# Apify Store Market Scanner (`honest_cherimoya/apify-store-scanner`) Actor Turn the whole Apify Store into a structured dataset with growth & opportunity metrics: rising actors, category concentration, and realistic expectations for new publishers. - **URL**: https://apify.com/honest\_cherimoya/apify-store-scanner.md - **Developed by:** [Ziv L](https://apify.com/honest_cherimoya) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## Apify Store Market Scanner Turn the whole Apify Store into a **structured dataset with opportunity metrics** — so you can decide *what to build* with data instead of vibes. Point it at the Store (optionally filtered by category or keyword) and it collects every listed actor with usage, growth, pricing, and review stats, computes per-actor opportunity metrics, and produces a market summary: which actors are rising right now, how concentrated each category is, and what a realistic first month looks like for a new publisher. ### Why this exists Before building anything, I scanned 15,000 actors by hand to answer one question: *where is real, active demand?* That study produced findings like: - ~97% of actors get runs every month — the ecosystem is alive — but **only ~30% gain 5+ new users per month**. - For small actors (10–200 total users), median new users per month is **3–7, in every category**. Category choice barely changes what a newcomer gets. - The fastest-rising actors are overwhelmingly **data pipelines for well-known platforms**, not clever niche tools. This actor is that study, productized: the same collection and analysis, repeatable in one click, always current. ### Input | Field | Default | What it does | |---|---|---| | `search` | — | Keyword filter, same as the Store search box | | `category` | — | Store category (e.g. `SOCIAL_MEDIA`, `LEAD_GENERATION`, `AI`, `MCP_SERVERS`) | | `sortBy` | `popularity` | `popularity` = top of the market first; `newest` = recent launches first | | `maxItems` | `5000` | How many actors to collect | | `riserMinGrowthPercent` | `30` | Riser flag: 30-day new users ≥ this % of all-time users | | `riserMinNewUsers30d` | `10` | Riser flag: minimum absolute 30-day new users | ### Output **Dataset** — one record per actor: - Identity: `title`, `name`, `username`, `url`, `description`, `categories`, `pricingModel` - Usage: `totalUsers`, `users7d/30d/90d`, `totalRuns`, `runs30d`, `succeeded30d`, `lastRunStartedAt` - Reputation: `reviewCount`, `reviewRating`, `bookmarks` - Computed: `successRate30d`, `growthShare30d` (30-day users ÷ all-time users), `runsPerNewUser30d`, `isRiser` **Key-value store `SUMMARY`** — the market report: - `categoryStats`: per category — actor count, total 30-day runs, top-actor concentration share, and median runs / new users for small players (your realistic first-month expectation) - `risersTop25`: the fastest-growing actors right now - `riserTitleKeywords`: what the rising actors call themselves - `pricingModelDistribution` ### Honest limitations - The public Store API stops paging around offset 15,000 — with `popularity` sort you get the most active ~15k actors, not the long tail. Use `category` filters to fully cover a segment. - The riser flag naturally includes recently launched actors (a new actor's 30-day share is high by definition); the absolute-users threshold filters pure noise but not this bias. - `runsPerNewUser30d` reflects heavy use by existing users, not retention. - The API exposes no creation date, so "time to traction" can't be computed. ### Typical uses - Pick a niche with evidence: demand volume vs. concentration per category. - Sanity-check your expectations before publishing an actor. - Track who is rising this month and what they're building. - Feed the dataset into your own analysis or LLM research pipeline. # Actor input Schema ## `search` (type: `string`): Optional keyword filter, same as the Store search box (e.g. "linkedin", "tiktok", "pdf"). Leave empty to scan without a keyword. ## `category` (type: `string`): Optional Store category filter. Known values include: SOCIAL_MEDIA, LEAD_GENERATION, AUTOMATION, ECOMMERCE, DEVELOPER_TOOLS, AI, AGENTS, SEO_TOOLS, JOBS, VIDEOS, NEWS, TRAVEL, REAL_ESTATE, BUSINESS, MCP_SERVERS. Leave empty to scan all categories. ## `sortBy` (type: `string`): Order in which the Store is paged through. With a maxItems cap, "popularity" gives you the top of the market, "newest" gives you recent launches. ## `maxItems` (type: `integer`): Stop after collecting this many actors. The public Store API stops paging around offset 15,000, so with popularity sort you effectively get the most active ~15k actors of the store. ## `riserMinGrowthPercent` (type: `integer`): An actor is flagged as a "riser" when its last-30-days new users are at least this percentage of its all-time users (default 30%). ## `riserMinNewUsers30d` (type: `integer`): Minimum absolute new users in the last 30 days for the riser flag, to filter out noise from tiny actors (default 10). ## Actor input object example ```json { "search": "", "category": "", "sortBy": "popularity", "maxItems": 5000, "riserMinGrowthPercent": 30, "riserMinNewUsers30d": 10 } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("honest_cherimoya/apify-store-scanner").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 = {} # Run the Actor and wait for it to finish run = client.actor("honest_cherimoya/apify-store-scanner").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 '{}' | apify call honest_cherimoya/apify-store-scanner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=honest_cherimoya/apify-store-scanner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Apify Store Market Scanner", "description": "Turn the whole Apify Store into a structured dataset with growth & opportunity metrics: rising actors, category concentration, and realistic expectations for new publishers.", "version": "0.1", "x-build-id": "8s14enxspIXsCcO7a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/honest_cherimoya~apify-store-scanner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-honest_cherimoya-apify-store-scanner", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/honest_cherimoya~apify-store-scanner/runs": { "post": { "operationId": "runs-sync-honest_cherimoya-apify-store-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/honest_cherimoya~apify-store-scanner/run-sync": { "post": { "operationId": "run-sync-honest_cherimoya-apify-store-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "search": { "title": "Search term", "type": "string", "description": "Optional keyword filter, same as the Store search box (e.g. \"linkedin\", \"tiktok\", \"pdf\"). Leave empty to scan without a keyword.", "default": "" }, "category": { "title": "Category", "type": "string", "description": "Optional Store category filter. Known values include: SOCIAL_MEDIA, LEAD_GENERATION, AUTOMATION, ECOMMERCE, DEVELOPER_TOOLS, AI, AGENTS, SEO_TOOLS, JOBS, VIDEOS, NEWS, TRAVEL, REAL_ESTATE, BUSINESS, MCP_SERVERS. Leave empty to scan all categories.", "default": "" }, "sortBy": { "title": "Sort order", "enum": [ "popularity", "newest" ], "type": "string", "description": "Order in which the Store is paged through. With a maxItems cap, \"popularity\" gives you the top of the market, \"newest\" gives you recent launches.", "default": "popularity" }, "maxItems": { "title": "Max actors to collect", "minimum": 10, "maximum": 20000, "type": "integer", "description": "Stop after collecting this many actors. The public Store API stops paging around offset 15,000, so with popularity sort you effectively get the most active ~15k actors of the store.", "default": 5000 }, "riserMinGrowthPercent": { "title": "Riser: min 30-day growth share (%)", "minimum": 1, "maximum": 100, "type": "integer", "description": "An actor is flagged as a \"riser\" when its last-30-days new users are at least this percentage of its all-time users (default 30%).", "default": 30 }, "riserMinNewUsers30d": { "title": "Riser: min new users in 30 days", "minimum": 1, "type": "integer", "description": "Minimum absolute new users in the last 30 days for the riser flag, to filter out noise from tiny actors (default 10).", "default": 10 } } }, "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 } } } } } } } } } } ```