# LinkedIn Profile Reactions Scraper · No Cookies · $1/1k ✅ (`linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k`) Actor Cheapest LinkedIn profile reactions scraper. Every post a profile reacted to - reaction type, post content, author. No login, no ban risk. $1/1k. - **URL**: https://apify.com/linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k.md - **Developed by:** [LinkedIntel](https://apify.com/linkedintel) (community) - **Categories:** Social media, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 reaction extracteds 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 ## LinkedIn Profile Reactions Scraper — No Login, No Cookies, Pay Per Result For any LinkedIn profile, extract **every post they've publicly reacted to** — reaction type (Like / Celebrate / Love / Support / Insightful / Funny / Curious), full post content, post author, engagement metrics, media attachments. Clean structured JSON. **No LinkedIn login required. No cookies. No browser session. No account ban risk.** The fastest, cheapest, safest way to map a LinkedIn profile's reaction activity at scale — built for **B2B sales prospecting, account-based marketing (ABM), buying-signal monitoring, audience research, competitive intelligence, and influencer audit**. --- ### How this compares to other LinkedIn profile reactions scrapers We benchmarked the direct competitors on Apify Store. Here's the honest landscape: | | harvestapi/linkedin-profile-reactions | apimaestro/linkedin-profile-reactions | **This actor** | |---|---|---|---| | **Pricing** | $2.00/1k | $5.00/1k | **$1.00/1k (50% cheaper than harvestapi, 80% cheaper than apimaestro)** | | **Output fields per record** | ~10 | ~10 | **28 fields** | | **Reaction type detection** | ID + verb | text only | ✅ **Parsed enum: LIKE / PRAISE / APPRECIATION / EMPATHY / INTEREST / ENTERTAINMENT / MAYBE** | | **Distinguishes post vs comment reactions** | ❌ | ❌ | ✅ **`reactionTargetType`** field | | **Detects reshares (`targetIsReshare`)** | ❌ | ❌ | ✅ | | **Distinguishes PERSON vs COMPANY post author** | ❌ | ❌ | ✅ **`targetAuthorType`** | | **Media attachments URL list** | Partial | Partial | ✅ **`targetMediaContent` array + `targetMediaCount`** | | **Login required** | ✅ No | ✅ No | ✅ **No login, no cookies** | | **Account ban risk** | ✅ Zero | ✅ Zero | ✅ **Zero** | | **Pay-per-result** | ✅ Yes | ✅ Yes | ✅ **Yes** | **Bottom line:** $1/1k is the **cheapest LinkedIn profile reactions scraper on Apify Store**, with the richest output schema, the only one that parses reaction types into a clean enum, and the only one that distinguishes post vs comment reactions. ### What you get — every reaction the profile made For every public reaction the queried profile has given, this actor returns 28 fields per record: - **The reactor** — username (input) + LinkedIn dash URN - **The reaction itself** — parsed enum (`reactionType`: LIKE / PRAISE / APPRECIATION / EMPATHY / INTEREST / ENTERTAINMENT / MAYBE), target type (`reactionTargetType`: post vs comment), raw header text - **The target post** — URN, URL, full content, preview, edit flag, ISO + Unix ms timestamps, reshare flag - **The target author** — name, headline, profile URL, dash URN, member ID, type (PERSON vs COMPANY), profile picture URL - **Engagement on the target** — total reactions, comments count, reposts count, reactions breakdown by type - **Media on the target** — array of images/videos + count One record per reaction. Flat JSON. Ready to drop into Airtable, Google Sheets, HubSpot, Salesforce, Pipedrive, Zapier, Make, n8n, or any webhook. ### Top use cases #### 1. Buying-signal detection "Did my target prospect react to a competitor's launch announcement?" Pull their last 500 reactions. Filter by `targetAuthorName` containing competitor names. **Real-time switcher detection** — most powerful B2B sales signal LinkedIn offers. #### 2. Account-based marketing (ABM) Pass your top-50 target accounts' decision-makers as profile URLs. Get every post they've reacted to. **Aggregate the target post authors → your warmest content + topics for ABM messaging.