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 deep-researchgit 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/deep-research)<a href="https://agentmods.dev/skills/nexu-io/nexu/deep-research"><img src="https://agentmods.dev/badge/skills/nexu-io/nexu/deep-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/nexu-io/nexu/deep-research"><img src="https://agentmods.dev/badge/skills/nexu-io/nexu/deep-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 | $0.00078 | $0.03802 |
| Opus 5 | $0.00039 | $0.01901 |
| Sonnet 5 | $0.00016 | $0.00760 |
| Haiku 4.5 | $0.00008 | $0.00380 |
Grade A, and why
deep-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 9d 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:
- deep-research — 95% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research（深度调研 8 步法）
将用户提出的模糊主题，通过系统化方法转化为高质量、可交付的调研报告。
核心理念
- 结论来自机制对比，不是「我感觉像」
- 先钉牢事实，再做推导
- 资料权威优先：L1 > L2 > L3 > L4
- 中间结果必须保存，便于回溯和复用
参考文档
| 文档 | 内容 |
|---|---|
templates/intermediate-outputs.md |
中间产物格式模板 |
templates/comparison-framework.md |
对比框架模板 |
templates/fact-card.md |
事实卡片模板 |
templates/report-structure.md |
报告结构模板 |
工作目录结构
~/Downloads/research/<topic>/
├── 00_问题拆解.md # Step 0-1 产出
├── 01_资料来源.md # Step 2 产出
├── 02_事实卡片.md # Step 3 产出
├── 03_对比框架.md # Step 4 产出
├── 04_推导过程.md # Step 6 产出
├── 05.5_校验记录.md # Step 6.5 产出（独立 Agent 校验）
├── 05_验证记录.md # Step 7 产出
├── FINAL_调研报告.md # Step 8 产出
└── raw/ # 原始资料存档
中间产物格式详见 templates/intermediate-outputs.md
资料分层
| 层级 | 资料类型 | 可信度 |
|---|---|---|
| L1 | 官方文档、论文、规范、RFC | ✅ 高 |
| L2 | 官方博客、技术演讲、白皮书 | ✅ 高 |
| L3 | 权威媒体、专家解读、教程 | ⚠️ 中 |
| L4 | 社区讨论、个人博客、论坛 | ❓ 低 |
L4 社区来源（产品对比调研必查）：GitHub Issues/Discussions、Reddit、Hacker News
执行流程（8 步法）
每步完成后，立即写入对应文件。
Step 0: 问题类型判断
| 问题类型 | 核心任务 | 侧重维度 |
|---|---|---|
| 概念对比型 | 建立对比框架 | 机制差异、适用边界 |
| 决策支持型 | 权衡取舍 | 成本、风险、收益 |
| 趋势分析型 | 梳理演进脉络 | 历史、驱动因素、预测 |
| 问题诊断型 | 根因分析 | 症状、原因、证据链 |
| 知识梳理型 | 系统整理 | 定义、分类、关系 |
Step 0.5: 时效敏感性判断（BLOCKING）
| 敏感级别 | 典型领域 | 资料时间窗口 |
|---|---|---|
| 🔴 极高 | AI/大模型、区块链 | 3-6 个月 |
| 🟠 高 | 云服务、前端框架 | 6-12 个月 |
| 🟡 中 | 编程语言、数据库 | 1-2 年 |
| 🟢 低 | 算法原理、设计模式 | 无限制 |
🔴 极高敏感领域强制规则：
- 搜索时带时间约束（
time_range: "month"） - 官方源优先（文档、博客、Changelog）
- 版本号强制标注（禁止「最新版本支持」）
- 超过 6 个月的博客仅作历史参考
- 关键信息至少 2 个独立来源确认
- 直接访问官方下载页面验证（不依赖搜索缓存）
- 搜索产品支持的协议名称（MCP、ACP 等）
Step 1: 问题拆解与边界界定
把模糊主题拆成 2-4 个可调研的子问题，并明确研究对象边界（人群/地域/时间/层级）。
→ 保存：00_问题拆解.md
Step 2: 资料分层与权威锁定
搜索策略（高敏感领域）：
- 官方源定向搜索（限定官方域名）
- 官方下载页面直接验证（BLOCKING）
- 协议/功能名称搜索（BLOCKING）
- 限时广泛搜索
- 版本核实
- 社区声音挖掘（GitHub Issues、Reddit）
What ships with it
4 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.
- 9d ago First seen · 331 lines · 78 tokens per session scan A 263ac10fa1db
deep-research is a skill published in the GitHub repository nexu-io/nexu (3,266 stars, last pushed 4mo ago), licensed MIT. It adds 78 tokens to every session and 3,802 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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