# Sweatybetty Category Scraper (`getdataforme/sweatybetty-category-scraper`) Actor
The Sweatybeety Category Scraper efficiently extracts detailed product data from the Sweatybeety website, ideal for market research and competitive analysis....
- **URL**: https://apify.com/getdataforme/sweatybetty-category-scraper.md
- **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community)
- **Categories:** AI, Automation, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $9.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## Sweatybeety Category Scraper
The Sweatybeety Category Scraper is a powerful tool designed to extract detailed product information from the Sweatybeety website. Whether you're conducting market research, monitoring prices, or gathering competitive intelligence, this actor provides reliable and comprehensive data to meet your needs.
### Features
- **Comprehensive Data Extraction**: Scrapes detailed product information including prices, categories, and availability.
- **High Data Quality**: Ensures accurate and up-to-date information by using reliable proxy configurations.
- **Scalable Performance**: Capable of handling large volumes of data with a maximum of 10,000 items per run.
- **Customizable Searches**: Allows keyword-based searches to focus on specific product categories or types.
- **Proxy Support**: Utilizes Apify's residential proxies to maintain anonymity and avoid IP bans.
- **Flexible Output Formats**: Exports data in JSON, CSV, or Excel formats for easy integration into your workflows.
### Input Parameters
| Parameter | Type | Required | Description | Example |
|---------------------|---------|----------|-----------------------------------------------------------------------------|-----------|
| `searchQuery` | String | Yes | Keyword to search for in GoDaddy auctions (e.g. 'chat', 'ai', 'shop'). | `chat` |
| `maxItems` | Integer | No | Maximum number of auction results to scrape. Default is 100. | `100` |
| `proxyConfiguration`| Object | No | Specifies proxy servers used by the scraper to hide its origin. | `{ "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }` |
### Example Usage
#### Input JSON
```json
{
"searchQuery": "chat",
"maxItems": 100,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": ["RESIDENTIAL"]
}
}
````
#### Output JSON
```json
[
{
"productId": "SB10148_LuxeBlue",
"masterId": "SB10148",
"productName": "Athlete Featherweight Seamless Muscle Tank",
"currency": "GBP",
"price": 16,
"primaryCategoryId": "sale-vests",
"allCategoryIds": [
"sale-bestsellers",
"sale-tops",
"sale",
"sale-over50",
"sale-tshirts-vests",
"sale-threshold-1",
"sale-threshold",
"athlete-family",
"sale-run",
"sale-vests",
"tops",
"workout",
"vests",
"run"
],
"averageRating": 0,
"color": "luxeblue",
"activities": [
"GymWorkout",
"Run"
],
"productTypes": [
"TOPS",
"VT",
"VT002"
],
"inStock": true,
"imageUrl": "https://cdn.media.amplience.net/i/SweatyBetty/sb10148_luxeblue.jpg",
"allImages": [
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_khakibrown.jpg",
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_khakibrown_1.jpg",
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_lilywhite.jpg",
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_lilywhite_1.jpg",
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_luxeblue.jpg",
"https://cdn.media.amplience.net/i/SweatyBetty/sb10148_luxeblue_1.jpg"
],
"colors": [
{
"name": "Lily White",
"value": "lilywhite",
"orderable": true
},
{
"name": "Luxe Blue",
"value": "luxeblue",
"orderable": true
},
{
"name": "Khaki Brown",
"value": "khakibrown",
"orderable": true
}
],
"sizes": [
{
"name": "XXS",
"value": "xxs",
"orderable": true
},
{
"name": "XS",
"value": "xs",
"orderable": true
},
{
"name": "S",
"value": "s",
"orderable": true
},
{
"name": "M",
"value": "m",
"orderable": true
},
{
"name": "L",
"value": "l",
"orderable": true
},
{
"name": "XL",
"value": "xl",
"orderable": true
},
{
"name": "XXL",
"value": "xxl",
"orderable": true
}
],
"variationGroups": [
{
"productId": "SB10148_KhakiBrown",
"color": "khakibrown",
"orderable": true
},
{
"productId": "SB10148_LilyWhite",
"color": "lilywhite",
"orderable": true
},
{
"productId": "SB10148_LuxeBlue",
"color": "luxeblue",
"orderable": true
}
],
"badge": "FURTHERS"
}
]
```
### Use Cases
- **Market Research and Analysis**: Gather comprehensive data for market trends and consumer preferences.
- **Competitive Intelligence**: Monitor competitor pricing and product offerings.
- **Price Monitoring**: Track price changes and promotions over time.
- **Content Aggregation**: Collect product data for content creation and aggregation.
- **Academic Research**: Use detailed product data for research and analysis.
- **Business Automation**: Automate data collection processes for efficiency.
### Installation and Usage
1. Search for "Sweatybeety category scraper" in the Apify Store.
2. Click "Try for free" or "Run".
3. Configure input parameters.
4. Click "Start" to begin extraction.
5. Monitor progress in the log.
6. Export results in your preferred format (JSON, CSV, Excel).
### Output Format
The output is a JSON array where each object contains detailed product information including `productId`, `productName`, `price`, `currency`, `categories`, `averageRating`, `color`, `sizes`, and `imageUrl`. This structure allows for easy integration into various data processing workflows.
### Support
For custom/simplified outputs or bug reports, please contact:
- Email: support@getdataforme.com
- Subject line: "custom support"
- Contact form:
We're here to help you get the most out of this Actor!
# Actor input Schema
## `searchQuery` (type: `string`):
Keyword to search for in GoDaddy auctions (e.g. 'chat', 'ai', 'shop').
## `maxItems` (type: `integer`):
Maximum number of auction results to scrape.
## `proxyConfiguration` (type: `object`):
Specifies proxy servers that will be used by the scraper in order to hide its origin.
## Actor input object example
```json
{
"searchQuery": "chat",
"maxItems": 100,
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"searchQuery": "chat",
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("getdataforme/sweatybetty-category-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"searchQuery": "chat",
"proxyConfiguration": {
"useApifyProxy": True,
"apifyProxyGroups": ["RESIDENTIAL"],
},
}
# Run the Actor and wait for it to finish
run = client.actor("getdataforme/sweatybetty-category-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"searchQuery": "chat",
"proxyConfiguration": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}' |
apify call getdataforme/sweatybetty-category-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=getdataforme/sweatybetty-category-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Sweatybetty Category Scraper",
"description": "The Sweatybeety Category Scraper efficiently extracts detailed product data from the Sweatybeety website, ideal for market research and competitive analysis....",
"version": "0.0",
"x-build-id": "MyWMhA0vUMJbOsXbE"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/getdataforme~sweatybetty-category-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-getdataforme-sweatybetty-category-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/getdataforme~sweatybetty-category-scraper/runs": {
"post": {
"operationId": "runs-sync-getdataforme-sweatybetty-category-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/getdataforme~sweatybetty-category-scraper/run-sync": {
"post": {
"operationId": "run-sync-getdataforme-sweatybetty-category-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"searchQuery"
],
"properties": {
"searchQuery": {
"title": "Search Query",
"type": "string",
"description": "Keyword to search for in GoDaddy auctions (e.g. 'chat', 'ai', 'shop')."
},
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 10000,
"type": "integer",
"description": "Maximum number of auction results to scrape.",
"default": 100
},
"proxyConfiguration": {
"title": "Proxy configuration",
"type": "object",
"description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.",
"default": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```