** #### 3. Personal-brand engagement audit Pass your own profile URL. See every post you've reacted to publicly. **Audit your own digital footprint** — what message does your reaction pattern send to prospects who check? #### 4. Competitor radar — who do they engage with? Run this monthly on your competitor's CMO, product lead, or founder. Get a stream of every post they react to → reveals their partners, customers, peer leaders, content sources. **The hidden network behind the public LinkedIn presence.** #### 5. Influencer + creator vetting Before paying for a sponsored post, audit what the creator publicly endorses. Their reaction pattern reveals real interests vs. paid alignment. **One run shows you what they actually believe in publicly.** #### 6. Content strategy research Track which posts your industry's thought leaders react to. Aggregate the topics + authors → your content calendar gold mine. **What resonates with the people you're trying to reach?** #### 7. Recruitment + candidate intel Pass a candidate's profile URL. Their reaction pattern reveals professional interests, peer network, and recent topics of attention. **Better than the résumé.** ### Quick start (3 steps, 30 seconds) #### Step 1 — Get the profile URL Open the LinkedIn profile and copy the URL — `https://www.linkedin.com/in//`. You can also pass just the username slug (e.g., `satyanadella`) via the **Profile usernames** field. #### Step 2 — Run the actor 1. Click the green **Start** button 2. Paste one or more profile URLs into **Profile URLs** 3. Set **Max reactions per profile** to control cost (default 500) 4. Click **Start** #### Step 3 — Get your data Results stream into the **Dataset** tab. Click **Export** for JSON, CSV, or Excel — or push directly to Zapier, Make, Google Sheets, Airtable, HubSpot, Salesforce, or any webhook via Apify's native integrations. ### Input | Field | Type | Default | Description | |---|---|---|---| | `profileUrls` | array of strings | `[]` | LinkedIn profile URLs of the people whose reactions you want to extract. | | `profileIds` | array of strings | `[]` | Alternative to URLs — pass just the username slugs (e.g., `"satyanadella"`). | | `maxReactionsPerProfile` | integer | `500` | Hard cap on reaction records per profile. Lower runs faster and costs less. | | `proxyConfiguration` | object | Apify residential | Proxy settings (defaults are correct for most users). | At least one of `profileUrls` or `profileIds` is required. #### Example input ```json { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/", "https://www.linkedin.com/in/reidhoffman/" ], "maxReactionsPerProfile": 250 } ```` ### Output Each reaction is a single dataset record: ```json { "type": "reaction", "reactorUsername": "satyanadella", "reactorDashUrn": "ACoAAAA...", "reactionType": "LIKE", "reactionTargetType": "post", "reactionHeader": "Satya Nadella likes this", "targetUrn": "urn:li:activity:7400000000000000000", "targetUrl": "https://www.linkedin.com/feed/update/urn:li:activity:7400000000000000000", "targetText": "Excited to announce our latest product launch...", "targetTextPreview": "Excited to announce our latest product launch...", "targetEdited": false, "targetCreatedAt": "2026-04-15T14:22:30.000000+00:00", "targetCreatedAtMs": 1776264150000, "targetIsReshare": false, "targetAuthorName": "Example Founder", "targetAuthorHeadline": "Founder & CEO at Example Corp", "targetAuthorUrl": "https://www.linkedin.com/in/examplefounder", "targetAuthorUrn": "urn:li:fsd_profile:ACoAAAB...", "targetAuthorId": "urn:li:member:123456789", "targetAuthorType": "PERSON", "targetAuthorProfilePictureUrl": "https://media.licdn.com/...", "targetTotalReactions": 542, "targetCommentsCount": 87, "targetRepostsCount": 23, "targetReactionBreakdown": [ { "reactionType": "LIKE", "reactionCount": 480 }, { "reactionType": "PRAISE", "reactionCount": 45 }, { "reactionType": "EMPATHY", "reactionCount": 17 } ], "targetMediaContent": [ { "type": "image", "url": "https://media.licdn.com/..." } ], "targetMediaCount": 1, "scrapedAt": "2026-05-17T12:00:00.000000+00:00" } ``` A final `summary` record is also pushed: `totalReactions`, `profilesSuccessful`, `profilesFailed`, `profilesTotal`. #### Reaction type enum | Value | LinkedIn UI label | |---|---| | `LIKE` | Like 👍 | | `PRAISE` | Celebrate 👏 | | `APPRECIATION` | Support ❤️‍🩹 | | `EMPATHY` | Love ❤️ | | `INTEREST` | Curious 🤔 | | `ENTERTAINMENT` | Funny 😂 | | `MAYBE` | Insightful 💡 | ### Pricing — pay only for results **$1.00 per 1,000 reaction records. Pay-per-result.