mirror of
https://github.com/hansjone/oclaw.git
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oliver
b0e0c05d8a
Migrate prompt templates into runtime workspaces, add network_ops netx toolchain (latest batch injection, raw fields, SQL query), and align docs/tests/admin/runtime wiring so ops analysis can query netx detail data through stable role-scoped paths. Co-authored-by: Cursor <cursoragent@cursor.com>
212 lines
8.6 KiB
Python
212 lines
8.6 KiB
Python
from __future__ import annotations
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import threading
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from typing import Any
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from oclaw.runtime.memory_stage import render_memory_context_block
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from oclaw.runtime.project_context_prompt import build_project_context_block
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from oclaw.runtime.skills_prompt import build_skills_catalog_block
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from oclaw.runtime.skills_workspace_lane import (
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fs_safe_workspace_lane_segment,
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workspace_lane_segment,
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)
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from oclaw.runtime.types import OclawMemoryContext
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from oclaw.runtime.workspaces.experts import expert_workspace_signature_token
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from oclaw.runtime.prompt_templates import render_runtime_prompt
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from oclaw.runtime.tools.base import ToolRegistry
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_EXECUTOR_STATIC_PROMPT_CACHE_LOCK = threading.Lock()
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_EXECUTOR_STATIC_PROMPT_CACHE: dict[tuple[Any, ...], str] = {}
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def _executor_prompt_settings_signature(store: Any) -> tuple[str, ...]:
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keys = (
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"AIA_SKILL_RUNTIME_ENABLED",
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"AIA_SKILLS_PROMPT_IN_SYSTEM",
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"AIA_SKILL_DISABLED_NAMES",
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"AIA_SKILL_ROLE_BINDING_ENABLED",
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"AIA_SKILL_ROLE_BINDING_MANAGER_INHERIT",
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"AIA_PROJECT_CONTEXT_MAX_FILE_CHARS",
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"AIA_PROJECT_CONTEXT_MAX_TOTAL_CHARS",
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)
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parts: list[str] = []
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for key in keys:
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try:
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val = str(store.get_setting(key) or "")
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except Exception:
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val = ""
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parts.append(f"{key}={val}")
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return tuple(parts)
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def _unified_skill_policy_guidance() -> str:
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# Global policy for all agents (including dynamic/ephemeral) — appended to base_system in build_executor_system_prompt.
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return (
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"- 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"
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"- 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"
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"- 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。\n"
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"- 当对话涉及长期记忆、用户身份/偏好、项目背景延续、复发问题沉淀时，优先启用 wiki-first-autonomy 技能，并优先使用 memory_wiki_search/memory_wiki_get 检索上下文，再执行与回复。\n"
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"- memory wiki 请仅传相对 wiki 根的路径（相对 `data/wiki`），例如 `improvement/learnings.md`；不要传 `data/wiki/...` 或绝对路径。\n"
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"- 当新增事实会影响后续决策时，完成当前任务后使用 memory_wiki_apply 写入结构化记忆，并用 memory_wiki_lint 做质量检查。\n"
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"- 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"
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"- internal hooks 是独立系统，也不会自动执行这些脚本。\n"
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"- 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"
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"- 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"
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"- 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"
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"- 在 Windows 上，`.sh` 可能需要 Git Bash、WSL 或等效环境。\n"
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"- 当用户目标是“安装 skill/技能”时，必须遵循 `oclaw-skill-manager` 的安装策略，并以其为唯一规范来源。\n"
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"- 安装路径强约束：仅允许 `skill_auto_install`（`_workspace` lane）；不得改用任何非 auto 路径或脚本绕过。\n"
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"- 严禁臆测前置条件：不要把未在规范中声明的环境变量、端口、服务启动状态当作必需前提。\n"
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"- 若安装失败，仅输出可验证事实（至少包含 `error_code` 与 `detail`）和最小下一步，不得编造基础设施依赖。\n"
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)
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def _skill_catalog_lane_flags(
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*,
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skill_binding_role: str | None,
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workspace_owner_session_id: str | None,
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session_id: str | None,
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) -> tuple[bool, str | None]:
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role = str(skill_binding_role or "").strip().lower()
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if role:
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return True, fs_safe_workspace_lane_segment(role)
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o = str(workspace_owner_session_id or "").strip()
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s = str(session_id or "").strip()
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if not o and not s:
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return False, None
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seg = workspace_lane_segment(workspace_owner_session_id=o or None, session_id=s or None)
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return True, seg
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def build_executor_system_prompt(
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*,
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store: Any,
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tools: ToolRegistry | None,
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base_url: str,
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base_system: str,
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memory_context: OclawMemoryContext | None,
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lang: str,
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workspace_dir: str | None = None,
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skill_binding_role: str | None = None,
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workspace_owner_session_id: str | None = None,
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session_id: str | None = None,
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) -> str:
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"""Build the final system string for the oclaw executor (memory block + skills catalog).
