navclaw

Smart driving — exhaustive route search, may outperform default navigation. 导航/自驾/极限避堵, dozens of routes. One-tap iOS/Android deep link. Supports 高德/Amap. 智能避堵,极限搜索绕行方案,一键跳转手机导航APP.

3,891 stars

Best use case

navclaw is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Smart driving — exhaustive route search, may outperform default navigation. 导航/自驾/极限避堵, dozens of routes. One-tap iOS/Android deep link. Supports 高德/Amap. 智能避堵,极限搜索绕行方案,一键跳转手机导航APP.

Teams using navclaw should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/navclaw/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/ai4mse/navclaw/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/navclaw/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How navclaw Compares

Feature / AgentnavclawStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Smart driving — exhaustive route search, may outperform default navigation. 导航/自驾/极限避堵, dozens of routes. One-tap iOS/Android deep link. Supports 高德/Amap. 智能避堵,极限搜索绕行方案,一键跳转手机导航APP.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# NavClaw 🦀 - 出行智能导航AI助手 / Smart Driving Navigator

## 概述 / Overview

**智能避堵导航 — 极限搜索数十条路线,可能比官方导航更优。一键跳转手机导航APP(iOS/Android)。**

**Smart congestion-avoidance navigator — exhaustive search of dozens of routes, may outperform default navigation. One-tap deep link to mobile nav apps.**

五阶段流水线规划(广撒网 → 精筛选 → 深加工 → 迭代优化 → 路线固化),导航 APP 通常只返回 2-3 条路线,NavClaw 短时间内探索数十种绕行组合。

5-phase pipeline (Wide Search → Fine Filter → Deep Processing → Iterative Optimization → Route Finalization). While navigation apps typically return 2-3 routes, NavClaw explores dozens of bypass combinations in seconds.

导航平台:目前支持高德,后续扩展更多平台。

Navigation platform: currently supports Amap (高德), more platforms coming soon.

---

**⚠️ 前置条件 / Prerequisites**:需要高德 Web 服务 API Key(免费申请)。获取优先级:

1. 从记忆中查找用户是否已提供过高德 API Key
2. 如果没有,询问用户是否有高德 API Key
3. 如果用户没有,引导获取:[高德开放平台](https://lbs.amap.com/) → 控制台 → 创建应用 → 添加 Key(Web 服务)

拿到 Key 后填入 `config.py` 的 `API_KEY` 字段:

```python
API_KEY = "你的高德API Key"
```

---

**触发方式 / Trigger**:

用户说"从 [起点] 到 [终点] 导航"、"导航从 [起点] 到 [终点]"或"navigate from [A] to [B]"即可自动执行。说"到家"时自动替换为 `config.py` 中的 `DEFAULT_DEST`。

Trigger when user says "navigate from [A] to [B]", "从 [起点] 到 [终点] 导航", or "drive to [destination]". Saying "到家" (go home) auto-substitutes `DEFAULT_DEST` from `config.py`.

---

**工作流程 / Workflow**:

调用 `wrapper.py --origin "起点" --dest "终点"`,经过五阶段规划(广撒网 → 精筛选 → 深加工 → 迭代优化 → 路线固化),生成大量路线方案(含绕行优化),自动发送 3 条消息到聊天平台 + 日志附件:

- 消息 1 — 完整对比表格
- 消息 2 — 快速导航链接
- 消息 3 — 最终推荐 + iOS/Android 一键深度链接

---

**输出格式 / Output**:

- **Mattermost(原生支持,推荐优先使用)**:

  需要先在 `config.py` 中配置 `MM_BASEURL`、`MM_BOT_TOKEN`、`MM_CHANNEL_ID`,然后直接运行 `wrapper.py --origin "起点" --dest "终点"`,自动发送 3 条消息 + 日志附件(优先采用Mattermost 消息和日志附件,如果不成功则用备份方案)。

### Mattermost 发文件附件

OpenClaw Mattermost 插件不支持原生附件,用 curl 直调 API:

1. POST /api/v4/files 上传文件,拿 file_id
2. POST /api/v4/posts 发帖,带 file_ids 字段

- **其他聊天工具(备份方案)**:

  运行 `wrapper.py --origin "起点" --dest "终点" --no-send`,结果输出到 stdout,OpenClaw 读取后转发给用户即可。

  OpenClaw 可读取 stdout 按 `📨 消息 1/2/3` 分段转发给用户。日志文件路径在末尾 `📝 日志: log/navclaw/...` 行中,不要发路径,要读取后发出来,如果不能发附件,给发原文内容。

  (一定要原样发给用户,各个消息,特别是链接要保留,不能舍弃)

**强烈建议先用原生方法 / Native method recommended**

---

**安装配置 / Setup**:

`pip install requests` → `cp config_example.py config.py` → 编辑填入高德 API Key、默认终点、Mattermost 配置(可选,包括MM_BASEURL,MM_BOT_TOKEN,MM_CHANNEL_ID,如果记忆或者配置没有,提示用户给出,如果用户没有就忽略。如果有,要写入config.py对应位置)。

---

**文件位置 / Files**:

- 调用入口:`wrapper.py`
- 核心引擎:`navclaw.py`
- 配置文件:`config.py`(需用户创建)
- 配置模板:`config_example.py`
- 日志目录:`log/`

---

**聊天平台 / Chat Platforms**:

目前内置支持 Mattermost(通过 `wrapper.py`),其他聊天工具 OpenClaw 帮我转发。

最简单的办法是直接聊天告诉 OpenClaw 运行并读取结果发送给你,支持任何聊天平台,稳定性和上下文长度取决于你的大模型 API。如果想节约 token、防止上下文截断、加快响应速度,可以自行扩展 `wrapper.py` 或让 OpenClaw AI 阅读现有 Mattermost 代码帮你适配新平台。

---

**性能参考 / Performance**:

- 短途无拥堵(迭代=0):约 6 秒、15 次 API、10 条路线
- 长途有拥堵(迭代=1):约 30 秒、150 次 API、40 条路线

首次使用建议 `MAX_ITER = 0` 验证配置正确,`MAX_ITER = 1` 深度优化可能找到比官方更快的路线。

---

**依赖 / Dependencies**:

- Python 3.8+
- `requests`(唯一第三方依赖)
- 高德 Web 服务 API Key

---

**作者 / Author**:

公益技能,免费开源。 / Community-driven, open-source, free for everyone.

- **Email**: nuaa02@gmail.com
- **小红书 / Xiaohongshu**: @深度连接
- **GitHub**: [AI4MSE](https://github.com/AI4MSE)

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