   645→大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒
   646→鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键
   647→油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾
   648→伪口语: 说白了、说实话、折腾这玩意儿
   649→
   650→=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
   651→1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X" → 直接说判断，删掉"很多人"铺垫
   652→2. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
   653→3. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源）→ 用最朴素的说法
   654→4. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
   655→5. 比喻堆砌：全篇最多1个比喻，多余全删换直说
   656→6. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
   657→7. 首尾呼应：禁止结尾重复开头
   658→8. 对比式定义："不是X而是Y"、"那不叫X那叫Y" → 直接陈述
   659→9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
   660→10. 防御性引号：删除套在归纳性词汇上的引号
   661→11. 强行总结：删除"一句话建议"、"综上"式收尾
   662→12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落
   663→
   664→=== PRIORITY 3: ZHIHU-SPECIFIC RULES ===
   665→{zhihu_bot_style}
   666→
   667→=== PRIORITY 4: FULL REVISION GUIDE ===
   668→{powell_revise_guide}
   669→
   670→=== DRAFT TO REVIEW ===
   671→{draft}
   672→
   673→=== OUTPUT ===
   674→Return ONLY the corrected Chinese text. No explanation, no meta-commentary, no English.
   675→"""
   676→            logger.info("Running second-pass rule enforcement on Zhihu draft...")
   677→            response, _ = self.llm.generate_response(review_prompt, [])
   678→        else:
   679→            response = draft

大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X" → 直接说判断，删掉"很多人"铺垫
2. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
3. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源）→ 用最朴素的说法
4. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
5. 比喻堆砌：全篇最多1个比喻，多余全删换直说
6. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
7. 首尾呼应：禁止结尾重复开头
8. 对比式定义："不是X而是Y"、"那不叫X那叫Y" → 直接陈述
9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
10. 防御性引号：删除套在归纳性词汇上的引号
11. 强行总结：删除"一句话建议"、"综上"式收尾
12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落

=== PRIORITY 3: ZHIHU-SPECIFIC RULES ===
{zhihu_bot_style}

=== PRIORITY 4: FULL REVISION GUIDE ===
{powell_revise_guide}

=== DRAFT TO REVIEW ===
{draft}

=== OUTPUT ===
Return ONLY the corrected Chinese text. No explanation, no meta-commentary, no English.
"""
            logger.info("Running second-pass rule enforcement on Zhihu draft...")
            response, _ = self.llm.generate_response(review_prompt, [])
        else:
            response = draft


Edit

大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X" → 直接说判断，删掉"很多人"铺垫
2. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
3. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源）→ 用最朴素的说法
4. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
5. 比喻堆砌：全篇最多1个比喻，多余全删换直说
6. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
7. 首尾呼应：禁止结尾重复开头
8. 对比式定义："不是X而是Y"、"那不叫X那叫Y" → 直接陈述
9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
10. 防御性引号：删除套在归纳性词汇上的引号
11. 强行总结：删除"一句话建议"、"综上"式收尾
12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落

大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒、分水岭、沉淀（用作动词修饰数据/经验时）、云端办公室
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键、不是建议是必须
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾、"把X关在Y里...把Y放在Z上"式排比收尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X"、"有人X，有人已经Y了" → 直接说判断，删掉"很多人/有人"铺垫
2. 对比式定义："不是X，是Y"、"不是X而是Y"、"那不叫X那叫Y"、"别把X当Y" → 删除对比句式，直接陈述后半句即可
3. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
4. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源、"云端办公室"=云服务器、"牢笼"=限制）→ 用最朴素的说法
5. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
6. 比喻堆砌：全篇最多1个比喻，多余全删换直说
7. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
8. 首尾呼应：禁止结尾重复开头
9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
10. 防御性引号：删除套在归纳性词汇上的引号
11. 强行总结：删除"一句话建议"、"综上"式收尾
12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落
13. 关键词重复：同一个修辞性词汇（如"分水岭"、"命门"、"杀手锏"）全篇只能出现一次，重复出现时删除后续的或换成普通说法


The file /root/projects/butler/slack_bot/obsidian/generators.py has been updated successfully.

大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒、分水岭、沉淀（用作动词修饰数据/经验时）、云端办公室
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键、不是建议是必须
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾、"把X关在Y里...把Y放在Z上"式排比收尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X"、"有人X，有人已经Y了" → 直接说判断，删掉"很多人/有人"铺垫
2. 对比式定义："不是X，是Y"、"不是X而是Y"、"那不叫X那叫Y"、"别把X当Y" → 删除对比句式，直接陈述后半句即可
3. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
4. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源、"云端办公室"=云服务器、"牢笼"=限制）→ 用最朴素的说法
5. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
6. 比喻堆砌：全篇最多1个比喻，多余全删换直说
7. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
8. 首尾呼应：禁止结尾重复开头
9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
10. 防御性引号：删除套在归纳性词汇上的引号
11. 强行总结：删除"一句话建议"、"综上"式收尾
12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落
13. 关键词重复：同一个修辞性词汇（如"分水岭"、"命门"、"杀手锏"）全篇只能出现一次，重复出现时删除后续的或换成普通说法

import os
from pathlib import Path
from typing import List, Dict, Tuple, Optional, TYPE_CHECKING
from slack_bot.llm.gemini import GeminiLLM
from slack_bot.obsidian.indexer import ObsidianIndexer
from slack_bot.tools.web import WebSearchTool
from health.utils.logging_config import setup_logger
from health.utils.time_utils import get_current_time_str

if TYPE_CHECKING:
    from slack_bot.obsidian.vector_store import ChromaVectorStore

logger = setup_logger(__name__)

OBSIDIAN_SYSTEM_PROMPT = """You are Butler (Obsidian Edition), an intelligent knowledge assistant connected to the user's second brain.
Current Time: {current_time}

Your Role:
You are NOT a health assistant. You are a Knowledge Partner designed to help the user think, write, and communicate. You draw directly from the user's local Obsidian notes, values, and methodology.

Core Capabilities:
1. Writing: You mimic the user's unique writing style (as defined in `writing_style.md`).
2. Communication: You draft high-EQ, logically rigorous replies for professional contexts (as defined in `REPLY-SAMPLE.md`).
3. Decision: You act as a "Devil's Advocate" and strategic advisor using the user's decision frameworks (GPA, IPO).

Guidelines:
- **Style Alignment**: Strictly adhere to the tone and sentence structures found in the provided samples.
- **Data Source**: Rely primarily on the provided context (RAG) and loaded markdown files.
- **Identity**: You are pragmatic, rational, and value "technological optimism" and "intellectual honesty".
"""

class BaseGenerator:
    def __init__(
        self,
        indexer: ObsidianIndexer,
        vector_store: Optional["ChromaVectorStore"] = None,
    ):
        formatted_prompt = OBSIDIAN_SYSTEM_PROMPT.format(current_time=get_current_time_str())
        self.llm = GeminiLLM(system_instruction=formatted_prompt)
        self.indexer = indexer
        self.vector_store = vector_store
        self.workspace_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))

    def _read_file(self, filename: str) -> str:
        """Reads a file from the workspace root."""
        path = os.path.join(self.workspace_root, filename)
        try:
            with open(path, "r", encoding="utf-8") as f:
                return f.read()
        except Exception as e:
            logger.error(f"Failed to read {filename}: {e}")
            return ""

    def _semantic_search(self, query: str, top_k: int = 3) -> str:
        """Run semantic search against the vector store and format results.

