技能库智客分类：其他 local-places OpenClaw

Local Places

在当地主机上通过Google Places API代理搜索地点(restaurants,咖啡馆等).

官方网址：作者主页

技能介绍

先看中文介绍；官方 description 原文单独保留，不改写 SKILL.md。

做什么

在当地主机上通过Google Places API代理搜索地点(restaurants,咖啡馆等).

何时用

官方 description 未单独写出 Use when。按规范，代理会在用户任务与这段 description 的关键词匹配时激活本技能。

代理如何加载

按 Agent Skills 渐进披露：启动时只加载 name 与 description（约 100 token）；任务匹配后才读入整份 SKILL.md 正文；scripts/、references/、assets/ 仅在需要时再读。本文件正文结构：📍 Local Places、Setup、Quick Start、Conversation Flow、Filter Constraints、Response Format。

文件分析

文件分析：这是一份仅含 SKILL.md 的指令型技能，代理激活后整份正文进入上下文。

官方 description（原文）

Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.

📍 Local PlacesSetupQuick StartConversation FlowFilter ConstraintsResponse Format

来源与网址

市场来源：ClawHub

开源榜 Skill 分类

SKILL.md 与 Agent 调用

官方规范 ↗

name: local-places
description: Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.

发现技能客户端向 Agent 提供名称与描述目录。
匹配与调用用户指定或任务匹配后，载入 SKILL.md 指令。
按需加载按步骤读取参考文档、使用脚本与素材。

具体调用语法与可用工具以目标 Agent 客户端为准。查看调用机制说明 ↗

安装这个技能

Skills CLI ↗

先选择目标 Agent 和安装范围，保留技能包的附属文件，安装后检查客户端能否发现该技能。

交给 Agent 安装

复制安装指令给支持 Agent Skills 的代理，确认其中的目标目录与客户端匹配。

把 Agent Skill「local-places」安装到我的项目：SKILL.md 原文与官方 description 见 https://zicq.com/zh/skills/skl-935b395a62b0e7bc-Local-Places.html
请存为 .cursor/skills/local-places/SKILL.md 或 .claude/skills/local-places/SKILL.md，frontmatter 的 name 与 description 保持原样，不要改写。

当前没有明确的 GitHub 技能包地址，请按来源页面的安装器说明操作。

ClawHub ↗

SKILL.md 原文可复制，不会改动源文件

---
name: local-places
description: Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
homepage: https://github.com/Hyaxia/local_places
metadata: {"clawdbot":{"emoji":"📍","requires":{"bins":["uv"],"env":["GOOGLE_PLACES_API_KEY"]},"primaryEnv":"GOOGLE_PLACES_API_KEY"}}
---

# 📍 Local Places

*Find places, Go fast*

Search for nearby places using a local Google Places API proxy. Two-step flow: resolve location first, then search.

## Setup

```bash
cd {baseDir}
echo "GOOGLE_PLACES_API_KEY=your-key" > .env
uv venv && uv pip install -e ".[dev]"
uv run --env-file .env uvicorn local_places.main:app --host 127.0.0.1 --port 8000
```

Requires `GOOGLE_PLACES_API_KEY` in `.env` or environment.

## Quick Start

1. **Check server:** `curl http://127.0.0.1:8000/ping`

2. **Resolve location:**
```bash
curl -X POST http://127.0.0.1:8000/locations/resolve \
  -H "Content-Type: application/json" \
  -d '{"location_text": "Soho, London", "limit": 5}'
```

3. **Search places:**
```bash
curl -X POST http://127.0.0.1:8000/places/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "coffee shop",
    "location_bias": {"lat": 51.5137, "lng": -0.1366, "radius_m": 1000},
    "filters": {"open_now": true, "min_rating": 4.0},
    "limit": 10
  }'
```

4. **Get details:**
```bash
curl http://127.0.0.1:8000/places/{place_id}
```

## Conversation Flow

1. If user says "near me" or gives vague location → resolve it first
2. If multiple results → show numbered list, ask user to pick
3. Ask for preferences: type, open now, rating, price level
4. Search with `location_bias` from chosen location
5. Present results with name, rating, address, open status
6. Offer to fetch details or refine search

## Filter Constraints

- `filters.types`: exactly ONE type (e.g., "restaurant", "cafe", "gym")
- `filters.price_levels`: integers 0-4 (0=free, 4=very expensive)
- `filters.min_rating`: 0-5 in 0.5 increments
- `filters.open_now`: boolean
- `limit`: 1-20 for search, 1-10 for resolve
- `location_bias.radius_m`: must be > 0

## Response Format

```json
{
  "results": [
    {
      "place_id": "ChIJ...",
      "name": "Coffee Shop",
      "address": "123 Main St",
      "location": {"lat": 51.5, "lng": -0.1},
      "rating": 4.6,
      "price_level": 2,
      "types": ["cafe", "food"],
      "open_now": true
    }
  ],
  "next_page_token": "..." 
}
```

Use `next_page_token` as `page_token` in next request for more results.

阅读排版

name: local-places description: Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost. homepage: https://github.com/Hyaxia/local_places metadata: {"clawdbot":{"emoji":"📍","requires":{"bins":["uv"],"env":["GOOGLE_PLACES_API_KEY"]},"primaryEnv":"GOOGLE_PLACES_API_KEY"}}

📍 Local Places

Find places, Go fast

Search for nearby places using a local Google Places API proxy. Two-step flow: resolve location first, then search.

Setup

cd {baseDir}
echo "GOOGLE_PLACES_API_KEY=your-key" > .env
uv venv && uv pip install -e ".[dev]"
uv run --env-file .env uvicorn local_places.main:app --host 127.0.0.1 --port 8000

Requires GOOGLE_PLACES_API_KEY in .env or environment.

Quick Start

Check server: curl http://127.0.0.1:8000/ping
Resolve location:

curl -X POST http://127.0.0.1:8000/locations/resolve \
  -H "Content-Type: application/json" \
  -d '{"location_text": "Soho, London", "limit": 5}'

Search places:

curl -X POST http://127.0.0.1:8000/places/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "coffee shop",
    "location_bias": {"lat": 51.5137, "lng": -0.1366, "radius_m": 1000},
    "filters": {"open_now": true, "min_rating": 4.0},
    "limit": 10
  }'

Get details:

curl http://127.0.0.1:8000/places/{place_id}

Conversation Flow

If user says "near me" or gives vague location → resolve it first
If multiple results → show numbered list, ask user to pick
Ask for preferences: type, open now, rating, price level
Search with location_bias from chosen location
Present results with name, rating, address, open status
Offer to fetch details or refine search

Filter Constraints

filters.types: exactly ONE type (e.g., "restaurant", "cafe", "gym")
filters.price_levels: integers 0-4 (0=free, 4=very expensive)
filters.min_rating: 0-5 in 0.5 increments
filters.open_now: boolean
limit: 1-20 for search, 1-10 for resolve
location_bias.radius_m: must be > 0

Response Format

{
  "results": [
    {
      "place_id": "ChIJ...",
      "name": "Coffee Shop",
      "address": "123 Main St",
      "location": {"lat": 51.5, "lng": -0.1},
      "rating": 4.6,
      "price_level": 2,
      "types": ["cafe", "food"],
      "open_now": true
    }
  ],
  "next_page_token": "..." 
}

Use next_page_token as page_token in next request for more results.