# Afesupport Event Detail Opp Spider (`getdataforme/afesupport-event-detail-opp-spider`) Actor Automate data extraction from Afesupport event pages with customizable parameters, receiving structured JSON output. Ideal for market research, content aggregation, and business automation, offering scalable performance and user-friendly setup via Apify Store.... - **URL**: https://apify.com/getdataforme/afesupport-event-detail-opp-spider.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, 0 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 --- ## README.md ### Afesupport Event Detail Opp Spider #### Introduction The Afesupport Event Detail Opp Spider is a powerful web scraping tool designed to extract detailed information from specified event pages on the Afesupport website. This actor automates data collection, allowing users to efficiently gather insights and content for analysis or reporting. #### Features - **Automated Data Extraction**: Seamlessly scrape structured data from event detail pages. - **Customizable Input Parameters**: Tailor scraping tasks with specific URLs and item limits. - **High-Quality Output**: Receive well-structured JSON output containing comprehensive event details. - **Scalable Performance**: Efficiently handle multiple URLs within specified limits. - **User-Friendly Setup**: Easy configuration through the Apify Store interface. #### Input Parameters | Parameter | Type | Required | Description | Example | |---------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------| | ProductUrls | array | Yes | The product URLs for the spider. Must be valid HTTP/HTTPS links. | `["https://afesupport.org/example-url"]` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | #### Example Usage ##### Input JSON ```json { "ProductUrls": [ "https://afesupport.org/leading-the-way-upmc-sets-the-standard-in-afe-preparedness-through-simulation/" ], "item_limit": 10 } ```` ##### Output JSON ```json [ { "post_title": "Leading the Way: UPMC Sets the Standard in AFE Preparedness Through Simulation", "description": "At the AFE Foundation, we are always inspired by the commitment of hospitals and healthcare teams who take extraordinary steps to prepare for the unexpected...", "section_headings": [ "Comprehensive Training Before Simulation", "Full-Scale Multidisciplinary Simulations", "Community-Wide Impact", "Lasting Changes in Practice", "Gratitude and Commitment", "A Model for Others to Follow" ], "gallery_images": [ "https://afesupport.org/wp-content/uploads/2025/09/AFE-Preparedness-action-1.jpg", "https://afesupport.org/wp-content/uploads/2025/09/AFE-Preparedness-action-2.jpg", ... ], "og_image": "https://afesupport.org/wp-content/uploads/2025/09/AFE-Preparedness-action-6.jpg", "related_posts": [ { "title": "Advancing Maternal and Child Health: AFE Foundationโ€™s Advocacy in Action", "url": "https://afesupport.org/advancing-maternal-and-child-health-afe-foundations-advocacy-in-action/" }, ... ], "prev_post": { "title": "The Impact of AFE on the gastrointestinal System", "url": "https://afesupport.org/the-impact-of-afe-on-the-gastrointestinal-system/" }, "next_post": { "title": "Advancing Maternal Safety: Sharon Dey-Layneโ€™s Doctoral Project Validates the AFE Stabilization Checklist", "url": "https://afesupport.org/advancing-maternal-safety-sharon-dey-laynes-doctoral-project-validates-the-afe-stabilization-checklist/" }, "source_url": "https://afesupport.org/leading-the-way-upmc-sets-the-standard-in-afe-preparedness-through-simulation/", "actor_id": "1XWi7Gl5njcPbW1Jq", "run_id": "hU4AXrFKbbCbMyihe" } ] ``` #### Use Cases - **Market Research and Analysis**: Extract detailed event information for competitive analysis. - **Content Aggregation**: Compile comprehensive reports from multiple event pages. - **Academic Research**: Gather data for studies on healthcare preparedness initiatives. - **Business Automation**: Automate content collection for marketing or informational purposes. - **Competitive Intelligence**: Monitor competitor activities and strategies through event details. #### Installation and Usage 1. Search for "Afesupport Event Detail Opp Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters with desired URLs and item limits. 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 containing objects with detailed event information: - **post\_title**: Title of the event post. - **description**: Detailed description of the event. - **section\_headings**: List of section headings within the content. - **gallery\_images**: URLs to images associated with the event. - **og\_image**: URL to the Open Graph image for social media sharing. - **related\_posts**: Information on related posts, including titles and URLs. - **prev\_post** & **next\_post**: Links to previous and next posts. - **source\_url**: Original URL of the scraped content. - **actor\_id** & **run\_id**: Unique identifiers for the actor run. #### Error Handling The spider is designed to handle common web scraping errors, such as invalid URLs or network issues. If an error occurs, it will be logged in the Apify console, allowing you to troubleshoot and adjust your input parameters accordingly. #### Rate Limiting and Best Practices To ensure optimal performance and avoid being blocked by the target website: - Respect the `item_limit` parameter to control the number of pages scraped. - Use valid and unique URLs for each run. - Monitor logs for any rate-limiting warnings or errors. #### Limitations and Considerations - The spider is tailored for Afesupport event detail pages; it may not work with other websites without modifications. - Ensure compliance with the target website's terms of service when using web scraping tools. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Get Data For Me](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `ProductUrls` (type: `array`): The product urls for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "ProductUrls": [ "https://afesupport.org/leading-the-way-upmc-sets-the-standard-in-afe-preparedness-through-simulation/" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/afesupport-event-detail-opp-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/afesupport-event-detail-opp-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call getdataforme/afesupport-event-detail-opp-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/afesupport-event-detail-opp-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Afesupport Event Detail Opp Spider", "description": "Automate data extraction from Afesupport event pages with customizable parameters, receiving structured JSON output. Ideal for market research, content aggregation, and business automation, offering scalable performance and user-friendly setup via Apify Store....", "version": "0.0", "x-build-id": "YJ1eFyXOO2nEjqXnW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~afesupport-event-detail-opp-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-afesupport-event-detail-opp-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~afesupport-event-detail-opp-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-afesupport-event-detail-opp-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~afesupport-event-detail-opp-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-afesupport-event-detail-opp-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "ProductUrls": { "title": "Product Urls", "minItems": 1, "type": "array", "description": "The product urls for the spider.", "default": [ "https://afesupport.org/leading-the-way-upmc-sets-the-standard-in-afe-preparedness-through-simulation/" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```