
Airbnb Search Listing Extraction
FreeEfficiently extract Airbnb listings from search results.
Free · Opens the source repo
What Airbnb Search Listing Extraction does
The Airbnb Search Listing Extraction skill allows users to extract accommodation listings directly from Airbnb's search results using server-side rendered (SSR) data embedded in the page. By navigating to a specified Airbnb search URL, this skill retrieves important details for each listing, including ID, URL, name, geographic coordinates, rating, price, photos, and badge information. This is particularly useful for developers and data analysts who need to gather rental data for analysis or integration into other applications.
To use this skill, users must first ensure that the target search page is open in their browser. The skill does not require authentication, as it only accesses publicly available data. Once the page is loaded, users can execute a simple command to extract the listings. The output is structured in JSON format, making it easy to parse and utilize in various programming contexts.
This skill also supports pagination, allowing users to navigate through multiple pages of search results. Each page can yield up to 18 listings, and the skill provides cursors for seamless navigation through the results. This feature is particularly beneficial for users looking to gather comprehensive data across a wide range of listings without manual effort.
The skill is designed for users familiar with executing scripts in a browser environment and who have a need for automated data extraction from Airbnb. It is ideal for those working in real estate, travel analytics, or any domain where access to rental data is crucial.
When to use it
Use this skill when you need to extract multiple Airbnb listings from a specific destination search, especially for data analysis or integration into other applications.
When not to use it
This skill is not suitable for extracting data from pages requiring user authentication or for scraping data in violation of Airbnb's terms of service.
What you can build with it
Data Analysis for Real Estate
Extract Airbnb listings for a specific area to analyze market trends and rental prices.
Vacation Rental Aggregation
Use the skill to compile listings from multiple destinations for a travel planning application.
Research on Short-Term Rentals
Gather data on Airbnb properties for academic research or market studies.
How to install Airbnb Search Listing Extraction
View source1. Install with the skills CLI
npx skills add browser-act/skills/airbnb-search-listing --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by browser-actAirbnb — Search Listing Extraction
Navigate to Airbnb search URL → extract listing results from SSR-embedded data
Language
All process output to user (progress updates, process notifications) follows the user's language.
Objective
Extract accommodation listing results from an Airbnb search page using SSR-embedded niobeClientData JSON.
Prerequisites
- Target search page is already open in the browser:
https://www.airbnb.com/s/{destination}/homes - No login required — search results are publicly accessible
Pre-execution Checks
1. Tool Readiness
If browser-act has been confirmed available in the current session → skip this step.
Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.
Capability Components
This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. JS code is encapsulated in Python files under the
scripts/directory, invoked viaeval "$(python scripts/xxx.py {params})".$(...)is bash syntax; it is recommended to use the bash tool for execution.
DOM: extract search results (SSR niobeClientData)
Navigate to the search URL first, wait for the page to load, then run:
eval "$(python scripts/search-listing.py)"
URL construction pattern:
https://www.airbnb.com/s/{destination}/homes?checkin={YYYY-MM-DD}&checkout={YYYY-MM-DD}&adults={N}&children={N}&infants={N}&pets={N}&price_min={N}&price_max={N}&min_beds={N}&min_bedrooms={N}&min_bathrooms={N}&cursor={base64_cursor}
All URL parameters are optional except destination. Omit any parameter to use the Airbnb default.
Full invocation sequence:
navigate https://www.airbnb.com/s/{destination}/homes?{params}wait stableeval "$(python scripts/search-listing.py)"
Output example:
{
"items": [
{
"id": "5476930",
"url": "https://www.airbnb.com/rooms/5476930",
"name": "Bright Studio in Notting Hill",
"lat": 51.5101,
"lng": -0.1949,
"rating": "4.85",
"title": "Entire studio in London",
"price_total": "$120 total",
"price_qualifier": "before taxes",
"photos": ["https://a0.muscache.com/im/pictures/...jpeg"],
"badges": ["Guest favorite"]
}
],
"count": 18,
"total_pages": 13,
"cursors": ["eyJzZWN0aW9uX29mZnNldCI6MCwiaXRlbXNfb2Zmc2V0IjoxOCwidmVyc2lvbiI6MX0="]
}
Error handling: If error: true is returned, verify the current page is an Airbnb search results page (URL contains /s/ and /homes), then retry once after wait stable. If niobeClientData is not found, the page may still be loading — wait and retry.
Pagination
URL Pagination: URL pattern https://www.airbnb.com/s/{destination}/homes?{filters}&cursor={cursor}. Each page returns a cursors array where cursors[0] is the current page, cursors[1] is page 2, cursors[2] is page 3, etc. total_pages equals the length of cursors. Termination: index >= total_pages OR count is 0.
Paginate by taking the cursor from the previous result and navigating:
- First page: navigate without cursor; extract
cursorsarray from result - Page 2:
navigate https://www.airbnb.com/s/{destination}/homes?{filters}&cursor={cursors[1]} wait stableeval "$(python scripts/search-listing.py)"- Page N: use
cursors[N-1]from the original page-1cursorsarray - Stop when page index >= total_pages or count is 0
Success Criteria
count >= 1 AND items[0].id is not null AND items[0].url is not null
Known Limitations
- Returns up to 18 listings per page (Airbnb's default page size)
- Maximum ~240 results total across all pages (Airbnb's search cap)
ratingmay be null for new listings with no reviewsprice_totalis null when no dates are specified in search
Execution Efficiency
- Batch orchestration: Write a bash script to loop through cursors serially; do not parallelize within one browser session
- Test before batch execution: After writing a batch script, test with 1-2 pages before running full pagination
- Error resumption: Save results page by page; resume from the last successful page on failure
Experience Notes
Path: {working-directory}/browser-act-skill-forge-memories/airbnb-scraper-airbnb-search-listing.memory.md
Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.
After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line:
{YYYY-MM-DD}: {what happened} → {conclusion}
Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.
Frequently asked questions about Airbnb Search Listing Extraction
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