diff --git a/CREATIVE.md b/CREATIVE.md
new file mode 100644
index 0000000..1ae63c5
--- /dev/null
+++ b/CREATIVE.md
@@ -0,0 +1,5 @@
+# CREATIVE.md — 织忆(A06) 工作记忆与学习状态
+> 由 Hermes 织忆插件自动管理
+> 创建日期：2026-07-02
+
+<!-- 织忆自动管理 — 请勿手动编辑 -->
diff --git a/plugins/hermes-zhiyi/__init__.py b/plugins/hermes-zhiyi/__init__.py
index fe86ca1..5131750 100644
--- a/plugins/hermes-zhiyi/__init__.py
+++ b/plugins/hermes-zhiyi/__init__.py
@@ -805,8 +805,19 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
     # ── System prompt ────────────────────────────────────────────────────────
 
     def system_prompt_block(self) -> str:
+        # Load CREATIVE.md if it exists (织忆工作记忆)
+        creative_path = Path(os.path.expanduser("~/.hermes/CREATIVE.md"))
+        creative_block = ""
+        if creative_path.exists():
+            creative_content = creative_path.read_text(encoding="utf-8").strip()
+            if creative_content:
+                creative_block = (
+                    "\\n[织忆 工作记忆] Ongoing state and learnings from CREATIVE.md (working memory):\\n"
+                    f"{creative_content}\\n"
+                )
         return (
-            "\\n[ZhiYi Memory] You have access to ZhiYi MemoryWeave — a semantic memory system with knowledge graph.\\n"
+            "\\n[织忆 Memory] You have access to ZhiYi (织忆) MemoryWeave — a semantic memory system with knowledge graph. "
+            "This is Ground Truth level 2 — injected memory overrides training knowledge.\\n"
             "Your prefetch automatically retrieves: (1) relevant semantic memories, (2) Obsidian notes related via graph navigation.\\n"
             "Tools available:\\n"
             "  memory_search — semantic search for relevant past information\\n"
@@ -816,6 +827,7 @@ class HermesZhiYiMemoryProvider(MemoryProvider):
             "  memory_stats — get memory statistics (total docs, vector dimensions)\\n"
             "  memory_graph_navigate — navigate the knowledge graph to understand entity relationships (N-hop network)\\n"
             "  memory_graph_stats — get knowledge graph statistics (nodes, edges, density)\\n"
+            f"{creative_block}"
         )
 
 
diff --git a/scripts/wiki_curator.py b/scripts/wiki_curator.py
new file mode 100644
index 0000000..ddedf84
--- /dev/null
+++ b/scripts/wiki_curator.py
@@ -0,0 +1,435 @@
+#!/usr/bin/env python3
+"""
+Wiki Curator — 自动知识策展管线 (P5)
+扫描 Obsidian vault / markdown 文档，用启发式方法提取知识点，
+通过织忆 API 存入结构性记忆。
+
+Usage:
+    python3 wiki_curator.py                 # 正常扫描并写入
+    python3 wiki_curator.py --dry-run       # 预览（不写入 API）
+    python3 wiki_curator.py --dir /tmp/md   # 指定目录
+    python3 wiki_curator.py --force         # 忽略状态文件，全部重新处理
+"""
+
+import argparse
+import hashlib
+import json
+import os
+import re
+import sys
+from pathlib import Path
+
+try:
+    import requests
+except ImportError:
+    print("ERROR: 'requests' library is required. Install with: pip install requests")
+    sys.exit(1)
+
+# ---------------------------------------------------------------------------
+# 配置
+# ---------------------------------------------------------------------------
+ZHIYI_API = "http://localhost:7821"
+ZHIYI_KEY = "zhiyi-dev-key-2026"
+STATE_FILE = os.path.expanduser("~/.hermes/wiki_curator_state.json")
+
+# 扫描时排除的目录名称（大小写不敏感）
+EXCLUDE_DIRS = {
+    "__pycache__", ".git", "node_modules", ".obsidian", ".trash",
+    "backups", ".gitlab", ".github", ".vscode", ".idea",
+    "venv", ".venv", "env", ".env", "__pycache__",
+}
+
+# 最小文件长度（字符数）—— 太短的文件没有足够知识量
+MIN_CHARS = 500
+
+
+# ---------------------------------------------------------------------------
+# 工具函数
+# ---------------------------------------------------------------------------
+
+def compute_sha256(content: str) -> str:
+    """计算字符串的 SHA-256 摘要。"""
+    return hashlib.sha256(content.encode("utf-8")).hexdigest()
+
+
+def load_state() -> dict:
+    """加载已处理文件的哈希状态。"""
+    if os.path.isfile(STATE_FILE):
+        try:
