# Kalshi Profile Scraper (`saswave/kalshi-profile-scraper`) Actor $0.8 / 1000 results. Extraction tool designed to unlock the social and financial layers of the Kalshi prediction market. Targeting individual trader profiles. Get data on profile, open positions, closed positions, trades, top categories, posts, comments, likes - **URL**: https://apify.com/saswave/kalshi-profile-scraper.md - **Developed by:** [SASWAVE](https://apify.com/saswave) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $0.80 / 1,000 results 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 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 ### πŸ“Š Kalshi Profile Scraper Extraction tool designed to unlock the social and financial layers of the Kalshi prediction market. By targeting individual trader profiles, this scraper provides a deep look into the "social betting" ecosystem, allowing you to track the exact movements, sentiments, and track records of the exchange's most successful participants. ### ✨ Features Financial Deep-Dive: Capture a user's total volume, pnl (Profit and Loss), open_interest, and the total num_markets_traded. Social Activity Tracking: Extract full histories of trader Posts, Comments, and Likes to understand the "why" behind their positions. Portfolio Analysis: Scrape both open_positions (current bets) and closed_positions (historical results) including ticker-level detail. Performance Badges: Identify elite status with badge data, including specific rankings like Top 50 Predictions or #6 in Financials. Category Affinity: Pull the top_categories data to see if a trader specializes in Sports, Crypto, Economics, or Science and Technology. Trade-Level Resolution: Access individual trades data, revealing the specific price, side (Yes/No), and timestamp of execution. ### πŸš€ Use Cases Leaderboard Forensics: Go beyond simple ranks to analyze the specific trade patterns of top-tier performers identified by the Kalshi Leaderboard Scraper. Sentiment & Signal Correlation: Match trader commentary and "Ideas" (posts) with their actual financial positions to separate noise from high-conviction signals. Whale Watching: Monitor users with high open_interest to see where institutional-grade capital is moving in real-time. Educational Benchmarking: Study the historical closed_positions of successful traders to learn optimal entry and exit strategies for binary options. Risk Management: Analyze the diversification of elite traders across top_categories to understand how they hedge across different market sectors. ### INPUT Feature Select: profile, open_positions, closed_positions, trades, top_categories, posts, comments, likes ### OUTPUT Profile ```json { "social_id": "6d479151-102d-4b4b-b881-f86c7c2fc94c", "nickname": "weatherman.allday", "follower_count": 125, "following_count": 0, "posts_count": 1, "profile_image_path": "weatherman.allday-2026-01-17 20:45:23", "description": "", "pending_profile_image_path": "", "blocked": false, "joined_at": "2026-01-06", "profile_view_count": 231568, "top_categories": [ "Sports", "Crypto" ], "inner_circle": { "enabled": true, "viewer_status": "none" }, "open_interest": 49770, "open_interest_fp": "49770.00", "volume": 3086866, "volume_fp": "3086866.00", "pnl": 181431186, "num_markets_traded": 111275, "badges": [ { "badge_id": "rank_top_0050__metric_num_markets_traded__category_all__period_all_time", "badge_type": "RANK", "threshold": 50, "metric_name": "num_markets_traded", "category": "all", "time_period": "all_time", "user_rank": 26, "percentile": 0, "display_name": "#26 Predictions", "description": "Ranked 26th on the Predictions leaderboard for All", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_top_0050__num_markets_traded__all__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_top_0050__num_markets_traded__all__all_time.webp", "color_hex": "#1E3A8A" }, { "badge_id": "rank_0006__metric_num_markets_traded__category_financials__period_all_time", "badge_type": "RANK", "threshold": 6, "metric_name": "num_markets_traded", "category": "financials", "time_period": "all_time", "user_rank": 6, "percentile": 