# Australia Super Product & Pathway Structure - APRA (`nexgensignal/au-super-product-pathway-structure`) Actor

Australia superannuation product & pathway structure (APRA product-structure XLSX) as clean typed per-record data - product-profile (Table 1a) and investment-pathway (Table 1b) rows with fund/product/option identifiers and structural fields. 131,118 live rows. CC BY 4.0. $0.05 per record.

- **URL**: https://apify.com/nexgensignal/au-super-product-pathway-structure.md
- **Developed by:** [NexGen Signal](https://apify.com/nexgensignal) (community)
- **Categories:** Business, Developer tools, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $33.50 / 1,000 product/pathway records

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/actors/running/actors-in-store.md#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

## Australia Super Product & Pathway Structure - APRA

Australia's superannuation product and investment-pathway structure from APRA's quarterly product-structure workbook,
as clean typed records - one row per product profile or per investment pathway, with fund, product and option
identifiers and the current snapshot's structural fields.

### What one record represents

The source is APRA's **Quarterly Superannuation Product Statistics - Product Structure** workbook (XLSX) at
`www.apra.gov.au`, downloaded directly. The workbook holds two data tables and this Actor emits a typed record from
each, tagged by `record_type`: **product\_profile** (Table 1a - one row per superannuation product, with member
accounts and assets, product type/category/phase, open-to indicators, inception/end dates and the product disclosure
statement URL) and **investment\_pathway** (Table 1b - one row per investment pathway, with the product, investment
menu and investment option identifiers and names, member accounts and assets, proportions, option codes and
indicators).

### Coverage and volume

The live workbook holds **830 product-profile rows and 130,288 investment-pathway rows (131,118 records total)**. This
is stated plainly because it differs from the supply index's figure of 238,940: that figure was taken from the
workbook's padded row dimension, which includes trailing empty rows; the Actor delivers only the populated data rows,
and this listing quotes the live populated count. The workbook's SHA-256 is verified on the build. You raise **Maximum
records** to pull the full set or lower it to sample.

### Person data

Every identifier and name is organisation- or product-level (fund/RSE, product, investment menu and option). The
workbook carries no individual-trustee or contact column.

### Licence

Australian Prudential Regulation Authority (APRA). Published under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence: you are free to copy, redistribute and adapt the material for any purpose, including commercially, provided you attribute APRA and do not suggest APRA endorses you. The attribution rides on every record.

### Fields, scheduling and joins

Every field in the record is either a source-native identifier, a source-native attribute, or one of the six
provenance fields (`source`, `source_dataset`, `licence`, `attribution`, `caveat`, `observed_at`) the fleet attaches to
every record. Nothing is derived or inferred beyond the small, documented transforms noted above, and nothing is
dropped silently - the person-handling section spells out exactly which fields are excluded and why. The grain is one
record per the natural unit of the source, which keeps each row independently meaningful, keeps the key stable across
runs so re-running is a cheap upsert rather than a re-import, and lets you aggregate up to whatever unit you need
without unpicking a pre-joined table.

Because the source republishes on its own cadence, a scheduled run keeps a downstream table current: new and changed
records upsert over the old ones on the stable key, and the `observed_at` stamp on every record tells you when each was
last seen live. Set **Maximum records** low to sample the shape of the data cheaply, then raise it once the cell fits
your use; the Actor streams its source, so memory stays flat regardless of how many records you request, and you are
billed only for what is delivered. There is no subscription and no minimum - the per-record price and the record cap
together mean the spend on any run is known in advance and matched exactly to the data you receive.

### Reconciling counts honestly

Where the live count differs from any previously published figure, the live measure is the honest one and is what this
listing quotes; sources re-issue and re-version their files over time, and a workbook's declared row dimension can
include trailing empty rows. The run receipt always states what was actually delivered and charged and confirms the
two agree, so every run is auditable against itself regardless of what any external index expected.

