# Skip Trace (`twoapi/skip-trace`) Actor - **URL**: https://apify.com/twoapi/skip-trace.md - **Developed by:** [twoapi](https://apify.com/twoapi) (community) - **Categories:** Lead generation, Real estate, SEO tools - **Stats:** 266 total users, 26 monthly users, 100.0% runs succeeded, 6 bookmarks - **User rating**: 2.47 out of 5 stars ## Pricing from $10.00 / 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 ## Skip Trace - People Search Actor Batch skip tracing actor that performs **name**, **phone**, **address**, and **email** lookups. Returns full profiles with addresses, phone numbers, emails, relatives, associates, and confidence scoring. ### Features - **4 search types**: Name, Address, Phone Number, Email - **Batch processing**: Submit multiple queries across all search types in a single run - **Automatic fallback**: If the primary source returns no results, a secondary source is tried automatically - **Confidence scoring**: Each result gets a confidence level (high / medium / low) based on match quality - **Convenience fields**: `best_phone` and `best_email` as top-level fields for easy integration with n8n, Make, or Zapier ### Input | Field | Type | Description | |-------|------|-------------| | `max_results_per_query` | integer | Max profile results per query (default: 1, max: 10) | | `name` | string[] | Name searches. Format: `Full Name` or `Name; City, ST Zip` | | `street_citystatezip` | string[] | Address searches. Format: `Street; City, ST Zip` | | `phone_number` | string[] | Phone searches. Any format accepted | | `email` | string[] | Email searches | #### Example Input ```json { "max_results_per_query": 1, "name": [ "James E Whitsitt", "Amalia Castillo; Dallas, TX 75228" ], "street_citystatezip": [ "3828 Double Oak Ln; Irving, TX 75061", "2334 Falcon Rd, Charleston, SC 29406" ], "phone_number": [ "(214)349-3972", "(956) 536-1441" ], "email": [ "nakialove6@gmail.com" ] } ```` ### Output Each result is pushed to the default dataset with the following fields: | Field | Type | Description | |-------|------|-------------| | `query` | object | The original search input | | `source` | string | Data source used | | `profile_url` | string | URL of the profile page | | `first_name` | string | First name | | `last_name` | string | Last name | | `full_name` | string | Full name | | `age` | string | Age | | `addresses` | array | List of addresses (street, city, state, zip\_code, full\_address) | | `phones` | array | List of phone numbers (number, phone\_type) | | `emails` | array | List of email addresses (address, provider) | | `relatives` | array | List of relatives (name, relation) | | `associates` | array | List of associates (name, relation) | | `best_phone` | string|null | First/most relevant phone number | | `best_email` | string|null | First/most relevant email address | | `confidence` | string | Match confidence: "high", "medium", or "low" | | `error` | string|null | Error message if the query failed | #### Example Output ```json { "query": { "search_type": "name", "first_name": "James", "last_name": "Whitsitt" }, "source": "truepeoplesearch", "profile_url": "https://www.truepeoplesearch.com/find/person/pnr264490294u6460962", "first_name": "James", "last_name": "Whitsitt", "full_name": "James Whitsitt Jr", "age": "82", "addresses": [ { "full_address": "8705 Aldwick Dr, Dallas, TX, 75238", "street": "8705 Aldwick Dr", "city": "Dallas", "state": "TX", "zip_code": "75238" } ], "phones": [ { "number": "(214) 349-3972", "phone_type": "landline" }, { "number": "(214) 707-3872", "phone_type": "mobile" } ], "emails": [], "relatives": [ { "name": "Mallary Whitsitt", "relation": "relative" }, { "name": "James Whitsitt", "relation": "relative" } ], "associates": [ { "name": "John Smith", "relation": "associate" } ], "best_phone": "(214) 349-3972", "best_email": null, "confidence": "high", "error": null } ``` ### Confidence Scoring - **High**: Exact first + last name match with age or location confirmation - **Medium**: Partial name match, location match, or reverse lookup (phone/address/email) with a result - **Low**: No strong match signals — result may be a different person ### Pricing **$10 per 1,000 results** — simple, flat-rate pricing with no hidden fees. ### Integrations The `best_phone` and `best_email` convenience fields make it easy