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home / skills / benchflow-ai / skillsbench / search-accommodations

search-accommodations skill

/tasks/travel-planning/environment/skills/search-accommodations

This skill locates accommodations for a specified city from the bundled dataset to help plan stays and filter lodging options.

npx playbooks add skill benchflow-ai/skillsbench --skill search-accommodations

Review the files below or copy the command above to add this skill to your agents.

Files (2)
SKILL.md
515 B
---
name: search-accommodations
description: Lookup accommodations by city from the bundled dataset. Use this skill when you need to recommend places to stay in a given city or filter lodging options before building an itinerary.
---

# Search Accommodations

Find the accommodations for a specific city. Notice all the

## Installation

```bash
pip install pandas
```

## Quick Start

```python
from search_accommodations import Accommodations

acc = Accommodations()
result = acc.run("Seattle")
print(result)
```

Overview

This skill looks up accommodations by city from a bundled dataset and returns matching lodging records. It is designed to quickly filter and present places to stay when building itineraries or making recommendations. Use it to narrow options before applying further business rules or user preferences.

How this skill works

The skill loads a bundled dataset of accommodations and filters entries by the requested city name. It returns structured results that include basic lodging details so callers can sort, display, or further filter them. The interface is simple: provide a city string and receive a list of matching accommodations.

When to use it

  • You need quick recommendations for places to stay in a specified city.
  • Filtering lodging options before adding them to an itinerary or booking flow.
  • Validating available accommodation data during travel planning or prototyping.
  • Populating a UI with sample accommodations for a demo or testing.

Best practices

  • Normalize city names (case, diacritics) before calling to improve match accuracy.
  • Use additional filtering (price, rating, type) after receiving results for precise recommendations.
  • Cache results for popular cities to reduce repeated dataset reads and latency.
  • Validate the returned structure before integrating with downstream components.

Example use cases

  • A travel planner that shows available hotels and hostels when the user selects a city.
  • An itinerary builder that filters accommodation options to include only budget or boutique stays.
  • A prototype demo where you need sample lodging data without calling external APIs.
  • Automated tests that require deterministic accommodation results for given cities.

FAQ

What input does the skill expect?

A plain city name string; normalizing case and whitespace improves matching.

Does it return live availability or prices?

No. It returns records from the bundled dataset, intended for lookup and prototyping, not live booking data.

How should I handle no-results responses?

Fall back to a fuzzy match, suggest nearby cities, or show a message asking the user to refine the city name.