        Args:
            query: Search query text.
            top_k: Maximum number of chunks to retrieve.

        Returns:
            Formatted string block, or empty string if no results.
        """
        if self.vector_store is None:
            return ""
        try:
            results = self.vector_store.search_knowledge(query, top_k=top_k)
        except Exception as e:
            logger.warning(f"_semantic_search failed: {e}")
            return ""
        if not results:
            return ""
        lines = ["=== SEMANTIC KNOWLEDGE FROM VAULT ==="]
        for r in results:
            lines.append(f"[{Path(r.source_path).name} / {r.header_hierarchy}]")
            # Truncate individual chunks to avoid prompt bloat (~500 tokens ≈ 2000 chars)
            chunk_text = r.text[:2000]
            lines.append(chunk_text)
            lines.append("")
        return "\n".join(lines)

class WritingAssistant(BaseGenerator):
    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        is_first_turn = (len(history) == 0)

        if is_first_turn:
            # === First Turn: Build Rich Prompt ===
            # user_input is treated as "Topic"

            # Naive splitting for context if formatted as "Topic | Context"
            parts = user_input.split("|", 1)
            topic = parts[0].strip()
            extra_context = parts[1].strip() if len(parts) > 1 else ""

            # 1. RAG & Samples
            style_samples = self.indexer.get_writing_samples(count=3)
            style_text = "\n\n".join(style_samples)

            rag_notes = self.indexer.search(topic, limit=5)
            rag_text = "\n\n".join(rag_notes)

            writing_style_guide = self._read_file("writing_style.md")
            methodology = self._read_file("methodology.md")
            powell_revise_guide = self._read_file("POWELL_REVISE.md")

            semantic_context = self._semantic_search(topic, top_k=3)

            prompt = f"""You are a ghostwriter for the user. Your goal is to write an article on the TOPIC provided.

            === YOUR IDENTITY & METHODOLOGY ===
            {methodology}

            === YOUR WRITING STYLE GUIDE ===
            {writing_style_guide}

            === ANTI-AI-TONE GUIDE (APPLY WHILE WRITING, NOT JUST AT THE END) ===
            {powell_revise_guide}

            === YOUR WRITING SAMPLES (MIMIC THIS TONE) ===
            {style_text}

            === RELEVANT NOTES FROM OBSIDIAN VAULT ===
            {rag_text}

            {semantic_context}

            === TASK ===
            Topic: {topic}
            Extra Context: {extra_context}

            Write the article in Chinese (unless the topic implies English).
            Adhere strictly to the "Identity" and "Style Guide".
            """

            logger.info(f"Generating article for topic: {topic}")
            actual_user_message = prompt # We inject the big prompt

        else:
            # === Follow-up Turn ===
            # user_input is just instructions like "Make it shorter"
            logger.info(f"Follow-up instruction: {user_input}")
            actual_user_message = user_input
            prompt = user_input

        # Call LLM — first pass: content generation
        draft, _ = self.llm.generate_response(prompt, history)

        # === Second pass: de-AI review (first turn only) ===
        if is_first_turn:
            powell_revise_guide = self._read_file("POWELL_REVISE.md")
            review_prompt = f"""You are a strict editor. A draft article has been written. Your ONLY job is to remove AI tone. Do NOT change facts or add new content.

=== TONE TARGET (最高优先级) ===
目标语感：像一个懂行的人在跟朋友聊天时随口说的。不端着，也不刻意耍帅。
两种AI味都要杀：
- 正式AI味：过渡词堆砌、排比对仗、空洞修饰、鸡汤结尾
- 油腻AI味：刻意装随意（"折腾去吧"）、强行造金句（"搞坏了不心疼，跑通了就是资产"）、给普通概念起酷名字（"隔离舱"、"暴力碰撞"、"最干净的契约"）、用一句俏皮话收尾装潇洒
正确的语感：平铺直叙，该说什么说什么，说完就停。不需要让读者觉得你很酷。

=== PRIORITY 1: VOCABULARY KILL LIST (逐词扫描，命中即删或换大白话) ===
夸张修饰: 极度脆弱、毁灭性的、灾难级的、压倒性的、远超想象、不止一个量级、颠覆性的、革命性的、折磨人、天翻地覆、风险极高
大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、可扩展的执行信用、终极归宿、壁垒、分水岭、沉淀（用作动词修饰数据/经验时）、云端办公室
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键、不是建议是必须
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾、"把X关在Y里...把Y放在Z上"式排比收尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"有人X，有人已经Y了" → 直接说判断，删掉"很多人/有人"铺垫
2. 对比式定义："不是X，是Y"、"不是X而是Y"、"那不叫X那叫Y"、"别把X当Y" → 删除对比句式，直接陈述后半句即可
3. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
4. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源、"云端办公室"=云服务器、"牢笼"=限制）→ 用最朴素的说法
5. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
6. 比喻堆砌：全篇最多保留1个比喻，多余的全部删除换成直说
7. 鸡汤/口号结尾：删除所有励志或耍帅收尾，改为具体行动建议或直接停住
8. 首尾呼应：禁止结尾重复开头观点
9. 语气漂移：全文语气必须统一，不要中途切换
10. 编号标题：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落过渡；如果是并列对比不同方案（如方案A vs 方案B vs 方案C），可以保留编号
11. 过渡词堆积：删除"首先...其次...最后..."
12. 排比对仗：打破字数相近的并列句
13. 关键词重复：同一个修辞性词汇（如"分水岭"、"命门"、"杀手锏"）全篇只能出现一次，重复出现时删除后续的或换成普通说法

=== PRIORITY 3: FULL REVISION GUIDE ===
{powell_revise_guide}

=== DRAFT TO REVIEW ===
{draft}

=== OUTPUT ===
Return ONLY the corrected text. No explanation, no meta-commentary.
"""
            logger.info("Running second-pass de-AI review on writing draft...")
            response, _ = self.llm.generate_response(review_prompt, [])
        else:
            response = draft

        # Update History
        new_history = history.copy()
        new_history.append({"role": "user", "content": actual_user_message})
        new_history.append({"role": "assistant", "content": response})

        return response, new_history

class ReplyGenerator(BaseGenerator):
    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        is_first_turn = (len(history) == 0)

        if is_first_turn:
             # === First Turn ===
            static_samples = self._read_file("REPLY-SAMPLE.md")
            dynamic_samples = self.indexer.get_reply_samples(count=3)
            dynamic_samples_text = "\n\n".join(dynamic_samples)
            methodology = self._read_file("methodology.md")

            semantic_context = self._semantic_search(user_input, top_k=3)

            prompt = f"""You are an experienced Product/R&D Manager.
            Your task is to draft a reply to a challenging situation.

            === CORE PHILOSOPHY ===
            {methodology}

            === REPLY GUIDELINES ===
            1. Logic: Be clear and structured (e.g., numbered lists).
            2. Responsibility: Don't take unnecessary blame, but don't shirk core duties. Explain objective causes.
            3. Emotion: Be empathetic but professional.
            4. Conciseness: No fluff.

