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-researchgit 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-research)<a href="https://agentmods.dev/skills/wanikua/danghuangshang/novel-research"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-research/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-research"><img src="https://agentmods.dev/badge/skills/wanikua/danghuangshang/novel-research.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 | $0.00047 | $0.01165 |
| Opus 5 | $0.00023 | $0.00583 |
| Sonnet 5 | $0.00009 | $0.00233 |
| Haiku 4.5 | $0.00005 | $0.00117 |
Grade A, and why
novel-research 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.
What it actually says
深度调研技能
你已被注入 novel-research 技能。你的职责是为小说创作提供高质量的信息检索和逻辑推演支持。
1. 真实细节检索
定位
搜索真实世界信息，为虚构叙事注入可信度。细节是谎言的伪装，让虚构更可信。
使用场景
| 场景 | 示例查询 |
|---|---|
| 历史场景 | "19世纪伦敦东区街道布局和日常生活" |
| 专业知识 | "氰化物中毒的症状和发作时间" |
| 地理文化 | "京都祇园花街的建筑风格和艺伎文化" |
| 军事/武器 | "二战德国U型潜艇内部结构" |
| 医学/科学 | "失血过多时人体的生理反应" |
| 社会制度 | "古罗马共和国的元老院运作方式" |
使用方法
通过可用的搜索工具（web_search 等）进行检索：
- 构造明确的搜索查询（中文或英文均可）
- 获取搜索结果
- 提取关键细节融入叙事
使用原则
- 精准查询：查询语句要具体，避免过于宽泛
- 提取细节：从搜索结果中提取具体的名称、数字、描述
- 融入叙事：将事实自然地编织进故事，不要像百科全书式的罗列
- 适度使用：不是每个场景都需要查证，只在需要可信度的关键场景使用
2. 逻辑推演
定位
用于复杂情节的深层逻辑推演，确保故事经得起推敲。
使用场景
| 场景 | 推演内容 |
|---|---|
| 复杂反转 | 提前验证反转是否有足够铺垫，读者是否能接受 |
| 多线收束 | 推演多条故事线如何自然汇合 |
| 智斗环节 | 设计攻防逻辑，确保双方行为合理 |
| 逻辑验证 | 检查情节因果链，发现潜在漏洞 |
| 时间线推演 | 验证多角色同一时间段的行动是否冲突 |
| 动机分析 | 推演角色在特定情境下的合理反应 |
使用原则
- 问题明确：将待推演的问题清晰地表述为一个具体命题
- 分步推演：逐步推理，不要一次给出太大的命题
- 批判采纳：推演结果是参考，用创作者的判断力筛选
- 留有余地：推演出多个可能路径时，选最有戏剧性且合逻辑的那个
3. 风格参考
使用场景
- 卡文需要灵感时
- 需要参考特定场景描写方式时
- 学习某种叙事风格时
工作流
- 在工作区
novel/{书名}/或参考素材目录中检索相关风格参考 - 分析检索结果：韵味、节奏、手法、切入点
- 参考而非抄袭
调研与创作的结合
"参考而非抄袭"原则
- 搜索结果：提取事实细节（名称、数据、描述），用自己的语言重新叙述
- 风格参考：学习风格和手法（节奏、视角、氛围营造），用自己的内容重新创作
- 逻辑推演结果：采纳逻辑结论，但用创作者的方式表达
调研强度选择
| 场景类型 | 调研强度 | 方法 |
|---|---|---|
| 日常对话/情感场景 | 低 | 无需调研 |
| 专业知识相关场景 | 中 | 网络检索 |
| 复杂反转/多线汇合 | 中 | 逻辑推演 |
| 历史/真实场景还原 | 高 | 检索 + 推演 |
| 卡文/风格探索 | 中 | 风格参考 |
注意事项
- 不要过度调研：调研是为创作服务的，不要陷入无休止的搜索
- 不要堆砌细节：搜索到的信息要自然融入叙事，不要变成知识百科
- 时间管理：调研应在写作前完成，不要边写边查打断创作节奏
- 结果存储：重要的调研结果写入
设定/对应文件（见 novel-memory 技能），避免重复搜索
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 · 113 lines · 47 tokens per session scan A 0202bb265603
novel-research is a skill published in the GitHub repository wanikua/danghuangshang (2,700 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 1,165 once invoked, about $0.0002 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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