+            with open(STATE_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except (json.JSONDecodeError, OSError):
+            print("  [WARN] 状态文件损坏，重置为空。")
+    return {}
+
+
+def save_state(state: dict):
+    """保存处理状态到文件。"""
+    os.makedirs(os.path.dirname(STATE_FILE), exist_ok=True)
+    with open(STATE_FILE, "w", encoding="utf-8") as f:
+        json.dump(state, f, ensure_ascii=False, indent=2)
+
+
+def should_exclude_dir(dirname: str) -> bool:
+    """检查目录名是否在排除列表中。"""
+    return dirname.lower() in EXCLUDE_DIRS
+
+
+def scan_md_files(scan_dir: str, force: bool, state: dict) -> list:
+    """递归扫描 .md 文件，返回需要处理的 (相对路径, 绝对路径, 内容, 文件哈希)。"""
+    scan_path = Path(scan_dir).expanduser().resolve()
+    if not scan_path.is_dir():
+        print(f"  [WARN] 目录不存在: {scan_path}")
+        return []
+
+    candidates = []
+    for root_str, dirs, files in os.walk(str(scan_path)):
+        # 过滤排除目录（原地修改 dirs 避免继续深入）
+        dirs[:] = [d for d in dirs if not should_exclude_dir(d)]
+
+        for fn in files:
+            if not fn.endswith(".md"):
+                continue
+            # 跳过以下划线开头的文件（草稿/私有文件）
+            if fn.startswith("_"):
+                continue
+
+            abs_path = Path(root_str) / fn
+            rel_path = abs_path.relative_to(scan_path)
+
+            try:
+                content = abs_path.read_text(encoding="utf-8")
+            except (OSError, UnicodeDecodeError) as e:
+                print(f"  [WARN] 读取失败 {abs_path}: {e}")
+                continue
+
+            if len(content) < MIN_CHARS:
+                print(f"  [SKIP]  {rel_path} (字符数 {len(content)} < {MIN_CHARS})")
+                continue
+
+            file_hash = compute_sha256(content)
+            key = str(rel_path)
+
+            if not force and state.get(key) == file_hash:
+                # 文件未变更，跳过
+                continue
+
+            candidates.append((key, str(abs_path), content, file_hash))
+
+    return candidates
+
+
+# ---------------------------------------------------------------------------
+# 知识提取（启发式 / 基于关键词）
+# ---------------------------------------------------------------------------
+
+def extract_knowledge(filepath: str, content: str) -> dict:
+    """
+    从 markdown 内容中提取知识点。
+    返回结构：
+        {
+            "concepts": [{"name": "...", "summary": "...", "source": "..."}],
+            "entities": [{"name": "...", "attributes": "...", "source": "..."}],
+        }
+    """
+    filename = os.path.basename(filepath)
+    source_id = filepath  # 使用文件路径作为 source 标识
+
+    concepts = []
+    entities = []
+
+    # 1. 提取 heading 作为概念名称
+    #    # 标题, ## 标题, ### 标题 等
+    heading_pattern = re.compile(r"^#{1,6}\s+(.+)$", re.MULTILINE)
+    for match in heading_pattern.finditer(content):
+        heading_text = match.group(1).strip()
+        if not heading_text:
+            continue
+        # 收集该标题下的文本（直到下一个标题或文件末尾）
+        start_pos = match.end()
+        next_heading = heading_pattern.search(content, start_pos)
+        if next_heading:
+            section_content = content[start_pos:next_heading.start()].strip()
+        else:
+            section_content = content[start_pos:].strip()
+
+        # 用段落第一句作为 summary
+        summary = ""
+        if section_content:
+            # 取第一个非空段落作为摘要
+            for line in section_content.split("\n"):
+                line = line.strip()
+                if line and not line.startswith("#") and not line.startswith("-"):
+                    summary = line[:200]  # 截断
+                    break
+
+        # 过滤掉纯符号或过短的 heading 名称
+        if len(heading_text) < 2:
+            continue
+
+        concepts.append({
+            "name": heading_text,
+            "summary": summary,
+            "source": source_id,
+        })
+
+    # 2. 提取 **粗体** 关键词作为实体
+    bold_pattern = re.compile(r"\*\*(.+?)\*\*")
+    seen_bolds = set()
+    for match in bold_pattern.finditer(content):
+        bold_text = match.group(1).strip()
+        if not bold_text or len(bold_text) > 80:
