# AI Content Repurposer (`enezli/ai-content-repurposer`) Actor Content repurposing in one click. Turn a URL or text into 9 publish-ready formats: tweet thread, LinkedIn post, SEO meta, summary, Instagram caption and carousel, Reels/Shorts script, YouTube title and description. Stop rewriting the same idea nine times. - **URL**: https://apify.com/enezli/ai-content-repurposer.md - **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community) - **Categories:** AI, Social media - **Stats:** 2 total users, 0 monthly users, 45.5% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $40.00 / 1,000 format generateds 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 ## AI Content Repurposer — One Input, Content Ready for Every Platform Turns a **URL or raw text** into **9 publish-ready formats** in a single run. One input → content ready for everywhere, natural and cliché-free (anti-slop). ### What it does Instead of rewriting a blog post, article, or transcript by hand for every platform, this actor produces your chosen formats in seconds. Content creators (social posts) and bloggers / SEO writers (meta + summary) get everything from one tool. ### Generated formats - **Tweet Thread (X)** — 4-7 tweets, each under 280 characters - **LinkedIn Post** — 120-220 words, professional yet warm - **SEO Meta Description** — 150-160 characters - **Short Summary** — 3-4 sentences - **Instagram Caption** — with relevant hashtags (8-15) - **Instagram Carousel** — 5-8 slides - **Reels/Shorts Script** — ~60 sec, strong first 3 seconds - **YouTube Title** — under 70 characters - **YouTube Description** — with hashtags (3-5) Every format is **deterministically verified** against its platform limit (the LLM requests it, the code guarantees it). Any output that doesn't fit is flagged in the dataset as `Publish Ready: No` with a warning — no surprises at publish time. ### Output language Set `language` to control the language of every generated piece. The content is written **entirely in your chosen language** (English, Türkçe, Deutsch, Español, Français), regardless of the source content's language. ### Input | Field | Description | |---|---| | `sourceText` | The raw text to repurpose (takes priority over the URL when provided) | | `url` | A URL instead of text; content is fetched automatically (used when `sourceText` is empty) | | `formats` | Which formats to generate (from the list above) — empty = all | | `language` | Output language: English / Türkçe / Deutsch / Español / Français | | `model` | (Advanced) LLM model — the default is fast and economical | ### Output (dataset) One row per format with the columns **Format · Size · Publish Ready · Warning · Preview**, plus the full text in `Content` (ready to copy directly). A final summary row reports how many formats were generated and whether they are all publish-ready. If a URL was provided, `source_url` is also included. ### How to use 1. Paste your raw text into `sourceText`, or provide a `url`. 2. Pick the formats you want (or leave empty for all) and set the output `language`. 3. Click **Start**. 4. Grab your ready-to-publish content from the Dataset tab. ### Why this actor? Most tools are either locked to a video/topic input or only produce social posts. This actor turns **raw text OR a URL into 9 formats at once** — and notably includes SEO meta + summary, which most social content generators leave out. A unified combo in one tool, with natural language. ### Pricing (Pay-Per-Event) You only pay for what you use: per run + per processed content + per generated format. No monthly subscription. # Actor input Schema ## `sourceText` (type: `string`): The raw content to repurpose (blog post / transcript / article text). If provided, it takes priority over the URL. ## `url` (type: `string`): Instead of text, you can provide a URL (blog/article). The content is fetched automatically. Used when sourceText is empty. ## `formats` (type: `array`): Which content formats should be generated? If left empty, all of them are produced. ## `language` (type: `string`): The language of the generated content. ## `model` (type: `string`): The LLM model. The default is cheap and fast; pick a stronger model for deeper, more creative output. ## Actor input object example ```json { "sourceText": "Remote work is no longer a perk — it is the default for millions of knowledge workers. Teams that thrive share three habits: they write things down instead of relying on meetings, they set clear expectations for response times, and they protect deep-focus blocks on the calendar. The result is fewer interruptions, better documentation, and happier people who can do their best work from anywhere.", "formats": [ "tweet_thread", "linkedin_post", "seo_meta", "summary" ], "language": "English", "model": "claude-haiku-4-5-20251001" } ```` # 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 = { "sourceText": "Remote work is no longer a perk — it is the default for millions of knowledge workers. Teams that thrive share three habits: they write things down instead of relying on meetings, they set clear expectations for response times, and they protect deep-focus blocks on the calendar. The result is fewer interruptions, better documentation, and happier people who can do their best work from anywhere.", "url": "" }; // Run the Actor and wait for it to finish const run = await client.actor("enezli/ai-content-repurposer").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 = { "sourceText": "Remote work is no longer a perk — it is the default for millions of knowledge workers. Teams that thrive share three habits: they write things down instead of relying on meetings, they set clear expectations for response times, and they protect deep-focus blocks on the calendar. The result is fewer interruptions, better documentation, and happier people who can do their best work from anywhere.", "url": "", } # Run the Actor and wait for it to finish run = client.actor("enezli/ai-content-repurposer").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 '{ "sourceText": "Remote work is no longer a perk — it is the default for millions of knowledge workers. Teams that thrive share three habits: they write things down instead of relying on meetings, they set clear expectations for response times, and they protect deep-focus blocks on the calendar. The result is fewer interruptions, better documentation, and happier people who can do their best work from anywhere.", "url": "" }' | apify call enezli/ai-content-repurposer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=enezli/ai-content-repurposer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Content Repurposer", "description": "Content repurposing in one click. Turn a URL or text into 9 publish-ready formats: tweet thread, LinkedIn post, SEO meta, summary, Instagram caption and carousel, Reels/Shorts script, YouTube title and description. Stop rewriting the same idea nine times.", "version": "0.1", "x-build-id": "6vezPEnrwlhyNER84" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/enezli~ai-content-repurposer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-enezli-ai-content-repurposer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/enezli~ai-content-repurposer/runs": { "post": { "operationId": "runs-sync-enezli-ai-content-repurposer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/enezli~ai-content-repurposer/run-sync": { "post": { "operationId": "run-sync-enezli-ai-content-repurposer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "sourceText": { "title": "Source text", "type": "string", "description": "The raw content to repurpose (blog post / transcript / article text). If provided, it takes priority over the URL." }, "url": { "title": "Source URL", "type": "string", "description": "Instead of text, you can provide a URL (blog/article). The content is fetched automatically. Used when sourceText is empty." }, "formats": { "title": "Formats to generate", "type": "array", "description": "Which content formats should be generated? If left empty, all of them are produced.", "items": { "type": "string", "enum": [ "tweet_thread", "linkedin_post", "seo_meta", "summary", "instagram_caption", "instagram_carousel", "reels_shorts_script", "youtube_title", "youtube_description" ], "enumTitles": [ "Tweet Thread (X)", "LinkedIn Post", "SEO Meta Description", "Short Summary", "Instagram Caption (+hashtags)", "Instagram Carousel", "Reels/Shorts Script", "YouTube Title", "YouTube Description (+hashtags)" ] }, "default": [ "tweet_thread", "linkedin_post", "seo_meta", "summary" ] }, "language": { "title": "Output language", "enum": [ "English", "Türkçe", "Deutsch", "Español", "Français" ], "type": "string", "description": "The language of the generated content.", "default": "English" }, "model": { "title": "Model (advanced)", "enum": [ "claude-haiku-4-5-20251001", "claude-sonnet-4-6" ], "type": "string", "description": "The LLM model. The default is cheap and fast; pick a stronger model for deeper, more creative output.", "default": "claude-haiku-4-5-20251001" } } }, "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 } } } } } } } } } } ```