# 📊 SEC XBRL Financial Frames — Peer Comparison (`nexgendata/sec-xbrl-financial-frames`) Actor Pull one XBRL financial concept (revenue, net income, assets, EPS, etc.) across ALL SEC filers for a given period — instant cross-company peer benchmarking. For quant and fundamental analysts. - **URL**: https://apify.com/nexgendata/sec-xbrl-financial-frames.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 heroku bill estimate + multi-host comparisons 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 ## 📊 SEC XBRL Financial Frames — Peer Comparison **Pay-per-result cross-filer fundamentals — $0.10 per company-value record. Pull one XBRL financial concept (revenue, net income, total assets, EPS, and hundreds more) reported by every SEC filer for a single period, in one call. Instant peer benchmarking from the official SEC data, no Bloomberg seat required.** Most financial data tools are built to answer "what did *this* company report?" The SEC's XBRL "frames" API flips that question on its head: it answers "what did *every* company report for this concept, in this period?" The SEC XBRL Financial Frames actor wraps that capability into a clean, paginated, filterable feed. Pick a us-gaap concept — `Revenues`, `NetIncomeLoss`, `Assets`, `EarningsPerShareBasic`, or any of the hundreds of standardized XBRL tags companies file — pick a period like `CY2025Q1` or `CY2024`, and get back one record per filer that reported that exact concept for that exact frame. The result is an instant cross-company benchmark table assembled straight from machine-readable SEC filings. This is the dataset behind sector screens, peer-multiple analysis, and "show me every company that reported revenue above X last quarter" research — sourced from the same XBRL filings the SEC stores, with no terminal subscription and no manual filing-by-filing extraction. ### Why use this Pulling one metric across the whole filing universe sounds simple until you try it against raw SEC infrastructure. The XBRL frames endpoint requires an exact taxonomy/concept/unit/period quadruple, returns unsorted JSON with no value filtering, demands a properly-formatted contact User-Agent on every request or it blocks you, and mixes duration concepts (revenue) with instant concepts (assets) that take different period syntaxes. This actor smooths all of that: - **One concept, every filer, one call.** You don't loop over thousands of CIK numbers — you ask for the concept once and the SEC returns every company that tagged it for the period. - **Value filtering built in.** Set `minValue` to drop the long tail of micro-filers and keep only companies above a materiality threshold, so a "$1B+ revenue" peer set is one parameter. - **Period syntax handled.** Annual (`CY2024`), quarterly (`CY2025Q1`), and instant (`CY2025Q1I`) frames are all supported — the actor passes them through correctly so you don't have to memorize the SEC's frame grammar. - **Compliant by construction.** The SEC requires a contact User-Agent; the actor sends one on every request, so you stay inside the SEC's fair-access policy. - **Audit-grade provenance.** Every record carries the exact `form` and `filed` date the value came from, so a benchmark figure traces back to a specific filing. ### What you get Each record is one filer's reported value for the requested concept and period, with the following real output fields: - `entityName` — the filer's company name as registered with the SEC - `concept` — the us-gaap XBRL concept the value represents (e.g. `Revenues`, `NetIncomeLoss`, `Assets`, `EarningsPerShareBasic`) - `period` — the frame period the value belongs to (e.g. `CY2025Q1`, `CY2024`) - `value` — the reported numeric value, in the requested unit (USD, USD-per-share, etc.) - `form` — the SEC form the value was filed on (e.g. `10-K`, `10-Q`) - `filed` — the date the filing that contained this value was submitted to the SEC Every value comes directly from the company's own XBRL-tagged filing as stored by the SEC — no estimates, no consensus, no proprietary normalization beyond the SEC's standardized taxonomy. ### Use cases - **Instant peer benchmarking.** Pull `Revenues` for `CY2024` with `minValue` set to your sector floor, and you have every comparable company's annual revenue in one table — no manual filing pulls. - **Sector revenue / profitability screens.