            === REFERENCE SAMPLES (FEW-SHOT) ===
            {static_samples}

            {dynamic_samples_text}

            {semantic_context}

            === THE SITUATION ===
            {user_input}

            Draft a reply. If the context implies a specific role (PM, Dev Lead), assume that role.
            """
            logger.info(f"Generating reply for query: {user_input}")
            actual_user_message = prompt

        else:
             # === Follow-up Turn ===
            logger.info(f"Follow-up reply instruction: {user_input}")
            actual_user_message = user_input
            prompt = user_input

        response, _ = self.llm.generate_response(prompt, history)

        new_history = history.copy()
        new_history.append({"role": "user", "content": actual_user_message})
        new_history.append({"role": "assistant", "content": response})

        return response, new_history

class DecisionSupport(BaseGenerator):
    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        is_first_turn = (len(history) == 0)

        if is_first_turn:
            decision_guide = self._read_file("decision.md")
            methodology = self._read_file("methodology.md")

            rag_notes = self.indexer.search(user_input, limit=3)
            rag_text = "\n\n".join(rag_notes)

            prompt = f"""You are a Decision Support Assistant.
            Your goal is to help the user think through a complex decision.

            === DECISION FRAMEWORK ===
            {decision_guide}

            === CORE PHILOSOPHY ===
            {methodology}

            === RELEVANT CONTEXT FROM NOTES ===
            {rag_text}

            === THE DECISION / ISSUE ===
            {user_input}

            === TASK ===
            1. Challenge the premise: Is this the right problem to solve?
            2. Apply the Framework: Use GPA (Goal, Priority, Alternatives) and IPO (Information, People, Objective reasoning) models.
            3. Pre-mortem: What is the worst that could happen?
            4. Provide a recommendation or a set of questions to clarify.
            """

            logger.info(f"Analyzing decision: {user_input}")
            actual_user_message = prompt
        else:
            logger.info(f"Follow-up decision instruction: {user_input}")
            actual_user_message = user_input
            prompt = user_input

        response, _ = self.llm.generate_response(prompt, history)

        new_history = history.copy()
        new_history.append({"role": "user", "content": actual_user_message})
        new_history.append({"role": "assistant", "content": response})

        return response, new_history

class SearchAnalyzer(BaseGenerator):
    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        # Tools definition for the LLM
        tools = [
            {
                "type": "function",
                "function": {
                    "name": "search_web",
                    "description": "Search the public web using DuckDuckGo. Use this when the internal notes are insufficient or when you need up-to-date external information.",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "query": {
                                "type": "string",
                                "description": "The search query"
                            }
                        },
                        "required": ["query"]
                    }
                }
            },
            {
                "type": "function",
                "function": {
                    "name": "list_recent_files",
                    "description": "List files in the Obsidian vault that have been modified recently. Use this when the user asks for 'recent updates', 'what's new', or changes in the last X days.",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "days": {
                                "type": "integer",
                                "description": "Number of days to check (default 5)",
                                "default": 5
                            }
                        },
                        "required": []
                    }
                }
            }
        ]

        is_first_turn = (len(history) == 0)

        # Current Turn Message
        if is_first_turn:
            rag_notes = self.indexer.search(user_input, limit=5)
            rag_text = "\n\n".join(rag_notes)

            logger.info(f"SearchAnalyzer retrieved {len(rag_notes)} local notes.")
            for note in rag_notes:
                # Extract source line for logging
                first_line = note.split('\n')[0]
                logger.debug(f" - Retrieved: {first_line}")

            prompt = f"""You are an Analyst using Butler (Obsidian Edition).

            === TASK ===
            Answer the user's question.
            CRITICAL: You MUST explicitly cite your sources in the output.

            === STRATEGY ===
            1. first: Check "RELEVANT LOCAL NOTES" provided below.
            2. second: If the notes answer the question, answer directly.
            3. third: If the notes are missing info, ambiguos, or outdated, USE THE `search_web` TOOL to find external info.

            === OUTPUT FORMAT ===
            - Begin with a summary.
            - When stating facts, append the source in brackets, e.g., "The server failed [Source: XLSmart 2025-12-25.md]" or "Market share is 20% [Source: Web Search]".
            - If data comes from BOTH, mention both.

            === RELEVANT LOCAL NOTES ===
            {rag_text}

            === USER QUESTION ===
            {user_input}
            """
            actual_user_message = prompt
        else:
            actual_user_message = user_input

        # Prepare execution context
        # We don't want to mutate the input history directly until the end
        exec_history = history.copy()
        exec_history.append({"role": "user", "content": actual_user_message})

        logger.info(f"Analyzer thinking on: {user_input}")

        # === ReAct Loop (Max 2 turns to prevent infinite loops) ===
        for i in range(2):
            response_text, tool_calls = self.llm.generate_response(
                message="", # Message is already in history
                context=exec_history,
                tools=tools
            )

            if tool_calls:
                # LLM wants to use tools
                logger.info(f"LLM requested tool calls: {tool_calls}")

                # Append the assistant's request (with tool calls) to history
                exec_history.append({
                    "role": "assistant",
                    "content": f"[Tool Call Request: {tool_calls}]"
                })

                for tc in tool_calls:
                    if tc["name"] == "search_web":
                        query = tc["args"].get("query")
                        search_result = WebSearchTool.search_web(query)

                        # Feed result back
                        exec_history.append({
                            "role": "user", # Using 'function' role would be better if our LLM class supports it fully, but 'user' works as system injection
                            "content": f"--- TOOL OUTPUT (search_web) ---\n{search_result}"
                        })

                    elif tc["name"] == "list_recent_files":
                        days = tc["args"].get("days", 5)
                        # Call indexer method
                        recent_list = self.indexer.get_recent_files(days=days)

                        exec_history.append({
                            "role": "user",
                            "content": f"--- TOOL OUTPUT (list_recent_files) ---\n{recent_list}"
                        })

                # Loop again to get the final answer using tool outputs
                try:
                     # Second pass with tool outputs
                    response_text, _ = self.llm.generate_response(
                        message="",
                        context=exec_history,
                        tools=tools
                    )
                    return response_text, exec_history
                except Exception as e:
                    logger.error(f"Error in second ReAct loop: {e}")
                    return response_text, exec_history

            else:
                # Final answer (no tools)
                return response_text, exec_history

        # Fallback if loop exhausted
        return response_text, exec_history

class DeAIReviser(BaseGenerator):
    """Remove AI tone from articles - text-in, text-out mode."""

    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        is_first_turn = (len(history) == 0)

        if is_first_turn:
            # === First Turn: User pastes the AI-generated article ===
            # Load the POWELL_REVISE.md guide
            powell_revise_guide = self._read_file("POWELL_REVISE.md")

            # Load user's writing samples for style reference
            style_samples = self.indexer.get_writing_samples(count=3)
            style_text = "\n\n".join(style_samples)

            prompt = f"""You are an expert editor specializing in removing AI-generated tone from articles.

Your task is to revise the article provided by the user to make it sound more human and authentic.

=== REVISION GUIDE ===
{powell_revise_guide}

=== USER'S AUTHENTIC WRITING SAMPLES (for style reference) ===
{style_text}

=== ARTICLE TO REVISE ===
{user_input}

=== TASK ===
1. Analyze the article and identify AI characteristics (过渡词堆积, 排比对仗, 空洞修饰词等)
2. Rewrite the article following the POWELL_REVISE.md principles
3. Preserve:
   - Original meaning and key information
   - Technical terms and proper nouns
   - Code blocks, lists, and formatting
   - Original language (Chinese/English)
4. Output the revised article directly (no meta-commentary like "Here's the revised version...")

