# US Brand Signal Batch Splitter (`zinin/us-brand-signal-batch-splitter`) Actor

Split already-normalized US brand signal rows into stable groups by one submitted grouping mode without network access or inference.

- **URL**: https://apify.com/zinin/us-brand-signal-batch-splitter.md
- **Developed by:** [Tim Zinin](https://apify.com/zinin) (community)
- **Categories:** MCP servers, Automation, Marketing
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## US Brand Signal Batch Splitter

Split a submitted batch of normalized US brand signal rows into stable machine-readable groups. Choose `brand`, `eventType`, `severity`, or `analysisStatus`; receive exact IDs, counts, and SHA-256 digests. No browsing, enrichment, inference, or child Actor calls.

### Input and output

Submit 1-100 unique closed rows with `signalId`, `brandName`, `eventType`, `severity`, and `analysisStatus`, plus exactly one `groupingMode`. Rows are already normalized buyer input. The Actor sorts rows, group keys, and IDs with stable ASCII ordering. The report contains one group per distinct selected value and no hidden claims about the underlying signals.

### Pricing

Pay-per-event pricing charges one `apify-actor-start` on start and at most one `result-found` after one confirmed Dataset row. Invalid input, unsafe pricing, and buyer-cap failures produce no result event. Six PPE tiers are configured:

| Tier | Discount | Start | Result |
| --- | ---: | ---: | ---: |
| FREE | 0% | $0.005000 | $0.010000 |
| BRONZE | 5% | $0.004750 | $0.009500 |
| SILVER | 10% | $0.004500 | $0.009000 |
| GOLD | 15% | $0.004250 | $0.008500 |
| PLATINUM | 18% | $0.004100 | $0.008200 |
| DIAMOND | 20% | $0.004000 | $0.008000 |

### Freshness

This is a closed inline transformation. It has no source freshness, scheduled refresh, network request, runtime clock, Dataset input, or persistent state. Re-run it when the buyer has a new normalized batch.

### Limits and privacy

The public input maximum is 100 rows and 64 KiB serialized input. Output is compact and runtime-capped below 48 KiB; every terminal receipt is strictly below 64 KiB. Runtime defaults are 512 MB and 90 seconds with `LIMITED_PERMISSIONS` and Standby disabled. No URL, proxy, BYOK key, secret, LLM, child Actor, queue, cron, or remote write is used.

### FAQ

#### Does this verify a signal or brand identity?

No. It groups only the exact submitted values and attributes no truth to them.

#### Can I send a Dataset ID or URL?

No. The contract is intentionally inline and closed.

#### Does input order affect the result?

No. Canonical row, group, ID, and digest ordering is deterministic.

#### Why are counts and digests trustworthy?

The runtime recomputes every derived field from the canonical request. Standalone schemas reject unknown fields, invalid ranges, duplicate group IDs, and malformed digest strings; adversarial tests cover forged counts, groups, and digests.

### Related public Actors

The following five public Store links are the verified HTTP-200 related-Actor links retained for this local build:

| Actor | Use it for |
| --- | --- |
| [US Brand Action Queue](https://apify.com/zinin/us-brand-action-queue) | Turn submitted signal rows into an ordered action queue. |
| [US Brand Offer Evidence Normalizer](https://apify.com/zinin/us-brand-offer-evidence-normalizer) | Normalize offer observations before downstream grouping. |
| [US Brand Federal Award Snapshot](https://apify.com/zinin/us-brand-federal-award-snapshot) | Collect federal-award evidence upstream of a signal workflow. |
| [Competitor Change Rollup](https://apify.com/zinin/competitor-change-rollup) | Consolidate competitor changes before splitting work. |
| [Trademark Multiregistry](https://apify.com/zinin/trademark-multiregistry) | Add trademark evidence to a buyer-owned signal batch. |

This local build does not contact those links or any remote service.

# Actor input Schema

## `schemaVersion` (type: `string`):

Required closed V1 contract.

## `groupingMode` (type: `string`):

Choose the submitted row field used as the group key.