### Integration notes

The output is a flat table of typed records, so it drops straight into whatever you already use: load the run's
dataset over the API or an export, key on the record id, and upsert. A common pattern is a light scheduled run that
pulls the newest slice into a staging table, then a merge on the stable key into the table your product reads, so you
never re-pay for rows you already hold and your history grows cleanly over time. Because identifiers are preserved
exactly as the source publishes them, joins across the fleet's cells - and onto your own systems - work without a
mapping layer: the same organisation, product, option or facility id lines up on both sides. If you only need a slice,
the record cap and any partition inputs bound the run precisely, so a targeted pull costs cents rather than the price
of the whole set. Nothing about the record shape assumes a particular warehouse, language or tool; it is deliberately
plain so the integration work is a load and a merge, not a cleaning project.

### Sibling Actors

The fleet's ETF-holdings cell is a different market and instrument; there is no row overlap. The two other APRA
superannuation cells in this batch (allocation history, performance history) share this host and licence but are
distinct datasets.

### Provenance and compliance

Every run reads the door host's `robots.txt` at runtime and records the result (URL, status, byte length and, where
a policy is served, its SHA-256) in the run's `RUN_RECEIPT`. Where the host serves no robots policy (HTTP 404), the
gate records that as a flag and proceeds on the licence, which grants re-use. The endpoint is keyless and the Actor
reads only the public data door - never a per-record detail page, never a mirror, and it never bypasses a block.

### Data quality and freshness

Numbers arrive as real numbers, booleans as real booleans, and every other value as a string or null, so the dataset
loads without a cleaning pass. Each record is keyed on a stable source identifier, so it is safe to diff, deduplicate
or upsert. Every run re-reads the live door, so the data is as fresh as the source publishes, and each record's
`observed_at` stamp dates the snapshot. The receipt records how many rows were delivered and charged and confirms
`charge_equals_delivered`.

### Billing, delivery and joins

Pricing is per record: you are billed only for records the Actor actually delivers, and the charge is raised after
each record is pushed (push-then-charge), so a failed or empty run costs nothing. The **Maximum records** cap bounds
every run, so spend is known before you start - sample cheaply, then raise it. Every record is a flat, typed object
keyed on a stable id, so it loads without a cleaning pass, diffs cleanly between runs, and upserts into a table you
keep over time; re-running keeps that table current without re-paying for rows you already hold, and each receipt
reconciles delivered against charged. Because the source's own identifiers are preserved verbatim, the dataset joins
onto other sources keyed on the same identifier.

### Who buys this, and how they use it

This cell is bought by teams that need the source's published set as a typed, keyed table they can hold and refresh
rather than a page they scrape: market-intelligence and analytics teams sizing and tracking a market, data engineers
wiring a clean upstream feed into a warehouse, and product teams building on a stable identifier. The grain and the
key are chosen so the output is a building block, not a one-off export - you run it on a schedule, keep the delta, and
join it to your other sources on the identifiers it preserves verbatim. The spend on any run is the per-record price
times the records delivered, matched exactly to what you receive.

# Actor input Schema

## `maxRecords` (type: `integer`):

Maximum records delivered and billed. You are billed only for records actually delivered. Raise it to pull the full set.

## Actor input object example

```json
{
  "maxRecords": 500
}
```

# Actor output Schema

## `results` (type: `string`):

The delivered APRA super product/pathway record.

# 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 = {
    "maxRecords": 500
};

// Run the Actor and wait for it to finish
const run = await client.actor("nexgensignal/au-super-product-pathway-structure").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 = { "maxRecords": 500 }

# Run the Actor and wait for it to finish
run = client.actor("nexgensignal/au-super-product-pathway-structure").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 '{
  "maxRecords": 500
}' |
apify call nexgensignal/au-super-product-pathway-structure --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,nexgensignal/au-super-product-pathway-structure"
        }
    }
}

```

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/Enr5DodW0nuH0rgIa/builds/xW7FaXDYk0HxQjPhb/openapi.json