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https://github.com/hansjone/oclaw.git
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oliver
c33ca303a2
Include skill_role_binding store JSON in executor and manager prompt cache signatures so skill catalog updates after binding edits without restart. Admin async prewarm now polls prewarm/status until not running before router() so parallel prompts fetch matches completed warm. Co-authored-by: Cursor <cursoragent@cursor.com>
279 lines
12 KiB
Python
279 lines
12 KiB
Python
from __future__ import annotations
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import hashlib
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import threading
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from pathlib import Path
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from typing import Any
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from oclaw.platform.config.paths import PROJECT_ROOT
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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.skill_role_binding import SKILL_ROLE_BINDING_KEY
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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 _file_stat_signature(path: Path) -> str:
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try:
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st = path.stat()
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return f"{path.as_posix()}:{int(st.st_mtime_ns)}:{int(st.st_size)}"
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except Exception:
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return f"{path.as_posix()}:missing"
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def _session_bootstrap_content_signature(*, skill_binding_role: str | None, workspace_dir: str | None) -> str:
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"""Best-effort signature for session-bootstrap content sources.
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When this signature changes, cached executor system prompts are invalidated,
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so the next turn rebuilds project context and re-runs bootstrap hooks.
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"""
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repo = Path(PROJECT_ROOT).resolve()
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role = str(skill_binding_role or "").strip().lower()
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ws = Path(workspace_dir).expanduser().resolve() if str(workspace_dir or "").strip() else None
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targets: list[Path] = []
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# session-bootstrap static source files
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targets.append((repo / "skills" / "session-bootstrap" / "SOUL.md").resolve())
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targets.append((repo / "skills" / "session-bootstrap" / "IDENTITY.md").resolve())
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# wiki global memory/improvement sources
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targets.append((repo / "data" / "wiki" / "improvement" / "learnings.md").resolve())
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targets.append((repo / "data" / "wiki" / "improvement" / "errors.md").resolve())
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targets.append((repo / "data" / "wiki" / "improvement" / "feature-requests.md").resolve())
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targets.append((repo / "data" / "wiki" / "users" / "current.md").resolve())
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core_dir = (repo / "data" / "wiki" / "core").resolve()
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if core_dir.exists() and core_dir.is_dir():
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for p in sorted(core_dir.glob("*.md"), key=lambda x: x.name.lower()):
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targets.append(p.resolve())
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if role:
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role_dir = (repo / "data" / "wiki" / "experts" / role).resolve()
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if role_dir.exists() and role_dir.is_dir():
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for p in sorted(role_dir.glob("*.md"), key=lambda x: x.name.lower()):
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targets.append(p.resolve())
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if ws is not None:
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mem_dir = (ws / "memory").resolve()
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if mem_dir.exists() and mem_dir.is_dir():
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mem_files = [p for p in mem_dir.glob("*.md") if p.is_file()]
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mem_files.sort(key=lambda p: p.stat().st_mtime if p.exists() else 0.0, reverse=True)
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if mem_files:
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targets.append(mem_files[0].resolve())
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h = hashlib.sha256()
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for p in targets:
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h.update(_file_stat_signature(p).encode("utf-8", errors="ignore"))
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h.update(b"\n")
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return h.hexdigest()[:24]
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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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SKILL_ROLE_BINDING_KEY,
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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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"## 技能使用政策（Skill Usage Policy）：\n"
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"- 每次会话启动后，在进行文件/路径相关操作前，必须先读取 `skills/_workspace/public/path-convention/SKILL.md` 了解当前路径规范。\n"
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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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"- 使用 memory_wiki_search 时默认采用三段检索：先 `query` 精确检索；若结果不足再启用 `expand_query=true`；仍不足时加 `path_prefix` 定向到候选目录后重检。\n"
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"- 三段检索建议参数：第一段 `limit=5~8`；第二段 `expand_query=true,max_rounds=2~3`；第三段在保留前述参数基础上增加 `path_prefix` 并分页（`offset`/`limit`）。\n"
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"- 每段检索后先依据 `queries_attempted`、`total_hits_estimate`、`next_offset` 判断是否继续，避免一次无命中就直接下结论。\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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"- 工具调用必须通过模型的原生 `tool_calls` 协议返回；不要在正文输出 DSML/XML/工具协议 JSON 模板。\n"
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"- 若当前模型链路不支持原生 `tool_calls`，请用自然语言明确说明“当前无法发起工具调用”，并请求切换到支持该协议的链路；不要伪造或模拟工具调用。\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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_session_bootstrap_content_signature(skill_binding_role=skill_binding_role, workspace_dir=workspace_dir),
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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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