# Job Posting Sales Trigger Detector (`fetchcraft/job-posting-sales-trigger`) Actor Drop in company URLs. Get back recent hires that signal sales triggers (new CRO, VP Sales, Head of Growth, Director of RevOps) plus the inferred trigger event. $0.10 per company analysed. - **URL**: https://apify.com/fetchcraft/job-posting-sales-trigger.md - **Developed by:** [Emily Ward](https://apify.com/fetchcraft) (community) - **Categories:** AI, Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $100.00 / 1,000 company trigger scanneds 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 ## Job Posting Sales Trigger Detector **Drop in company URLs. Get back open roles that signal a sales trigger (new CRO, VP Sales, Head of Growth, RevOps director) plus the inferred buying moment.** ### Why this exists Sales triggers are the single best predictor of cold outreach reply rate. A company that just hired a CRO is 5 to 10 times more likely to evaluate new sales tooling than one that didn't. Trigger data costs $99 to $499/mo at LinkedIn Sales Navigator, $300/mo at Crystal Knows, $250/mo at LeadSift. This actor charges $0.10 per company analysed. No subscription. ### What you get back per company | Field | Description | | --- | --- | | `company_url` | URL you submitted | | `company_name` | Pulled from the careers page | | `total_jobs_found` | Approximate count of all open roles | | `trigger_jobs` | Array of roles matching your trigger patterns | | `top_trigger_role` | The strongest single trigger (sorted by AI) | | `trigger_score` | 0 to 100. 100 = multiple senior triggers | | `inferred_trigger_event` | One sentence on the buying moment | | `suggested_outreach_angle` | One paragraph the seller can paste into a cold opener | | `evidence_quotes` | Exact substrings from the careers page that ground the analysis | ### Pricing **$0.10 USD per company analysed.** Failed scans (no careers page found, blocked) are not charged. | Use case | Companies | Cost | | --- | --- | --- | | Weekly trigger sweep | 50 | $5.00 | | Monthly target account refresh | 500 | $50.00 | | Quarterly book of business scan | 2,000 | $200.00 | Compare to LinkedIn Sales Navigator Advanced at $149/mo, $1,788/yr, or LeadSift at $250/mo. ### How it works 1. Tries `/careers`, `/jobs`, `/careers/jobs`, `/about/careers`, `/company/careers`, `/work-with-us`, `/join-us`, `/hiring` in order. 2. Falls back to scanning the homepage for a "Careers" link. 3. Sends cleaned careers content to Claude Sonnet 4.6 with your trigger role patterns and product pitch. 4. Claude returns structured job data + a buying-moment narrative grounded in evidence quotes. ### Inputs - **company_urls**: list of company homepage URLs (up to 100 per run). - **your_product_pitch**: paragraph describing what you sell. The AI uses this to tailor the buying-moment inference. - **trigger_roles**: roles that signal a trigger. Default = revenue leadership. Customize for your ICP (e.g. "Head of Data" for data tooling sellers, "VP Engineering" for dev tools). - **max_concurrency**: companies processed in parallel. ### Use cases - **SDR teams**: Weekly trigger sweep against your account list. Prioritise hot accounts. - **Lead-gen agencies**: Sell "trigger-detected" lists at a premium. - **Investors**: See which portfolio competitors are scaling sales. - **Recruiters**: Reverse use, find companies actively hiring trigger roles to pitch your candidates. ### What this actor does NOT do - It does not check LinkedIn. Public careers pages only. - It does not infer hires that already happened. Currently-open roles only. - It does not enrich with contact emails. Pair with Apollo or Clearbit. ### Tags `sales-triggers` `lead-generation` `b2b` `ai` `claude` `sales-intelligence` `revops` `careers-scraper` `sdr` --- Made with Claude Sonnet 4.6 by Emily Ward, Cancel Costs. # Actor input Schema ## `company_urls` (type: `array`): List of company homepage URLs. We check each one's /careers, /jobs, /careers/jobs and similar paths. ## `your_product_pitch` (type: `string`): Brief description of your product. The AI uses this to score how strongly each company's hiring pattern matches as a buyer trigger for you. ## `trigger_roles` (type: `array`): Job-title patterns that indicate a buying moment for you. Default = revenue-leadership hires. ## `max_concurrency` (type: `integer`): How many companies to scan in parallel. Higher = faster, more network load. ## `llm_provider` (type: `string`): Pick which LLM you want. Gemini is recommended (1500 free requests/day at https://aistudio.google.com/apikey). Groq is also good (14400 free/day at https://console.groq.com/keys). Leave blank to use the actor owner's configured key (if any). ## `llm_api_key` (type: `string`): Paste the API key for the