# GoldAnswer — High-Intent Lead Hunter (`gemlike_playbill/goldanswer-hunter`) Actor Finds high-intent questions on Reddit, Hacker News & RSS, scores them 0–100, and drafts a reply with your CTA (BYOK). - **URL**: https://apify.com/gemlike\_playbill/goldanswer-hunter.md - **Developed by:** [Yixing Chen](https://apify.com/gemlike_playbill) (community) - **Categories:** Lead generation, AI, Social media - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## 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/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 ## GoldAnswer — High-Intent Lead Hunter (Apify Actor) Automatically monitors **Hacker News / Reddit / any RSS feed**, finds questions that are highly relevant to your product yet haven't been answered well, scores each with a 0–100 intent score, and drafts a ready-to-post reply that ends with your CTA. Built on **BYOK (Bring Your Own Key)**: you supply your own LLM / Reddit credentials and pay for them on your own bill. The developer never sees or pays for your keys. --- ### 30-second start 1. Fill in `keywords` (**required**): your niche keywords, comma-separated, e.g. `analytics, saas, churn`. 2. Hit **Run**. 3. Open the **Dataset** tab for results; export to CSV anytime. > Runs with **no keys at all** — it defaults to Hacker News and produces **free rule-based drafts**, so you can try before connecting anything. --- ### Smarter replies: bring your own LLM (optional) Fill the three fields below to generate replies with your own model. Leave them empty to use the free rule-based drafts: | Field | What to put | Example | |-------|-------------|---------| | `llmBaseUrl` | Any OpenAI-compatible endpoint | `https://api.deepseek.com/v1` | | `llmApiKey` | The matching platform key (any non-empty string for local models) | `sk-...` | | `llmModel` | Model name, comma-separated for fallbacks | `deepseek-chat` | **Endpoints you can use (pick one):** | Platform | llmBaseUrl | llmModel example | |----------|-----------|------------------| | OpenAI | `https://api.openai.com/v1` | `gpt-4o-mini` | | DeepSeek | `https://api.deepseek.com/v1` | `deepseek-chat` | | Groq | `https://api.groq.com/openai/v1` | `llama3.1-8b` | | Alibaba Qwen | `https://dashscope.aliyuncs.com/compatible-mode/v1` | `qwen-plus` | | Local Ollama | `http://localhost:11434/v1` | `llama3.1:8b` | > `deepseek-chat` is a great low-friction choice — high quality without an OpenAI account. --- ### Scanning Reddit (optional) - `redditClientId` + `redditClientSecret`: create a `script` or `web app` at [reddit.com/prefs/apps](https://www.reddit.com/prefs/apps). - Required for reliable Reddit scanning (the public API is often rate-limited / 403). - Hacker News works with **no key at all**. --- ### Output fields | Field | Meaning | |-------|---------| | `source` | Where it came from (hackernews / reddit/r/...) | | `title` | The question / post title | | `url` | Link to the original thread | | `excerpt` | Snippet | | `engagement` | Engagement count | | `intentScore` | **Intent score 0–100** (higher = more worth pursuing) | | `draft` | Reply draft (with your CTA) | --- ### Input field cheat-sheet | Field | Required | Notes | |-------|----------|-------| | `keywords` | yes | Your product keywords, comma-separated | | `communities` | no | `hackernews` (no key), `r/SaaS` (needs Reddit creds), or an RSS URL | | `product` | no | One line about your product, to make drafts fit better | | `ctaUrl` | no | Landing / product link (add UTM for tracking) | | `minIntentScore` | no | Keep only leads ≥ this score (default 40; higher = more precise) | | `maxResults` | no | Max leads per run (default 30) | | `includeDrafts` | no | Generate reply drafts? (default yes) | | `llmBaseUrl` / `llmApiKey` / `llmModel` | no | Connect your own LLM (see above) | | `redditClientId` / `redditClientSecret` | no | Connect Reddit (see above) | --- ### Pricing This Actor is billed by subscription (see Pricing on the right). **LLM and Reddit API costs are covered by your own key accounts** and are separate from this Actor's subscription fee. ### Tips - Higher `minIntentScore` = more precise but fewer leads; lower it to cast a wider net. - Hacker News is news-heavy; genuine questions (Ask HN / help threads) score higher. For the most real questions, connect Reddit. - Drafts include deterministic variation so posting several in a row won't look like spam. # Actor input Schema ## `keywords` (type: `string`): Comma-separated buyer keywords, e.g. 