# Scribd Scraper - Low-costπŸ’²πŸ”₯πŸ“„πŸ“š (`delectable_incubator/scribd-scraper-low-cost`) Actor πŸ“šπŸ” Extract public Scribd documents by keyword with ease. Collect document titles, authors, page counts, upload dates, descriptions, categories, thumbnails, and document URLs. Ideal for academic research, content discovery, document indexing, knowledge management, and research dataset creation πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/scribd-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Automation, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

Scribd Document Scraper

--- ## πŸ“„πŸ“š Scribd Document Search & Scraper | Bulk Document Intelligence Tool | Apify Actor ### πŸš€ Extract Scribd Documents Data in Seconds (No Code) The **Scribd Document Search & Scraper (Apify Actor)** is a powerful, scalable, and SEO-optimized tool designed to extract **structured document data from Scribd.com** in bulk. Search documents by keyword, collect rich metadata, and export everything for **research, NLP datasets, trend analysis, content intelligence, and automation workflows.** --- ## πŸ” What This Scraper Does Just enter a keyword and define how many documents you want, and the scraper will automatically extract structured Scribd document results. #### πŸ“Œ It collects: βœ… Document metadata from Scribd search βœ… Structured fields per document βœ… Author & uploader information βœ… Engagement metrics (views, votes, ratings) βœ… Language detection βœ… Categories & classification data βœ… High-quality thumbnails βœ… Access & unlock status --- ## πŸ“Š Extracted Data Fields | Field | Description | |--------------------|-------------| | `id` | Unique Scribd document ID | | `title` | Document title | | `description` | Document description (if available) | | `type` | File type (document) | | `url` | Full Scribd document URL | | `downloadUrl` | Direct download endpoint (if available) | | `image_url` | Thumbnail image | | `retina_image_url` | High-resolution thumbnail | | `pageCount` | Number of pages | | `releasedAt` | Upload/publication date | | `views` | View count | | `consumptionTime` | Estimated reading time | | `isUnlocked` | Access status | | `upvoteCount` | Upvotes | | `downvoteCount` | Downvotes | | `ratingCount` | Total ratings | | `author` | Author name | | `authorUrl` | Author profile URL | | `authors` | Array of author objects | | `language` | Document language | | `language_iso` | ISO language code | | `categories` | Document categories | --- ## ⚑ How to Use (Simple & Fast) 1️⃣ Enter a search keyword 2️⃣ Set maximum number of documents (up to 100) 3️⃣ Run the Actor 4️⃣ Export data in your preferred format --- ## πŸ“₯ Input Configuration ### πŸ”₯ Bulk Keyword Input ```` { "keyword": "data", "maxitems": 80 } ``` --- ### πŸ“Œ Input Fields | Field | Type | Description | |------------|--------|-------------| | `keyword` | string | Search keyword on Scribd | | `maxitems` | integer | Max documents to scrape (≀100) | --- ## πŸ“€ Output Example ``` { "id": 751945245, "title": "2k data (2)", "description": "N/A", "type": "document", "url": "https://www.scribd.com/document/751945245/2k-data-2", "downloadUrl": "/document\_downloads/751945245", "image\_url": "https://imgv2-2-f.scribdassets.com/img/document/751945245/149x198/...", "retina\_image\_url": "https://imgv2-2-f.scribdassets.com/img/document/751945245/298x396/...", "pageCount": 90, "releasedAt": "2024-07-20", "views": "0", "consumptionTime": "N/A", "isUnlocked": false, "upvoteCount": 0, "downvoteCount": 0, "ratingCount": "N/A", "author": "chicamy9839", "authorUrl": "/users/768000436", "authors": \[ { "id": 768000436, "name": "chicamy9839", "url": "/users/768000436" } ], "language": "English", "language\_iso": "en", "categories": \[] } ```` --- ## πŸ’‘ Use Cases (SEO-Optimized) πŸ“š Academic research dataset creation πŸ€– AI / NLP training data extraction πŸ“Š Content trend analysis on Scribd πŸ”Ž Document discovery automation πŸ“₯ Lead generation via authors & topics πŸ“ˆ Market intelligence & knowledge mining βš™οΈ Automated document monitoring pipelines --- ## πŸš€ Key Features ⚑ Fast Scribd search scraping engine πŸ“„ Structured document extraction 🧠 Author & metadata