# Freecodecamp Discovery Parser Spider (`getdataforme/freecodecamp-discovery-parser-spider`) Actor The Freecodecamp Discovery Parser Spider is a web scraping tool that extracts detailed data from FreeCodeCamp articles using keyword searches. It offers features like customizable item limits, comprehensive metadata extraction, high-quality output, and user-friendly configuration.... - **URL**: https://apify.com/getdataforme/freecodecamp-discovery-parser-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, 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 --- ## Freecodecamp Discovery Parser Spider ### Introduction The Freecodecamp Discovery Parser Spider is a powerful web scraping tool designed to extract valuable data from the FreeCodeCamp website. It allows users to search for articles based on specific keywords and retrieve detailed information about each article, including titles, URLs, authors, publication dates, and more. ### Features - **Keyword-Based Search**: Customize searches using specific keywords. - **Flexible Item Limit**: Set a maximum number of items to scrape per run. - **Comprehensive Data Extraction**: Retrieve detailed metadata for each article. - **High-Quality Output**: Ensures data accuracy and reliability. - **User-Friendly Configuration**: Easy setup with intuitive input parameters. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|---------------| | SearchKey | string | No | The search key for the spider. | `"js"` | | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "SearchKey": "js", "item_limit": 10 } ```` #### Output JSON ```json [ { "object_id": "6a024b87fca21b0d4b6cbcd9", "title": "How to Build a Browser-Based PDF to Image Converter Using JavaScript", "url": "https://www.freecodecamp.org/news/pdf-to-image-converter/", "feature_image": "https://cdn.hashnode.com/uploads/covers/5e1e335a7a1d3fcc59028c64/d412f56a-2860-4b61-a300-ab3511c34e78.png", "published_at": "2026-05-11T21:35:03.786Z", "published_at_timestamp": 1778535303, "author_name": "Bhavin Sheth", "author_url": "https://www.freecodecamp.org/news/author/allinonetools", "author_profile_image": "https://cdn.hashnode.com/res/hashnode/image/upload/v1769591816718/c151b08b-2f7b-4e54-b68e-262a3b4d998a.png?w=500&h=500&fit=crop&crop=entropy&auto=compress,format&format=webp", "tags": [ { "name": "JavaScript", "url": "https://www.freecodecamp.org/news/tag/javascript/" }, { "name": "pdf", "url": "https://www.freecodecamp.org/news/tag/pdf/" }, { "name": "webdev", "url": "https://www.freecodecamp.org/news/tag/webdev/" }, { "name": "Tutorial", "url": "https://www.freecodecamp.org/news/tag/tutorial/" } ], "search_query": "js", "actor_id": null, "run_id": null }, { "object_id": "69fe1ae5f239332df4ec3436", "title": "How to Convert Images to PDF in the Browser Using JavaScript – A Step-by-Step Guide", "url": "https://www.freecodecamp.org/news/how-to-convert-images-to-pdf-using-javascript/", "feature_image": "https://cdn.hashnode.com/uploads/covers/5e1e335a7a1d3fcc59028c64/8902fd4f-fcfa-4f7b-8baf-9b595239254f.png", "published_at": "2026-05-08T17:18:29.368Z", "published_at_timestamp": 1778260709, "author_name": "Bhavin Sheth", "author_url": "https://www.freecodecamp.org/news/author/allinonetools", "author_profile_image": "https://cdn.hashnode.com/res/hashnode/image/upload/v1769591816718/c151b08b-2f7b-4e54-b68e-262a3b4d998a.png?w=500&h=500&fit=crop&crop=entropy&auto=compress,format&format=webp", "tags": [ { "name": "JavaScript", "url": "https://www.freecodecamp.org/news/tag/javascript/" }, { "name": "pdf", "url": "https://www.freecodecamp.org/news/tag/pdf/" }, { "name": "webdev", "url": "https://www.freecodecamp.org/news/tag/webdev/" }, { "name": "Programming Blogs", "url": "https://www.freecodecamp.org/news/tag/programming-blogs/" } ], "search_query": "js", "actor_id": null, "run_id": null }, { "object_id": "69fa2435a386d7f121b7c4af", "title": "Mastering the JavaScript Event Loop", "url": "https://www.freecodecamp.org/news/mastering-the-javascript-event-loop/", "feature_image": "https://cdn.hashnode.com/uploads/covers/5f68e7df6dfc523d0a894e7c/0f6a748e-4b2e-4fd8-9592-d6ab4eb83dd3.jpg", "published_at": "2026-05-05T17:09:09.784Z", "published_at_timestamp": 1778000949, "author_name": "Beau Carnes", "author_url": "https://www.freecodecamp.org/news/author/beaucarnes", "author_profile_image": "https://cdn.hashnode.com/res/hashnode/image/upload/v1713211849730/O5mmKs5h0.jpg", "tags": [ { "name": "JavaScript", "url": "https://www.freecodecamp.org/news/tag/javascript/" }, { "name": "youtube", "url": "https://www.freecodecamp.org/news/tag/youtube/" } ], "search_query": "js", "actor_id": null, "run_id": null } ] ``` ### Use Cases - **Market Research and Analysis**: Gather insights from FreeCodeCamp articles to understand market trends. - **Competitive Intelligence**: Monitor competitor content strategies by analyzing their published articles. - **Price Monitoring**: Track pricing information in tech-related articles. - **Content Aggregation**: Compile a comprehensive list of resources for educational purposes. - **Academic Research**: Use article data for research projects or studies. - **Business Automation**: Automate the collection of industry-specific news and updates. ### Installation and Usage 1. Search for "Freecodecamp Discovery Parser Spider" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters such as `SearchKey` and `item_limit`. 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 like: - **object\_id**: Unique identifier for the article. - **title**: Title of the article. - **url**: URL to the article. - **feature\_image**: URL to the featured image. - **published\_at**: Publication date and time. - **author\_name**: Name of the author. - **tags**: List of tags associated with the article. ### Error Handling The spider is designed to handle common web scraping errors such as network issues, missing elements, or changes in page structure. If an error occurs, it will be logged for review. Users can adjust configurations or contact support for assistance. ### Rate Limiting and Best Practices - **Respectful Scraping**: Ensure compliance with FreeCodeCamp's terms of service. - **Rate Limiting**: Implement delays between requests to avoid overloading the server. - **Error Handling**: Monitor logs for errors and adjust settings as needed. ### Support # Actor input Schema ## `SearchKey` (type: `string`): The search key 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 { "SearchKey": "js", "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/freecodecamp-discovery-parser-spider").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("getdataforme/freecodecamp-discovery-parser-spider").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 getdataforme/freecodecamp-discovery-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/freecodecamp-discovery-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Freecodecamp Discovery Parser Spider", "description": "The Freecodecamp Discovery Parser Spider is a web scraping tool that extracts detailed data from FreeCodeCamp articles using keyword searches. It offers features like customizable item limits, comprehensive metadata extraction, high-quality output, and user-friendly configuration....", "version": "0.0", "x-build-id": "FszbWPvMBE31dT6id" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~freecodecamp-discovery-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-freecodecamp-discovery-parser-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~freecodecamp-discovery-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-freecodecamp-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~freecodecamp-discovery-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-freecodecamp-discovery-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "SearchKey": { "title": "Search Key", "type": "string", "description": "The search key for the spider.", "default": "js" }, "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 } } } } } } } } } } ```