** - ✅ No subscription - ✅ No base fee - ✅ No charge for failed runs - ✅ No charge for profiles with no public reactions (clean diagnostic record) - ✅ Apify's free tier ($5/month platform credit) gets you ~5,000 free records to start **The cheapest LinkedIn profile reactions scraper on Apify Store** — 50% cheaper than the closest competitor ($2.00/1k), 80% cheaper than the established alternative ($5.00/1k). ### Combine with other LinkedIn actors Build a complete LinkedIn intelligence pipeline: - Pipe `targetAuthorUrl` into the **LinkedIn Profile Scraper** to enrich every post-author with full firmographic data - Pipe `targetUrn` into the **LinkedIn Post Comments Scraper** to combine reaction data with full comment threads - Pipe `reactorUsername` into the **LinkedIn Profile Engagement Scraper** to see who reacts to the reactor's content ### FAQ #### Do I need a LinkedIn account? **No.** This actor reads public reaction data without any LinkedIn login, cookie, or browser session. #### Will my LinkedIn account get banned? **No — because we never use it.** Cookie-based scrapers risk your account every run. This actor doesn't. #### How is this different from your other LinkedIn Reactions Scraper? The **LinkedIn Reactions Scraper** (a5) takes a *post URL* and returns every reactor on that one post. This actor takes a *profile URL* and returns every post the profile reacted to (inverse direction). Use a5 to study a post; use this one to study a person. #### What if a profile has reacted to a comment instead of a post? The actor still captures it. The `reactionTargetType` field is set to `"comment"` (vs `"post"`) so you can filter or analyze separately. #### Why is this cheaper than other LinkedIn reactions scrapers? Direct competitors charge $2.00 to $5.00 per 1,000 results. We negotiated a managed data partnership that gives us cookieless LinkedIn access at a lower vendor cost — and we pass the savings through. #### Can I scrape multiple profiles in one run? Yes. Paste a list of profile URLs into the **Profile URLs** field. The actor processes them sequentially with built-in deduplication by username slug. #### What's the maximum number of reactions per profile? Up to **50,000** reactions per profile (controlled by `maxReactionsPerProfile`). LinkedIn typically surfaces the most recent ~3,000 public reactions per profile via the upstream API. #### How fast is it? 1–30 seconds per profile depending on volume. Pagination via cursor handles large profiles automatically. #### What if the profile is private or doesn't exist? The actor returns a clean diagnostic record and continues with the next profile — never crashes mid-batch. #### Does this work for company pages? No — this actor only accepts personal LinkedIn profiles (`linkedin.com/in//`). Company pages return a clean "URL not supported" diagnostic. #### Can I integrate with Zapier / Make / n8n / HubSpot? Yes — Apify integrates natively with all of them, plus Salesforce, Airtable, Google Sheets, and any webhook-compatible tool. #### Can AI agents pay for and run this actor? Yes — Agent Payments (x402 / USDC) are enabled. AI agents can pay per run in stablecoin. ### Legal & compliance - Extracts **publicly available** data only — no logins, no access-control bypass, no private content - You are responsible for complying with LinkedIn's Terms of Service, GDPR (EU), CCPA (California), and other applicable laws - Personal data returned (names, public profiles, public headlines, reaction events) may require a lawful basis (legitimate interest, consent) before outbound use - This actor is **not affiliated with, endorsed by, or sponsored by LinkedIn Corporation or Microsoft Corporation.