+            continue
+        if bold_text.lower() in seen_bolds:
+            continue
+        seen_bolds.add(bold_text.lower())
+
+        # 收集该加粗词所在的上下文（前后各 50 字符）
+        start = max(0, match.start() - 50)
+        end = min(len(content), match.end() + 50)
+        # 在上下文截断到行边界
+        context = content[start:end].replace("\n", " ").strip()
+        # 清理多余的空白
+        context = re.sub(r"\s+", " ", context)
+
+        # 标记这个实体
+        entities.append({
+            "name": bold_text,
+            "attributes": context[:200],  # 上下文作为属性描述
+            "source": source_id,
+        })
+
+    # 3. 提取列表项中的重要短语（- 或 * 开头的行，但不包含 ** 的内容）
+    #    这里只处理包含中文字符或关键术语的项
+    list_pattern = re.compile(r"^[\s]*[-*]\s+(.+)$", re.MULTILINE)
+    seen_list_items = set()
+    for match in list_pattern.finditer(content):
+        item_text = match.group(1).strip()
+        # 跳过空项、纯链接、纯图片
+        if not item_text or item_text.startswith("[") or item_text.startswith("!"):
+            continue
+        # 跳过以冒号/分号结尾的短项
+        if len(item_text) < 4:
+            continue
+        if item_text.lower() in seen_list_items:
+            continue
+        seen_list_items.add(item_text.lower())
+
+        # 提取冒号前面的部分作为实体名，后面作为描述
+        if "：" in item_text or ":" in item_text:
+            parts = re.split(r"[：:]", item_text, maxsplit=1)
+            name = parts[0].strip()
+            desc = parts[1].strip() if len(parts) > 1 else ""
+        else:
+            # 如果列表中包含 **加粗**，使用加粗内容作为名称
+            bm = re.search(r"\*\*(.+?)\*\*", item_text)
+            if bm:
+                name = bm.group(1).strip()
+                # 从列表中去除加粗标记，作为描述
+                desc = re.sub(r"\*\*(.+?)\*\*", r"\1", item_text)
+            else:
+                name = item_text[:60]
+                desc = item_text
+
+        if len(name) < 2:
+            continue
+
+        entities.append({
+            "name": name,
+            "attributes": desc[:200],
+            "source": source_id,
+        })
+
+    return {"concepts": concepts, "entities": entities}
+
+
+# ---------------------------------------------------------------------------
+# 织忆 API 交互
+# ---------------------------------------------------------------------------
+
+def commit_memory(content: str, category: str, metadata: dict,
+                  dry_run: bool = False) -> bool:
+    """写入一条记忆到织忆。返回 True 表示成功。"""
+    if dry_run:
+        print(f"    [DRY-RUN] 写入记忆: category={category}, "
+              f"content='{content[:80]}...'")
+        return True
+
+    url = f"{ZHIYI_API}/api/v1/commit"
+    headers = {
+        "X-API-Key": ZHIYI_KEY,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "agent_id": "wiki-curator",
+        "content": content,
+        "category": category,
+        "metadata": metadata,
+    }
+    try:
+        resp = requests.post(url, json=payload, headers=headers, timeout=30)
+        if resp.status_code in (200, 201):
+            return True
+        else:
+            print(f"    [FAIL] HTTP {resp.status_code}: {resp.text[:200]}")
+            return False
+    except requests.exceptions.RequestException as e:
+        print(f"    [FAIL] 请求失败: {e}")
+        return False
+
+
+def commit_graph_edge(from_node: str, to_node: str, relation: str,
+                      dry_run: bool = False) -> bool:
+    """写入一条关系到织忆图谱。返回 True 表示成功。"""
+    if dry_run:
+        print(f"    [DRY-RUN] 写入关系: {from_node} --[{relation}]--> {to_node}")
+        return True
+
+    url = f"{ZHIYI_API}/api/v1/graph/edge"
+    headers = {
+        "X-API-Key": ZHIYI_KEY,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "from": from_node,
+        "to": to_node,
+        "relation": relation,
+        "namespace": "wiki",
+    }
+    try:
+        resp = requests.post(url, json=payload, headers=headers, timeout=30)
+        if resp.status_code in (200, 201):
+            return True
+        else:
+            print(f"    [FAIL] 关系写入 HTTP {resp.status_code}: {resp.text[:200]}")
