# Newsletter Sponsor Finder — Substack (`davidbenittah/newsletter-sponsor-finder`) Actor Scan Substack newsletters and extract who sponsors them: advertiser name, domain, sponsorship format, frequency and where each sponsor appeared — the data ad-space sellers and sponsor-hunting brands can't get anywhere. - **URL**: https://apify.com/davidbenittah/newsletter-sponsor-finder.md - **Developed by:** [David](https://apify.com/davidbenittah) (community) - **Categories:** Marketing, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $15.00 / 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 ## Newsletter Sponsor Finder — Substack Scan any list of Substack newsletters and extract **who sponsors them**: advertiser name and domain, sponsorship format, how often they appear, in which newsletters, and when they were last seen. Newsletter scrapers give you posts. Nobody gives you the **sponsors** — yet that is the data two groups pay for: - **Newsletter operators selling ad space**: every brand already sponsoring a newsletter in your niche is a warm prospect for yours — they have budget and proven intent. - **Brands & media buyers**: see where competitors spend, which advertisers are active in a niche, and which newsletters actually run sponsorships before you pitch or buy. ### What it does 1. Scrapes the recent posts (full content) of the newsletters you list — works with `*.substack.com` and custom domains. 2. Detects sponsorship sections via editorial markers: *brought to you by*, *sponsored by*, *today's sponsor*, *presented by*, *in partnership with*, *together with*... 3. Extracts the advertiser behind each sponsorship (link + brand anchor), ignoring CDN assets, social links, self-links and reference sites, and bounding the sponsor section so editorial links don't pollute the data. 4. Aggregates everything into **one item per sponsor** across all scanned newsletters, sorted by activity. ### Input example ```json { "newsletterUrls": [ "https://newsletter.pragmaticengineer.com", "https://www.lennysnewsletter.com" ], "maxPostsPerNewsletter": 20 } ```` The defaults work as-is: just list the newsletters of your niche. ### Output example ```json { "type": "sponsor", "sponsorName": "WorkOS", "sponsorDomain": "workos.com", "sponsorUrl": "https://workos.com/", "totalMentions": 7, "newslettersSponsored": 2, "newsletters": [ { "newsletterUrl": "https://newsletter.pragmaticengineer.com", "publicationName": "The Pragmatic Engineer", "subscriberCount": "1,100,000", "mentions": 5, "lastSeenAt": "2026-07-01T16:57:46.499Z" }, { "newsletterUrl": "https://www.lennysnewsletter.com", "publicationName": "Lenny's Newsletter", "subscriberCount": "1,000,000", "mentions": 2, "lastSeenAt": "2026-06-20T12:00:00.000Z" } ], "markers": ["brought to you by"], "firstSeenAt": "2026-04-02T16:00:00.000Z", "lastSeenAt": "2026-07-01T16:57:46.499Z", "sampleContext": "Antithesis is autonomous testing... WorkOS — auth for B2B SaaS..." } ``` A `SUMMARY` record in the run's key-value store gives the counts: newsletters scanned, posts scanned, posts with sponsors, sponsors found. ### How to use it - **Selling ad space**: scan the 5-10 newsletters closest to yours, sort sponsors by `lastSeenAt` — active advertisers first — and you have your outreach list, with proof of where they already spend. - **Competitive intel**: scan the newsletters of your category monthly (schedule + `postedAfter`) and diff which sponsors appear and disappear. - **Frequency = budget signal**: a sponsor with `totalMentions` 6+ across recent posts is running a campaign, not a one-off test. ### Fair use & data - Uses only **public newsletter posts** (no login, paywalled content is not accessed). - Outputs **company-level advertiser data only** — no author or reader personal data. - Inputs are hard-capped (10 newsletters, 100 posts each) so runs stay bounded and predictable. ### FAQ **Does it work on beehiiv/Ghost newsletters?** v1 covers Substack (including custom domains). Other platforms are on the roadmap. **Can detection be wrong?** Sponsorship is editorial text, so heuristics apply. Each sponsor item includes `sampleContext` and per-post provenance so you can verify in one click. **How far back can it look?** Up to 100 posts per newsletter, optionally bounded with `postedAfter`. # Actor input Schema ## `newsletterUrls` (type: `array`): Newsletters to scan for sponsors — homepage or custom-domain URLs (e.g. https://www.lennysnewsletter.com). Hard maximum: 10 per run. ## `maxPostsPerNewsletter` (type: `integer`): How many recent posts to analyze per newsletter. More posts = better frequency data, higher cost. Hard maximum: 100. ## `postedAfter` (type: `string`): Optional YYYY-MM-DD date — ignore posts older than this (e.g. last quarter only). ## Actor input object example ```json { "newsletterUrls": [ "https://newsletter.pragmaticengineer.com", "https://www.lennysnewsletter.com" ], "maxPostsPerNewsletter": 20 } ``` # 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 = { "newsletterUrls": [ "https://newsletter.pragmaticengineer.com", "https://www.lennysnewsletter.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("davidbenittah/newsletter-sponsor-finder").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 = { "newsletterUrls": [ "https://newsletter.pragmaticengineer.com", "https://www.lennysnewsletter.com", ] } # Run the Actor and wait for it to finish run = client.actor("davidbenittah/newsletter-sponsor-finder").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 '{ "newsletterUrls": [ "https://newsletter.pragmaticengineer.com", "https://www.lennysnewsletter.com" ] }' | apify call davidbenittah/newsletter-sponsor-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=davidbenittah/newsletter-sponsor-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Newsletter Sponsor Finder — Substack", "description": "Scan Substack newsletters and extract who sponsors them: advertiser name, domain, sponsorship format, frequency and where each sponsor appeared — the data ad-space sellers and sponsor-hunting brands can't get anywhere.", "version": "1.0", "x-build-id": "ThFQvJkSIdKJZqA3P" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/davidbenittah~newsletter-sponsor-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-davidbenittah-newsletter-sponsor-finder", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/davidbenittah~newsletter-sponsor-finder/runs": { "post": { "operationId": "runs-sync-davidbenittah-newsletter-sponsor-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/davidbenittah~newsletter-sponsor-finder/run-sync": { "post": { "operationId": "run-sync-davidbenittah-newsletter-sponsor-finder", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "newsletterUrls" ], "properties": { "newsletterUrls": { "title": "Substack newsletter URLs", "type": "array", "description": "Newsletters to scan for sponsors — homepage or custom-domain URLs (e.g. https://www.lennysnewsletter.com). Hard maximum: 10 per run.", "items": { "type": "string" } }, "maxPostsPerNewsletter": { "title": "Posts to scan per newsletter", "minimum": 1, "maximum": 100, "type": "integer", "description": "How many recent posts to analyze per newsletter. More posts = better frequency data, higher cost. Hard maximum: 100.", "default": 20 }, "postedAfter": { "title": "Only posts after", "type": "string", "description": "Optional YYYY-MM-DD date — ignore posts older than this (e.g. last quarter only)." } } }, "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 } } } } } } } } } } ```