# ats-hiring-velocity (`borisinc/my-actor`) Actor Turn public ATS boards (Greenhouse, Lever, Ashby) into a hiring-intent signal, not a raw job dump. See what opened/closed, net hiring velocity, a 30-day trend, and new roles tagged by seniority and function. Spot expanding companies before competitors. Public data, no PII. - **URL**: https://apify.com/borisinc/my-actor.md - **Developed by:** [Donk](https://apify.com/borisinc) (community) - **Categories:** Lead generation, Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $25.00 / 1,000 results 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 ## ATS Hiring Velocity & Signal Feed **Turn public job boards into a hiring-intent signal — not another raw job scraper.** This Actor tracks companies' **Greenhouse, Lever, and Ashby** job boards and tells you what actually matters: what roles **opened**, what **closed**, the net hiring direction, a rolling **30-day velocity trend**, and every new role tagged by **seniority** and **function**. Companies expanding a team are actively buying tools, services, and headcount — and job postings are the most honest growth signal a company emits. Catch them first. > A company that raises its open postings 30%+ in a quarter is ~2.4x likelier to buy new software the next quarter (LinkedIn Economic Graph). This Actor surfaces exactly that signal — the delta, not a raw dump. ### What you get For every company, every run: - `open_count`, `opened`, `closed`, `net_change`, `velocity_pct` - a rolling `hiring_signal` label: **strong_expansion → expanding → steady → cooling → contracting** - 30-day trend: `opened_30d`, `closed_30d`, `net_change_30d`, `velocity_pct_30d` - `new_by_function` and `new_by_seniority` breakdowns - (optional) one row per **new** role, tagged with **seniority** and **function** ### Who it's for - **B2B sales / RevOps** — spot accounts scaling a team (Engineering, Sales, Data…) and reach out before your competitors do - **Recruiters & staffing** — companies opening lots of roles in your niche are warm leads - **Investors / market intel** — track portfolio and competitor hiring velocity over time ### Sources Public JSON board feeds — **Greenhouse, Lever, Ashby** (Workable best-effort). No login, no CAPTCHAs, no personal data. ### Input Provide companies as `:`, where the token is the board slug in the company's careers URL (`boards.greenhouse.io/gitlab` → `greenhouse:gitlab`): ```json { "companies": ["greenhouse:stripe", "ashby:openai", "lever:spotify"], "emitJobRecords": true, "classifier": "heuristic" } ```` Comes **pre-filled with a dozen high-growth tech companies** so you can run it immediately. Optional `classifier: "llm"` uses any OpenAI-compatible endpoint for messy titles. ### Output example ```json { "company": "stripe", "ats": "greenhouse", "open_count": 497, "opened": 12, "closed": 4, "net_change": 8, "velocity_pct": 1.6, "velocity_pct_30d": 21.4, "hiring_signal": "expanding", "new_by_function": { "Engineering": 6, "Sales": 3, "Product": 2 }, "new_by_seniority": { "senior": 5, "mid": 4, "staff": 2 } } ``` Velocity accumulates across scheduled runs: the first run sets a baseline (no false spike), and every run after tracks what changed. **Schedule it daily or weekly** to build a live hiring-intent feed. ### Why this, not a raw job scraper Everyone ships the current job list. This ships the **change** — opened/closed deltas, a velocity trend, and seniority/function tags — which is the layer intent-data vendors charge $15–50k/yr for. It's also low-maintenance by design: stable public JSON APIs, no headless browser, no anti-bot arms race. ### Notes - **Public data only, no PII** — reads public job-board listings, never personal information. - **Near-zero maintenance** — stable JSON board APIs. - Point it at any companies you care about, or run the built-in high-growth seed as-is. # Actor input Schema ## `companies` (type: `array`): One entry per company as ':'. Supported ats: greenhouse, lever, ashby, workable. The token is the board slug in the company's careers URL (boards.greenhouse.io/gitlab -> 'greenhouse:gitlab'). ## `emitJobRecords` (type: `boolean`): Also output one row per NEW requisition (with seniority + function tags), not just the per-company summary. ## `classifier` (type: `string`): heuristic = zero-cost regex (default). llm = OpenAI-compatible