# US Brand Signal Confidence Gate (`zinin/us-brand-signal-confidence-gate`) Actor

Apply buyer-owned accept and review thresholds to normalized signal scores without verifying truth or inferring identity.

- **URL**: https://apify.com/zinin/us-brand-signal-confidence-gate.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 Confidence Gate

US Brand Signal Confidence Gate is a deterministic signal-scoring classifier for teams that already have normalized US brand signals. Give it 1-100 buyer-supplied scores and an explicit accept/review policy; it returns sorted accepted, review, and rejected signal IDs with closed reason codes, stable counts, and SHA-256 digests.

It does not verify the signal, fetch a source, identify a person or company, or claim that an accepted row is true.

### What you get

- Exact `acceptedIds`, `reviewIds`, and `rejectedIds` lists sorted by opaque buyer signal ID.
- One decision row and one closed reason code for every input row.
- Explicit threshold ordering: `reviewThreshold < acceptThreshold` and `thresholdOrder: "accept_gt_review"`.
- Reconciled counts plus input, row-list, outcome-list, and result digests.
- One bounded Dataset report and a truthful `OUTPUT` terminal receipt.
- Runs on Apify with JSON export, API access, schedules, monitoring, and pipeline integration after your own publication decision.

### How to run it

1. Click **Try for free** in the Actor Console.
2. Provide `thresholds` and `rows` in the JSON input. Each row needs an opaque `signalId` and an integer `normalizedSignal` from 1 to 100.
3. Set `acceptThreshold` higher than `reviewThreshold`, keep `thresholdOrder` exactly `accept_gt_review`, and click **Start**.
4. Read the one report row from the Dataset and the delivery receipt from `OUTPUT`.

No source URL, proxy, API key, Dataset ID, identity field, or runtime timestamp is accepted.

### Pricing

Pay per event: **$0.005 per run start + $0.010 per delivered result** on the 0% FREE tier. The result charge is emitted only for one confirmed Dataset write with the named `result-found` event. The six exact PPE tiers are:

| Tier | Discount | Run start | Delivered result |
| --- | ---: | ---: | ---: |
| FREE | 0% | $0.00500 | $0.01000 |
| BRONZE | 5% | $0.00475 | $0.00950 |
| SILVER | 10% | $0.00450 | $0.00900 |
| GOLD | 15% | $0.00425 | $0.00850 |
| PLATINUM | 18% | $0.00410 | $0.00820 |
| DIAMOND | 20% | $0.00400 | $0.00800 |

The runtime checks the buyer cap for the start plus one result before writing the Dataset. Invalid input, budget rejection, uncertain delivery, and uncharged delivery are not reported as successful results. One example batch of four rows costs $0.015 on FREE when delivered successfully.

### Freshness and determinism

This Actor has no source freshness to measure because it never fetches data and never reads the runtime clock. Freshness is the buyer's responsibility: rerun when the upstream signal set changes. The same canonical input always produces the same report and digests, regardless of row order.

### Input

```json
{
  "schemaVersion": "1.0",
  "thresholds": {"acceptThreshold": 80, "reviewThreshold": 50, "thresholdOrder": "accept_gt_review"},
  "rows": [
    {"signalId": "signal-001", "normalizedSignal": 96},
    {"signalId": "signal-002", "normalizedSignal": 82},
    {"signalId": "signal-003", "normalizedSignal": 61},
    {"signalId": "signal-004", "normalizedSignal": 39}
  ]
}
```

The inline input above is the complete runnable fixture. A successful run returns the one report row described below and a terminal receipt in `OUTPUT`.

### Output

The Dataset contains one report item. Its decision rows use these closed codes:

| Score rule | Outcome | Reason code |
| --- | --- | --- |
| `score >= acceptThreshold` | accepted | `at_or_above_accept_threshold` |
| `reviewThreshold <= score < acceptThreshold` | review | `at_or_above_review_below_accept_threshold` |
| `score < reviewThreshold` | rejected | `below_review_threshold` |

The result also includes `counts`, `inputDigest`, `rowsDigest`, one digest for each ID list, and `resultDigest`. `OUTPUT` records whether Dataset delivery and the `result-found` charge were confirmed. Validation, budget, counter, and uncertain delivery failures remain non-success receipts and never retry a paid Dataset write.

### Related Actors

Need to prepare, aggregate, and act on signal data?

| Actor | What it does |
| --- | --- |
| [US Brand Signal Metrics Aggregator](https://apify.com/zinin/us-brand-signal-metrics-aggregator) | Summarizes already collected signal rows into deterministic counts and metrics. |
| [US Brand Evidence Snapshot Diff](https://apify.com/zinin/us-brand-evidence-snapshot-diff) | Compares two submitted evidence snapshots without fetching either one. |
| [US Brand Action Queue](https://apify.com/zinin/us-brand-action-queue) | Turns selected signal rows into a bounded next-action proposal. |
| [US Brand Offer Evidence Normalizer](https://apify.com/zinin/us-brand-offer-evidence-normalizer) | Normalizes buyer-supplied offer evidence before policy gating. |
| [Website Content Crawler](https://apify.com/apify/website-content-crawler) | Collects website content upstream when a separate source-collection job is appropriate. |

These five links returned HTTP 200 on a read-only verification on 2026-08-05. The linked Actors are separate products; this gate does not call them.