CRITICAL: Output ONLY the revised article text. Do NOT add explanations before or after.
"""

            logger.info(f"DeAI Reviser: Processing article ({len(user_input)} chars)")
            actual_user_message = prompt

        else:
            # === Follow-up Turn: User gives refinement instructions ===
            logger.info(f"DeAI Reviser: Follow-up instruction - {user_input}")
            actual_user_message = user_input
            prompt = user_input

        # Call LLM
        response, _ = self.llm.generate_response(prompt, history)

        # Update History
        new_history = history.copy()
        new_history.append({"role": "user", "content": actual_user_message})
        new_history.append({"role": "assistant", "content": response})

        return response, new_history

class ZhihuGenerator(BaseGenerator):
    """Generate Zhihu-style answers based on style guide and user's knowledge base."""

    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:

        is_first_turn = (len(history) == 0)
        built_rich_prompt = False  # tracks whether we ran the full question->draft pipeline

        # Pre-load rule files (needed for both generation and second-pass review)
        zhihu_bot_style = self._read_file("zhihu-bot-style.md")
        powell_revise_guide = self._read_file("POWELL_REVISE.md")

        # Treat as a new question if:
        # - history is empty (genuine first turn), OR
        # - user input contains "|" (explicit "Question | Core Ideas" format), OR
        # - user input starts with known question triggers like "回答这个问题"
        is_new_question = (
            is_first_turn
            or "|" in user_input
            or user_input.strip().startswith("回答这个问题")
        )

        if is_new_question:
            # === First Turn: Build Rich Prompt ===
            # Expected input format: "Question | Core Ideas"
            parts = user_input.split("|", 1)
            question = parts[0].strip()
            core_ideas = parts[1].strip() if len(parts) > 1 else ""

            # 1. Load Style Guide (zhihu-style.md serves as writing standard, NOT as material)
            zhihu_style_guide = self._read_file("zhihu-style.md")
            # Bot-specific overrides (plain-text output, no bracket explanations, etc.)
            if zhihu_bot_style:
                zhihu_style_guide = zhihu_style_guide + "\n\n" + zhihu_bot_style

            # 2. RAG from Obsidian vault for actual content material
            rag_notes = self.indexer.search(question, limit=5)
            rag_text = "\n\n".join(rag_notes)

            # 3. Load methodology
            methodology = self._read_file("methodology.md")

            semantic_context = self._semantic_search(question, top_k=3)

            prompt = f"""You are answering a Zhihu question. Your goal is to write an authentic, experience-driven answer that sounds like a real human, not AI-generated.

=== YOUR IDENTITY & METHODOLOGY ===
{methodology}

=== ANTI-AI-TONE GUIDE (APPLY WHILE WRITING, NOT JUST AT THE END) ===
{powell_revise_guide}

=== ZHIHU STYLE GUIDE (FOLLOW THESE WRITING PRINCIPLES) ===
{zhihu_style_guide}

CRITICAL: The style guide above defines HOW to write (tone, structure, language patterns).
Do NOT treat zhihu-sample content as factual material. It's ONLY for style reference.

=== RELEVANT KNOWLEDGE FROM YOUR OBSIDIAN VAULT (USE AS ACTUAL MATERIAL) ===
{rag_text}

{semantic_context}

=== THE QUESTION ===
{question}

=== YOUR CORE IDEAS ===
{core_ideas}

=== TASK ===
1. Answer the question based on YOUR knowledge (from Obsidian vault) and core ideas
2. FOLLOW THE ANTI-AI-TONE GUIDE FIRST, then the Zhihu style guide
3. Share real experiences, examples, and insights from the vault material
4. Write in Chinese (unless question implies English)
5. Minimize first person "我": use it sparingly, not as filler. Observations and judgments can stand without "我认为" or "我觉得" as prefix.
6. Be direct and opinionated. Attach a concrete evaluation to every fact or feature you mention (e.g. "暴力但有效", "极度脆弱"). Pure feature listing is forbidden.
7. AVOID AT ALL COSTS: AI tone, empty buzzwords, hollow phrases, and 宏大叙事 (不可逆的浪潮, 范式转移, 时代大背景).
8. If recent web context is provided, weave it in naturally — like someone who reads a lot online
9. STRICT: Never add bracket explanations for any term. Write "大语言模型" not "大语言模型（LLM）". If a term needs explaining, use a sentence, not parentheses.
10. No public account names, no self-promotion, no引流 of any kind — this is the highest priority rule.
11. STRICT: Never reveal personal identity or professional background. Do not mention employer, team, job title, product name, or any detail that could identify who you are.
12. STRICT: Never reference past projects, articles, or talks by name.
13. No poetry, quotes, or "升华" endings. No首尾呼应 (repeating the opening point at the end). Stop when the point is made.
14. LENGTH: 500–1000 Chinese characters. Cut a point, not quality.

=== BEFORE SUBMITTING, CHECK THESE ===
- Is there any AI-generated tone or phrasing?
- Are there any buzzwords or jargon that need to be replaced with plain language?
- Is the answer direct and opinionated, not neutral or "balanced"?
- Does it sound like a real human wrote it, not a machine?

Begin writing the answer:
"""

            logger.info(f"Generating Zhihu answer for: {question[:50]}...")
            actual_user_message = prompt
            built_rich_prompt = True

        else:
            # === Follow-up Turn: User gives refinement instructions (e.g. "make it shorter") ===
            logger.info(f"Follow-up Zhihu instruction: {user_input}")
            actual_user_message = user_input
            prompt = user_input

        # Call LLM — first pass: content generation
        # For new questions, use empty history to avoid contamination from previous conversations
        llm_history = [] if is_new_question else history
        draft, _ = self.llm.generate_response(prompt, llm_history)

        # === Second pass: rule enforcement ===
        # Runs whenever a full draft was generated (built_rich_prompt=True),
        # regardless of whether history was empty or not.
        # Skipped on follow-up refinement turns (e.g. "make it shorter").
        run_review = built_rich_prompt
        if run_review:
            review_prompt = f"""You are a strict editor. A draft Zhihu answer has been written. Your ONLY job is to remove AI tone. Do NOT change facts or add new content.

=== TONE TARGET (最高优先级) ===
目标语感：像一个懂行的人在知乎随手打字回答。不端着，也不刻意耍帅。
两种AI味都要杀：
- 正式AI味：过渡词堆砌、排比对仗、空洞修饰、鸡汤结尾
- 油腻AI味：刻意装随意（"折腾去吧"）、强行造金句（"搞坏了不心疼，跑通了就是资产"）、给普通概念起酷名字（"隔离舱"、"暴力碰撞"、"最干净的契约"）、用一句俏皮话收尾装潇洒
正确的语感：平铺直叙，该说什么说什么，说完就停。不需要让读者觉得你很酷。

=== PRIORITY 1: VOCABULARY KILL LIST (逐词扫描，命中即删或换大白话) ===
夸张修饰: 极度脆弱、毁灭性的、灾难级的、压倒性的、远超想象、不止一个量级、颠覆性的、革命性的、折磨人、天翻地覆、风险极高、本质上
大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒
鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键
油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾
伪口语: 说白了、说实话、折腾这玩意儿

=== PRIORITY 2: SENTENCE PATTERN KILL LIST (命中即改写) ===
1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X" → 直接说判断，删掉"很多人"铺垫
2. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）
3. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源）→ 用最朴素的说法
4. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句
5. 比喻堆砌：全篇最多1个比喻，多余全删换直说
6. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停
7. 首尾呼应：禁止结尾重复开头
8. 对比式定义："不是X而是Y"、"那不叫X那叫Y" → 直接陈述
9. 编号分段：如果是线性论述（论点1→论点2→论点3），删除"一、二、三"序号改为自然段落；如果是并列对比不同方案（方案A vs 方案B vs 方案C），可以保留编号
10. 防御性引号：删除套在归纳性词汇上的引号
11. 强行总结：删除"一句话建议"、"综上"式收尾
12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落

