# Course Curriculum Skill Demand Extractor (`seeb/course-curriculum-skill-demand-extractor`) Actor Analyze course curricula for skill-demand signals, tools taught, modules, learner level, role alignment, and curriculum gaps. - **URL**: https://apify.com/seeb/course-curriculum-skill-demand-extractor.md - **Developed by:** [Techionik](https://apify.com/seeb) (community) - **Categories:** Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 skill demand rows This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Course Curriculum Skill Demand Extractor Analyze course curricula for skill-demand signals, tools taught, modules, learner level, role alignment, and curriculum gaps. Course Curriculum Skill Demand Extractor is built for one focused job: turning public source pages or pasted research snippets into usable skill demand row records. It does not try to be a generic webpage scraper. The parser, fields, examples, and dataset schema are shaped around this actor's specific business question. Use it when you already have public pages, listings, announcements, reviews, directories, or copied snippets and want a repeatable way to extract the information that matters without manually filling a spreadsheet. ### Who Uses It EdTech companies, course creators, hiring researchers, workforce analysts, bootcamps, and agencies tracking emerging skill demand. ### What It Finds The actor looks for signals connected to analyze course curricula for skill-demand signals, tools taught, modules, learner level, role alignment, and curriculum gaps. It keeps the output focused on fields a buyer can review, filter, enrich, or send into a CRM, spreadsheet, product backlog, procurement workflow, or research database. The extraction method is specific to this actor: Curriculum parsing with tool dictionary matching, learner-level detection, module/topic extraction, role alignment, and gap inference. ### How It Works 1. The actor accepts public URLs or pasted snippets in `courseSources`. 2. Public URLs are fetched when possible and converted into visible text. 3. Pasted snippets are processed directly, which is useful for pages that block automated fetching or require manual browsing. 4. Actor-specific parsing logic extracts only the fields listed below. 5. Empty or low-information inputs are skipped safely, with clean logs instead of noisy failed rows. ### Input Use `courseSources` for the sources you want to process. Each item can be a public URL or a focused text snippet. ```json { "courseSources": [ "DataCamp Advanced AI Automation course teaches Python, LangChain, vector databases, and prompt evaluation for RevOps analysts.", "Shopify Academy beginner curriculum covers Liquid, Klaviyo, checkout analytics, and retention flows for ecommerce marketers.", "SecureCloud Bootcamp teaches SOC 2, vendor risk, audit evidence, and GRC workflows for compliance managers." ], "maxResults": 3 } ```` ### Output Fields - `courseName` - Course, curriculum, bootcamp, or learning program name. - `providerName` - Education provider or platform. - `courseUrl` - Course or curriculum URL. - `learnerLevel` - Beginner, intermediate, or advanced level. - `skillCluster` - Skill category taught by the curriculum. - `toolsTaught` - Tools, frameworks, platforms, or methods taught. - `modulesCovered` - Modules or topics covered in the course. - `roleAlignment` - Role the course appears to prepare learners for. - `marketDemandSignal` - Why the curriculum suggests skill demand. - `curriculumGap` - Potential course, hiring, or market gap suggested by the curriculum. ### Example Output ```json { "courseName": "Advanced AI Automation course", "providerName": "DataCamp", "courseUrl": "https://example.com/course", "learnerLevel": "advanced", "skillCluster": "AI automation", "toolsTaught": "Python, LangChain, vector databases, prompt evaluation", "modulesCovered": "Python, LangChain, vector databases, and prompt evaluation", "roleAlignment": "RevOps analysts", "marketDemandSignal": "Curriculum teaches marketable tools: Python, LangChain, vector databases, prompt evaluation", "curriculumGap": "advanced specialization opportunity" } ``` ### Best Use Cases - Build a focused spreadsheet from repeated source pages. - Review public signals before sales, partnership, product, procurement, or market research work. - Monitor a category over time using the same output fields. - Turn copied notes from newsletters, communities, directories, review pages, or search results into a structured dataset. ### Tips For Better Results - Use source pages that visibly contain the signal you want this actor to extract. - For hard-to-fetch sites, paste the relevant public text snippet directly into the input. - Keep each input item focused on one page, listing, announcement, review, or profile. - Review extracted rows before making high-stakes business, legal, procurement, or security decisions. ### Limitations - The actor does not bypass logins, paywalls, private systems, CAPTCHA, or anti-bot walls. - The actor extracts from visible text and may miss information hidden behind scripts or user interactions. - Some fields may be empty when the source does not contain that information. - Output quality depends on source quality, so focused source text produces the strongest rows. # Actor input Schema ## `courseSources` (type: `array`): Add public URLs or paste focused text snippets. Use one source per row. The actor extracts only signals that match its specific purpose. ## `maxResults` (type: `integer`): Maximum number of useful dataset rows to save. ## Actor input object example ```json { "courseSources": [ "DataCamp Advanced AI Automation course teaches Python, LangChain, vector databases, and prompt evaluation for RevOps analysts.", "Shopify Academy beginner curriculum covers Liquid, Klaviyo, checkout analytics, and retention flows for ecommerce marketers.", "SecureCloud Bootcamp teaches SOC 2, vendor risk, audit evidence, and GRC workflows for compliance managers." ], "maxResults": 25 } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # 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("seeb/course-curriculum-skill-demand-extractor").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("seeb/course-curriculum-skill-demand-extractor").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 seeb/course-curriculum-skill-demand-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seeb/course-curriculum-skill-demand-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Course Curriculum Skill Demand Extractor", "description": "Analyze course curricula for skill-demand signals, tools taught, modules, learner level, role alignment, and curriculum gaps.", "version": "1.0", "x-build-id": "cezBWUQxxkd6i7RdZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seeb~course-curriculum-skill-demand-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seeb-course-curriculum-skill-demand-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seeb~course-curriculum-skill-demand-extractor/runs": { "post": { "operationId": "runs-sync-seeb-course-curriculum-skill-demand-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seeb~course-curriculum-skill-demand-extractor/run-sync": { "post": { "operationId": "run-sync-seeb-course-curriculum-skill-demand-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "courseSources" ], "properties": { "courseSources": { "title": "Course pages, syllabi, bootcamp modules, or curriculum snippets", "type": "array", "description": "Add public URLs or paste focused text snippets. Use one source per row. The actor extracts only signals that match its specific purpose.", "items": { "type": "string" }, "default": [ "DataCamp Advanced AI Automation course teaches Python, LangChain, vector databases, and prompt evaluation for RevOps analysts.", "Shopify Academy beginner curriculum covers Liquid, Klaviyo, checkout analytics, and retention flows for ecommerce marketers.", "SecureCloud Bootcamp teaches SOC 2, vendor risk, audit evidence, and GRC workflows for compliance managers." ] }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of useful dataset rows to save.", "default": 25 } } }, "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 } } } } } } } } } } ```