tools/yelp-search/SKILL.md
Search Yelp for local businesses, get contact info, ratings, and hours. Use when finding services (cleaners, groomers, restaurants, etc.), looking up business phone numbers to text, or checking ratings before booking. Triggers on queries about finding businesses, restaurants, services, or "look up on Yelp".
npx skillsauth add letta-ai/skills yelp-searchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Search for local businesses on Yelp to find services, get contact information, check ratings, and retrieve hours of operation.
Add to your .env file:
YELP_API_KEY=your_api_key_here
Only needed if you want to extract review text (slow, ~30-60s per request).
Install dependencies:
uv add browser-use playwright langchain-openai
uv run playwright install chromium
Add to .env:
OPENAI_API_KEY=your_openai_key_here
Note: Review extraction uses browser-use to search DuckDuckGo (since Yelp blocks direct scraping). For most use cases, the rating + review_count from the API is sufficient.
All scripts are in tools/yelp-search/scripts/ and should be run with uv run python.
uv run python tools/yelp-search/scripts/search.py "search term" --location "City, State"
Options:
| Flag | Description | Example |
|------|-------------|---------|
| --location, -l | City, address, or zip | "San Francisco" or "94123" |
| --latitude/--longitude | GPS coordinates | --latitude 37.78 --longitude -122.41 |
| --limit, -n | Number of results (default: 5) | -n 10 |
| --sort-by | Sort order | rating, distance, review_count, best_match |
| --price | Price filter (1-4) | --price 1,2 for $ and $$ only |
| --json | Output raw JSON | |
Examples:
# Find top-rated dog groomers
uv run python tools/yelp-search/scripts/search.py "dog groomer" -l "San Francisco" --sort-by rating
# Find cheap restaurants nearby
uv run python tools/yelp-search/scripts/search.py "restaurants" -l "94123" --price 1,2 --sort-by distance
# Search near a specific address
uv run python tools/yelp-search/scripts/search.py "laundry pickup" -l "123 Main St, San Francisco"
uv run python tools/yelp-search/scripts/details.py "business-alias"
The business alias is in the Yelp URL (e.g., the-laundry-corner-san-francisco).
uv run python tools/yelp-search/scripts/phone_search.py "+14155551234"
uv run python tools/yelp-search/scripts/get_reviews.py "Business Name" -l "City" -n 3
Note: Uses browser-use which is slow (~30-60s). Yelp blocks direct scraping, so it searches DuckDuckGo for cached reviews as a workaround.
uv run python tools/yelp-search/scripts/scrape_reviews.py "https://www.yelp.com/biz/business-alias" -n 5
Requires Browserbase credentials:
BROWSERBASE_API_KEY=your_key_here
BROWSERBASE_PROJECT_ID=your_project_id
Note: Uses Browserbase with proxies to bypass Yelp's CAPTCHA. More reliable than get_reviews.py but requires a Browserbase account.
The API provides rating + review_count which is usually sufficient:
| Rating | Review Count | Interpretation | |--------|--------------|----------------| | 4.5+ | 50+ | Excellent, reliable data | | 4.5+ | <20 | Promising but limited data | | 4.0-4.4 | 100+ | Good, well-established | | <4.0 | any | Proceed with caution |
When looking for services with specific requirements (weekend hours, pickup/delivery, etc.):
--sort-by rating to get best options"laundry pickup" not just "laundry"-l "123 Main St, City"rating first, then check distance on resultsreview_count - high ratings with few reviews may be unreliableEach business result includes:
| Feature | Status | Notes | |---------|--------|-------| | Business search | ✅ Works | Fast, reliable | | Business details | ✅ Works | Includes hours | | Phone lookup | ✅ Works | Reverse search | | Review text (API) | ❌ Paid only | Requires enterprise tier | | Review text (scraping) | ⚠️ Slow | browser-use workaround via DuckDuckGo |
testing
Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Use when recalling what a past assistant knew, searching old conversations, rendering specific chats, seeding reference memory from export sidecars, or mining historical context without doing a full import.
testing
Migrates deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking, local lexical search, and QMD semantic search via the memfs-search skill. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating open_file, grep_file, and search_file behavior.
data-ai
Configures Letta agent compaction settings and custom summarization prompts. Use when a user asks to change an agent's compaction prompt, improve summaries after context eviction, tune sliding-window or all-message compaction, or design companion/coding-agent continuity summaries.
development
Semantic search over agent memory files. Use when you need to find conceptually related memory blocks, discover forgotten reference files, check what you already know before creating new memory, or search beyond exact keyword matching. Currently supports QMD (local, no API keys).