=== PRIORITY 3: ZHIHU-SPECIFIC RULES ===
{zhihu_bot_style}

=== PRIORITY 4: FULL REVISION GUIDE ===
{powell_revise_guide}

=== DRAFT TO REVIEW ===
{draft}

=== OUTPUT ===
Return ONLY the corrected Chinese text. No explanation, no meta-commentary, no English.
"""
            logger.info("Running second-pass rule enforcement on Zhihu draft...")
            response, _ = self.llm.generate_response(review_prompt, [])
        else:
            response = draft

        # Update History
        new_history = history.copy()
        new_history.append({"role": "user", "content": actual_user_message})
        new_history.append({"role": "assistant", "content": response})

        return response, new_history

-大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒

-鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键

-油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾

+大词空转: 范式转移、范式突变、开发范式、数字化容器、数字雇员、温和池架构、非确定性、阅后即焚、毫秒级就绪、毫秒级响应、赋能、底层逻辑、仪式性努力、深刻变革、暴力碰撞、最干净的契约、服务化的潜力、不可逆的浪潮、时代大背景、可扩展的执行信用、终极归宿、壁垒、分水岭、沉淀（用作动词修饰数据/经验时）、云端办公室

+鸡汤/口号: 正途、值得跨越、安置灵魂、掌控感、才是王道、苦活累活交给、未来已来、危机就是转机、挑战与机遇并存、从玩具变成生产力、把环境变成了资产、安全底线、这才是成熟的做法、才是X的关键、不是建议是必须

+油腻收尾: "X去吧"式潇洒收尾、"搞坏了不心疼，跑通了就是Y"式金句、任何试图用一句俏皮话总结全文的结尾、"把X关在Y里...把Y放在Z上"式排比收尾

伪口语: 说白了、说实话、折腾这玩意儿

-1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X" → 直接说判断，删掉"很多人"铺垫

-2. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）

-3. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源）→ 用最朴素的说法

-4. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句

-5. 比喻堆砌：全篇最多1个比喻，多余全删换直说

-6. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停

-7. 首尾呼应：禁止结尾重复开头

-8. 对比式定义："不是X而是Y"、"那不叫X那叫Y" → 直接陈述

+1. 防御性写法："很多人觉得X，实际上Y"、"有些人认为X，但其实Y"、"很多人没意识到"、"不少人觉得X"、"有人X，有人已经Y了" → 直接说判断，删掉"很多人/有人"铺垫

+2. 对比式定义："不是X，是Y"、"不是X而是Y"、"那不叫X那叫Y"、"别把X当Y" → 删除对比句式，直接陈述后半句即可

+3. 换皮夸张：用另一个夸张词替换夸张词不算修好。"灾难级"→"毁灭性"→"风险极高"都是换皮。正确做法：换成具体后果（如"可能误删整个home目录"）

+4. 概念包装：给普通事物起酷名字（"隔离舱"=容器、"暴力碰撞"=冲突、"数字雇员"=自动化脚本、"执行信用"=服务器资源、"云端办公室"=云服务器、"牢笼"=限制）→ 用最朴素的说法

+5. 油腻金句：短句对仗、押韵、刻意工整的收尾句 → 要么删掉，要么拆成普通陈述句

+6. 比喻堆砌：全篇最多1个比喻，多余全删换直说

+7. 鸡汤/口号结尾：删除励志或耍帅收尾，给具体建议或直接停

+8. 首尾呼应：禁止结尾重复开头

10. 防御性引号：删除套在归纳性词汇上的引号

11. 强行总结：删除"一句话建议"、"综上"式收尾

12. 嵌套数字列表：删除正文中"1. 2. 3."子列表，改为连续段落

+13. 关键词重复：同一个修辞性词汇（如"分水岭"、"命门"、"杀手锏"）全篇只能出现一次，重复出现时删除后续的或换成普通说法


两个 kill list 同步更新完毕。这轮改了：

**词汇新增**：分水岭、沉淀、云端办公室、不是建议是必须

**句式新增**：
- 写作 assistant 补上了"对比式定义"（"不是X，是Y"），之前只有知乎有
- 防御性写法补上了"有人X，有人已经Y了"变体
- 概念包装补上了"牢笼"=限制
- 油腻收尾补上了"把X关在Y里...把Y放在Z上"式排比

**新规则**：第 13 条——关键词重复检查，同一个修辞性词汇全篇只能出现一次

重启后测试。


是不是还可以了：


[Request interrupted by user]


是不是还可以了：让 AI 在你的主力电脑上跑脚本、改文件，这事儿听起来挺酷，实际操作起来很容易出问题。第一个绕不开的坎是权限和逻辑错误的死结。目前大模型处理任务还是概率性的，执行指令时难免有理解偏差。如果在本地给它高权限，它可能一个误操作就把系统文件删了；如果限制权限，它又会频繁报错，导致你得不停地人工确认，这种体验极其糟糕。要真想让 AI Agent 稳当运行，必须准备一个隔离环境。目前的解决方案无非三种：虚拟机、闲置物理机、云服务器。本地虚拟机太重了。为了跑个轻量级 Python 脚本，得分出好几个 GB 内存和几十 GB 硬盘，还得折腾网络透传和文件共享。最关键的是，虚拟机运行起来不仅风扇狂转，环境也极度脆弱。闲置电脑就更不用提了，维护成本高，数据还没法随时迁移，基本就是花钱买个心理安慰。真正能跑通的路径在云上。从基础设施看，云服务器的环境是预先配好的，依赖库、运行环境、网络策略都处于就绪状态。用户请求进来，环境几毫秒就能准备好。这种即刻响应的体验，本地折腾虚拟机确实比不了。更核心的原因在于数据的稳定性和工作流的扩展性。AI 执行任务产生的中间产物、长期记忆、配置文件，这些都是核心资产。放在云上，数据天然有多副本冗余和远程访问能力。你在公司给 AI 下指令，下班路上用手机就能看结果，这种衔接在本地很难实现。而且，一旦你在云上跑通了一套自动抓取数据或自动运维的流程，这套工作流可以很快变成一个标准化的服务分享出去。从个人工具到服务化产品的路径，在云上是顺畅的。现在市面上流行的一些 Agent 沙箱产品虽然启动快，但限制太多。一旦业务变复杂，需要连接特定数据库或者调用冷门 C 库，沙箱就会变成限制。给 AI 准备环境，最直接的办法就是开一台云上的 IaaS 资源。你可以完全控制环境，挂载需要的磁盘，配置偏好的镜像。虽然这需要一点点运维能力，比如加固安全、管理守护进程，但这种掌控感才是构建应用的基础。别试图在主力机上调教 AI，那是浪费时间。去云上给 AI 找个独立房间，把生成的逻辑、代码和数据彻底隔离，然后通过协议去调用。这种架构早期看着有点复杂，但它是通往自动化、可控化 Agent 的路。


The user is asking me to evaluate the latest output. Let me analyze it carefully.

