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oliver
2aff012b48
Drop the hard WhatsApp body cap and tighten playbook prompts so final answers stay concise but finished. Co-authored-by: Cursor <cursoragent@cursor.com>
217 lines
8.8 KiB
Python
217 lines
8.8 KiB
Python
from __future__ import annotations
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from typing import Any
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from runtime.scheduler.recipe import compile_playbook_instruction, recipe_has_playbook
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def format_scheduled_user_reminder(prompt_text: str, *, lang: str = "en") -> str:
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body = str(prompt_text or "").strip()
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if not body:
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return ""
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if body.startswith("⏰"):
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return body
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if str(lang or "").lower().startswith("zh"):
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return f"⏰ 提醒：{body}"
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return f"⏰ Reminder: {body}"
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def format_scheduled_success_summary(
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*,
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job_name: str = "",
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job_id: str = "",
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reply_text: str = "",
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attachment_count: int = 0,
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lang: str = "en",
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) -> str:
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"""User-facing success notice; keeps the model reply intact (no hard truncate)."""
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name = str(job_name or "").strip() or str(job_id or "").strip() or "scheduled job"
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body = str(reply_text or "").strip()
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# Drop pure reminder fallbacks that just echo the job prompt.
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if body.startswith("⏰"):
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body = ""
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att_n = max(0, int(attachment_count or 0))
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if str(lang or "").lower().startswith("zh"):
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head = f"[定时任务完成] {name}"
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if att_n:
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head += f"\n附件：{att_n} 个"
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if body:
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return f"{head}\n{body}"
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return head + ("\n（本次无文字摘要，请查看附件。）" if att_n else "\n（本次无摘要内容。）")
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head = f"[Scheduled job done] {name}"
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if att_n:
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head += f"\nAttachments: {att_n}"
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if body:
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return f"{head}\n{body}"
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return head + ("\n(No text summary; see attachment(s).)" if att_n else "\n(No summary content.)")
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def format_scheduled_failure_summary(
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*,
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job_name: str = "",
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job_id: str = "",
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error: str = "",
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lang: str = "en",
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) -> str:
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"""Short user-facing failure notice for WhatsApp/WeChat scheduled delivery."""
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name = str(job_name or "").strip() or str(job_id or "").strip() or "scheduled job"
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err = " ".join(str(error or "").strip().split())
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if len(err) > 240:
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err = err[:237] + "..."
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if not err:
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err = "unknown error"
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if str(lang or "").lower().startswith("zh"):
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return f"[定时任务失败] {name}\n错误：{err}\n请检查任务配置或手动重试。"
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return f"[Scheduled job failed] {name}\nError: {err}\nCheck the job config or retry manually."
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def format_scheduled_skip_summary(
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*,
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job_name: str = "",
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job_id: str = "",
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overlapping_run_id: str = "",
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lang: str = "en",
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) -> str:
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"""User-facing notice when a due tick is skipped because a prior run is still active."""
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name = str(job_name or "").strip() or str(job_id or "").strip() or "scheduled job"
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oid = str(overlapping_run_id or "").strip()
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if str(lang or "").lower().startswith("zh"):
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lines = [
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f"[定时任务跳过] {name}",
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"原因：上一轮仍在运行（overlapping），本轮已跳过以免叠跑。",
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]
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if oid:
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lines.append(f"进行中的 run：{oid[:12]}")
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return "\n".join(lines)
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lines = [
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f"[Scheduled job skipped] {name}",
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"Reason: previous run still active (overlapping); this tick was skipped.",
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]
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if oid:
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lines.append(f"Active run: {oid[:12]}")
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return "\n".join(lines)
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def build_scheduled_turn_instruction(
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*,
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prompt_text: str,
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mode: str,
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lang: str,
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recipe: dict[str, Any] | None = None,
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previous_run: dict[str, Any] | None = None,
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) -> str:
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"""Internal LLM instruction for proactive scheduled reminders/playbooks (not user-facing)."""
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_ = str(mode or "scheduled").strip()
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if recipe_has_playbook(recipe):
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text = compile_playbook_instruction(recipe=recipe or {}, lang=lang)
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else:
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intent = str(prompt_text or "").strip()
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is_en = str(lang or "").lower().startswith("en")
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if is_en:
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text = (
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"[Scheduled proactive reminder — internal instruction, not a user message]\n"
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f"Reminder intent: {intent}\n"
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"Write a short, friendly proactive reminder TO the user (second person). "
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"Do not say you received a reminder or that you will remind someone; speak directly to the user."