to use results directly in: - **n8n** workflows - **Make** (Integromat) scenarios - **Zapier** zaps - Any tool that calls the Apify API No array parsing needed for the most common use case of getting the primary phone/email. # Actor input Schema ## `max_results_per_query` (type: `integer`): Maximum number of profile results to return per search query. ## `name` (type: `array`): Full\_Name(James E Whitsitt) or Name; Citystatezip(Amalia Castillo; Dallas, TX 75228), Separated by semicolon(;) ## `street_citystatezip` (type: `array`): Enter Street and Citystatezip(3828 Double Oak Ln; Irving, TX 75061), separated by semicolon(;) ## `phone_number` (type: `array`): Phone number in any format, e.g. (214)349-3972 or 214-349-3972. ## `email` (type: `array`): Email address to search, e.g. nakialove6@gmail.com. ## Actor input object example ```json { "max_results_per_query": 1, "name": [ "James E Whitsitt", "Amalia Castillo; Dallas, TX 75228" ], "street_citystatezip": [ "3828 Double Oak Ln; Irving, TX 75061", "2334 Falcon Rd, Charleston, SC 29406" ], "phone_number": [ "(214)349-3972", "(956) 536-1441" ], "email": [ "nakialove6@gmail.com" ] } ``` # Actor output Schema ## `results` (type: `string`): Person profiles matching your search queries. # 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 = { "max_results_per_query": 1, "name": [ "James E Whitsitt", "Amalia Castillo; Dallas, TX 75228" ], "street_citystatezip": [ "3828 Double Oak Ln; Irving, TX 75061", "2334 Falcon Rd, Charleston, SC 29406" ], "phone_number": [ "(214)349-3972", "(956) 536-1441" ], "email": [ "nakialove6@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("twoapi/skip-trace").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 = { "max_results_per_query": 1, "name": [ "James E Whitsitt", "Amalia Castillo; Dallas, TX 75228", ], "street_citystatezip": [ "3828 Double Oak Ln; Irving, TX 75061", "2334 Falcon Rd, Charleston, SC 29406", ], "phone_number": [ "(214)349-3972", "(956) 536-1441", ], "email": ["nakialove6@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("twoapi/skip-trace").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 '{ "max_results_per_query": 1, "name": [ "James E Whitsitt", "Amalia Castillo; Dallas, TX 75228" ], "street_citystatezip": [ "3828 Double Oak Ln; Irving, TX 75061", "2334 Falcon Rd, Charleston, SC 29406" ], "phone_number": [ "(214)349-3972", "(956) 536-1441" ], "email": [ "nakialove6@gmail.com" ] }' | apify call twoapi/skip-trace --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=twoapi/skip-trace", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Skip Trace", "description": null, "version": "0.1", "x-build-id": "xNXDJSWzyUFQCGFGd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/twoapi~skip-trace/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-twoapi-skip-trace", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/twoapi~skip-trace/runs": { "post": { "operationId": "runs-sync-twoapi-skip-trace", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/twoapi~skip-trace/run-sync": { "post": { "operationId": "run-sync-twoapi-skip-trace", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "max_results_per_query": { "title": "Max Results From Each Search", "minimum": 1, "maximum": 10, "type": "integer", "description": "Maximum number of profile results to return per search query.", "default": 1 }, "name": { "title": "Full_Name(James E Whitsitt) or Name; Citystatezip(Amalia Castillo; Dallas, TX 75228), Separated by semicolon(;)", "type": "array", "description": "Full_Name(James E Whitsitt) or Name; Citystatezip(Amalia Castillo; Dallas, TX 75228), Separated by semicolon(;)", "items": { "type": "string" } }, "street_citystatezip": { "title": "Enter Street and Citystatezip(3828 Double Oak Ln; Irving, TX 75061), separated by semicolon(;)", "type": "array", "description": "Enter Street and Citystatezip(3828 Double Oak Ln; Irving, TX 75061), separated by semicolon(;)", "items": { "type": "string" } }, "phone_number": { "title": "Phone Number", "type": "array", "description": "Phone number in any format, e.g. (214)349-3972 or 214-349-3972.", "items": { "type": "string" } }, "email": { "title": "Email Address", "type": "array", "description": "Email address to search, e.g. nakialove6@gmail.com.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```