## `rows` (type: `array`):

One to 100 rows. Rows are submitted facts; this Actor does not verify, enrich, or infer them.

## Actor input object example

```json
{
  "schemaVersion": "1.0",
  "groupingMode": "brand",
  "rows": [
    {
      "signalId": "sig-nike-launch-001",
      "brandName": "Nike",
      "eventType": "product_launch",
      "severity": "high",
      "analysisStatus": "unreviewed"
    },
    {
      "signalId": "sig-nike-price-002",
      "brandName": "Nike",
      "eventType": "price_change",
      "severity": "medium",
      "analysisStatus": "reviewed"
    },
    {
      "signalId": "sig-acme-recall-003",
      "brandName": "Acme Labs",
      "eventType": "recall",
      "severity": "critical",
      "analysisStatus": "reviewed"
    },
    {
      "signalId": "sig-contoso-news-004",
      "brandName": "Contoso",
      "eventType": "news_item",
      "severity": "low",
      "analysisStatus": "unknown"
    }
  ]
}
```

# Actor output Schema

## `OUTPUT` (type: `string`):

API link to the terminal receipt after the bounded OUTPUT write.

## `report` (type: `string`):

API link to the one report row after confirmed Dataset delivery.

# 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 = {
    "schemaVersion": "1.0",
    "groupingMode": "brand",
    "rows": [
        {
            "signalId": "sig-nike-launch-001",
            "brandName": "Nike",
            "eventType": "product_launch",
            "severity": "high",
            "analysisStatus": "unreviewed"
        },
        {
            "signalId": "sig-nike-price-002",
            "brandName": "Nike",
            "eventType": "price_change",
            "severity": "medium",
            "analysisStatus": "reviewed"
        },
        {
            "signalId": "sig-acme-recall-003",
            "brandName": "Acme Labs",
            "eventType": "recall",
            "severity": "critical",
            "analysisStatus": "reviewed"
        },
        {
            "signalId": "sig-contoso-news-004",
            "brandName": "Contoso",
            "eventType": "news_item",
            "severity": "low",
            "analysisStatus": "unknown"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/us-brand-signal-batch-splitter").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 = {
    "schemaVersion": "1.0",
    "groupingMode": "brand",
    "rows": [
        {
            "signalId": "sig-nike-launch-001",
            "brandName": "Nike",
            "eventType": "product_launch",
            "severity": "high",
            "analysisStatus": "unreviewed",
        },
        {
            "signalId": "sig-nike-price-002",
            "brandName": "Nike",
            "eventType": "price_change",
            "severity": "medium",
            "analysisStatus": "reviewed",
        },
        {
            "signalId": "sig-acme-recall-003",
            "brandName": "Acme Labs",
            "eventType": "recall",
            "severity": "critical",
            "analysisStatus": "reviewed",
        },
        {
            "signalId": "sig-contoso-news-004",
            "brandName": "Contoso",
            "eventType": "news_item",
            "severity": "low",
            "analysisStatus": "unknown",
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/us-brand-signal-batch-splitter").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 '{
  "schemaVersion": "1.0",
  "groupingMode": "brand",
  "rows": [
    {
      "signalId": "sig-nike-launch-001",
      "brandName": "Nike",
      "eventType": "product_launch",
      "severity": "high",
      "analysisStatus": "unreviewed"
    },
    {
      "signalId": "sig-nike-price-002",
      "brandName": "Nike",
      "eventType": "price_change",
      "severity": "medium",
      "analysisStatus": "reviewed"
    },
    {
      "signalId": "sig-acme-recall-003",
      "brandName": "Acme Labs",
      "eventType": "recall",
      "severity": "critical",
      "analysisStatus": "reviewed"
    },
    {
      "signalId": "sig-contoso-news-004",
      "brandName": "Contoso",
      "eventType": "news_item",
      "severity": "low",
      "analysisStatus": "unknown"
    }
  ]
}' |
apify call zinin/us-brand-signal-batch-splitter --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=zinin/us-brand-signal-batch-splitter",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

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