**Overall**: This is significantly better than all previous versions. The tone is much more natural and grounded. But let me check for remaining issues:

1. **"极其糟糕"** - "极其" is a mild intensifier, borderline acceptable but could be simpler
2. **"极度脆弱"** - This is on the kill list! It should have been caught.
3. **"花钱买个心理安慰"** - This is actually fine, it's a natural expression
4. **"真正能跑通的路径在云上"** - Slightly sloganish but acceptable

5. **"更核心的原因"** - Fine
6. **"天然有多副本冗余"** - "天然" is a bit buzzwordy when used this way
7. **"从个人工具到服务化产品的路径，在云上是顺畅的"** - This reads like a marketing tagline
8. **"这种掌控感才是构建应用的基础"** - "掌控感" is on the kill list!
9. **"别试图在主力机上调教 AI，那是浪费时间"** - Slightly preachy, but the core idea is solid. The main issues are the buzzwords and phrases that feel too polished or marketing-like. I need to flag these for revision to keep the language more conversational and grounded. Looking at the remaining issues more carefully—"极度脆弱" and "掌控感" are definitely kill-list words, and "独立房间" falls into the same packaging trap as "云端办公室". The ending about "通往自动化、可控化 Agent 的路" has that motivational tone too. But these are relatively minor problems now, and the overall improvement is substantial. I should give honest feedback while acknowledging how much better the text reads.


比前几轮好很多了，整体读起来像正常人在写东西。还有几个漏网的小问题：

1. **"极度脆弱"** — 在 kill list 里，没被杀掉
2. **"掌控感"** — 在 kill list 里，也漏了
3. **"去云上给 AI 找个独立房间"** — 跟"云端办公室"一个意思，概念包装
4. **"通往自动化、可控化 Agent 的路"** — 结尾还是在指向宏大未来，微弱的鸡汤味

但这些已经是挑刺级别了。结构合理、语气基本统一、没有油腻金句、编号用在方案对比上也合适。kill list 里的词偶尔漏杀是 Gemini 的执行力问题，不是规则缺失。

要不要把"独立房间"也加进 kill list？还是你觉得目前这个质量可以收工了？


这个回答是错误的呀，请看看原因：https://www.zhihu.com/question/2016845353517806681 回答定义，优势和建议
powellllll  [下午 2:22]
间歇性禁食并不是一种单纯的节食手段，它在生物黑客和代谢医学领域的本质定义是：通过设定进食与断食的时间窗口，强制机体完成从葡萄糖代谢向脂肪/酮体代谢的模式切换。

这种切换被称为代谢转换。现代人一天三餐加零食，胰岛素常年维持在高位，身体已经丧失了高效燃烧脂肪的能力。间歇性禁食就是通过人为制造能量匮乏，把那个生锈的脂肪代谢开关重新合上。

这事儿的核心价值不在于少吃了多少热量，而在于身体在特定时间窗口内发生的生化反应。

第一个优势是胰岛素敏感性的重置。

长期的高频进食会让细胞对胰岛素产生脱敏，这是很多代谢问题的根源。禁食期间，胰岛素水平会降到极低，这给受体提供了喘息和重新敏感化的机会。这种重置不仅能优化血糖曲线，更关键的是它让身体重新获得了代谢灵活性，即在葡萄糖和脂肪供能之间丝滑切换。

第二个优势是细胞自噬，这是最硬核的部分。

当断食时间超过 16 小时，人体内的机械靶向雷帕霉素复合物 1 会受到抑制，细胞开始启动大扫除。它会降解并回收受损的线粒体和蛋白质。这是一种进化带来的自我保护机制，在能量充裕时它是关闭的。只有通过禁食，你才能触发这种细胞层面的深度清洁。

对于想要尝试的人，我有几条非常直接的建议。

首先，不要搞仪式性努力。

很多人一上来就挑战 24 小时断食，结果饿得低血糖发作，然后报复性暴食。这纯属自虐。最稳妥的起步方式是 14:10 模式，即晚上 7 点后不吃，第二天早 9 点吃早饭。这能让你的昼夜节律先对齐，消除深夜进食对褪黑素和次日血糖的干扰。

其次，重视电解质，别只喝白水。

禁食期间，随着糖原耗尽和胰岛素下降，肾脏会排出大量的钠和水。很多人的头痛、乏力其实不是饿的，是脱水和电解质失衡。在水里加点海盐或者喝无糖的电解质水，能解决 80% 的不适感。

再者，进食窗口的质量至关重要。

如果在 8 小时的进食窗口里塞满了奶茶和炸鸡，那这种禁食基本白费。由于你的胰岛素已经处于低位，第一餐对血糖的冲击会非常敏感。建议执行纤维到蛋白质再到碳水化合物的进食顺序，这能让你的血糖峰值下降至少 30%。

最后，如果你在出差、高压工作或者感冒初期，请立刻停止激进的禁食协议。

禁食本身是一种温和的应激。当外部压力已经爆表时，再叠加代谢压力，皮质醇会飙升，反而会导致肌肉分解和免疫力下降。

间歇性禁食是一种身体管理工具，不是技术宗教。把它当成一个可以随时调用的功能模块，根据当天的身体负荷去灵活编排窗口，这才是真正职业的代谢管理。


是不是还可以了：让 AI 在你的主力电脑上跑脚本、改文件，这事儿听起来挺酷，实际操作起来很容易出问题。第一个绕不开的坎是权限和逻辑错误的死结。目前大模型处理任务还是概率性的，执行指令时难免有理解偏差。如果在本地给它高权限，它可能一个误操作就把系统文件删了；如果限制权限，它又会频繁报错，导致你得不停地人工确认，这种体验极其糟糕。要真想让 AI Agent 稳当运行，必须准备一个隔离环境。目前的解决方…


Conversation compacted


This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.

Summary:
1. Primary Request and Intent:
   The user's Obsidian bot generates Chinese articles/blog posts that have detectable "AI味" (AI tone). The user wanted to identify and fix AI tone issues in the bot's output pipeline. The work evolved through multiple iterations:
   - First: Diagnose where in the pipeline the problem exists (guide docs vs execution)
   - Second: Add a second-pass review to `WritingAssistant` (like `ZhihuGenerator` already had) and fix the `/deai` Claude Code command to use `POWELL_REVISE.md`
   - Third through sixth: Iteratively refine the review prompts' kill lists and rules based on real generated outputs, catching progressively subtler forms of AI tone — from formal AI tone, to "油腻" (try-hard casual) tone, to concept packaging, to keyword repetition

2. Key Technical Concepts:
   - Two-pass LLM generation: first pass generates content, second pass runs rule enforcement review
   - AI tone taxonomy: "正式AI味" (formal AI tone) vs "油腻AI味" (oily/try-hard casual tone)
   - Kill list approach: prioritized vocabulary and sentence pattern lists for the review pass
   - "换皮夸张" (skin-swapping exaggeration): replacing one dramatic word with another equally dramatic word is not a valid fix
   - Conditional rules: e.g., numbered sections OK for parallel comparisons (A vs B vs C) but not for linear arguments
   - The bot uses Gemini LLM via `GeminiLLM` class
   - RAG pipeline with Obsidian vault, semantic search via ChromaVectorStore
   - Multiple generator classes: WritingAssistant, ZhihuGenerator, DeAIReviser, ReplyGenerator, SearchAnalyzer

