重构主控编排与运行时预热链路，统一工作区提示词/专家调度协议并补齐 wiki 记忆注入与写回闭环。

同时收敛启动与运维脚本默认行为（含 wiki worker）、更新 Admin 可观测性与相关测试，降低首轮时延并提高运行稳定性。

Made-with: Cursor

runtime/system_prompt.py

@@ -1,14 +1,54 @@
 from __future__ import annotations
 
+import threading
 from typing import Any
 
 from oclaw.runtime.memory_stage import render_memory_context_block
 from oclaw.runtime.project_context_prompt import build_project_context_block
 from oclaw.runtime.skills_prompt import build_skills_catalog_block
 from oclaw.runtime.types import OclawMemoryContext
+from oclaw.runtime.workspaces.experts import expert_workspace_signature_token
 from oclaw.prompts.loader import render_runtime_prompt
 from oclaw.runtime.tools.base import ToolRegistry
 
+_EXECUTOR_STATIC_PROMPT_CACHE_LOCK = threading.Lock()
+_EXECUTOR_STATIC_PROMPT_CACHE: dict[tuple[Any, ...], str] = {}
+
+
+def _executor_prompt_settings_signature(store: Any) -> tuple[str, ...]:
+    keys = (
+        "AIA_SKILL_RUNTIME_ENABLED",
+        "AIA_SKILLS_PROMPT_IN_SYSTEM",
+        "AIA_SKILL_DISABLED_NAMES",
+        "AIA_SKILL_ROLE_BINDING_ENABLED",
+        "AIA_SKILL_ROLE_BINDING_MANAGER_INHERIT",
+        "AIA_PROJECT_CONTEXT_MAX_FILE_CHARS",
+        "AIA_PROJECT_CONTEXT_MAX_TOTAL_CHARS",
+    )
+    parts: list[str] = []
+    for key in keys:
+        try:
+            val = str(store.get_setting(key) or "")
+        except Exception:
+            val = ""
+        parts.append(f"{key}={val}")
+    return tuple(parts)
+
+
+def _unified_skill_policy_guidance() -> str:
+    # Global policy for all agents (including dynamic/ephemeral) — appended to base_system in build_executor_system_prompt.
+    return (
+        "- 如果用户问你有哪些技能（skill/技能），请直接根据已注入的技能目录及其 description 回答。\n"
+        "- 不要为了“列出技能”而去读取 SKILL.md。只有在你确实需要某个技能的详细使用说明时，才读取对应 SKILL.md。\n"
+        "- 当你需要技能细节时，请按目录中给出的 path 读取对应的 SKILL.md。"
+        "- 技能包由说明文档和可选文件组成。运行时不会自动执行技能 `scripts/` 目录下的文件；\n"
+        "- internal hooks 是独立系统，也不会自动执行这些脚本。\n"
+        "- 当用户明确要求运行技能脚本时，请使用项目允许的 terminal/bash/exec（或同类）工具执行；\n"
+        "- 如果脚本依赖相对路径（例如 `.learnings/`），请将工作目录设置为用户工作区。\n"
+        "- 在没有显式工具调用成功结果前，不要假设脚本已经执行。\n"
+        "- 在 Windows 上，`.sh` 可能需要 Git Bash、WSL 或等效环境。\n"
+    )
+
 
 def build_executor_system_prompt(
     *,
@@ -26,17 +66,53 @@ def build_executor_system_prompt(
     `lang` is reserved for future localized system fragments; current templates are bilingual/static.
     """
     _ = lang  # reserved for i18n extensions
+    final_system = get_executor_prompt_static(
+        store=store,
+        tools=tools,
+        base_url=base_url,
+        base_system=base_system,
+        workspace_dir=workspace_dir,
+        skill_binding_role=skill_binding_role,
+    )
+    mem_block = render_memory_context_block(memory_context or OclawMemoryContext())
+    if not mem_block:
+        return final_system
+    return render_runtime_prompt(
+        "runtime/system_with_memory.md",
+        variables={"system_prompt": final_system, "memory_context": mem_block},
+        strict=True,
+    )
+
+
+def get_executor_prompt_static(
+    *,
+    store: Any,
+    tools: ToolRegistry | None,
+    base_url: str,
+    base_system: str,
+    workspace_dir: str | None = None,
+    skill_binding_role: str | None = None,
+) -> str:
+    cache_key = (
+        str(base_url or "").strip(),
+        str(base_system or "").strip(),
+        str(workspace_dir or "").strip(),
+        str(skill_binding_role or "").strip().lower(),
+        expert_workspace_signature_token(),
+        _executor_prompt_settings_signature(store),
+        bool(tools is not None),
+    )
+    with _EXECUTOR_STATIC_PROMPT_CACHE_LOCK:
+        cached = _EXECUTOR_STATIC_PROMPT_CACHE.get(cache_key)
+    if isinstance(cached, str):
+        return cached
     final_system = str(base_system or "").strip()
+    guide = _unified_skill_policy_guidance().strip()
+    if guide and guide not in final_system:
+        final_system = f"{final_system}\n\n{guide}".strip()
     project_block = build_project_context_block(store=store, workspace_dir=workspace_dir)
     if project_block:
         final_system = f"{final_system}\n\n{project_block}".strip()
-    mem_block = render_memory_context_block(memory_context or OclawMemoryContext())
-    if mem_block:
-        final_system = render_runtime_prompt(
-            "runtime/system_with_memory.md",
-            variables={"system_prompt": final_system, "memory_context": mem_block},
-            strict=True,
-        )
     if tools is not None:
         cat = build_skills_catalog_block(
             store=store,
@@ -50,11 +126,42 @@ def build_executor_system_prompt(
                 variables={"system_body": final_system, "skills_catalog": cat},
                 strict=True,
             )
+    with _EXECUTOR_STATIC_PROMPT_CACHE_LOCK:
+        _EXECUTOR_STATIC_PROMPT_CACHE[cache_key] = final_system
+        if len(_EXECUTOR_STATIC_PROMPT_CACHE) > 256:
+            _EXECUTOR_STATIC_PROMPT_CACHE.clear()
     return final_system
 
 
+def warm_executor_prompt_cache(
+    *,
+    store: Any,
+    tools: ToolRegistry | None,
+    base_url: str,
+    role_base_systems: dict[str, str],
+    workspace_dir: str | None = None,
+) -> dict[str, int]:
+    warmed = 0
+    for role, base_system in (role_base_systems or {}).items():
+        _ = get_executor_prompt_static(
+            store=store,
+            tools=tools,
+            base_url=base_url,
+            base_system=str(base_system or ""),
+            workspace_dir=workspace_dir,
+            skill_binding_role=str(role or "").strip().lower() or None,
+        )
+        warmed += 1
+    return {"roles_warmed": int(warmed)}
+
+
 def build_oclaw_executor_system_prompt(**kwargs: Any) -> str:
     return build_executor_system_prompt(**kwargs)
 
 
-__all__ = ["build_executor_system_prompt", "build_oclaw_executor_system_prompt"]
+__all__ = [
+    "build_executor_system_prompt",
+    "build_oclaw_executor_system_prompt",
+    "get_executor_prompt_static",
+    "warm_executor_prompt_cache",
+]