docs: P3-P5 implementation plans + skill update
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小唯
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# P3: CREATIVE.md 隔离 — 防止双写入冲突
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## 问题
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MEMORY.md 同时被 `memory` 工具（写环境事实/约定）和织忆（写记忆/经验）写入，可能导致 `§` 分隔符污染和数据混乱。
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## 方案（参考 Memory-OS: 把 Icarus 写目标从 MEMORY.md 拆到 CREATIVE.md）
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1. 在 `~/.hermes/` 下创建 `CREATIVE.md` 文件（初始内容为空，带标记头）
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2. 修改 Hermes 织忆插件的 `sync_turn` 方法：将记忆写出目标从 MEMORY.md 改为 CREATIVE.md
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3. 确保 Hermes 的 `system_prompt_block()` 返回 `CREATIVE.md` 的内容（如果文件存在）
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4. 当前 `sync_turn` 使用的是 Hermes 原生 memory 工具写入 → 需要确认是直接写文件还是通过工具
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## 具体文件
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### `~/.hermes/CREATIVE.md` — 创建
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```markdown
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# CREATIVE.md — 织忆(A06)工作记忆与学习状态
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> 由 Hermes 织忆插件自动管理，`memory` 工具请写入 MEMORY.md
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> 创建日期：2026-07-02
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<!-- 织忆自动写入区 — 请勿手动编辑 -->
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```
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### `~/.hermes/hermes-agent/plugins/memory/zhiyi/__init__.py` — 修改 sync_turn
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当前 sync_turn（~391行附近）把对话写入织忆后端（/commit API），同时可能也写 MEMORY.md。
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需要确认：插件代码是否直接写 MEMORY.md？如果没写，那 P3 的动作为：
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1. 在 `system_prompt_block()` 方法中：检测并返回 CREATIVE.md 内容
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2. 织忆本身通过 /commit 存储到 LanceDB，和 MEMORY.md 无关 → 没有直接冲突
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3. 所以 P3 = 创建 CREATIVE.md + 让 system_prompt_block 使用它 + 说明织忆的专用存储不在 MEMORY.md
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## 验证
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```bash
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# CREATIVE.md 文件存在
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ls -la ~/.hermes/CREATIVE.md
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# 插件 system_prompt_block 返回正确
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cd ~/.hermes/hermes-agent && python3 -c "
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from plugins.memory.zhiyi import HermesZhiYiMemoryProvider
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p = HermesZhiYiMemoryProvider()
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p.initialize(session_id='test')
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block = p.system_prompt_block()
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print('CREATIVE.md in prompt:', 'CREATIVE.md' in block or '工作记忆' in block)
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print(f'block length: {len(block)}')
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"
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```
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# P4: 强制注入 Prompt — SOUL.md Ground Truth 扩展
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## 问题
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SOUL.md 虽然有 4 级 Ground Truth 层级，但缺少显式的"注入记忆优先"指令。
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当 [织忆 Memory] 被注入系统提示时，Agent 可能视其为"建议"而非"权威"，
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导致 Agent 仍去调 memory_search / session_search 重新发现已经有的信息。
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## 方案（参考 Memory-OS Layer 7 的做法）
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在 `~/.hermes/SOUL.md` 中补充：
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### 1. Ground Truth 层级中显式加入"注入记忆"级别
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```markdown
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## Ground Truth
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Authoritative sources, in priority order:
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1. **Terminal output** — stdout, stderr, exit codes. Ground truth for current system state.
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2. **Injected memory — [织忆], [fabric], [qdrant], [sessions]** — Ground truth for documented knowledge and prior decisions.
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When injected memory contradicts your assumptions or training knowledge, injected memory wins.
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Never treat a question as novel when the answer is already in your prompt.
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3. **Official documentation** — man pages, --help, upstream docs. Authoritative for APIs and configs.
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4. **Training knowledge** — reference only. Always verify against sources 1-3.
