danghuangshang is a multi-agent collaboration system that organizes specialized AI agents into a hierarchy modeled on historical Chinese government institutions. Users delegate tasks to these agents through platforms such as Discord or Feishu, with roles for coordination, coding, review, memory, and automation. Its catalogue entries are the project's agents and skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wanikua/danghuangshang --skill novel-memorygit clone --depth 1 https://github.com/wanikua/danghuangshangWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wanikua/danghuangshang/novel-memory)<a href="https://agentmods.dev/skills/wanikua/danghuangshang/novel-memory"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wanikua/danghuangshang/novel-memory"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.01115 |
| Opus 5 | $0.00026 | $0.00558 |
| Sonnet 5 | $0.00010 | $0.00223 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
novel-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
小说记忆系统
你已被注入 novel-memory 技能。本技能指导如何使用工作区文件系统作为小说创作的持久化记忆。
文件结构
novel/{书名}/
├── plan.md # 总控大纲（由 novel-worldbuilding 生成）
├── 设定/
│ ├── characters.md # 人物档案（全部角色）
│ ├── world.md # 世界观规则
│ ├── foreshadowing.md # 伏笔台账
│ ├── timeline.md # 时间线
│ └── relations.md # 人物关系网络
├── 正文/
│ ├── 第1章_标题.md
│ └── ...
└── summary/
├── chapter_01.md # 章节摘要
└── ...
所有路径基于工作区根目录。文件即记忆，目录即分类。
记忆分类
1. 静态设定（长期有效）
存储于 novel/{书名}/设定/ 目录：
| 文件 | 内容 |
|---|---|
characters.md |
人物档案：姓名、年龄、外貌、性格、背景、动机、能力 |
world.md |
世界观：力量体系、科技水平、政治制度、地理环境 |
relations.md |
关系网络：角色间关系类型、阵营、势力层级 |
2. 动态日志（按章推进）
| 文件 | 内容 |
|---|---|
summary/chapter_XX.md |
每章摘要：核心事件、角色状态变化、新/回收伏笔 |
设定/foreshadowing.md |
伏笔台账：ID、描述、埋设章节、预计回收章节、状态 |
设定/timeline.md |
时间线：故事内时间进度、各章时间跨度 |
设定/characters.md |
角色当前状态（追加更新，不覆盖历史） |
操作规范
读取（每章开始前必做）
- 读取上一章摘要：
summary/chapter_XX.md - 读取角色当前状态：
设定/characters.md末尾的最新状态段 - 读取未回收伏笔：
设定/foreshadowing.md中状态为"未回收"的条目 - 读取时间线：
设定/timeline.md获取当前故事时间
查询技巧：当需要搜索特定设定时，用 grep 在 设定/ 目录下搜索关键词。
写入（每章完成后必做）
- 创建本章摘要文件：
summary/chapter_XX.md - 更新角色状态：在
设定/characters.md中追加变化记录 - 更新伏笔台账：
设定/foreshadowing.md中标记新增/回收 - 推进时间线：
设定/timeline.md中记录本章时间
初始化（新书启动时）
- 创建
novel/{书名}/设定/目录及所有初始文件 - 填入初始人物档案、世界观、空伏笔台账、时间线起点
- 创建
novel/{书名}/正文/和summary/目录
操作原则
- 即时记录：定义新实体/规则时立即写入对应文件
- 更新同步：设定演变时及时更新，保持文件与正文一致
- 避免冗余：只记录核心的、长期有效的信息
- 防遗忘：依赖文件而非记忆，第 100 章时读文件就能回忆第 1 章的设定
- 追加优先：角色状态变化用追加而非覆盖，保留变化历史
伏笔台账格式
设定/foreshadowing.md 中每条伏笔格式：
### F001: {伏笔描述}
- 埋设：第 X 章，{具体场景描述}
- 预计回收：第 Y 章
- 状态：未回收 / 已回收（第 Z 章）
- 关联角色：{角色名}
查询流程
需要核实设定 → 读取 设定/ 下对应文件 → grep 关键词定位 → 获取信息
需要前文回顾 → 读取 summary/ 下近几章摘要 → 了解上下文
需要全局检查 → 遍历 设定/ 全部文件 → 交叉验证一致性
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 115 lines · 51 tokens per session scan A 6433bb68c30d
novel-memory is a skill published in the GitHub repository wanikua/danghuangshang (2,701 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,115 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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project-context
Use PowerContext project memory and handoff tools through MCP when continuing prior work, recalling decisions, maintaining durable memory, or transferring work across tasks, sessions, or agents.
ralph-memory
Manage Al semantic memory entries — list, query, and clear lessons learned across loop iterations.
session-harvest
Extract cited decisions, requirements, risks, entities, and relationships from session history and review exact candidate versions before memory promotion.