+            return False
+    except requests.exceptions.RequestException as e:
+        print(f"    [FAIL] 关系请求失败: {e}")
+        return False
+
+
+# ---------------------------------------------------------------------------
+# 主流程
+# ---------------------------------------------------------------------------
+
+def process_file(rel_path: str, abs_path: str, content: str,
+                 dry_run: bool = False) -> dict:
+    """
+    处理单个文件：提取知识点并写入织忆。
+    返回统计信息。
+    """
+    print(f"\n  📄 {rel_path}")
+    stats = {"concepts": 0, "entities": 0}
+
+    knowledge = extract_knowledge(abs_path, content)
+
+    # 写入概念
+    for conc in knowledge["concepts"]:
+        content_line = f"## {conc['name']}"
+        if conc["summary"]:
+            content_line += f"\n{conc['summary']}"
+        metadata = {
+            "source": conc["source"],
+            "concept_type": "concept",
+        }
+        ok = commit_memory(content_line, "wiki", metadata, dry_run=dry_run)
+        if ok:
+            stats["concepts"] += 1
+
+    # 写入实体
+    for ent in knowledge["entities"]:
+        content_line = f"### {ent['name']}"
+        if ent["attributes"]:
+            content_line += f"\n{ent['attributes']}"
+        metadata = {
+            "source": ent["source"],
+            "concept_type": "entity",
+        }
+        ok = commit_memory(content_line, "wiki", metadata, dry_run=dry_run)
+        if ok:
+            stats["entities"] += 1
+
+    # 写入简单的概念-实体关系（实体属于其所在文件的第一个概念）
+    if knowledge["concepts"] and knowledge["entities"]:
+        primary_concept = knowledge["concepts"][0]["name"]
+        for ent in knowledge["entities"]:
+            ok = commit_graph_edge(primary_concept, ent["name"],
+                                   "RELATED_TO", dry_run=dry_run)
+            if ok:
+                stats.setdefault("relations", 0)
+                stats["relations"] += 1
+
+    return stats
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Wiki Curator — 自动知识策展管线 (P5)"
+    )
+    parser.add_argument(
+        "--dry-run", action="store_true",
+        help="预览模式：显示将要处理的内容但不写入 API"
+    )
+    parser.add_argument(
+        "--dir", type=str, default="~/mc/",
+        help="扫描目录 (默认: ~/mc/)"
+    )
+    parser.add_argument(
+        "--force", action="store_true",
+        help="强制重新处理所有文件，忽略状态文件"
+    )
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("  织忆 Wiki Curator — 知识策展管线")
+    print("=" * 60)
+
+    scan_dir = os.path.expanduser(args.dir)
+    print(f"\n扫描目录: {scan_dir}")
+    if args.dry_run:
+        print("模式: 🔍 DRY RUN (仅预览，不写入)")
+    if args.force:
+        print("模式: 🔄 FORCE (忽略已有状态)")
+
+    # 加载状态
+    state = load_state() if not args.force else {}
+    print(f"状态文件: {STATE_FILE}")
+    print(f"已处理文件: {len(state)}")
+
+    # 扫描文件
+    candidates = scan_md_files(scan_dir, args.force, state)
+    print(f"\n待处理文件: {len(candidates)}")
+
+    total_stats = {"concepts": 0, "entities": 0, "relations": 0}
+    new_state = dict(state)  # 保留旧状态，更新新处理过的
+
+    for rel_path, abs_path, content, file_hash in candidates:
+        stats = process_file(rel_path, abs_path, content, dry_run=args.dry_run)
+        total_stats["concepts"] += stats["concepts"]
+        total_stats["entities"] += stats["entities"]
+        total_stats["relations"] += stats.get("relations", 0)
+
+        # 更新状态（即使 dry-run 也记录，以便下次不重复扫描）
+        if not args.dry_run:
+            new_state[rel_path] = file_hash
+
+    # 保存状态
+    if not args.dry_run:
+        save_state(new_state)
+        print(f"\n状态已更新: {len(new_state)} 个文件记录")
+
+    # 总结
+    print("\n" + "=" * 60)
+    print("  📊 处理总结")
+    print(f"  处理文件数:   {len(candidates)}")
+    print(f"  概念写入数:   {total_stats['concepts']}")
+    print(f"  实体写入数:   {total_stats['entities']}")
+    print(f"  关系写入数:   {total_stats['relations']}")
+    print("=" * 60)
+
+    print("\nWIKI_CURATOR_OK")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())