# Apify Store Radar — New and Rising Actors (`intelscrape/apify-store-radar`) Actor

Scan the Apify store for newly-published actors that are gaining traction (new + rising user growth).

- **URL**: https://apify.com/intelscrape/apify-store-radar.md
- **Developed by:** [IntelScrape](https://apify.com/intelscrape) (community)
- **Categories:** AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 dataset items

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Apify Store Radar — find new Actors that are gaining traction

![Actor Banner](https://api.apify.com/v2/key-value-stores/PyvJzfQg02FD3RYhd/records/apify-store-radar-banner.png)

Scans the public Apify Store and surfaces the Actors that are **young and growing**,
not the ones that have been sitting at the top of the charts for two years.

Useful if you build Actors and want to see which niches are opening up, or if you
buy Actors and want the rising tools before they get crowded.

### SoftCTA mesh (0.2.7)

Every riser row (and optional `risers-summary`) includes a `softCta` object with
next-step links into IntelScrape tools for maker outreach, phone validation,
reviews/licenses, and people enrichment. **Skip Trace PRO is SoftCTA-only**
(links — code frozen). No self-link to this Actor. No extra SoftCTA PPE events.

### How "new & rising" is decided

The Store feed does not expose a creation date, so newness is inferred from the
user-count windows the Store publishes for every Actor:

| Metric | Formula | Meaning |
|---|---|---|
| `newnessPct` | `totalUsers90Days / totalUsers` | 100% = every user this Actor has ever had arrived in the last 90 days, i.e. it is young |
| `users30d` | `totalUsers30Days` | recent active volume — the traction signal |
| `accel` | `(users7d × 30/7) / users30d` | above 1.0 = this week is outpacing the 30-day average |
| `score` | `users30d × newness` | recent volume, weighted toward fresh Actors |

Results are filtered by `minUsers30d` and `minNewness`, sorted by `score`, and cut
to `topN`.

**`newnessPct` is a proxy, not a creation date.** An older Actor that recently went
viral will score as "new" because most of its users are recent. That is usually
what you want from a radar, but it is not the same as "published in the last 90 days".

### Input

| Field | Default | Notes |
|---|---|---|
| `scanLimit` | 2000 | How many Actors to pull from the Store before filtering. Higher = deeper, slower. |
| `minUsers30d` | 30 | Minimum recent users to count as traction. |
| `minNewness` | 0.65 | Minimum fraction of users acquired in the last 90 days (0–1). |
| `topN` | 30 | How many risers to return. |
| `category` | — | Optional single Store category, e.g. `LEAD_GENERATION`, `SOCIAL_MEDIA`, `AI`, `SEO_TOOLS`. |
| `sortBy` | popularity | Ordering used while scanning: `popularity`, `totalUsers`, `relevance`, `lastUpdate`. |
| `demoMode` | false | Synthetic risers + SoftCTA for Store demos. |
| `includeRisersSummary` | true | Push one `risers-summary` row with SoftCTA. |

### Output

One dataset item per riser (plus optional summary):

```json
{
  "rank": 1,
  "title": "Some New Scraper",
  "handle": "username/some-new-scraper",
  "url": "https://apify.com/username/some-new-scraper",
  "totalUsers": 412,
  "users30d": 388,
  "users7d": 121,
  "newnessPct": 94.2,
  "accel": 1.34,
  "score": 365.5,
  "rating": 4.8,
  "reviews": 6,
  "category": "LEAD_GENERATION",
  "source": "apify-store",
  "softCta": {
    "next": "Nice — rising Actor ...",
    "skipTracePro": "https://apify.com/intelscrape/skip-trace-pro"
  }
}
```

A `SUMMARY` record is also written to the key-value store with how many Actors were
scanned, how many passed the filters, and the filter values used.

### Notes

- Reads only the **public** Apify Store API. No token, no login, no private data.
- If your filters are strict you can legitimately get zero results — the run reports
  that in its status message rather than failing.
- SEEK untouched. SoftCTA deepen only.

# Actor input Schema

## `scanLimit` (type: `integer`):

How many top actors (by the chosen sort) to scan. Higher = deeper but slower.

## `minUsers30d` (type: `integer`):

Minimum recent active users to count as 'traction'.

## `minNewness` (type: `number`):

Min fraction of an actor's users acquired in the last 90 days. 1.0 = brand new.

## `topN` (type: `integer`):

How many top risers to output.

## `category` (type: `string`):

Limit to one store category, e.g. LEAD\_GENERATION, SOCIAL\_MEDIA, AI, SEO\_TOOLS.

## `sortBy` (type: `string`):

Which ordering to scan through.

## `demoMode` (type: `boolean`):

Return synthetic rising-actor rows with SoftCTA mesh (no Store scan). For Store listing demos.

## `includeRisersSummary` (type: `boolean`):

Push one risers-summary dataset row with SoftCTA mesh after risers.

## Actor input object example

```json
{
  "scanLimit": 2000,
  "minUsers30d": 30,
  "minNewness": 0.65,
  "topN": 30,
  "sortBy": "popularity",
  "demoMode": false,
  "includeRisersSummary": true
}
```

# Actor output Schema

## `actors` (type: `string`):

Scored Store actors in the default dataset.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("intelscrape/apify-store-radar").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("intelscrape/apify-store-radar").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 intelscrape/apify-store-radar --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,intelscrape/apify-store-radar"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/fGHfiJcItsuG4sRHL/builds/NtLb1QFpRceQTuZ0Z/openapi.json