3. Files and Code Sections:
   - **`/root/projects/butler/slack_bot/obsidian/generators.py`**
     - Core file containing all generator classes. Modified extensively across multiple iterations.
     - **WritingAssistant.chat()** (around line 83-201): Added second-pass de-AI review after first-turn draft generation. The review prompt evolved through 5 iterations to include TONE TARGET, three-priority kill list structure, and 13 sentence pattern rules.
     - **ZhihuGenerator review prompt** (around line 621-677): Updated kill lists to match WritingAssistant, with additional Zhihu-specific rules.
     - Current WritingAssistant review prompt structure:
       ```
       TONE TARGET (最高优先级) — defines two AI tone types to kill
       PRIORITY 1: VOCABULARY KILL LIST — categories: 夸张修饰, 大词空转, 鸡汤/口号, 油腻收尾, 伪口语
       PRIORITY 2: SENTENCE PATTERN KILL LIST — 13 rules including 对比式定义, 换皮夸张, 概念包装, 油腻金句, conditional 编号, 关键词重复
       PRIORITY 3: FULL REVISION GUIDE (powell_revise_guide)
       ```
     - Key code change in WritingAssistant — the second-pass review block:
       ```python
       # Call LLM — first pass: content generation
       draft, _ = self.llm.generate_response(prompt, history)

       # === Second pass: de-AI review (first turn only) ===
       if is_first_turn:
           powell_revise_guide = self._read_file("POWELL_REVISE.md")
           review_prompt = f"""You are a strict editor..."""
           logger.info("Running second-pass de-AI review on writing draft...")
           response, _ = self.llm.generate_response(review_prompt, [])
       else:
           response = draft
       ```
     - The ZhihuGenerator already had a two-pass system; its review prompt was updated with the same kill list expansions and TONE TARGET.

   - **`/root/projects/butler/.claude/commands/deai.md`**
     - Complete rewrite. Previously had generic English rules ("Overly formal language", "Repetitive sentence structures"). Now references `POWELL_REVISE.md` and `writing_style.md` as sole authority. 4-step analysis process following POWELL_REVISE sections. Output format changed to cite specific rules applied.

   - **`/root/projects/butler/writing_style.md`** (read only)
     - Core writing style DNA. Contains style patterns, bilingual rules, zero-fluff principle, max 1-2 metaphors per article. Not modified.

   - **`/root/projects/butler/POWELL_REVISE.md`** (read only)
     - Comprehensive 430-line de-AI revision standard. Contains 10 writing principles, AI trace identification checklist, revision operations manual, examples, vocabulary blacklist/whitelist. Not modified.

   - **`/root/projects/butler/zhihu-bot-style.md`** (read only)
     - Additional constraints for bot auto-generation. Not modified.

4. Errors and Fixes:
   - **Edit tool found 2 matches**: When first trying to add the second-pass review to WritingAssistant, the old_string pattern (`# Call LLM\n        response, _ = self.llm.generate_response(prompt, history)...`) matched in both WritingAssistant and ReplyGenerator. Fixed by including more surrounding context (the `logger.info` line above) to make the match unique.
   - **"正式AI味" overcorrected to "油腻AI味"**: After adding de-formalization rules, the LLM output swung from stiff/formal to try-hard casual (concept packaging like "隔离舱", witty endings like "搞坏了不心疼，跑通了就是资产"). Fixed by adding TONE TARGET section explicitly defining both types of AI tone to kill, and adding "油腻收尾" and "油腻金句" as new kill categories.
   - **Kill list words not being caught by Gemini**: Words like "极度脆弱", "掌控感" are explicitly in the kill list but still appear in output. This is a Gemini execution fidelity issue, not a rule gap. Not fully resolved — acknowledged as an inherent LLM limitation.
   - **"一、二、三" numbering blanket removal was too aggressive**: User pointed out that numbered sections are fine when comparing distinct alternatives (虚拟机 vs 闲置电脑 vs 云服务器). Fixed by changing the rule to conditional: keep for parallel comparisons, remove for linear arguments.
   - **"不是X，是Y" pattern missing from WritingAssistant**: This "对比式定义" rule existed in ZhihuGenerator's kill list but was absent from WritingAssistant's. Fixed by adding it as rule #2 in WritingAssistant's sentence pattern kill list.
   - **"分水岭" repeated twice**: No rule existed for checking keyword repetition. Fixed by adding rule #13: same rhetorical word can only appear once in the full text.

5. Problem Solving:
   - **Diagnosed root cause**: The writing guide documents (`POWELL_REVISE.md`, `writing_style.md`) were already comprehensive. The problem was in the execution pipeline — `WritingAssistant` lacked a review pass, and the `/deai` command used its own simplified rules.
   - **Iterative refinement through real output testing**: User tested each change by regenerating the same article topic through the bot, pasting results, and identifying remaining issues. This drove 5 rounds of kill list expansion.
   - **Two-type AI tone taxonomy**: Identified that de-AI efforts can overcorrect from formal tone to oily/try-hard tone, requiring rules against both directions.
   - **Ongoing issue**: Gemini occasionally ignores kill list words even when explicitly listed. The user accepted this as a known limitation at current quality level.

6. All User Messages:
   - "下面这段是由obsidian bot生成的文字，你再看看哪里有ai味，并告诉我应该怎么修改..." (shared original AI-generated article about cloud servers for AI agents)
   - "那对于几个指导写作的md文档是否需要修改？还是说增加一个新的步骤"
   - "好的，就按你的建议来修改吧"
   - "需要重启bot吗？还是我直接在slack就可以看到新的表现"
   - "看看现在的结果：让 AI 运行代码..." (shared output v2 — better but still had issues)
   - "调整一下，尤其是知乎的回答，最好是越平实越好"
   - "新生成的是不是有点油腻？让大语言模型直接在自己办公电脑上跑代码..." (shared output v3 — identified 油腻/try-hard tone)
   - "是不是还有问题..." (shared output v4 with specific remaining issues noted: "可扩展的执行信用", "终极归宿", etc.)
   - "可以接受，把这几个问题加到kill list里就行，以后可以维护这个kill list。另外，一二三还是要慎重，看看怎么修改，这篇文章本身看着还行"
   - "好像还是有问题..." (shared output v5, noting "不是...是..." pattern and "分水岭" appearing twice)
   - "是不是还可以了：让 AI 在你的主力电脑上跑脚本..." (shared output v6 — much improved, few minor issues remaining)

7. Pending Tasks:
   - User's last question before summary request was: "要不要把'独立房间'也加进 kill list？还是你觉得目前这个质量可以收工了？" — This question was not answered before the summary request. The user may want to add "独立房间" to the kill list or may consider the work done.

8. Current Work:
   The most recent work was the fifth round of kill list refinement. After the user shared output v5 noting "不是...是..." AI pattern and "分水岭" appearing twice, I updated both review prompts in `generators.py`:
   - Added "对比式定义" rule to WritingAssistant's kill list (was only in Zhihu's)
   - Added vocabulary: 分水岭, 沉淀, 云端办公室, 不是建议是必须
   - Added sentence patterns: "有人X，有人已经Y了", concept packaging examples (牢笼=限制), "把X关在Y里...把Y放在Z上" parallel structure
   - Added rule #13: keyword repetition check

   The user then shared output v6 which was significantly improved, with only minor issues: "极度脆弱" and "掌控感" (kill list words not caught by Gemini), "独立房间" (concept packaging), and mild chicken-soup ending. I asked the user whether to add "独立房间" to the kill list or consider the work done.

9. Optional Next Step:
   The user's unanswered question was: "要不要把'独立房间'也加进 kill list？还是你觉得目前这个质量可以收工了？" If the user wants to continue, the next step would be adding "独立房间" (and similar housing metaphors like "单间", "房间") to the vocabulary kill list in both review prompts in `generators.py`, and potentially adding "通往...的路" to the chicken-soup/slogan kill list. The bot would need a restart after any changes.

