# Its Nice That Author Detail Opp Scraper (`getdataforme/itsnicethat-author-detail-opp-scraper`) Actor The Its Nice That Author Detail Opp Scraper is an Apify actor that extracts detailed metadata from itsnicethat.com articles, including author details, publication dates, and content.... - **URL**: https://apify.com/getdataforme/itsnicethat-author-detail-opp-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, 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ### Its Nice That Author Detail Opp Scraper #### Introduction The **Its Nice That Author Detail Opp Scraper** is an Apify Actor designed to extract detailed information about articles from itsnicethat.com. It efficiently gathers metadata such as author details, publication dates, and article content, providing a comprehensive dataset for analysis or integration into other systems. #### Features - **Comprehensive Data Extraction**: Captures detailed article metadata including titles, authors, URLs, and tags. - **High-Quality Output**: Ensures reliable and accurate data extraction with minimal errors. - **User-Friendly Configuration**: Simple input parameters make it easy to customize scraping tasks. - **Flexible Export Options**: Supports multiple output formats like JSON, CSV, and Excel for seamless integration. - **Scalable Performance**: Capable of handling multiple URLs efficiently. #### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------| | startUrls | array | Yes | URLs to start with. These are the initial pages from which data will be scraped. | `[{"url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426"}]` | #### Example Usage ##### Input JSON ```json { "startUrls": [ { "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426" } ] } ```` ##### Output JSON ```json [ { "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426", "title": "The Pet Shop Boys book Volume is a visual feast, exploring every inch of the icons’ creative legacy", "standfirst": "", "author": "Paul Moore", "authorUrl": "https://www.itsnicethat.com/authors/paul-moore", "date": "14 April 2026", "tags": [ "Work", "Photography", "Publication", "Book", "Installation", "Art Direction", "Archive", "Music", "History" ], "body": "...", "articleId": "QXJ0aWNsZToxODExNDQ=", "pageType": "Article", "disciplines": [ "Photography", "Publication" ], "requiresAuthentication": false, "requiresSubscription": false, "publicationDate": "2026-04-14T07:00:00+00:00", "authorBio": "Paul M (He/Him) is a Junior Writer at It’s Nice That since May 2025. He studied (BA) Fine Art and has a strong interest in digital kitsch, multimedia painting, collage, nostalgia, analogue technology and all matters of strange stuff. pcm@itsnicethat.comTo submit your work to be featured on the site, see our Submissions Guide." } ] ``` #### Use Cases - **Market Research and Analysis**: Extract insights from articles for market trends. - **Competitive Intelligence**: Monitor competitor publications and strategies. - **Content Aggregation**: Compile content for newsletters or reports. - **Academic Research**: Gather data for studies on media and publication trends. - **Business Automation**: Integrate with CRM systems for automated updates. #### Installation and Usage 1. Search for "Its Nice That Author Detail Opp Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters by providing the `startUrls`. 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 represents an article with fields such as: - **url**: The URL of the article. - **title**: Title of the article. - **author**: Name of the author. - **authorUrl**: Link to the author's profile. - **date**: Publication date. - **tags**: List of tags associated with the article. - **body**: Full text content of the article. - **articleId**: Unique identifier for the article. #### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! *** # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426" } ] } ``` # 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 = { "startUrls": [ { "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/itsnicethat-author-detail-opp-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "startUrls": [{ "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/itsnicethat-author-detail-opp-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.itsnicethat.com/articles/pet-shop-boys-thames-and-hudson-volume-photography-publication-project-140426" } ] }' | apify call getdataforme/itsnicethat-author-detail-opp-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/itsnicethat-author-detail-opp-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Its Nice That Author Detail Opp Scraper", "description": "The Its Nice That Author Detail Opp Scraper is an Apify actor that extracts detailed metadata from itsnicethat.com articles, including author details, publication dates, and content....", "version": "0.0", "x-build-id": "dUbhetKa9jVPSYfyz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~itsnicethat-author-detail-opp-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-itsnicethat-author-detail-opp-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~itsnicethat-author-detail-opp-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-itsnicethat-author-detail-opp-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~itsnicethat-author-detail-opp-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-itsnicethat-author-detail-opp-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "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 } } } } } } } } } } ```