0, "display_name": "#6 Predictions", "description": "Ranked 6th on the Predictions leaderboard for Financials", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_0006__num_markets_traded__financials__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_0006__num_markets_traded__financials__all_time.webp", "color_hex": "#5B21B6" }, { "badge_id": "rank_top_0025__metric_num_markets_traded__category_science_and_technology__period_all_time", "badge_type": "RANK", "threshold": 25, "metric_name": "num_markets_traded", "category": "science and technology", "time_period": "all_time", "user_rank": 23, "percentile": 0, "display_name": "#23 Predictions", "description": "Ranked 23rd on the Predictions leaderboard for Science And Technology", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_top_0025__num_markets_traded__science_and_technology__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_top_0025__num_markets_traded__science_and_technology__all_time.webp", "color_hex": "#0F4C5C" }, { "badge_id": "rank_top_0050__metric_num_markets_traded__category_sports__period_all_time", "badge_type": "RANK", "threshold": 50, "metric_name": "num_markets_traded", "category": "sports", "time_period": "all_time", "user_rank": 31, "percentile": 0, "display_name": "#31 Predictions", "description": "Ranked 31st on the Predictions leaderboard for Sports", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_top_0050__num_markets_traded__sports__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_top_0050__num_markets_traded__sports__all_time.webp", "color_hex": "#1E3A8A" }, { "badge_id": "rank_top_0050__metric_num_markets_traded__category_economics__period_all_time", "badge_type": "RANK", "threshold": 50, "metric_name": "num_markets_traded", "category": "economics", "time_period": "all_time", "user_rank": 32, "percentile": 0, "display_name": "#32 Predictions", "description": "Ranked 32nd on the Predictions leaderboard for Economics", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_top_0050__num_markets_traded__economics__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_top_0050__num_markets_traded__economics__all_time.webp", "color_hex": "#1E3A8A" }, { "badge_id": "rank_top_0050__metric_num_markets_traded__category_climate_and_weather__period_all_time", "badge_type": "RANK", "threshold": 50, "metric_name": "num_markets_traded", "category": "climate and weather", "time_period": "all_time", "user_rank": 43, "percentile": 0, "display_name": "#43 Predictions", "description": "Ranked 43rd on the Predictions leaderboard for Climate And Weather", "badge_path": "https://kalshi-social-public.s3.amazonaws.com/badges/compact/rank_top_0050__num_markets_traded__climate_and_weather__all_time.webp", "badge_expanded_path": "https://kalshi-social-public.s3.amazonaws.com/badges/expanded/rank_top_0050__num_markets_traded__climate_and_weather__all_time.webp", "color_hex": "#1E3A8A" } ] } ```` Open Position ```json { "market_id": "7b4d371f-b76c-4013-9260-9950eb1e0f3e", "market_ticker": "KXEPLTOP6-26-MUN", "name": "weatherman.allday", "open_position": true, "pnl": 40.0, "contracts": 500 } ``` Closed Position ```json { "market_id": "fda825f3-4425-4fba-892d-f0986f24337c", "market_ticker": "KXNCAAMBGAME-26FEB19IUINWRST-WRST", "name": "weatherman.allday", "open_position": false, "pnl": -20.88, "contracts": 2000 } ``` Trades ```json { "trade_id": "9fa464e9-a736-6b3c-9cf4-13398e30814f", "market_id": "e39ac7c1-b3ce-4b34-9738-ecce7d041121", "ticker": "KXTEMPNYCH-26MAY0403-T51.99", "price": 99, "price_dollars": "0.9900", "count": 1, "count_fp": "1.00", "taker_side": "yes", "maker_action": "buy", "taker_action": "buy", "maker_nickname": "weatherman.allday", "taker_nickname": "", "maker_social_id": "6d479151-102d-4b4b-b881-f86c7c2fc94c", "taker_social_id": "", "create_date": "2026-05-04T06:53:13.80876Z", "name": "weatherman.allday" } ``` Top Categories ```json { "value": 11538.776, "tag": "Sports", "category": "Sports", "social_id": "", "name": "weatherman.allday" } ``` Post ```json { "id": "7320b3a1-965e-48c0-a141-83fdae911829", "social_id": "9198dc7d-fb91-49c4-942e-4f6e5594e0a9", "market_title": "yes