If you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: /root/.claude/projects/-root-projects-butler/b2dcaddd-e1c9-4fc9-a931-03f322ed21b1.jsonl


<local-command-caveat>Caveat: The messages below were generated by the user while running local commands. DO NOT respond to these messages or otherwise consider them in your response unless the user explicitly asks you to.</local-command-caveat>


<command-name>/compact</command-name>
            <command-message>compact</command-message>
            <command-args></command-args>


<local-command-stdout>Compacted (ctrl+o to see full summary)
Tip: You have access to Opus 1M with 5x more context</local-command-stdout>


The user is saying that the answer generated by the bot is incorrect, and they want me to look at the Zhihu question to understand why. Let me first fetch the Zhihu question to understand what was asked, then analyze the bot's response.

The user shared a bot-generated answer about intermittent fasting. They say the answer is wrong and want me to check the original Zhihu question to understand the issue.

Let me fetch the Zhihu question first.


让我先看看原始知乎问题是什么。


WebFetch

Prompt: 提取这个知乎问题的完整标题和详细描述内容


Request failed with status code 403

Error: Request failed with status code 403


Bash

curl -s -L -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" "https://www.zhihu.com/question/2016845353517806681" 2>/dev/null | grep -oP '<title[^>]*>.*?</title>' | head -5

Fetch Zhihu question title


(Bash completed with no output)


curl -s -L -H "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36" "https://www.zhihu.com/question/2016845353517806681" 2>/dev/null | head -200

Fetch Zhihu page content


<!DOCTYPE html><meta id="zh-zse-ck" charset="UTF-8" content="w/JpbLrEBhkGd8wRK9KD2hYcrfzjQjztfVxYC/iVH+7V7z4ccIFc+MyPBfqF4PVQJsEf3qTEBs4vW12xDWOag3onP2EQgGP8TVnSqeus3smZci5C2x8DRQAM51S6fhKd">
知乎，让每一次点击都充满意义 —— 欢迎来到知乎，发现问题背后的世界。


The Zhihu page is returning a JavaScript challenge page, so I can't directly fetch the content. Let me try a different approach - maybe search for the question.


知乎页面有反爬保护，无法直接获取。让我搜索一下这个问题。


WebSearch

Question: site:zhihu.com 2016845353517806681


Question: site:zhihu.com 2016845353517806681


Question: site:zhihu.com 2016845353517806681


Web search results for query: "site:zhihu.com 2016845353517806681"

Links: [{"title":"X错误请重试怎么解决？2026最新修复X登录时出错与Attestation Denied指南 - 知乎","url":"https://zhuanlan.zhihu.com/p/2010752434948027143"},{"title":"付费咨询 - 知乎","url":"https://www.zhihu.com/consult"},{"title":"Corporate Information | Zhihu","url":"https://ir.zhihu.com/en/corporate-information/"},{"title":"Investor Relations | Zhihu","url":"https://ir.zhihu.com/"},{"title":"PM 周刊第 21 期 2016-04-11 - 知乎","url":"https://zhuanlan.zhihu.com/p/20736861"},{"title":"阿里桌面Agent QoderWork全面开放！人人可用的智能体来了 - 知乎","url":"https://zhuanlan.zhihu.com/p/2012215013922849049"},{"title":"find-skills skill：一个能为当前任务快速找到最匹配skills的元skill - 知乎","url":"https://zhuanlan.zhihu.com/p/2001068039613982168"},{"title":"知乎直答","url":"https://zhida.zhihu.com/"},{"title":"发现 - 知乎","url":"https://www.zhihu.com/explore"},{"title":"2026最新！阿里云百炼Coding Plan详解：收费标准 + 套餐 + 上手教程 - 知乎","url":"https://zhuanlan.zhihu.com/p/2010486807704409374"}]

The search for `site:zhihu.com 2016845353517806681` did not return a direct match for that specific ID/number on Zhihu. The results returned were general Zhihu pages, including:

1. **zhuanlan.zhihu.com** – Various Zhihu column (知乎专栏) articles on topics like X (Twitter) login troubleshooting, Alibaba's QoderWork AI agent, and Alibaba Cloud's Coding Plan.
2. **zhihu.com/consult** – Zhihu's paid consultation page.
3. **ir.zhihu.com** – Zhihu's Investor Relations website provides comprehensive information for investors, including company profiles, financial reports, news updates, and stock information.
4. **zhida.zhihu.com** – 知乎直答 (zhida.ai) is a product launched by Zhihu using AI large model technology, with Zhihu's quality content at its core.
5. **zhihu.com/explore** – Zhihu's discovery/explore page.

It appears that the number `2016845353517806681` does not correspond to a currently indexed or publicly accessible page on zhihu.com. It's possible the content associated with that ID has been deleted, made private, or is otherwise unavailable.

REMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks.

Question: site:zhihu.com 2016845353517806681

付费咨询 - 知乎

Corporate Information | Zhihu

Investor Relations | Zhihu

PM 周刊第 21 期 2016-04-11 - 知乎

阿里桌面Agent QoderWork全面开放！人人可用的智能体来了 - 知乎

知乎直答

发现 - 知乎


I can't access the Zhihu question directly. The user says the bot's answer is wrong, and from the URL it seems to be about "间歇性禁食" (intermittent fasting).

Let me look at what the user told me: "回答定义，优势和建议" - this seems to be the question's structure/requirements. But the user says the answer is "错误的" (wrong/incorrect).

Looking at the bot's generated answer, I need to understand what might be factually incorrect. Let me analyze the content:

1. The answer talks about intermittent fasting definitions, advantages, and suggestions

2. It covers several scientific concepts like metabolic switching, insulin sensitivity improvements, autophagy activation after 16 hours, and mTORC1 regulation
3. The practical suggestions include a 14:10 fasting pattern, electrolyte supplementation, and specific eating order recommendations

The user is saying this response contains errors, but without seeing the actual Zhihu answer or having access to verify the specific claims, I need to think about what might be problematic here. Looking at the bot's answer more carefully, I'm noticing some potential factual issues in the health/medical content. The explanation about metabolic switching during fasting seems oversimplified, and the claim about mTOR inhibition after 16 hours of fasting needs verification against current scientific understanding. Let me examine the ZhihuGenerator code to see how it's processing and generating these medical claims. Looking at the specific claims in the answer, I'm noticing some issues - the 30% blood sugar reduction figure comes from particular studies, and I'm also spotting that "仪式性努力" appears on the content filter list, which should have been caught during review. Let me examine the generator code to see how questions are being processed and whether the filtering is working correctly. I'm spotting some factual issues in the response—the Chinese translation for mTOR seems off, using "机械靶向" when it should be something like "哺乳动物雷帕霉素靶蛋白," and that specific 30% blood glucose reduction claim lacks solid support. The autophagy timeline also seems questionable, though structurally the answer does cover the definition, advantages, and suggestions the question asked for, so there might be something else the user is pointing out.

Wait, I'm realizing the instruction "回答定义，优势和建议" might not be the actual question itself—it could be a directive to the bot about what to include in the answer. If that's the case, the real Zhihu question might be asking something entirely different. The core issue here is probably that the ZhihuGenerator is confidently producing medical claims that aren't well-founded, which is a known problem with LLMs generating healthcare content. I should examine how the generator actually retrieves the question and constructs its response to understand where the hallucinations are coming from.


无法直接访问知乎页面（有反爬保护）。让我先看看 ZhihuGenerator 的代码逻辑，理解它是怎么处理问题和生成回答的。