# ContentRadar — Content Intelligence Brief (`el_proyecto_cubo/contentradar`) Actor Generate AI-scored content briefs from 5 real-time sources. One call returns trending topics, content gaps, People Also Ask questions, autocomplete keywords, YouTube insights, Reddit discussions, and AI-synthesized recommendations for what content to create. - **URL**: https://apify.com/el\_proyecto\_cubo/contentradar.md - **Developed by:** [TheCubeProject](https://apify.com/el_proyecto_cubo) (community) - **Categories:** AI, SEO tools, Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## ContentRadar — Content Intelligence Brief Stop guessing what content to create. Start knowing. ContentRadar scans **Google, YouTube, Reddit, and trend data** in real time to find what your audience is searching for, what questions they ask, and where the content gaps are. ### What you get One call returns a complete content brief: - **AI-scored content angles** — ranked by cross-platform convergence - **Content gaps** — topics with high demand but little existing content - **Suggested topics** — with competition level (very_low / low / medium / high) - **People Also Ask** — real questions from Google - **Autocomplete keywords** — long-tail suggestions - **YouTube insights** — top videos with view counts - **Reddit discussions** — active posts with engagement scores - **Google Trends** — interest direction + rising queries - **Recommended actions** — specific content pieces to create ### How to use #### 1. Get a free RapidAPI key Go to [ContentRadar on RapidAPI](https://rapidapi.com/elproyectocubo/api/contentradar) and subscribe to the free plan (5 briefs/month, no credit card). #### 2. Run the Actor **Input:** ```json { "topic": "email marketing", "lang": "en", "country": "US", "rapidapi_key": "your-rapidapi-key" } ```` #### 3. Get your brief The output includes market pulse, top opportunities, suggested topics with competition levels, content gaps, and specific recommendations for what to create. ### Parameters | Parameter | Required | Description | |-----------|----------|-------------| | topic | Yes | Topic to research | | lang | No | Output language (default: en) | | country | No | Geo-localize results (ISO 3166: US, ES, MX...) | | rapidapi\_key | Yes | Your RapidAPI key ([get one free](https://rapidapi.com/elproyectocubo/api/contentradar)) | ### Pricing This Actor is free to run. You pay for ContentRadar API usage through RapidAPI: | Plan | Price | Briefs/month | |------|-------|-------------| | Free | $0 | 5 | | Starter | $19/mo | 100 | | Pro | $49/mo | 500 | | Business | $149/mo | 2,000 | ### Use cases - **AI agents** that need real-time content intelligence - **Content SaaS** adding smart topic suggestions - **Marketing agencies** automating research for multiple clients - **SEO tools** finding content gaps competitors miss # Actor input Schema ## `topic` (type: `string`): The topic to research (e.g. 'email marketing', 'yoga for beginners', 'trail running') ## `lang` (type: `string`): Output language — ISO 639-1 code (en, es, fr, de, pt...) ## `country` (type: `string`): Geo-localize results — ISO 3166 code (US, ES, MX, GB, DE...). Leave empty for global results. ## `rapidapi_key` (type: `string`): Your RapidAPI key for ContentRadar. Get one free at https://rapidapi.com/elproyectocubo/api/contentradar ## Actor input object example ```json { "topic": "content marketing", "lang": "en" } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "topic": "content marketing", "lang": "en" }; // Run the Actor and wait for it to finish const run = await client.actor("el_proyecto_cubo/contentradar").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "topic": "content marketing", "lang": "en", } # Run the Actor and wait for it to finish run = client.actor("el_proyecto_cubo/contentradar").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 '{ "topic": "content marketing", "lang": "en" }' | apify call el_proyecto_cubo/contentradar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=el_proyecto_cubo/contentradar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ContentRadar — Content Intelligence Brief", "description": "Generate AI-scored content briefs from 5 real-time sources. One call returns trending topics, content gaps, People Also Ask questions, autocomplete keywords, YouTube insights, Reddit discussions, and AI-synthesized recommendations for what content to create.", "version": "1.0", "x-build-id": "oh47wvk10UbJbfAEh" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/el_proyecto_cubo~contentradar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-el_proyecto_cubo-contentradar", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/el_proyecto_cubo~contentradar/runs": { "post": { "operationId": "runs-sync-el_proyecto_cubo-contentradar", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/el_proyecto_cubo~contentradar/run-sync": { "post": { "operationId": "run-sync-el_proyecto_cubo-contentradar", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "topic", "rapidapi_key" ], "properties": { "topic": { "title": "Topic", "type": "string", "description": "The topic to research (e.g. 'email marketing', 'yoga for beginners', 'trail running')" }, "lang": { "title": "Language", "type": "string", "description": "Output language — ISO 639-1 code (en, es, fr, de, pt...)", "default": "en" }, "country": { "title": "Country (optional)", "type": "string", "description": "Geo-localize results — ISO 3166 code (US, ES, MX, GB, DE...). Leave empty for global results." }, "rapidapi_key": { "title": "RapidAPI Key", "type": "string", "description": "Your RapidAPI key for ContentRadar. Get one free at https://rapidapi.com/elproyectocubo/api/contentradar" } } }, "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 } } } } } } } } } } ```