OG Anunoby: 2+,yes Shai Gilgeous-Alexander: 1+,yes Dylan Harper: 10+,yes Stephon Castle: 15+,yes Victor Wembanyama: 25+,yes Jalen Brunson: 25+,yes Karl-Anthony Towns: 15+,yes OG Anunoby: 15+,yes Paul George: 10+,yes Tyrese Maxey: 20+,yes VJ Edgecombe: 10+,yes LeBron James: 15+,yes Chet Holmgren: 10+,yes Shai Gilgeous-Alexander: 25+,yes Jaden McDaniels: 2+,yes Rudy Gobert: 10+,yes OG Anunoby: 4+,yes Kelly Oubre Jr.: 2+,yes Deandre Ayton: 8+,yes San Antonio wins by over 6.5 points,yes Oklahoma City wins by over 7.5 points", "event_title": "MVE", "event_long_title": "Philadelphia at New York", "series_title": "MVE Cross Category", "market_ticker": "KXMVECROSSCATEGORY-S2026BF6B0BFD21E-39C1E0D1080", "event_ticker": "KXMVECROSSCATEGORY-S2026BF6B0BFD21E", "series_tickers": [ "KXMVECROSSCATEGORY" ], "nickname": "galamex1", "post_type": "idea", "content": "let’s go for tomorrow", "image_paths": null, "media": null, "dollar_roi": 22.272, "percentage_roi": 0.4864, "side": "yes", "cost_cents": 4578, "payout_if_win_cents": 2268700, "paid_out_cents": null, "value_cents": 6806, "num_positions_on_event": 1, "has_ever_sold": false, "likes_count": 13, "comments_count": 14, "bookmarks_count": 4, "liked": false, "bookmarked": false, "created_ts": 1777862457267517, "weight": 0, "recent_comment_content": "this one’s a lock", "recent_comment_nickname": "kalebwittaplay", "recent_comment_social_id": "862b0d41-1b30-4958-be34-f0558d3ac869", "profile_image_path": "galamex1-2026-04-20 01:11:16", "market_status": "", "market_close": 0, "settlement_source": "", "suggested_market_title": "", "product_metadata": {}, "post_subtype": "", "position_odds": 0, "name": "galamex1", "created": "2026-05-04T04:40:57.267517" } ``` Comment ```json { "id": "b9f7ae96-4e7a-4bad-8436-aa56f5eb0dd4", "post_id": "2938ce60-0855-4dd2-9431-f15cc819bafd", "social_id": "9198dc7d-fb91-49c4-942e-4f6e5594e0a9", "nickname": "galamex1", "content": "no one is buying that shit", "image_path": "", "profile_image_path": "galamex1-2026-04-20 01:11:16", "created_ts": 1777683411512761, "depth": 0, "likes_count": 2, "liked": false, "is_deleted": false, "media": null, "name": "galamex1", "created": "2026-05-02T02:56:51.512761", "post": { "id": "2938ce60-0855-4dd2-9431-f15cc819bafd", "social_id": "5b1cf5f4-6914-4a91-978f-9b1100b35141", "market_title": "yes Donovan Mitchell: 25+,yes Evan Mobley: 10+,yes Jarrett Allen: 10+,yes James Harden: 15+,yes Jakob Poeltl: 10+,yes RJ Barrett: 20+,yes Scottie Barnes: 15+,yes Ausar Thompson: 10+,yes Cade Cunningham: 25+,yes Duncan Robinson: 10+,yes Jalen Duren: 10+,yes Tobias Harris: 15+,yes Anthony Black: 10+,yes Desmond Bane: 10+,yes Jalen Suggs: 10+,yes Paolo Banchero: 20+,yes Wendell Carter Jr.: 10+,yes Alperen Sengun: 15+,yes Amen Thompson: 15+,yes Jabari Smith Jr.: 15+,yes Reed Sheppard: 10+,yes Tari Eason: 10+,yes Austin Reaves: 20+,yes Deandre Ayton: 10+,yes LeBron James: 20+,yes Marcus Smart: 10+,yes Over 202.5 points scored,yes Over 200.5 points scored", "event_title": "MVE", "event_long_title": "Combo", "series_title": "MVE Sport Mutli Game", "market_ticker": "KXMVESPORTSMULTIGAMEEXTENDED-S20268B7DBDFCB56-B1CF5859BC5", "event_ticker": "KXMVESPORTSMULTIGAMEEXTENDED-S20268B7DBDFCB56", "series_tickers": [ "KXMVESPORTSMULTIGAMEEXTENDED" ], "nickname": "Yoboykail", "post_type": "idea", "content": "trust and buy!", "image_paths": null, "media": null, "dollar_roi": 9.545, "percentage_roi": 0.3042, "side": "yes", "cost_cents": 3137, "payout_if_win_cents": 1364100, "paid_out_cents": 0, "value_cents": 4092, "num_positions_on_event": 1, "has_ever_sold": false, "likes_count": 0, "comments_count": 2, "bookmarks_count": 0, "liked": false, "bookmarked": false, "created_ts": 1777683374624911, "weight": 0, "recent_comment_content": "", "recent_comment_nickname": "", "recent_comment_social_id": "", "profile_image_path": "Yoboykail-2026-04-18 17:00:21", "market_status": "", "market_close": 0, "settlement_source": "", "suggested_market_title": "", "product_metadata": null, "post_subtype": "", "position_odds": 0 } } ``` Likes ```json { "id": "d861e528-76f6-4ff3-a162-b444ae738264", "social_id": "b13c3389-6417-8bca-89cf-a619648ff986", "market_title": "Liverpool", "event_title": "WOL vs LFC (Mar 3)", "event_long_title": "Wolverhampton vs Liverpool", "series_title": "English Premier League Game", "market_ticker": "KXEPLGAME-26MAR03WOLLFC-LFC", "event_ticker": "KXEPLGAME-26MAR03WOLLFC", "series_tickers": [ "KXEPLGAME" ], "nickname": "snipe9", "post_type": "roi", "content": "", "image_paths": null, "media": [ { "provider": "giphy", "provider_id": "ujTVMASREzuRbH6zy5", "url": "https://media.giphy.com/media/ujTVMASREzuRbH6zy5/200h.webp", "content_type": "image/webp" } ], "dollar_roi": 0, "percentage_roi": 0, "side": "no", "cost_cents": 68000, "payout_if_win_cents": 200000, "paid_out_cents": 0, "value_cents": 198000, "num_positions_on_event": 0, "has_ever_sold": false, "likes_count": 3, "comments_count": 5, "bookmarks_count": 0, "liked": true, "bookmarked": false, "created_ts": 1772575657137029, "weight": 0, "recent_comment_content": "how are u so good at this", "recent_comment_nickname": "rich.mindet", "recent_comment_social_id": "4e61739e-0b50-4d5b-9b86-36e8ce4d6cc0", "profile_image_path": "hacker", "market_status": "", "market_close": 0, "settlement_source": "", "suggested_market_title": "", "product_metadata": {}, "post_subtype": "", "position_odds": 34, "name": "galamex1", "created": "2026-03-03T23:07:37.137029" } ``` ### πŸ›Ÿ SUPPORT Share your runs with the developer team and create issues on error to help us improve actor quality. You might discover edge case we didn't test yet We stay available anytime # Actor input Schema ## `feature` (type: `string`): Select Feature to extract data from ## `names` (type: `array`): Provide a list of kalshi profile ## `max_results` (type: `integer`): Maximum results per input name ## Actor input object example ```json { "feature": "profile", "names": [ "weatherman.allday" ], "max_results": 20 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "feature": "profile", "names": [ "weatherman.allday" ], "max_results": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("saswave/kalshi-profile-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "feature": "profile", "names": ["weatherman.allday"], "max_results": 20, } # Run the Actor and wait for it to finish run = client.actor("saswave/kalshi-profile-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "feature": "profile", "names": [ "weatherman.allday" ], "max_results": 20 }' | apify call saswave/kalshi-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=saswave/kalshi-profile-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Kalshi Profile Scraper", "description": "$0.8 / 1000 results. Extraction tool designed to unlock the social and financial layers of the Kalshi prediction market. Targeting individual trader profiles. Get data on profile, open positions, closed positions, trades, top categories, posts, comments, likes", "version": "0.0", "x-build-id": "jsfLmelu7fc6UcKrr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/saswave~kalshi-profile-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-saswave-kalshi-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/saswave~kalshi-profile-scraper/runs": { "post": { "operationId": "runs-sync-saswave-kalshi-profile-scraper", "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/saswave~kalshi-profile-scraper/run-sync": { "post": { "operationId": "run-sync-saswave-kalshi-profile-scraper", "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", "required": [ "feature", "names" ], "properties": { "feature": { "title": "Feature", "enum": [ "profile", "open_positions", "closed_positions", "trades", "top_categories", "posts", "comments", "likes" ], "type": "string", "description": "Select Feature to extract data from" }, "names": { "title": "Profile Name", "type": "array", "description": "Provide a list of kalshi profile", "items": { "type": "string" } }, "max_results": { "title": "Maximum results", "minimum": 0, "type": "integer", "description": "Maximum results per input name" } } }, "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 } } } } } } } } } } ```