### Limitations

- A score is an input value, not a truth probability. An accepted ID is not a verified lead, event, brand, person, or company.
- IDs are opaque and must be unique. Duplicate IDs are rejected rather than silently collapsed.
- Scores must be integers from 1 to 100; decimals, strings, missing fields, and extra fields fail closed.
- The maximum batch is 100 rows, and the report is intentionally compact.
- There is no live lookup, evidence validation, source freshness check, identity resolution, network access, proxy, BYOK, LLM, child Actor, state, or clock use.

### FAQ

**Can I use equal accept and review thresholds?** No. The ordering is deliberately strict so every score has exactly one outcome: `reviewThreshold < acceptThreshold`.

**Does accepted mean true or verified?** No. It means only that the submitted integer met the buyer's stated threshold.

**What happens at a boundary?** Accept is inclusive. Review is inclusive at `reviewThreshold` and exclusive at `acceptThreshold`. Rejected is strictly below `reviewThreshold`.

**Can I submit brand names or person names?** They are outside this contract. Submit an opaque buyer ID; the Actor performs no identity inference.

**What happens if a Dataset write or charge is uncertain?** The Actor makes no second Dataset push. `OUTPUT` reports `delivery_unknown`, `result_uncharged`, or `result_charge_delta` rather than claiming success.

**How do I report a problem?** Use the issue form on the Actor page and include the schema version, error code, and a redacted input shape. Do not include secrets.

Built by [zinin](https://apify.com/zinin).

# Actor input Schema

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

The only supported closed contract version.

## `thresholds` (type: `object`):

Accept is inclusive at acceptThreshold; review is inclusive from reviewThreshold up to, but excluding, acceptThreshold. The required ordering marker prevents ambiguity.

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

One to 100 unique signal IDs with integer normalized scores from 1 to 100. IDs are sorted by the runtime before classification.

## Actor input object example

```json
{
  "schemaVersion": "1.0",
  "thresholds": {
    "acceptThreshold": 80,
    "reviewThreshold": 50,
    "thresholdOrder": "accept_gt_review"
  },
  "rows": [
    {
      "signalId": "signal-001",
      "normalizedSignal": 96
    },
    {
      "signalId": "signal-002",
      "normalizedSignal": 82
    },
    {
      "signalId": "signal-003",
      "normalizedSignal": 61
    },
    {
      "signalId": "signal-004",
      "normalizedSignal": 39
    }
  ]
}
```

# Actor output Schema

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

API link to the terminal receipt when OUTPUT was stored.

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

API link to the one deterministic report row after confirmed result 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",
    "thresholds": {
        "acceptThreshold": 80,
        "reviewThreshold": 50,
        "thresholdOrder": "accept_gt_review"
    },
    "rows": [
        {
            "signalId": "signal-001",
            "normalizedSignal": 96
        },
        {
            "signalId": "signal-002",
            "normalizedSignal": 82
        },
        {
            "signalId": "signal-003",
            "normalizedSignal": 61
        },
        {
            "signalId": "signal-004",
            "normalizedSignal": 39
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("zinin/us-brand-signal-confidence-gate").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",
    "thresholds": {
        "acceptThreshold": 80,
        "reviewThreshold": 50,
        "thresholdOrder": "accept_gt_review",
    },
    "rows": [
        {
            "signalId": "signal-001",
            "normalizedSignal": 96,
        },
        {
            "signalId": "signal-002",
            "normalizedSignal": 82,
        },
        {
            "signalId": "signal-003",
            "normalizedSignal": 61,
        },
        {
            "signalId": "signal-004",
            "normalizedSignal": 39,
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("zinin/us-brand-signal-confidence-gate").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",
  "thresholds": {
    "acceptThreshold": 80,
    "reviewThreshold": 50,
    "thresholdOrder": "accept_gt_review"
  },
  "rows": [
    {
      "signalId": "signal-001",
      "normalizedSignal": 96
    },
    {
      "signalId": "signal-002",
      "normalizedSignal": 82
    },
    {
      "signalId": "signal-003",
      "normalizedSignal": 61
    },
    {
      "signalId": "signal-004",
      "normalizedSignal": 39
    }
  ]
}' |
apify call zinin/us-brand-signal-confidence-gate --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/m0IAXzHCVjUKWCnIT/builds/tCLQ8TKOhdjSyVjS8/openapi.json