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健身房
作者
mikeclaw007
· GitHub ↗
· v0.1.0
· MIT-0
111
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当前安装
1
版本数
在 OpenClaw 中安装
/install gyms
功能描述
Find nearby gyms. Invoke when user asks for fitness centers near me.
使用说明 (SKILL.md)
Nearby Gyms
用途
- 提供用户当前位置附近的 Gyms 列表
- 统一返回字段与查询行为，便于前端/接口复用
触发条件
- 用户询问“健身房 附近 / gyms near me / nearby fitness centers”
输入参数
- location: 经纬度 { lat, lng }，必填
- radius_meters: 查询半径，默认 3000
- limit: 返回数量上限，默认 20，最大 50
- filters: 可选筛选（是否私教、是否游泳、评分等）
响应字段
- 统一参见 STANDARD_RESPONSE.md
- 本技能 category 固定为 "gyms"
错误码
- INVALID_LOCATION: 经纬度不合法
- RADIUS_TOO_LARGE: 超过最大查询半径
- PROVIDER_UNAVAILABLE: 数据源不可用
- RATE_LIMITED: 触发速率限制
示例
- 输入: { location: { lat: 30.123, lng: 120.456 }, radius_meters: 2500, limit: 10 }
- 输出: 标准 POI 列表（见 STANDARD_RESPONSE.md）
隐私与速率限制
- 仅在用户授权定位后查询
- 避免保留精确坐标，必要时进行网格化模糊处理
安全使用建议
This skill appears to be a lightweight, instruction-only formatter for returning nearby gyms, but the runtime instructions omit how to fetch POI data and reference a local STANDARD_RESPONSE.md that likely won't exist. Before installing or enabling it: (1) ask the publisher which data provider/API the skill uses (Google/Foursquare/OSM/etc.) and whether any API keys are required; (2) confirm how the platform will supply user location and that the skill will not persist precise coordinates (it claims to grid/blur coordinates — get specifics); (3) request a corrected reference or the actual STANDARD_RESPONSE schema so you know the output format; (4) test in a safe environment to verify the agent doesn't perform broad web scraping or unexpected network requests. These steps will reduce the uncertainty caused by incomplete instructions.
功能分析
Type: OpenClaw Skill
Name: gyms
Version: 0.1.0
The skill bundle consists of metadata and a markdown instruction file (SKILL.md) designed to help an AI agent find nearby gyms. It defines standard input parameters like coordinates and radius, includes privacy considerations for location data, and contains no executable code, network requests, or malicious instructions.
能力评估
Purpose & Capability
Name and description match: a simple 'nearby gyms' skill reasonably requires no extra binaries, env vars, or installs. The declared inputs (location, radius, filters) are appropriate for the purpose.
Instruction Scope
SKILL.md defines inputs, outputs, and privacy considerations but does not specify how to obtain gym data (no provider, API endpoints, or query mechanism). It also references STANDARD_RESPONSE.md via a local file:///Users/... path that will not exist in most runtimes. Because the instructions are incomplete and leave unspecified how external data is retrieved, an agent may fall back to broad behaviors (web scraping, generic web requests, or other platform tools) — this is vagueness that affects safety and predictability.
Install Mechanism
No install spec and no code files — instruction-only skill. Low installation risk because nothing is written to disk or downloaded.
Credentials
No environment variables, credentials, or config paths are requested. This is proportional to the described functionality (a simple POI lookup doesn't inherently require secrets).
Persistence & Privilege
Flags show default behavior (not always-on, user-invocable, autonomous invocation allowed). Nothing in the skill requests elevated persistence or modifies other skills/config.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install gyms - 安装完成后，直接呼叫该 Skill 的名称或使用
/gyms触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
- Initial release of the "附近健身房" skill for finding nearby gyms.
- Supports search by user location with customizable radius and result limit.
- Allows optional filters (personal training, swimming pool, rating, etc.).
- Standardized response format for easy frontend/API integration.
- Includes detailed error codes and privacy controls (requires location permission, protects user coordinates).
元数据
常见问题
健身房 是什么？
Find nearby gyms. Invoke when user asks for fitness centers near me. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 111 次。
如何安装 健身房？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install gyms」即可一键安装，无需额外配置。
健身房 是免费的吗？
是的，健身房 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
健身房 支持哪些平台？
健身房 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 健身房？
由 mikeclaw007（@mikeclaw007）开发并维护，当前版本 v0.1.0。
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