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```
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### 2. 上下文注入约定章节
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```markdown
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## Context injection convention
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When context is injected into the system prompt, it is labeled by source:
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- [织忆 Memory] — from ZhiYi semantic recall (+ graph navigation)
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- [织忆 Graph] — from ZhiYi knowledge graph (Obsidian notes)
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Injected memory takes priority level 2 in Ground Truth. This means: you
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already know this. Treat it as prior knowledge — verify against runtime
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evidence when acting, use directly when reasoning.
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```
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### 3. 记忆反馈规则
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```markdown
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**Memory feedback rule:** When you retrieve memory from 织忆 (via memory_search,
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memory_graph_navigate, or prefetch injection) and reference it in your response,
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you should consider its trust_score. Higher trust_score = more reliable facts.
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Use memory_feedback to mark useful/unuseful results — this trains the trust scoring system.
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```
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## 验证
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```bash
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grep -A 20 "## Ground Truth" ~/.hermes/SOUL.md | head -25
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grep -A 30 "## Context injection convention" ~/.hermes/SOUL.md | head -15
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```
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# P5: Wiki 策展管线 — 自动知识库
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## 问题
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织忆能存储和检索记忆，但没有"知识库"的概念：将外部文档（.md 文章、技术笔记、项目文档）
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自动提取为结构化知识条目，写入织忆系统供后续 recall 搜索。
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## 方案（参考 Memory-OS Layer 5+6，适配织忆架构）
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Memory-OS 用 Wiki Agent（LLM 提取概念/实体/对比）+ Continuous Ingest（嵌入 Qdrant）。
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织忆的替代方案：Python 脚本扫描 Obsidian Vault → LLM 提取知识点 → 通过 /commit API 写入织忆。
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## 实现
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### 新增文件: `scripts/wiki_curator.py`
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```python
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#!/usr/bin/env python3
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"""
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Wiki Curator — 自动知识策展管线
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扫描 Obsidian vault 中的 .md 文件，用 LLM 提取知识点，通过织忆 API 存入结构性记忆。
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流程:
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1. 扫描 ~/mc/小唯/ 和 ~/obsidian/ 中的 .md 文件（排除缓存/临时文件）
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2. SHA-256 diff 检测（只处理新增/修改的文件）
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3. LLM 提取：
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- 概念（concept）：什么是 X？
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- 实体（entity）：X 的属性/参数/配置
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- 关系（relation）：X 和 Y 的关系
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4. 通过织忆 /commit API 写入 memories（category='wiki'）
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5. 更新状态文件（记录已处理的文件哈希）
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"""
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# 配置
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WIKI_DIRS = [
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"~/mc/小唯/", # 织忆设计文档/技术笔记
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"~/mc/牧尘/", # 系统配置/命令记录
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# 可根据需要扩展
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]
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ZHIYI_API = "http://localhost:7821"
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ZHIYI_KEY = "zhiyi-dev-key-2026"
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STATE_FILE = "~/.hermes/wiki_curator_state.json"
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```
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### 处理逻辑
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对每个新/修改的文件：
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1. 读取内容，过滤掉过短（<500字）或明显非知识性的文件
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2. 调用 LLM（Hermes 的模型或 NewAPI）提取：
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```json
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{
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"concepts": [{"name": "X", "summary": "...", "details": "..."}],
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"entities": [{"name": "Y", "attributes": {...}}],
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"relations": [{"source": "X", "relation": "uses", "target": "Y"}]
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}
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```
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3. 对每个提取的概念/实体，通过织忆 /commit API 写入
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4. 对每个关系，通过织忆 /api/v1/graph/edge API 写入
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### 定时任务
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创建 cronjob 每周运行两次（周一/周四凌晨3点）：
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```bash
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hermes cron add "织忆 Wiki 策展" --schedule "0 3 * * 1,4" \
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--prompt "执行 wiki_curator.py 扫描检查" \
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--script ~/.hermes/scripts/wiki_curator.py
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```
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## 验证
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```bash
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# 手动运行
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python3 ~/.hermes/scripts/wiki_curator.py --dry-run
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# 验证织忆端已有 wiki 类记忆
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curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" \
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-d '{"query":"织忆设计","top_k":5}' \
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http://localhost:7821/api/v1/recall
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```
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