'analytics, saas, churn'. These define the intent you hunt for. ## `communities` (type: `string`): Comma-separated. Use 'hackernews' (works with no key), 'r/SUBREDDIT' or 'reddit:SUBREDDIT' (needs the Reddit credentials below), or a full RSS/Atom feed URL. Empty = Hacker News only. ## `product` (type: `string`): Used inside the drafted answer so it reads naturally. Optional. ## `ctaUrl` (type: `string`): The link the draft points to, e.g. 'https://yoursite.com?utm=goldanswer'. Optional. ## `minIntentScore` (type: `integer`): Only leads at or above this score are output. 40 = good volume, 60 = solid, 70 = hot. Lower it to include more HN news; raise it to keep only real questions. ## `maxResults` (type: `integer`): Stop after this many high-intent leads (keeps runs cheap). ## `includeDrafts` (type: `boolean`): If true, each lead comes with a ready-to-post draft. Disable to output questions only. ## `redditClientId` (type: `string`): Create a 'script' or 'web app' at reddit.com/prefs/apps and paste its ID here. Needed for reliable Reddit scanning (the public API is often rate-limited / returns 403). Hacker News needs no key. Billed to your own Reddit app quota. ## `redditClientSecret` (type: `string`): Pair this with the Client ID above. ## `llmBaseUrl` (type: `string`): Any OpenAI-compatible endpoint. Provide it (with a key + model) to use your own LLM for smarter drafts. Examples: OpenAI https://api.openai.com/v1 | DeepSeek https://api.deepseek.com/v1 | Groq https://api.groq.com/openai/v1 | Alibaba Qwen https://dashscope.aliyuncs.com/compatible-mode/v1 | local Ollama http://localhost:11434/v1. Leave empty to use free rule-based drafts (no key needed). ## `llmApiKey` (type: `string`): The API key for the base URL above (get it from OpenAI / DeepSeek / Groq / Qwen, etc.). For local models like Ollama, any non-empty string works. Leave empty to skip the LLM. Billed to your own account. ## `llmModel` (type: `string`): Model name, comma-separated for fallbacks, e.g. gpt-4o-mini, deepseek-chat, llama3.1:8b. deepseek-chat is a great low-friction choice. Empty defaults to gpt-4o-mini. ## Actor input object example ```json { "keywords": "analytics, saas, churn", "communities": "hackernews", "product": "GoldAnswer", "ctaUrl": "https://goldanswer.ai?utm=goldanswer", "minIntentScore": 40, "maxResults": 30, "includeDrafts": true, "llmModel": "gpt-4o-mini" } ```` # 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 = { "keywords": "analytics, saas, churn", "communities": "hackernews", "product": "GoldAnswer", "ctaUrl": "https://goldanswer.ai?utm=goldanswer", "redditClientId": "", "redditClientSecret": "", "llmBaseUrl": "", "llmApiKey": "", "llmModel": "gpt-4o-mini" }; // Run the Actor and wait for it to finish const run = await client.actor("gemlike_playbill/goldanswer-hunter").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 = { "keywords": "analytics, saas, churn", "communities": "hackernews", "product": "GoldAnswer", "ctaUrl": "https://goldanswer.ai?utm=goldanswer", "redditClientId": "", "redditClientSecret": "", "llmBaseUrl": "", "llmApiKey": "", "llmModel": "gpt-4o-mini", } # Run the Actor and wait for it to finish run = client.actor("gemlike_playbill/goldanswer-hunter").