endpoint for messy titles (works with a local vLLM server). ## `velocityWindowDays` (type: `integer`): Window for the rolling opened/closed/net trend across scheduled runs. ## `llmBaseUrl` (type: `string`): OpenAI-compatible base, e.g. https://api.openai.com/v1 . Only used when Tagging mode = llm. ## `llmModel` (type: `string`): e.g. gpt-4o-mini or a local model name. Only used when Tagging mode = llm. ## `llmApiKey` (type: `string`): Bearer token for the LLM endpoint (blank for a keyless local server). ## Actor input object example ```json { "companies": [ "greenhouse:stripe", "ashby:openai" ], "emitJobRecords": true, "classifier": "heuristic", "velocityWindowDays": 30 } ``` # 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 = { "companies": [ "greenhouse:stripe", "greenhouse:anthropic", "ashby:openai", "greenhouse:databricks", "ashby:ramp", "greenhouse:figma", "ashby:notion", "lever:spotify", "greenhouse:reddit", "greenhouse:coinbase", "ashby:snowflake", "greenhouse:gitlab" ] }; // Run the Actor and wait for it to finish const run = await client.actor("borisinc/my-actor").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 = { "companies": [ "greenhouse:stripe", "greenhouse:anthropic", "ashby:openai", "greenhouse:databricks", "ashby:ramp", "greenhouse:figma", "ashby:notion", "lever:spotify", "greenhouse:reddit", "greenhouse:coinbase", "ashby:snowflake", "greenhouse:gitlab", ] } # Run the Actor and wait for it to finish run = client.actor("borisinc/my-actor").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 '{ "companies": [ "greenhouse:stripe", "greenhouse:anthropic", "ashby:openai", "greenhouse:databricks", "ashby:ramp", "greenhouse:figma", "ashby:notion", "lever:spotify", "greenhouse:reddit", "greenhouse:coinbase", "ashby:snowflake", "greenhouse:gitlab" ] }' | apify call borisinc/my-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=borisinc/my-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ats-hiring-velocity", "description": "Turn public ATS boards (Greenhouse, Lever, Ashby) into a hiring-intent signal, not a raw job dump. See what opened/closed, net hiring velocity, a 30-day trend, and new roles tagged by seniority and function. Spot expanding companies before competitors. Public data, no PII.", "version": "0.0", "x-build-id": "MmghnSpugLVecGAqP" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/borisinc~my-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-borisinc-my-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/borisinc~my-actor/runs": { "post": { "operationId": "runs-sync-borisinc-my-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/borisinc~my-actor/run-sync": { "post": { "operationId": "run-sync-borisinc-my-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "companies" ], "properties": { "companies": { "title": "Companies to track", "type": "array", "description": "One entry per company as ':'. Supported ats: greenhouse, lever, ashby, workable. The token is the board slug in the company's careers URL (boards.greenhouse.io/gitlab -> 'greenhouse:gitlab').", "items": { "type": "string" } }, "emitJobRecords": { "title": "Emit per-new-job records", "type": "boolean", "description": "Also output one row per NEW requisition (with seniority + function tags), not just the per-company summary.", "default": true }, "classifier": { "title": "Tagging mode", "enum": [ "heuristic", "llm" ], "type": "string", "description": "heuristic = zero-cost regex (default). llm = OpenAI-compatible endpoint for messy titles (works with a local vLLM server).", "default": "heuristic" }, "velocityWindowDays": { "title": "Rolling velocity window (days)", "minimum": 1, "maximum": 365, "type": "integer", "description": "Window for the rolling opened/closed/net trend across scheduled runs.", "default": 30 }, "llmBaseUrl": { "title": "LLM base URL", "type": "string", "description": "OpenAI-compatible base, e.g. https://api.openai.com/v1 . Only used when Tagging mode = llm." }, "llmModel": { "title": "LLM model", "type": "string", "description": "e.g. gpt-4o-mini or a local model name. Only used when Tagging mode = llm." }, "llmApiKey": { "title": "LLM API key", "type": "string", "description": "Bearer token for the LLM endpoint (blank for a keyless local server)." } } }, "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 } } } } } } } } } } ```