enrichment πŸ“Š Clean JSON/CSV/Excel output πŸ” Scalable bulk keyword support πŸ’Ύ Export-ready datasets 🌍 Cloud-based Apify execution πŸ“ˆ SEO-optimized scraping system --- ## πŸ“€ Supported Export Formats βœ” JSON βœ” CSV βœ” Excel (XLSX) βœ” XML βœ” HTML --- ## πŸ’° Pricing This scraper runs on a **pay-per-result model**. You only pay for successfully extracted records. πŸ’³ **Price:** **$3.99 / 1,000 results** --- ## πŸ”₯ Why This is the BEST Scribd Scraper on Apify? βœ” Built for structured document intelligence βœ” Optimized for large-scale data extraction βœ” Supports research & AI dataset pipelines βœ” Clean & normalized metadata output βœ” High-performance scraping engine βœ” Enterprise-ready scalability βœ” SEO optimized for marketplace visibility --- ## ❓ FAQ #### Can I scrape multiple documents at once? Yes β€” simply increase `maxitems`. #### Does it support metadata extraction? Yes β€” author, views, language, ratings, and more. #### Can I use it for AI datasets? Absolutely β€” it’s ideal for NLP and training pipelines. #### Is coding required? No β€” 100% no-code Apify Actor. #### Is it scalable? Yes β€” designed for bulk document extraction. --- ## ⚠️ Disclaimer This tool is an independent data extraction solution and is **not affiliated with, endorsed by, or sponsored by Scribd**. --- ## πŸ“¬ Support ⭐⭐⭐⭐⭐ If you like this scraper, please leave a review. For custom scraping solutions or enterprise requests, contact us via **Apify platform or email**. # Actor input Schema ## `keywords` (type: `array`): One or more search keywords. Each keyword generates its own paginated search. Examples: β€’ NBA β€’ AI β€’ Crypto β€’ cooking β€’ fitness β€’ finance β€’ climate change ## `max_items_per_keyword` (type: `integer`): The maximum number of Scribd documents to retrieve per keyword. Must be between 1 and 500. ## Actor input object example ```json { "keywords": [ "Crypto", "climate" ], "max_items_per_keyword": 80 } ```` # Actor output Schema ## `overview` (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 = { "keywords": [ "Crypto", "climate" ], "max_items_per_keyword": 80 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/scribd-scraper-low-cost").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 = { "keywords": [ "Crypto", "climate", ], "max_items_per_keyword": 80, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/scribd-scraper-low-cost").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 '{ "keywords": [ "Crypto", "climate" ], "max_items_per_keyword": 80 }' | apify call delectable_incubator/scribd-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/scribd-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Scribd Scraper - Low-costπŸ’²πŸ”₯πŸ“„πŸ“š", "description": "πŸ“šπŸ” Extract public Scribd documents by keyword with ease. Collect document titles, authors, page counts, upload dates, descriptions, categories, thumbnails, and document URLs. Ideal for academic research, content discovery, document indexing, knowledge management, and research dataset creation πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "lMj2cmcDa6QmE4jYh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~scribd-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-scribd-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~scribd-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-scribd-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~scribd-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-scribd-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords", "max_items_per_keyword" ], "properties": { "keywords": { "title": "Search Keywords πŸ”", "type": "array", "description": "One or more search keywords. Each keyword generates its own paginated search.\n\nExamples:\nβ€’ NBA\nβ€’ AI\nβ€’ Crypto\nβ€’ cooking\nβ€’ fitness\nβ€’ finance\nβ€’ climate change", "items": { "type": "string" }, "default": [ "Crypto", "climate" ] }, "max_items_per_keyword": { "title": "Maximum Documents per Keyword πŸ“„", "type": "integer", "description": "The maximum number of Scribd documents to retrieve per keyword. Must be between 1 and 500.", "default": 80 } } }, "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 } } } } } } } } } } ```