** Rank all filers by `NetIncomeLoss` for a quarter to find the most (and least) profitable names, then drill into the outliers. - **Cross-company multiple construction.** Combine two runs — `NetIncomeLoss` and a share-count or EPS concept — to build trailing P/E or margin comparisons across an entire industry. - **Macro / sector aggregation.** Sum `Revenues` across all filers for successive quarters to build a bottom-up, filing-sourced read on aggregate corporate revenue trends. - **Anomaly and restatement detection.** Trend one company's reported concept across periods (run the actor for each period) to spot restatements or unusual swings against the peer distribution. - **Quant fundamental factor pipelines.** Use cross-sectional concept values as raw inputs to value, quality, or growth factors without licensing a fundamentals vendor. - **Research and journalism.** Answer "which companies reported the highest [concept] last quarter?" directly from the primary source, with each figure linked to its form and filing date. - **Data-quality and coverage checks.** Use the frames feed to see which filers did and did not tag a given concept in a period — a coverage map of XBRL reporting practice. ### Sample output A single record returned by the actor: ```json { "entityName": "APPLE INC.", "concept": "Revenues", "period": "CY2024", "value": 391035000000, "form": "10-K", "filed": "2024-11-01" } ```` An EPS example (unit `USD-per-shares`): ```json { "entityName": "MICROSOFT CORPORATION", "concept": "EarningsPerShareBasic", "period": "CY2025Q1", "value": 3.24, "form": "10-Q", "filed": "2025-04-30" } ``` ### Input parameters | Parameter | Label | Description | |-----------|-------|-------------| | `concept` | Concept (XBRL tag) | The us-gaap concept to pull, e.g. `Revenues`, `NetIncomeLoss`, `Assets`, `EarningsPerShareBasic`. | | `taxonomy` | Taxonomy | The XBRL taxonomy (default `us-gaap`). | | `unit` | Unit | The reporting unit, e.g. `USD`, `USD-per-shares`. | | `period` | Period | Frame period: `CY2025Q1` (quarter), `CY2024` (annual), `CY2025Q1I` (instant). | | `minValue` | Min value | Only return filers whose reported value is greater than or equal to this. | | `maxResults` | Max results | Maximum number of filers to return. | | `userAgentContact` | SEC User-Agent contact | Contact string (the SEC requires a User-Agent with contact info on every request). | ### How to use #### Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/sec-xbrl-financial-frames").call(run_input={ "concept": "Revenues", "taxonomy": "us-gaap", "unit": "USD", "period": "CY2024", "minValue": 1000000000, "maxResults": 500, "userAgentContact": "Your Name your@email.com" }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["entityName"], item["value"], item["form"], item["filed"]) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~sec-xbrl-financial-frames/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "concept": "Revenues", "taxonomy": "us-gaap", "unit": "USD", "period": "CY2024", "minValue": 1000000000, "maxResults": 500, "userAgentContact": "Your Name your@email.com" }' ``` Schedule it quarterly via Apify's built-in scheduler — once a reporting season's 10-Qs and 10-Ks are filed and XBRL-indexed, a run for the new period rebuilds your peer table. Export as JSON, JSONL, CSV, or Excel, or push to your warehouse for a period-over-period fundamentals store. ### Pricing This actor runs on Apify's **pay-per-event (PPE)** model — you pay only for the company-value records returned, never for run-time: - **$0.10 per company-value record** (the primary event — one charge per filer value pushed to the dataset) - A negligible actor-start cost per run (sub-cent at typical memory) No subscription, no minimum. A run that returns zero rows (e.g. a `minValue` higher than any filer reported) costs nothing for results. #### Cost worked example - Top 100 filers by revenue for a year → 100 × $0.10 = **$10.00** per run - 500-filer peer set above a $1B floor → 500 × $0.10 = **$50.00** per run - A 4-concept fundamentals pull (revenue, net income, assets, EPS) at 200 filers each → 800 × $0.10 = **$80.00** - Quarterly schedule of a 100-name sector screen → 100 × $0.10 × 4/year = **$40.00/year** You know the cost from the row count before the run starts — no compute or storage add-ons. ### How this compares to Bloomberg | | Bloomberg Terminal | SEC XBRL Financial Frames | |---|---|---| | Cross-company concept comparison | Yes (EQS / FA screens) | Yes (one concept across all filers) | | Source | Aggregated / normalized vendor data | Filers' own SEC XBRL filings (primary source) | | Annual cost | ~$24,000 / seat / year | Pay-per-record, no subscription | | Programmatic API | BLPAPI (seat-locked) | Apify REST + webhooks | | Provenance per figure | Vendor-normalized | Exact `form` + `filed` date per value | | Best for | Full cross-asset workstation | Filing-sourced peer screens & factor pipelines | Bloomberg gives you a polished, normalized fundamentals layer with global coverage and a full workstation around it — and you pay ~$24k/seat/year for the whole thing. This actor does one thing the terminal does, the same way an analyst would if they read the filings themselves: it pulls a concept across every filer straight from the source XBRL, with each value traceable to a specific form and filing date. For a peer screen, a sector aggregate, or a quant factor input, that's a 95%+ cost saving and arguably cleaner provenance, because every number points back to a real filing rather than a vendor's normalization. ### FAQ **Q: What concepts can I request?** A: Any standardized us-gaap XBRL tag companies file — `Revenues`, `NetIncomeLoss`, `Assets`, `Liabilities`, `EarningsPerShareBasic`, `StockholdersEquity`, and hundreds more. Pass the exact tag in `concept`. **Q: What's the difference between annual, quarterly, and instant periods?** A: Duration concepts (revenue, net income) use annual (`CY2024`) or quarterly (`CY2025Q1`) frames. Balance-sheet "instant" concepts (assets, equity) measured at a point in time use the instant frame syntax with a trailing `I` (e.g. `CY2025Q1I`). Match the period type to the concept. **Q: Why do I have to supply a User-Agent contact?** A: The SEC's fair-access policy requires every automated request to identify itself with a contact (name/email). The actor sends your `userAgentContact` so the SEC can reach the operator if needed — it's a condition of using the SEC's data programmatically. **Q: Why might a company I expect be missing from the results?** A: A filer only appears if it tagged that exact concept, in that exact unit, for that exact frame. Companies that use a different (but economically similar) tag, report in a different period, or filed late may not be in a given frame — that's a property of XBRL tagging practice, not a gap in the actor. **Q: Are the values normalized or adjusted?** A: No. Values are exactly as the company reported them in XBRL, in the requested unit. The SEC's taxonomy provides standardization; the actor applies no further adjustment. **Q: Can I build trailing or multi-period comparisons?** A: Yes — run the actor once per period and join on `entityName`. Each record's `period`, `form`, and `filed` fields keep the time dimension explicit. ### Schema stability & versioning This actor follows NexGenData's **additive-only schema** contract. New fields may be added over time as new keys, absent on older runs. The existing fields (`entityName`, `concept`, `period`, `value`, `form`, `filed`) are never renamed or removed without a major-version bump and advance changelog notice, and their semantics (the value is the as-reported XBRL figure in the requested unit) are never silently changed. Build period-over-period fundamentals pipelines on this actor with confidence. ### Compliance & legal - The actor reads the **SEC's public XBRL frames data** — machine-readable structured data the SEC publishes from filers' own EDGAR submissions. No authentication, no anti-bot evasion, no private endpoints. - Corporate financial filings are public disclosure mandated by the federal securities laws; the SEC publishes the XBRL data explicitly for researchers and market participants. - Every request identifies itself with the contact User-Agent you supply, per the SEC's fair-access policy, and is paced politely. - You are responsible for ensuring your downstream use complies with the SEC's terms and your jurisdiction's rules. Read-only research, benchmarking, and factor-modeling use of public filing data is widely accepted; consult counsel before bulk commercial