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)
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else:
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text = (
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"【定时主动提醒·内部指令，不是用户发言】\n"
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f"提醒意图：{intent}\n"
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"请生成一条简短、自然、第二人称的主动提醒消息直接对用户说。"
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"不要写「收到提醒」「好的我来提醒用户」等元对话；不要假装用户刚说了话。"
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)
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return append_previous_run_context(text, previous_run=previous_run, lang=lang)
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def append_previous_run_context(
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instruction: str,
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*,
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previous_run: dict[str, Any] | None,
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lang: str = "en",
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max_body_chars: int = 800,
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) -> str:
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"""Append a short prior-run note so recurring jobs can compare deltas."""
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base = str(instruction or "").rstrip()
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if not previous_run or not isinstance(previous_run, dict):
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return base
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status = str(previous_run.get("status") or "").strip() or "unknown"
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finished = str(previous_run.get("finished_at") or previous_run.get("created_at") or "").strip()
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err = " ".join(str(previous_run.get("error") or "").split())
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body = " ".join(str(previous_run.get("reply_text") or "").split())
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# Prefer error text on failures; otherwise the outbound summary.
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if status.lower() in {"failed", "error"} and err:
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body = err
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cap = max(120, int(max_body_chars or 800))
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if len(body) > cap:
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body = body[: cap - 3].rstrip() + "..."
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if not body and not finished:
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return base
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is_en = str(lang or "").lower().startswith("en")
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if is_en:
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lines = [
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"",
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"[Previous run context — for continuity only; still execute this run fully]",
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f"Status: {status}" + (f" | Finished: {finished}" if finished else ""),
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]
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if body:
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lines.append(f"Summary: {body}")
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lines.append(
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"Use this for deltas/comparisons when useful; do not skip work just because the prior run succeeded."
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)
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else:
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lines = [
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"",
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"【上一轮运行摘要·仅供对照；本轮仍须完整执行】",
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f"状态：{status}" + (f"｜完成时间：{finished}" if finished else ""),
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]
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if body:
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lines.append(f"摘要：{body}")
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lines.append("可参考做环比/差异，但不要因上轮成功而跳过本轮步骤。")
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return base + "\n" + "\n".join(lines)
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def scheduled_turn_system_suffix(*, lang: str, playbook: bool = False) -> str:
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is_en = str(lang or "").lower().startswith("en")
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if playbook:
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if is_en:
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return (
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"\n\n[Scheduled playbook mode] You are executing a recurring workflow for the user. "
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"Follow the playbook steps, use tools as needed, and deliver a useful update "
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"(including save_deliverable_attachment for generated files). "
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"Final reply must be concise and complete: lead with conclusions "
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"(what ran, key counts, ok/failed highlights, top issues), then only the detail "
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"needed to act—no raw dumps, no unfinished sentences. Prefer a short full answer "
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"over a long partial one. "
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"For multi-NE CLI, prefer one execManagedNe(ne_ids|ume_ne_ids=..., commands=...) batch. "
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"Do not pretend the user just messaged you."
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)
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return (
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"\n\n【定时工作流模式】你正在执行周期性工作流。"
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"按 playbook 步骤完成任务，按需调用工具；若生成文件须 save_deliverable_attachment。"
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"最终回复须简明且写完：先给结论（做了什么、关键计数、成败、主要问题），"
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"再只保留可行动的细节；不要堆原始数据，不要半截句子。"
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"宁可短而完整，也不要长而截断感。"
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"多台 CLI 优先一次 execManagedNe(ne_ids|ume_ne_ids=..., commands=...) 批量并发。"
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"不要假装用户刚刚发了消息，不要只回一句空提醒。"
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)
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if is_en:
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return (
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"\n\n[Scheduled job mode] You are sending a proactive reminder to the user. "
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"Reply with the reminder text only; do not role-play as the user."
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)
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return (
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"\n\n【定时任务模式】你正在主动向用户发送提醒。"
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"只输出提醒正文，不要扮演用户，不要写「收到/好的」等对话式应答。"
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)
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__all__ = [
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"append_previous_run_context",
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"build_scheduled_turn_instruction",
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"format_scheduled_failure_summary",
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"format_scheduled_skip_summary",
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"format_scheduled_success_summary",
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"format_scheduled_user_reminder",
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"scheduled_turn_system_suffix",
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]
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