Nearby Budget Hotels
Find nearby budget hotels. Invoke when user asks for cheap hotels near me.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/clawkk/budget-hotelsWhat This Skill Does
The Nearby Budget Hotels skill is a specialized OpenClaw agent tool designed to help users identify affordable, high-value accommodation options based on their current geographical position. By leveraging real-time location data, this skill interfaces with hotel databases to return a curated list of budget-friendly establishments. It standardizes the retrieval process, ensuring that whether a user is looking for a quick overnight stay or a cost-effective base for a trip, the returned data is clean, relevant, and formatted according to global standards.
The skill supports customizable parameters, allowing users to define their search radius, specify the number of results, and apply filters such as minimum ratings or specific amenities like complimentary breakfast. It is a vital tool for travel-focused AI agents that need to balance location-aware constraints with budget-conscious decision-making.
Installation
To integrate this skill into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/clawkk/budget-hotels
Ensure you have the necessary permissions enabled for location services within your agent configuration to allow the skill to process proximity requests.
Use Cases
- Travel Planning: Assisting budget travelers in finding hostels or economy hotels within walking distance of landmarks.
- Emergency Stays: Helping users find an affordable place to sleep when travel plans unexpectedly change late at night.
- Cost Management: Allowing business or leisure travelers to filter accommodation by price level to stay within a specific daily allowance.
- Regional Discovery: Enabling users to explore cheaper accommodation in a new city without manually browsing endless lists on booking aggregators.
Example Prompts
- "Find me some budget hotels nearby, ideally with a rating above 4 stars."
- "I'm stuck in the city, can you look for any cheap hotels within 2 kilometers of my current location?"
- "Show me budget-friendly hotels with breakfast included near my current spot."
Tips & Limitations
- Accuracy: Always ensure your location services are active. If GPS data is unavailable, the skill may fail or fall back to inaccurate city-level coordinates.
- Privacy: This skill is designed to respect user privacy. It handles coordinate data with caution and avoids storing precise location history long-term. Consider using coarse location data if extreme precision is not required for your search.
- Caching: The skill implements a caching mechanism for identical queries (location + radius + category). If you perform the same search multiple times in a short window, you will receive cached results to minimize API overhead and improve speed.
- Rate Limiting: Heavy usage may trigger rate limits from the underlying data providers. If you encounter a
RATE_LIMITEDerror, wait a few minutes before attempting your next search.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-clawkk-budget-hotels": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: external-api, data-collection
Related Skills
data-move
Deep data migration workflow—scope, mapping, validation, batching and ordering, dual-write and cutover, rollback, and reconciliation. Use when moving tenants, bulk backfills, or changing stores without losing trust in data correctness.
data-model
Deep data modeling workflow—grain, facts and dimensions, keys, slowly changing dimensions, normalization trade-offs, and analytics query patterns. Use when designing warehouse/analytics models or reviewing star/snowflake schemas.
guard
Deep AI safety guardrails workflow—policy definition, input/output filtering, monitoring, escalation, and false-positive handling. Use when reducing harmful outputs, misuse, or policy violations in LLM products.
prompts
Deep prompt engineering workflow—task spec, constraints, examples, evaluation sets, iteration protocol, regression testing, and safety alignment. Use when improving LLM outputs, shipping prompt changes, or building reusable prompt templates.
cost-opt
Cloud cost review: rightsizing, reservations, waste. Use when reducing infra spend.