# My Actor (`jane_honda/my-actor`) Actor My ActorMy ActorMy ActorMy ActorMy ActorMy Actor - **URL**: https://securitybyobscurity.apify.com/jane\_honda/my-actor.md - **Developed by:** [Jane Honda \[staging\]](https://securitybyobscurity.apify.com/jane_honda) (community) - **Categories:** Agents, Developer tools, Lead generation - **Stats:** 5 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $10.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### Scrape single-page in TypeScript template A template for scraping data from a single web page in TypeScript (Node.js). The URL of the web page is passed in via input, which is defined by the [input schema](https://docs.apify.com/platform/actors/development/input-schema). The template uses the [Axios client](https://axios-http.com/docs/intro) to get the HTML of the page and the [Cheerio library](https://cheerio.js.org/) to parse the data from it. The data are then stored in a [dataset](https://docs.apify.com/sdk/js/docs/guides/result-storage#dataset) where you can easily access them. The scraped data in this template are page headings but you can easily edit the code to scrape whatever you want from the page. ### Included features - **[Apify SDK](https://docs.apify.com/sdk/js/)** - a toolkit for building [Actors](https://apify.com/actors) - **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input - **[Dataset](https://docs.apify.com/sdk/js/docs/guides/result-storage#dataset)** - store structured data where each object stored has the same attributes - **[Axios client](https://axios-http.com/docs/intro)** - promise-based HTTP Client for Node.js and the browser - **[Cheerio](https://cheerio.js.org/)** - library for parsing and manipulating HTML and XML ### How it works 1. `Actor.getInput()` gets the input where the page URL is defined 2. `axios.get(url)` fetches the page 3. `cheerio.load(response.data)` loads the page data and enables parsing the headings 4. This parses the headings from the page and here you can edit the code to parse whatever you need from the page ```javascript $("h1, h2, h3, h4, h5, h6").each((_i, element) => {...}); ``` 5. `Actor.pushData(headings)` stores the headings in the dataset ### Resources - [Web scraping in Node.js with Axios and Cheerio](https://blog.apify.com/web-scraping-with-axios-and-cheerio/) - [Web scraping with Cheerio in 2023](https://blog.apify.com/web-scraping-with-cheerio/) - [Video tutorial](https://www.youtube.com/watch?v=yTRHomGg9uQ) on building a scraper using CheerioCrawler - [Written tutorial](https://docs.apify.com/academy/web-scraping-for-beginners/challenge) on building a scraper using CheerioCrawler - [Integration with Zapier](https://apify.com/integrations), Make, Google Drive, and others - [Video guide on getting scraped data using Apify API](https://www.youtube.com/watch?v=ViYYDHSBAKM) - A short guide on how to build web scrapers using code templates: [web scraper template](https://www.youtube.com/watch?v=u-i-Korzf8w) ### Getting started For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). In short, you will: 1. Build the Actor 2. Run the Actor ### Pull the Actor for local development If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI: 1. Install `apify-cli` **Using Homebrew** ```bash brew install apify-cli ``` **Using NPM** ```bash npm -g install apify-cli ``` 2. Pull the Actor by its unique ``, which is one of the following: - unique name of the Actor to pull (e.g. "apify/hello-world") - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb") You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID. This command will copy the Actor into the current directory on your local machine. ```bash apify pull ``` ### Documentation reference To learn more about Apify and Actors, take a look at the following resources: - [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js) - [Apify SDK for Python documentation](https://docs.apify.com/sdk/python) - [Apify Platform documentation](https://docs.apify.com/platform) - [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU) # Actor input Schema ## `url` (type: `string`): The URL of website you want to get the data from. ## Actor input object example ```json { "url": "https://www.apify.com" } ```` # 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 = { "url": "https://www.apify.com" }; // Run the Actor and wait for it to finish const run = await client.actor("jane_honda/my-actor").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 = { "url": "https://www.apify.com" } # Run the Actor and wait for it to finish run = client.actor("jane_honda/my-actor").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 '{ "url": "https://www.apify.com" }' | apify call jane_honda/my-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jane_honda/my-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "My Actor", "description": "My ActorMy ActorMy ActorMy ActorMy ActorMy Actor", "version": "0.0", "x-build-id": "gA0vJS3ch5OgrWPBN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jane_honda~my-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jane_honda-my-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jane_honda~my-actor/runs": { "post": { "operationId": "runs-sync-jane_honda-my-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jane_honda~my-actor/run-sync": { "post": { "operationId": "run-sync-jane_honda-my-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "url" ], "properties": { "url": { "title": "URL of the page", "type": "string", "description": "The URL of website you want to get the data from." } } }, "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 } } } } } } } } } } ```