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 '{ "keywords": "analytics, saas, churn", "communities": "hackernews", "product": "GoldAnswer", "ctaUrl": "https://goldanswer.ai?utm=goldanswer", "redditClientId": "", "redditClientSecret": "", "llmBaseUrl": "", "llmApiKey": "", "llmModel": "gpt-4o-mini" }' | apify call gemlike_playbill/goldanswer-hunter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gemlike_playbill/goldanswer-hunter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GoldAnswer — High-Intent Lead Hunter", "description": "Finds high-intent questions on Reddit, Hacker News & RSS, scores them 0–100, and drafts a reply with your CTA (BYOK).", "version": "0.0", "x-build-id": "TrH4j9g2Dedmn28Rv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gemlike_playbill~goldanswer-hunter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gemlike_playbill-goldanswer-hunter", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gemlike_playbill~goldanswer-hunter/runs": { "post": { "operationId": "runs-sync-gemlike_playbill-goldanswer-hunter", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gemlike_playbill~goldanswer-hunter/run-sync": { "post": { "operationId": "run-sync-gemlike_playbill-goldanswer-hunter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keywords": { "title": "Keywords (niche)", "type": "string", "description": "Comma-separated buyer keywords, e.g. 'analytics, saas, churn'. These define the intent you hunt for." }, "communities": { "title": "Communities / sources", "type": "string", "description": "Comma-separated. Use 'hackernews' (works with no key), 'r/SUBREDDIT' or 'reddit:SUBREDDIT' (needs the Reddit credentials below), or a full RSS/Atom feed URL. Empty = Hacker News only." }, "product": { "title": "Your product / offer", "type": "string", "description": "Used inside the drafted answer so it reads naturally. Optional." }, "ctaUrl": { "title": "CTA / lead link (with UTM)", "type": "string", "description": "The link the draft points to, e.g. 'https://yoursite.com?utm=goldanswer'. Optional." }, "minIntentScore": { "title": "Minimum intent score (0-100)", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only leads at or above this score are output. 40 = good volume, 60 = solid, 70 = hot. Lower it to include more HN news; raise it to keep only real questions.", "default": 40 }, "maxResults": { "title": "Max leads to output", "minimum": 1, "maximum": 500, "type": "integer", "description": "Stop after this many high-intent leads (keeps runs cheap).", "default": 30 }, "includeDrafts": { "title": "Include drafted answers", "type": "boolean", "description": "If true, each lead comes with a ready-to-post draft. Disable to output questions only.", "default": true }, "redditClientId": { "title": "Reddit app ID (optional)", "type": "string", "description": "Create a 'script' or 'web app' at reddit.com/prefs/apps and paste its ID here. Needed for reliable Reddit scanning (the public API is often rate-limited / returns 403). Hacker News needs no key. Billed to your own Reddit app quota." }, "redditClientSecret": { "title": "Reddit app secret (optional)", "type": "string", "description": "Pair this with the Client ID above." }, "llmBaseUrl": { "title": "LLM base URL (optional — powers AI drafts)", "type": "string", "description": "Any OpenAI-compatible endpoint. Provide it (with a key + model) to use your own LLM for smarter drafts. Examples: OpenAI https://api.openai.com/v1 | DeepSeek https://api.deepseek.com/v1 | Groq https://api.groq.com/openai/v1 | Alibaba Qwen https://dashscope.aliyuncs.com/compatible-mode/v1 | local Ollama http://localhost:11434/v1. Leave empty to use free rule-based drafts (no key needed)." }, "llmApiKey": { "title": "LLM API key (optional)", "type": "string", "description": "The API key for the base URL above (get it from OpenAI / DeepSeek / Groq / Qwen, etc.). For local models like Ollama, any non-empty string works. Leave empty to skip the LLM. Billed to your own account." }, "llmModel": { "title": "LLM model (optional)", "type": "string", "description": "Model name, comma-separated for fallbacks, e.g. gpt-4o-mini, deepseek-chat, llama3.1:8b. deepseek-chat is a great low-friction choice. Empty defaults to gpt-4o-mini." } } }, "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 } } } } } } } } } } ```