feat: 批量重评分片并发（老爸：这么多key不能并发？）

- batch_reassess --shard K/N: index%N==K分片,N进程互不重叠
- llm_client LLM_KEY_OFFSET: key池起始位错开,避免N进程压同一首选key
- SKIP_FLUSH=1时worker不发摘要,parallel_batch.sh收尾统一flush
- parallel_batch.sh: N路并行+等待+统一digest

deploy/profile-scripts/llm_client.py

@@ -107,14 +107,19 @@ _KEY_POOL_TS = 0
 
 
 def _get_key_pool():
-    """key 池缓存 5 分钟。"""
+    """key 池缓存 5 分钟。LLM_KEY_OFFSET 环境变量：并发 worker 起始 key 错开，
+    避免 N 个进程同时压同一个首选 key（2026-07-24 并发分片配套）。"""
     global _KEY_POOL, _KEY_POOL_TS
-    import time as _t
+    import time as _t, os as _os
     if not _KEY_POOL or (_t.time() - _KEY_POOL_TS) > 300:
         _KEY_POOL = _load_key_pool()
         _KEY_POOL_TS = _t.time()
         if _KEY_POOL:
-            print(f"  [LLM] key池: {[k for k, _ in _KEY_POOL]}", flush=True)
+            _off = int(_os.environ.get("LLM_KEY_OFFSET", "0") or 0)
+            if _off and len(_KEY_POOL) > 1:
+                _off = _off % len(_KEY_POOL)
+                _KEY_POOL = _KEY_POOL[_off:] + _KEY_POOL[:_off]
+            print(f"  [LLM] key池: {[k for k, _ in _KEY_POOL]}" + (f" (offset={_off})" if _off else ""), flush=True)
     return _KEY_POOL