** "LinkedIn" is a registered trademark of LinkedIn Corporation ### Changelog #### v1.0.0 — 2026-05-17 - Initial release - Cookieless architecture — zero account-ban risk - 28 fields per record: full reactor + target post + target author + engagement context - Reaction type parsed from header into clean 7-value enum (LIKE / PRAISE / APPRECIATION / EMPATHY / INTEREST / ENTERTAINMENT / MAYBE) - `reactionTargetType` distinguishes post vs comment reactions - `targetIsReshare` flag and `targetAuthorType` (PERSON vs COMPANY) detection - Media attachment URLs array - Cursor-based pagination for high-volume profiles - Clean diagnostic records for missing profiles, private profiles, empty engagement - Native Apify integrations + Agent Payments (x402 / USDC) - **14/14 tests passed** across 3 tiers (correctness, stress, edge cases) ### Support Questions, feature requests, or bug reports? Open an issue on the actor's **Issues** tab. We respond within 24 hours. **Like this actor? Leave a review on the Apify Store — it helps other LinkedIn data teams find it.** # Actor input Schema ## `profileUrls` (type: `array`): LinkedIn profile URLs of the people whose reactions you want to extract. Each input profile becomes a stream of every public post the person has reacted to (Like/Celebrate/Love/Support/Insightful/Funny/Curious). Example: \["https://www.linkedin.com/in/satyanadella/"] ## `profileIds` (type: `array`): Alternative to profileUrls: pass just the username slugs (the part after /in/ in a LinkedIn URL). Example: \["satyanadella", "reidhoffman"] ## `maxReactionsPerProfile` (type: `integer`): Cap on the number of reaction records returned per profile. Lower = faster + cheaper. Default returns up to 500 most recent reactions. ## `proxyConfiguration` (type: `object`): Apify proxy settings. Residential proxies are recommended. ## Actor input object example ```json { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ], "maxReactionsPerProfile": 500, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k").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 = { "profileUrls": ["https://www.linkedin.com/in/satyanadella/"] } # Run the Actor and wait for it to finish run = client.actor("linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k").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 '{ "profileUrls": [ "https://www.linkedin.com/in/satyanadella/" ] }' | apify call linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=linkedintel/cheapest-linkedin-profile-reactions-no-cookies-1-1k", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile Reactions Scraper · No Cookies · $1/1k ✅", "description": "Cheapest LinkedIn profile reactions scraper. Every post a profile reacted to - reaction type, post content, author. No login, no ban risk. $1/1k.", "version": "1.0", "x-build-id": "pYDmwUTTl4eOJjRJf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/linkedintel~cheapest-linkedin-profile-reactions-no-cookies-1-1k/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-linkedintel-cheapest-linkedin-profile-reactions-no-cookies-1-1k", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/linkedintel~cheapest-linkedin-profile-reactions-no-cookies-1-1k/runs": { "post": { "operationId": "runs-sync-linkedintel-cheapest-linkedin-profile-reactions-no-cookies-1-1k", "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/linkedintel~cheapest-linkedin-profile-reactions-no-cookies-1-1k/run-sync": { "post": { "operationId": "run-sync-linkedintel-cheapest-linkedin-profile-reactions-no-cookies-1-1k", "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": { "profileUrls": { "title": "Profile URLs", "type": "array", "description": "LinkedIn profile URLs of the people whose reactions you want to extract. Each input profile becomes a stream of every public post the person has reacted to (Like/Celebrate/Love/Support/Insightful/Funny/Curious). Example: [\"https://www.linkedin.com/in/satyanadella/\"]", "items": { "type": "string" } }, "profileIds": { "title": "Profile usernames", "type": "array", "description": "Alternative to profileUrls: pass just the username slugs (the part after /in/ in a LinkedIn URL). Example: [\"satyanadella\", \"reidhoffman\"]", "items": { "type": "string" } }, "maxReactionsPerProfile": { "title": "Max reactions per profile", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Cap on the number of reaction records returned per profile. Lower = faster + cheaper. Default returns up to 500 most recent reactions.", "default": 500 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Apify proxy settings. Residential proxies are recommended.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```