query

Search for places using natural language with Camino AI's location intelligence API. Returns relevant results with coordinates, distances, and metadata. Use when you need to find real-world locations like restaurants, shops, landmarks, or any point of interest.

7 stars

Best use case

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

Search for places using natural language with Camino AI's location intelligence API. Returns relevant results with coordinates, distances, and metadata. Use when you need to find real-world locations like restaurants, shops, landmarks, or any point of interest.

Teams using query 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/query/SKILL.md --create-dirs "https://raw.githubusercontent.com/Demerzels-lab/elsamultiskillagent/main/public/skills/barneyjm/query/SKILL.md"

Manual Installation

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

How query Compares

Feature / AgentqueryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Search for places using natural language with Camino AI's location intelligence API. Returns relevant results with coordinates, distances, and metadata. Use when you need to find real-world locations like restaurants, shops, landmarks, or any point of interest.

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

## Installation

**Companion Skills**: This is part of the Camino AI location intelligence suite. Install all 6 skills (query, places, relationship, context, route, journey) for comprehensive coverage.

```bash
# Install all skills from repo
npx skills add https://github.com/barneyjm/camino-skills

# Or install specific skills
npx skills add https://github.com/barneyjm/camino-skills --skill query
```

**Via clawhub:**
```bash
npx clawhub@latest install query
# or: pnpm dlx clawhub@latest install query
# or: bunx clawhub@latest install query
```

# Query - Natural Language Place Search

Search for places using natural language. The AI automatically generates coordinates for known locations when not provided.

## Setup

1. Get your API key from [https://app.getcamino.ai](https://app.getcamino.ai)
2. Add to your `~/.claude/settings.json`:

```json
{
  "env": {
    "CAMINO_API_KEY": "your-api-key-here"
  }
}
```

3. Restart Claude Code

## Usage

### Via Shell Script

```bash
# Search for coffee shops near Times Square
./scripts/query.sh '{"query": "coffee shops near Times Square", "limit": 5}'

# Search with specific coordinates
./scripts/query.sh '{"query": "quiet cafes with wifi", "lat": 40.7589, "lon": -73.9851, "radius": 500}'

# Get AI-generated answer with results
./scripts/query.sh '{"query": "best pizza in Manhattan", "answer": true, "rank": true}'
```

### Via curl

```bash
curl -H "X-API-Key: $CAMINO_API_KEY" \
  "https://api.getcamino.ai/query?query=coffee+shops+near+Times+Square&limit=5"
```

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| query | string | Yes* | - | Natural language query (e.g., "coffee shops near Times Square") |
| lat | float | No | - | Latitude for search center. AI generates if omitted for known locations. |
| lon | float | No | - | Longitude for search center. AI generates if omitted for known locations. |
| radius | int | No | 1000 | Search radius in meters (100-50000) |
| rank | bool | No | true | Use AI to rank results by relevance |
| limit | int | No | 20 | Maximum results (1-100) |
| offset | int | No | 0 | Pagination offset |
| answer | bool | No | false | Generate human-readable summary |
| time | string | No | - | Temporal query: "2020-01-01", "2020..", or "2020..2024" |
| osm_ids | string | No | - | Comma-separated OSM IDs (e.g., "node/123,way/456") |
| mode | string | No | "basic" | "basic" (OSM only) or "advanced" (web enrichment) |

*Either `query` or `osm_ids` is required.

## Response Format

```json
{
  "query": "quiet coffee shops with wifi",
  "results": [
    {
      "name": "Blue Bottle Coffee",
      "lat": 40.7601,
      "lon": -73.9847,
      "type": "cafe",
      "distance_m": 150,
      "relevance_score": 0.95,
      "address": "..."
    }
  ],
  "ai_ranked": true,
  "pagination": {
    "total_results": 23,
    "limit": 5,
    "offset": 0,
    "has_more": true
  },
  "answer": "I found several quiet coffee shops with wifi near Times Square..."
}
```

## Examples

### Find nearby restaurants
```bash
./scripts/query.sh '{"query": "Italian restaurants", "lat": 40.7128, "lon": -74.0060, "limit": 10}'
```

### Search with AI answer
```bash
./scripts/query.sh '{"query": "best brunch spots in Brooklyn", "answer": true}'
```

### Historical data query
```bash
./scripts/query.sh '{"query": "restaurants", "lat": 40.7589, "lon": -73.9851, "time": "2020-01-01"}'
```

## Best Practices

- For known locations (cities, landmarks), you can omit lat/lon and let the AI generate coordinates
- Use `rank: true` for more relevant results when searching by attributes (e.g., "quiet", "cheap")
- Enable `answer: true` when you need a natural language summary of results
- Use `mode: "advanced"` for richer place data from web sources
- Keep queries descriptive but concise for best AI interpretation

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