Pricing

from $8.50 / 1,000 delivered policy simulation reports

Signal Threshold Policy Scenario Simulator

Compare up to 20 explicit buyer-owned threshold policies over normalized signal scores. Get complete outcomes, pairwise changes, sensitive signals, stable digests, evidence gaps, human review action, and current-run billing truth without selecting a winner.

Pricing

from $8.50 / 1,000 delivered policy simulation reports

Rating

0.0

(0)

Developer

Tim Zinin

Actor stats

Bookmarked

Total users

Monthly active users

11 days ago

Last modified

Categories

MCP servers

Automation

Marketing

You can access the Signal Threshold Policy Scenario Simulator programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = {
9    "schemaVersion": "1.1",
10    "rows": [
11        {
12            "signalId": "signal-001",
13            "normalizedScore": 96,
14        },
15        {
16            "signalId": "signal-002",
17            "normalizedScore": 82,
18        },
19        {
20            "signalId": "signal-003",
21            "normalizedScore": 61,
22        },
23        {
24            "signalId": "signal-004",
25            "normalizedScore": 39,
26        },
27    ],
28    "policies": [
29        {
30            "policyId": "balanced",
31            "reviewThreshold": 50,
32            "acceptThreshold": 80,
33            "thresholdOrder": "review_lt_accept",
34        },
35        {
36            "policyId": "strict",
37            "reviewThreshold": 70,
38            "acceptThreshold": 90,
39            "thresholdOrder": "review_lt_accept",
40        },
41        {
42            "policyId": "broad",
43            "reviewThreshold": 30,
44            "acceptThreshold": 70,
45            "thresholdOrder": "review_lt_accept",
46        },
47    ],
48}
49
50# Run the Actor and wait for it to finish
51run = client.actor("zinin/us-brand-signal-policy-simulator").call(run_input=run_input)
52
53# Fetch and print Actor results from the run's dataset (if there are any)
54print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
55for item in client.dataset(run.default_dataset_id).iterate_items():
56    print(item)
57
58# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Signal Policy Simulator — Threshold Comparison API in Python

The Apify API client for Python is the official library that allows you to use Signal Threshold Policy Scenario Simulator API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Signal Threshold Policy Scenario Simulator API in JavaScript

Signal Threshold Policy Scenario Simulator API through CLI

Signal Threshold Policy Scenario Simulator OpenAPI definition

Signal Threshold Policy Scenario Simulator API

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Summarize buyer-supplied signal rows by brand in one deterministic report. Preserve event, severity, status, and recorded-source counts with stable digests, evidence gaps, and a human review action. No source fetching or external verification.

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Compare two buyer-supplied signal sets in one deterministic union report. Preserve left-only, right-only, exact duplicate, and field-conflict evidence without selecting a winner or fetching recorded source URLs.

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