provider you selected. Never leaves the Apify run, never logged. ## Actor input object example ```json { "company_urls": [ "https://www.snowflake.com", "https://www.databricks.com" ], "your_product_pitch": "We help RevOps and Sales teams ramp new SDRs from week 1 with AI-personalized outbound. Self-serve, $99/seat/mo.", "trigger_roles": [ "CRO", "VP Sales", "Head of Sales", "Head of Growth", "Director RevOps", "Director of Revenue Operations", "Head of GTM" ], "max_concurrency": 5, "llm_provider": "" } ```` # 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 = { "company_urls": [ "https://www.snowflake.com", "https://www.databricks.com" ], "your_product_pitch": "We help RevOps and Sales teams ramp new SDRs from week 1 with AI-personalized outbound. Self-serve, $99/seat/mo.", "trigger_roles": [ "CRO", "VP Sales", "Head of Sales", "Head of Growth", "Director RevOps", "Director of Revenue Operations", "Head of GTM" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fetchcraft/job-posting-sales-trigger").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "company_urls": [ "https://www.snowflake.com", "https://www.databricks.com", ], "your_product_pitch": "We help RevOps and Sales teams ramp new SDRs from week 1 with AI-personalized outbound. Self-serve, $99/seat/mo.", "trigger_roles": [ "CRO", "VP Sales", "Head of Sales", "Head of Growth", "Director RevOps", "Director of Revenue Operations", "Head of GTM", ], } # Run the Actor and wait for it to finish run = client.actor("fetchcraft/job-posting-sales-trigger").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 '{ "company_urls": [ "https://www.snowflake.com", "https://www.databricks.com" ], "your_product_pitch": "We help RevOps and Sales teams ramp new SDRs from week 1 with AI-personalized outbound. Self-serve, $99/seat/mo.", "trigger_roles": [ "CRO", "VP Sales", "Head of Sales", "Head of Growth", "Director RevOps", "Director of Revenue Operations", "Head of GTM" ] }' | apify call fetchcraft/job-posting-sales-trigger --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fetchcraft/job-posting-sales-trigger", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Posting Sales Trigger Detector", "description": "Drop in company URLs. Get back recent hires that signal sales triggers (new CRO, VP Sales, Head of Growth, Director of RevOps) plus the inferred trigger event. $0.10 per company analysed.", "version": "0.1", "x-build-id": "kw2vUdjUiRh2lAaqH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fetchcraft~job-posting-sales-trigger/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fetchcraft-job-posting-sales-trigger", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/fetchcraft~job-posting-sales-trigger/runs": { "post": { "operationId": "runs-sync-fetchcraft-job-posting-sales-trigger", "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/fetchcraft~job-posting-sales-trigger/run-sync": { "post": { "operationId": "run-sync-fetchcraft-job-posting-sales-trigger", "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": [ "company_urls", "your_product_pitch" ], "properties": { "company_urls": { "title": "Company URLs", "type": "array", "description": "List of company homepage URLs. We check each one's /careers, /jobs, /careers/jobs and similar paths.", "items": { "type": "string" } }, "your_product_pitch": { "title": "What you sell (one paragraph)", "minLength": 20, "maxLength": 1500, "type": "string", "description": "Brief description of your product. The AI uses this to score how strongly each company's hiring pattern matches as a buyer trigger for you." }, "trigger_roles": { "title": "Roles that signal a trigger", "type": "array", "description": "Job-title patterns that indicate a buying moment for you. Default = revenue-leadership hires.", "items": { "type": "string" } }, "max_concurrency": { "title": "Max parallel companies", "minimum": 1, "maximum": 20, "type": "integer", "description": "How many companies to scan in parallel. Higher = faster, more network load.", "default": 5 }, "llm_provider": { "title": "LLM provider (bring your own key)", "enum": [ "", "gemini", "groq", "cerebras", "openrouter", "huggingface", "anthropic" ], "type": "string", "description": "Pick which LLM you want. Gemini is recommended (1500 free requests/day at https://aistudio.google.com/apikey). Groq is also good (14400 free/day at https://console.groq.com/keys). Leave blank to use the actor owner's configured key (if any).", "default": "" }, "llm_api_key": { "title": "Your LLM API key (paired with llm_provider above)", "type": "string", "description": "Paste the API key for the provider you selected. Never leaves the Apify run, never logged." } } }, "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 } } } } } } } } } } ```