local-places

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

40 stars

Best use case

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

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

Teams using local-places 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/local-places/SKILL.md --create-dirs "https://raw.githubusercontent.com/lllooollpp/clawdbot-cn/main/skills/local-places/SKILL.md"

Manual Installation

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

How local-places Compares

Feature / Agentlocal-placesStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

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.

SKILL.md Source

# 📍 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.

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