A comprehensive integration of Tencent Map Web Services for advanced location search, route planning, and coordinate transformations.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install qqmap
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install qqmap using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Tencent Map API skill enables AI agents to interact with professional geospatial data services provided by Tencent LBS. By utilizing this Openclaw Skills integration, developers can equip their agents with the ability to perform complex location-based tasks such as finding businesses, calculating travel times, and converting between physical addresses and geographic coordinates. It is designed to be a bridge between LLM reasoning and real-world spatial data.
This skill is particularly valuable for applications requiring localized knowledge within China, offering high-precision data for POI (Points of Interest) and optimized routing algorithms for various transport modes. Whether you are building a travel assistant or a logistics automation tool, this integration provides the necessary infrastructure for robust map-based workflows.
To use this skill within the Openclaw Skills framework, follow these steps:
curl and python3 installed.export TENCENT_MAP_KEY="YOUR_TENCENT_MAP_KEY"
The skill provides structured responses for all operations. The following table describes the standard response fields:
| Key | Description |
|---|---|
| status | Success or error code (0 for success) |
| message | Status description or error message |
| result | The core payload containing POIs, route paths, or address components |
| count | The number of results returned (for search queries) |
All data is delivered in a clean JSON format, making it easy for AI agents to parse and utilize in subsequent reasoning steps.
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