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`lang` is reserved for future localized system fragments; current templates are bilingual/static.
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"""
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_ = lang # reserved for i18n extensions
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final_system = get_executor_prompt_static(
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store=store,
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tools=tools,
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base_url=base_url,
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base_system=base_system,
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workspace_dir=workspace_dir,
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skill_binding_role=skill_binding_role,
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workspace_owner_session_id=workspace_owner_session_id,
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session_id=session_id,
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)
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mem_block = render_memory_context_block(memory_context or OclawMemoryContext())
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if not mem_block:
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return final_system
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return render_runtime_prompt(
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"runtime/system_with_memory.md",
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variables={"system_prompt": final_system, "memory_context": mem_block},
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strict=True,
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)
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def get_executor_prompt_static(
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*,
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store: Any,
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tools: ToolRegistry | None,
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base_url: str,
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base_system: str,
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workspace_dir: str | None = None,
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skill_binding_role: str | None = None,
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workspace_owner_session_id: str | None = None,
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session_id: str | None = None,
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) -> str:
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excl, lane_seg = _skill_catalog_lane_flags(
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skill_binding_role=skill_binding_role,
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workspace_owner_session_id=workspace_owner_session_id,
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session_id=session_id,
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)
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cache_key = (
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str(base_url or "").strip(),
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str(base_system or "").strip(),
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str(workspace_dir or "").strip(),
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str(skill_binding_role or "").strip().lower(),
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expert_workspace_signature_token(),
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_executor_prompt_settings_signature(store),
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bool(tools is not None),
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int(bool(excl)),
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str(lane_seg or ""),
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)
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with _EXECUTOR_STATIC_PROMPT_CACHE_LOCK:
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cached = _EXECUTOR_STATIC_PROMPT_CACHE.get(cache_key)
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if isinstance(cached, str):
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return cached
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final_system = str(base_system or "").strip()
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guide = _unified_skill_policy_guidance().strip()
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if guide and guide not in final_system:
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final_system = f"{final_system}\n\n{guide}".strip()
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project_block = build_project_context_block(store=store, workspace_dir=workspace_dir)
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if project_block:
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final_system = f"{final_system}\n\n{project_block}".strip()
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if tools is not None:
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cat = build_skills_catalog_block(
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store=store,
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registry=tools,
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base_url=str(base_url or ""),
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skill_binding_role=skill_binding_role,
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exclude_foreign_private_workspace_skills=excl,
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private_workspace_lane_segment=lane_seg,
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)
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if cat.strip():
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final_system = render_runtime_prompt(
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"runtime/system_with_skills.md",
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variables={"system_body": final_system, "skills_catalog": cat},
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strict=True,
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)
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with _EXECUTOR_STATIC_PROMPT_CACHE_LOCK:
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_EXECUTOR_STATIC_PROMPT_CACHE[cache_key] = final_system
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if len(_EXECUTOR_STATIC_PROMPT_CACHE) > 256:
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_EXECUTOR_STATIC_PROMPT_CACHE.clear()
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return final_system
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def warm_executor_prompt_cache(
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*,
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store: Any,
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tools: ToolRegistry | None,
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base_url: str,
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role_base_systems: dict[str, str],
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workspace_dir: str | None = None,
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) -> dict[str, int]:
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warmed = 0
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for role, base_system in (role_base_systems or {}).items():
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_ = get_executor_prompt_static(
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store=store,
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tools=tools,
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base_url=base_url,
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base_system=str(base_system or ""),
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workspace_dir=workspace_dir,
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skill_binding_role=str(role or "").strip().lower() or None,
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workspace_owner_session_id=None,
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session_id=None,
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)
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warmed += 1
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return {"roles_warmed": int(warmed)}
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def build_oclaw_executor_system_prompt(**kwargs: Any) -> str:
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return build_executor_system_prompt(**kwargs)
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__all__ = [
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"build_executor_system_prompt",
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"build_oclaw_executor_system_prompt",
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"get_executor_prompt_static",
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"warm_executor_prompt_cache",
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]
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