# Wiki Grabber (`shahabuddin38/wiki-grabber`) Actor Find Wikipedia pages with citation-needed tags, dead links, broken link signals, and cleanup issues using keyword search. Great for SEO, link building, outreach, and research workflows. - **URL**: https://apify.com/shahabuddin38/wiki-grabber.md - **Developed by:** [Shahab Uddin](https://apify.com/shahabuddin38) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.35 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 bash -lc 'cd wiki-grabber-apify && cat > README.md <<'\''EOF'\'' ## WikiGrabber - Wikipedia Citation Finder, Dead Link Finder, and Broken Link Replacement Tool Find Wikipedia pages that need citations, contain dead links, show broken link signals, or require source cleanup by entering a single keyword. WikiGrabber is an Apify Actor for SEO teams, link builders, outreach specialists, researchers, publishers, content marketers, and automation workflows that want fast Wikipedia opportunity discovery from keyword-based searches. ### Why use WikiGrabber? Wikipedia pages often contain: - Citation needed tags - Dead link templates - Broken link signals - Cleanup and verification warnings - Weak or missing references WikiGrabber helps you discover pages where you may be able to: - Replace dead links with better live sources - Find link building opportunities - Discover citation opportunities - Support digital PR and outreach - Research content gaps - Export results to CSV for campaigns and reporting ### Best use cases WikiGrabber is useful for: - Wikipedia citation finder workflows - Dead link building - Broken link replacement research - SEO outreach campaigns - Digital PR prospecting - Content marketing research - Authority link opportunity discovery - Researching missing references on Wikipedia - Finding Wikipedia pages by keyword with citation issues ### Features - Keyword-based Wikipedia search - Exact wikitext template detection - Detects citation-needed templates - Detects dead-link templates - Detects broken-link signals - Detects cleanup-related source issues - Saves search results into an Apify dataset - Built-in pagination for browsing results - CSV export for outreach and SEO workflows - Simple UI: enter keyword and search ### How it works 1. Enter a keyword such as `SEO`, `AI`, `Dentist`, `SaaS`, `Cybersecurity`, or `Renewable Energy` 2. WikiGrabber searches relevant Wikipedia pages 3. The Actor checks page content and wikitext for maintenance templates and source-quality signals 4. Results are scored and ranked 5. You can browse paginated results, save data, and export to CSV ### Input Use one keyword or topic. Examples: - SEO - Local SEO - Link building - Cybersecurity - Renewable energy - Artificial intelligence - Health supplements - Dental implants - SaaS marketing - Real estate ### Output Each result can include: - Keyword - Page title - Wikipedia URL - Snippet - Word count - Timestamp - Citation-needed template count - Dead-link template count - Broken-link signal count - Cleanup template count - Bare URL count - Reference count - Opportunity score ### Who is this for? This Actor is built for: - SEO agencies - Freelance SEOs - Outreach specialists - Link builders - PR teams - Researchers - Journalists - Publishers - Content marketers - Automation users on Apify ### SEO benefits WikiGrabber helps users discover Wikipedia-based opportunities for: - Broken link building - Dead link replacement - Authority link prospecting - Topical research - Content gap analysis - Source reclamation - Citation opportunity discovery This makes it valuable for anyone looking for scalable SEO research and high-quality prospecting data. ### Why this Actor stands out on Apify - Fast keyword-to-results workflow - Easy for beginners - Useful for professionals - Actionable SEO output - Designed for real outreach workflows - Export-ready results - Clear use case for search, automation, and data collection ### Example keywords to try - SEO - Citation - Backlinks - Digital marketing - Lawyer - Plastic surgery - Solar energy - VPN - Crypto - Education - Finance - Travel - Startups - Healthcare - Roofing - Immigration ### Export and automation You can use the dataset output for: - CSV exports - Campaign prospecting - Internal research databases - Apify integrations - SEO reporting pipelines - Lead enrichment workflows ### Related keywords Wikipedia citation finder, Wikipedia dead link finder, broken link finder, citation needed finder, dead link building tool, Wikipedia SEO tool, Wikipedia outreach tool, Wikipedia keyword search, broken link replacement tool, citation opportunity finder, Apify SEO Actor, Wikipedia research automation ### Tips for better results - Use broad keywords first, then narrow down - Try commercial and informational keywords - Export CSV and cluster pages by topic - Prioritize high-score pages for manual review - Use results as a starting point for outreach and content validation ### Support If you want improvements such as bulk keyword search, advanced filters, competitor workflows, or API-first exports, extend this Actor and publish updated versions on Apify. ### Disclaimer This tool is for research, discovery, and workflow automation. Always review Wikipedia editing policies and citation standards before making live edits. ## Actor input object example ```json {} ```` # 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("shahabuddin38/wiki-grabber").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 = {} # Run the Actor and wait for it to finish run = client.actor("shahabuddin38/wiki-grabber").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 '{}' | apify call shahabuddin38/wiki-grabber --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shahabuddin38/wiki-grabber", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wiki Grabber", "description": "Find Wikipedia pages with citation-needed tags, dead links, broken link signals, and cleanup issues using keyword search. Great for SEO, link building, outreach, and research workflows.", "version": "0.0", "x-build-id": "ILc1XvpwmCPitz04w" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shahabuddin38~wiki-grabber/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shahabuddin38-wiki-grabber", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shahabuddin38~wiki-grabber/runs": { "post": { "operationId": "runs-sync-shahabuddin38-wiki-grabber", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shahabuddin38~wiki-grabber/run-sync": { "post": { "operationId": "run-sync-shahabuddin38-wiki-grabber", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```