# Creative Lead Scorer (`dr_amp/creative-lead-scorer`) Actor Score and prioritize brand leads for creative, product photography and video agencies. - **URL**: https://apify.com/dr\_amp/creative-lead-scorer.md - **Developed by:** [Diego Jurado Garcia](https://apify.com/dr_amp) (community) - **Categories:** Business, Marketing, 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 lead scoreds 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 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 ## Creative Lead Scorer Prioritize brand leads for creative agencies, product photographers, content studios, and video freelancers. This actor takes leads you already have and returns deterministic scores, bands, reasons, and outreach-safe pitch angles. It does not scrape, send emails, use an LLM, or contact anyone. ### What it does - Scores each lead from 0-100. - Labels leads as `hot`, `warm`, or `cold`. - Explains the score with transparent reasons. - Suggests a concise creative pitch angle. - Filters by `min_score`. ### Input ```json { "leads": [ { "brand": "Aurora Skincare", "sector": "beauty", "website": "https://aurora.example", "email": "hello@aurora.example", "signal": "launching a new DTC premium line", "idea": "hero product photography + short video", "notes": "current visuals look weak", "priority": 5, "source_type": "referral" } ], "min_score": 45, "include_pitch_angles": true } ```` ### Output Each dataset item contains: - `brand` - `score` - `band` - `reasons` - `recommended_action` - `pitch_angle` - `input_echo` ### Local smoke ```bash python3 -m src.main --input samples/input.json --output /tmp/creative-lead-scorer-output.json python3 -m pytest tests/ ``` ### Privacy and limitations This actor only processes the input you provide. It performs no outreach and no enrichment by default. Scores are deterministic heuristics, not guarantees of conversion. # Actor input Schema ## `leads` (type: `array`): Array of brand/lead objects to score. ## `min_score` (type: `integer`): Only output leads with score >= this value. ## `include_pitch_angles` (type: `boolean`): Include deterministic suggested pitch angle per lead. ## Actor input object example ```json { "leads": [ { "brand": "Aurora Skincare", "sector": "beauty", "website": "https://example.com", "email": "hello@example.com", "signal": "launching a premium DTC product line", "idea": "hero product photography + short video", "notes": "current visuals look weak", "priority": 5, "source_type": "referral" } ], "min_score": 0, "include_pitch_angles": true } ``` # 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 = { "leads": [ { "brand": "Aurora Skincare", "sector": "beauty", "website": "https://example.com", "email": "hello@example.com", "signal": "launching a premium DTC product line", "idea": "hero product photography + short video", "notes": "current visuals look weak", "priority": 5, "source_type": "referral" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("dr_amp/creative-lead-scorer").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 = { "leads": [{ "brand": "Aurora Skincare", "sector": "beauty", "website": "https://example.com", "email": "hello@example.com", "signal": "launching a premium DTC product line", "idea": "hero product photography + short video", "notes": "current visuals look weak", "priority": 5, "source_type": "referral", }] } # Run the Actor and wait for it to finish run = client.actor("dr_amp/creative-lead-scorer").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 '{ "leads": [ { "brand": "Aurora Skincare", "sector": "beauty", "website": "https://example.com", "email": "hello@example.com", "signal": "launching a premium DTC product line", "idea": "hero product photography + short video", "notes": "current visuals look weak", "priority": 5, "source_type": "referral" } ] }' | apify call dr_amp/creative-lead-scorer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dr_amp/creative-lead-scorer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Creative Lead Scorer", "description": "Score and prioritize brand leads for creative, product photography and video agencies.", "version": "0.1", "x-build-id": "6fYtateUiV3xCS0zQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dr_amp~creative-lead-scorer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dr_amp-creative-lead-scorer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dr_amp~creative-lead-scorer/runs": { "post": { "operationId": "runs-sync-dr_amp-creative-lead-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dr_amp~creative-lead-scorer/run-sync": { "post": { "operationId": "run-sync-dr_amp-creative-lead-scorer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "leads" ], "properties": { "leads": { "title": "Leads", "type": "array", "description": "Array of brand/lead objects to score." }, "min_score": { "title": "Minimum score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only output leads with score >= this value.", "default": 0 }, "include_pitch_angles": { "title": "Include pitch angles", "type": "boolean", "description": "Include deterministic suggested pitch angle per lead.", "default": true } } }, "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 } } } } } } } } } } ```