nexu is an open-source desktop client that connects an OpenClaw AI agent to messaging services such as WeChat, Feishu, Slack, and Discord. It is for people who want to chat with their agent from those channels using their own model credentials while keeping data on their computer, and the catalogue entries support its agent workflows.
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 nexu-io/nexu --skill research-to-diagramgit clone --depth 1 https://github.com/nexu-io/nexuWrote 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/nexu-io/nexu/research-to-diagram)<a href="https://agentmods.dev/skills/nexu-io/nexu/research-to-diagram"><img src="https://agentmods.dev/badge/skills/nexu-io/nexu/research-to-diagram/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/nexu-io/nexu/research-to-diagram"><img src="https://agentmods.dev/badge/skills/nexu-io/nexu/research-to-diagram.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 | $0.00079 | $0.02427 |
| Opus 5 | $0.00039 | $0.01213 |
| Sonnet 5 | $0.00016 | $0.00485 |
| Haiku 4.5 | $0.00008 | $0.00243 |
Grade A, and why
research-to-diagram 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research-to-diagram — 94% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research to Diagram
深度调研主题并自动生成知识关系图谱PDF。从研究到可视化的一站式工具。
Description
这个 Skill 接收用户提供的研究主题，自动进行深度网络调研、信息收集、知识整理、结构设计，最终生成专业的可视化关系图谱PDF。与 structure-to-pdf 不同，本 Skill 专注于主动研究和知识挖掘，而非被动的数据转换。
核心特性
- 自动调研：使用 WebSearch 进行多轮深度调研
- 智能整理：自动提取、分类、结构化信息
- 专业设计：根据主题特点选择最佳可视化方案
- 多种输出：支持 Graphviz、PlantUML、Mermaid 等工具
- 高质量PDF：生成矢量图形，可无限缩放
Trigger Conditions
当用户想要：
- 研究某个复杂主题的知识结构（如"红楼梦人物关系"）
- 生成人物关系图、概念图谱、知识图谱
- 理解某个领域的组织架构、技术架构
- 可视化复杂的关系网络
- 需要从零开始研究并可视化某个主题
关键词：
- "调研...并做图"
- "研究...的关系"
- "深度分析...并可视化"
- "生成...知识图谱"
- "画...关系图"（无现成数据）
Workflow
1. 任务规划（TodoWrite）
- 深度调研主题和相关知识
- 设计图谱结构和层次
- 创建可视化图表
- 生成PDF文档
2. 深度调研阶段
- 多轮 WebSearch：从不同角度收集信息
- 主题概述和背景
- 核心要素和人物/概念
- 关系和联系
- 层次和分类
- 信息源记录：保存所有参考资料链接
- 知识提取：识别关键实体和关系
3. 结构设计阶段
根据主题类型选择最佳结构：
人物关系图：
- 家族谱系：多层次树状结构
- 社会网络：网状关系图
- 组织架构：层次化布局
概念图谱：
- 知识分类：树状或思维导图
- 概念关系：有向图
- 流程图：线性或分支流程
技术架构：
- 系统组件：模块化布局
- 依赖关系：层次或网络图
- 数据流向：流程图
4. 可视化实现
优先使用 Graphviz (DOT 语言)：
digraph G {
// 全局样式
graph [rankdir=TB bgcolor="#fdfdf5" fontname="Arial Unicode MS"]
node [shape=box style="rounded,filled" fillcolor="#e8f4f8"]
edge [fontname="Arial Unicode MS"]
// 使用 subgraph cluster 分组
subgraph cluster_group1 {
label="分组名称"
node1 [label="节点1"]
node2 [label="节点2"]
}
// 定义关系
node1 -> node2 [label="关系类型" color=red]
}
备选工具：
- PlantUML：UML 图、时序图
- Mermaid：简单流程图、时序图
5. PDF 生成
dot -Tpdf diagram.dot -o output.pdf
6. 文档整理
可选生成说明文档，包含：
- 研究主题概述
- 图谱说明
- 参考资料来源（Sources）
- 使用说明
设计原则
视觉设计
- 颜色编码：使用不同颜色区分类别
- 形状区分：不同类型实体用不同形状
- 层次清晰：使用 subgraph cluster 分组
- 关系标注：边的颜色、样式、标签表达关系类型
- 中文支持：使用 "Arial Unicode MS" 或系统中文字体
信息层次
- 标题层：主标题
- 分组层：主要类别/家族
- 实体层：具体人物/概念
- 关系层：连接和标注
- 图例层：说明符号含义
布局策略
- rankdir=TB：自上而下（家族树、组织架构）
- rankdir=LR：从左到右（流程图、时间线）
- rankdir=BT：自下而上（依赖图）
- splines=ortho：正交边（清晰的组织图）
- splines=curved：曲线边（美观的关系网）
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 281 lines · 79 tokens per session scan A 7e69f2eff7dd
research-to-diagram is a skill published in the GitHub repository nexu-io/nexu (3,268 stars, last pushed 4mo ago), licensed MIT. It adds 79 tokens to every session and 2,427 once invoked, about $0.0004 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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