，配合前端 spinner 体现进行中。
             _emit_skill_step({
                 "type": "invoke_skill",
-                "message": f"使用技能「{sk.name}」开始检索…",
+                "message": f"使用技能「{sk.name}」检索：{skill_query[:60]}",
                 "skill_name": sk.name,
             })
-            skill_query = f"{user_query} {sk.description or ''}".strip() or intent
             try:
-                res = await web_provider.search(skill_query, max_results=per_skill)
+                # 加超时兜底：单个技能检索最多等 _SKILL_SEARCH_TIMEOUT_SECONDS 秒，
+                # 超时不再阻塞后续技能，避免整段过程看起来"冻结"。
+                res = await asyncio.wait_for(
+                    web_provider.search(skill_query, max_results=per_skill),
+                    timeout=_SKILL_SEARCH_TIMEOUT_SECONDS,
+                )
+            except asyncio.TimeoutError:
+                logger.warning("skill[%s] search timed out after %ss", sk.name, _SKILL_SEARCH_TIMEOUT_SECONDS)
+                search_errors.append(f"skill[{sk.name}]: 检索超时（>{_SKILL_SEARCH_TIMEOUT_SECONDS}s）")
+                res = []
             except Exception as e:
                 logger.warning("skill[%s] search failed: %s", sk.name, e)
                 search_errors.append(f"skill[{sk.name}]: {e}")

offline-backend-20260512/backend/packages/harness/deerflow/agents/ai_writing/search/skill_search.py

@@ -16,6 +16,7 @@
 from __future__ import annotations
 
 import logging
+import time
 from typing import Callable, List, Optional
 
 from langchain_core.messages import HumanMessage, SystemMessage
@@ -140,7 +141,14 @@ class SkillPicker:
         parser = ThinkTagParser()
         full_text = ""
 
+        _chunk_count = {"n": 0}
+
         def _emit_chunk(text: str, is_thinking: bool) -> None:
+            # 只在首个 chunk 打一条 timing，避免逐 token 刷屏；用来和 skill_search_step
+            # 的 [skill-timing] 对比"选择打字"与"步骤事件"谁先到。
+            if _chunk_count["n"] == 0:
+                logger.info("[skill-timing] t=%.3f first skill_select_chunk", time.monotonic())
+            _chunk_count["n"] += 1
             writer({
                 "type": "skill_select_chunk",
                 "text": text,