How do I install the Local Places skill?

Run npx skillmds@latest add mahabdalla/local-places in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Local Places safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Local Places?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Local Places free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Local Places?

mahabdalla (@mahabdalla) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Local Places by mahabdalla — Agent Skill

Name: Local Places
Rating: 4.2 (5 reviews)
Author: mahabdalla

Local Places

Searches for local businesses and points of interest via a Google Places API proxy running on localhost. Resolves vague locations, applies filters for type, rating, and price, and returns structured place details. Use when the user asks to find restaurants, nearby places, coffee shops, business search, local recommendations, or anything involving place discovery by location.

mahabdalla Updated Sep 22, 2026

File contents

📍 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.

Converted and distributed by TomeVault | Claim this content

---name: local-placesdescription: Searches for local businesses and points of interest via a Google Places API proxy running on localhost. Resolves vague locations, applies filters for type, rating, and price, and returns structured place details. Use when the user asks to find restaurants, nearby places, coffee shops, business search, local recommendations, or anything involving place discovery by location.---# 📍 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```bashcd {baseDir}echo "GOOGLE_PLACES_API_KEY=your-key" > .envuv 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 Start1. **Check server:** `curl http://127.0.0.1:8000/ping`2. **Resolve location:**```bashcurl -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:**```bashcurl -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:**```bashcurl http://127.0.0.1:8000/places/{place_id}```## Conversation Flow1. If user says "near me" or gives vague location → resolve it first2. If multiple results → show numbered list, ask user to pick3. Ask for preferences: type, open now, rating, price level4. Search with `location_bias` from chosen location5. Present results with name, rating, address, open status6. 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.---> Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/elizaos/eliza)

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