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-openvikinggit 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-openviking)<a href="https://agentmods.dev/skills/wanikua/danghuangshang/novel-openviking"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-openviking/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-openviking"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-openviking.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 | $0.00063 | $0.01029 |
| Opus 5 | $0.00032 | $0.00515 |
| Sonnet 5 | $0.00013 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
novel-openviking 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
翰林院 OpenViking 记忆增强
OpenViking 已接管 OpenClaw 默认记忆后端（qmd），成为你的记忆系统。两者都是文件语义，切换无感。
本技能指导你如何将小说写作 pipeline 的记忆操作映射到 memory_recall / memory_store / memory_forget。
记忆操作映射
novel-memory 技能中的文件读写操作，在 OpenViking 下对应为：
| novel-memory（文件模式） | OpenViking（语义模式） |
|---|---|
写入 设定/characters.md |
memory_store 人物档案 |
写入 设定/world.md |
memory_store 世界观规则 |
写入 设定/foreshadowing.md |
memory_store 伏笔条目 |
写入 summary/chapter_XX.md |
memory_store 章节摘要 |
| grep 搜索设定文件 | memory_recall 语义查询 |
| 读 foreshadowing.md 查伏笔 | memory_recall "未回收伏笔" |
| 逐个读 summary 回顾前文 | memory_recall 一步定位相关章节 |
| 删除过时设定 | memory_forget 清理旧记忆 |
存入：什么时候 memory_store
新书初始化
memory_store: "{角色名}的人物档案：{性格}、{背景}、{动机}、{能力}"
memory_store: "世界观核心规则：{力量体系}、{社会结构}、{地理环境}"
memory_store: "故事主线：{核心冲突}、{主角目标}、{主要矛盾}"
每章归档后（配合 novel-archiving）
memory_store: "第X章摘要：{核心事件}；{角色状态变化}"
memory_store: "伏笔F{XXX}：{描述}，第X章埋设，预计第Y章回收"
memory_store: "{角色名}当前状态：位于{地点}，情绪{状态}，与{角色}关系变为{关系}"
设定变更时
memory_store: "{角色名}获得新能力：{能力描述}，来源：第X章{事件}"
memory_store: "新势力出现：{势力名}，{立场}，与{现有势力}的关系"
查询：什么时候 memory_recall
写作前（novel-prose）
memory_recall: "{角色名}的性格特征和当前状态" → 确保人设一致
memory_recall: "第X章到第Y章的情节发展" → 回顾上下文
memory_recall: "与{场景关键词}相关的世界设定" → 确认设定细节
审核时（novel-review）
memory_recall: "{角色名}在前文中的行为模式" → 验证角色一致性
memory_recall: "未回收的伏笔" → 检查伏笔遗漏
memory_recall: "{设定关键词}" → 交叉验证设定冲突
架构设计时（novel-worldbuilding）
memory_recall: "已有的世界观规则" → 避免设定矛盾
memory_recall: "现有角色的关系网络" → 设计新角色时考虑已有关系
清理：什么时候 memory_forget
memory_forget: "角色{名}的旧状态" → 角色状态大幅变化后清理旧版本
memory_forget: "伏笔F{XXX}" → 伏笔回收后清理埋设记录
注意事项
- store 内容要精炼：存入的是摘要级信息，不是正文全文
- recall 结果要验证：语义搜索可能返回相关但不精确的结果，关键设定需交叉确认
- auto-capture 会自动工作：日常对话中的设定讨论会被自动捕获，无需手动 store 一切
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.
- 12d ago First seen · 99 lines · 63 tokens per session scan A 86a7c728eb98
novel-openviking is a skill published in the GitHub repository wanikua/danghuangshang (2,701 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,029 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.
Other skills, from other repositories
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Two-layer persistent memory system (longterm facts + daily recall) backed by memory.db. Use when the user mentions remembering, recalling past events, storing preferences, or asks about past context. Also use automatically to persist important user preferences, project facts, and relationship data. Do NOT use for…
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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.