redistribution. ### Related NexGenData actors Part of NexGenData's SEC / disclosure intelligence cluster — pair this actor with: - [SEC Form 4 Insider Monitor](https://apify.com/nexgendata/sec-form-4-insider-monitor?fpr=2ayu9b) — corporate-insider buys and sells - [SEC 8-K Event Monitor](https://apify.com/nexgendata/sec-8k-event-monitor?fpr=2ayu9b) — material corporate events - [SEC Exec Comp Proxy Tracker](https://apify.com/nexgendata/sec-exec-comp-proxy-tracker?fpr=2ayu9b) — executive-compensation disclosures from proxy filings - [SEC Activist Proxy Solicitation Tracker (DFAN14A)](https://apify.com/nexgendata/sec-activist-proxy-solicitation-tracker?fpr=2ayu9b) — dissident proxy-solicitation filings - [Stock Buyback Announcement Tracker](https://apify.com/nexgendata/stock-buyback-announcement-tracker?fpr=2ayu9b) — repurchase-program announcements - [SEC IPO Prospectus Tracker](https://apify.com/nexgendata/sec-ipo-prospectus-tracker?fpr=2ayu9b) — registration and prospectus filings Explore the full catalog of 200+ buyer-intent actors at **https://apify.com/nexgendata?fpr=2ayu9b**. # Actor input Schema ## `concept` (type: `string`): us-gaap concept, e.g. Revenues, NetIncomeLoss, Assets, EarningsPerShareBasic. ## `taxonomy` (type: `string`): Taxonomy (default us-gaap). ## `unit` (type: `string`): Unit (USD, USD-per-shares, etc.). ## `period` (type: `string`): Frame period, e.g. CY2025Q1, CY2024 (annual), CY2025Q1I (instant). ## `minValue` (type: `number`): Only filers with value >= this. ## `maxResults` (type: `integer`): Max filers to return. ## `userAgentContact` (type: `string`): SEC requires a UA with contact info. ## Actor input object example ```json { "concept": "Revenues", "taxonomy": "us-gaap", "unit": "USD", "period": "CY2025Q1", "maxResults": 5000 } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "concept": "Revenues", "period": "CY2025Q1" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/sec-xbrl-financial-frames").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "concept": "Revenues", "period": "CY2025Q1", } # Run the Actor and wait for it to finish run = client.actor("nexgendata/sec-xbrl-financial-frames").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 '{ "concept": "Revenues", "period": "CY2025Q1" }' | apify call nexgendata/sec-xbrl-financial-frames --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/sec-xbrl-financial-frames", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📊 SEC XBRL Financial Frames — Peer Comparison", "description": "Pull one XBRL financial concept (revenue, net income, assets, EPS, etc.) across ALL SEC filers for a given period — instant cross-company peer benchmarking. For quant and fundamental analysts.", "version": "0.0", "x-build-id": "ZmbCisgQ5Zp6ZHqz2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~sec-xbrl-financial-frames/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-sec-xbrl-financial-frames", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~sec-xbrl-financial-frames/runs": { "post": { "operationId": "runs-sync-nexgendata-sec-xbrl-financial-frames", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~sec-xbrl-financial-frames/run-sync": { "post": { "operationId": "run-sync-nexgendata-sec-xbrl-financial-frames", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "concept": { "title": "Concept (XBRL tag)", "type": "string", "description": "us-gaap concept, e.g. Revenues, NetIncomeLoss, Assets, EarningsPerShareBasic.", "default": "Revenues" }, "taxonomy": { "title": "Taxonomy", "type": "string", "description": "Taxonomy (default us-gaap).", "default": "us-gaap" }, "unit": { "title": "Unit", "type": "string", "description": "Unit (USD, USD-per-shares, etc.).", "default": "USD" }, "period": { "title": "Period", "type": "string", "description": "Frame period, e.g. CY2025Q1, CY2024 (annual), CY2025Q1I (instant).", "default": "CY2025Q1" }, "minValue": { "title": "Min value", "type": "number", "description": "Only filers with value >= this." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 20000, "type": "integer", "description": "Max filers to return.", "default": 5000 }, "userAgentContact": { "title": "SEC User-Agent contact", "type": "string", "description": "SEC requires a UA with contact info." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```