# Twitter Auto Tweet Poster (`bhansalisoft/twitter-auto-tweet-poster`) Actor Twitter Auto Tweet Poster : Schedule & Auto Post Tweets Online using automation. - **URL**: https://apify.com/bhansalisoft/twitter-auto-tweet-poster.md - **Developed by:** [bhansalisoft](https://apify.com/bhansalisoft) (community) - **Categories:** Automation, Lead generation, Social media - **Stats:** 4 total users, 1 monthly users, 90.0% runs succeeded, 0 bookmarks - **User rating**: 3.00 out of 5 stars ## Pricing $20.00/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 ### Twitter Auto Tweet Poster Twitter Auto Tweet Poster– is Advance Software with Unique Feature for Send auto post to Twitter profile with rendom pick unique message. Boost your Twitter marketing with our Twitter Profile Poster Tool. Easily schedule, auto-post, and manage content across Twitter. Save time and grow your reach with unique message. - ✅ Schedule this tool for daily auto post to Twitter profile with unique message daily. - ✅ Software with send message to all Twitter group with delay feature you can add. - ✅ 100% Real Time Twitter Profile Auto Poster with log show you success and failed message history. - ✅ Schedule this tool for daily auto post to Twitter profile. ### How to use - Enter Your Text Message : Insert you message in json format , our tool will take rendom one unique message..it will be usefull when you want to send unique post to multiple Twitter page. [ "Check our tool \n Instagram follower scraper\n https://apify.com/bhansalisoft/instagram-follower-scraper", "Check our tool \n Instagram Email Scraper\n https://apify.com/bhansalisoft/instagram-email-scraper" ] - Delay(Second)- You can specified delay on Each message sent to profiles.its good for Twitter account. - Cookies : Insert Twitter Cookies - Click on Save and Start Button for Start Process of Twitter Mass message sender. ### How to get Twitter Cookies 1. ✅ Step 1 : Install Chrome Extension [Export cookie JSON file for Puppeteer](https://chromewebstore.google.com/detail/export-cookie-json-file-f/nmckokihipjgplolmcmjakknndddifde/reviews) in chrome browser 2. ✅ Step 2 : Login to your Twitter 3. ✅ Step 3 : After login, open chrome Extension "[Export cookie JSON file for Puppeteer" and Click Export icon, it will download Cookie file..copy cookie from file 4. ✅ Step 4 : Go to Software input Area,Paste Cookie on Box "Twitter Cookie" Demo video For Get Cookie from any website.. so as per this video, just get cookie for Twitter https://youtu.be/yYJ6l2jm83Q if you face any issue , you can email us at bhansalisoft781986@gmail.com # Actor input Schema ## `Message` (type: `array`): Enter Your Message which you want to send ## `Twitter_cookie` (type: `array`): Insert your Twitter cookie here : Please check Product information page ## Actor input object example ```json { "Message": [ "Test message 1", "Test message 2" ] } ```` # 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 = { "Message": [ "Test message 1", "Test message 2" ] }; // Run the Actor and wait for it to finish const run = await client.actor("bhansalisoft/twitter-auto-tweet-poster").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 = { "Message": [ "Test message 1", "Test message 2", ] } # Run the Actor and wait for it to finish run = client.actor("bhansalisoft/twitter-auto-tweet-poster").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 '{ "Message": [ "Test message 1", "Test message 2" ] }' | apify call bhansalisoft/twitter-auto-tweet-poster --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bhansalisoft/twitter-auto-tweet-poster", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Twitter Auto Tweet Poster", "description": "Twitter Auto Tweet Poster : Schedule & Auto Post Tweets Online using automation.", "version": "0.0", "x-build-id": "KDB71ElvzuAXJIIjs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bhansalisoft~twitter-auto-tweet-poster/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bhansalisoft-twitter-auto-tweet-poster", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/bhansalisoft~twitter-auto-tweet-poster/runs": { "post": { "operationId": "runs-sync-bhansalisoft-twitter-auto-tweet-poster", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/bhansalisoft~twitter-auto-tweet-poster/run-sync": { "post": { "operationId": "run-sync-bhansalisoft-twitter-auto-tweet-poster", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "Message", "Twitter_cookie" ], "properties": { "Message": { "title": "Enter Your Message List(We will select rendom unique message from list) ", "type": "array", "description": "Enter Your Message which you want to send" }, "Twitter_cookie": { "title": "Twitter Cookie : Please check Product information page", "type": "array", "description": "Insert your Twitter cookie here : Please check Product information page" } } }, "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 } } } } } } } } } } ```