# Instagram Reel Hooks & Competitor Analyzer (`coregent/instagram-reel-hooks-competitor-analyzer`) Actor Analyze Instagram Reels from competitor accounts. Extract hooks, captions, hashtags, audio, posting cadence, and engagement metrics in flat CSV-friendly output for benchmarking, content research, and creative strategy. - **URL**: https://apify.com/coregent/instagram-reel-hooks-competitor-analyzer.md - **Developed by:** [Delowar Munna](https://apify.com/coregent) (community) - **Categories:** Social media, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.80 / 1,000 reel 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 ## Instagram Reel Hooks & Competitor Analyzer ![Instagram Reel Hooks & Competitor Analyzer](https://raw.githubusercontent.com/coregentdevspace/instagram-reel-hooks-competitor-analyzer-assets/main/thumbnail-instagram-reel-hooks-competitor-analyzer.jpg) Analyze Instagram Reels from competitor accounts. Extract hooks, captions, hashtags, audio, posting cadence, and engagement metrics in flat CSV-friendly output for benchmarking, content research, and creative strategy. ### What this actor does **Instagram Reel Hooks & Competitor Analyzer** turns public Reels from any public Instagram profile (or direct Reel URLs) into a structured competitive-intelligence dataset. It is **not** a generic Reel downloader. The focus is on **analysis-ready output**: - opening hook extraction + rule-based hook-style classification - CTA type detection (`link_in_bio`, `dm`, `save`, `follow`, `share`, `buy`, …) - hashtag / mention / audio extraction - posting cadence (weekday, hour, days since published) - engagement ratios and **account-level benchmarks** (views vs the profile's sample average, performance bucket) Output is one flat row per Reel — ideal for CSV, Google Sheets, BI tools, or as clean input for downstream LLM summarization. ### How it works 1. **Input** — provide public profile URLs, bare usernames, or direct Reel URLs. 2. **Profile fetch** — for each profile, call Instagram's public `web_profile_info` endpoint and the Reels tab endpoint to collect up to `maxReelsPerProfile` recent Reels. 3. **Direct Reel fetch** — for each Reel URL, resolve the shortcode and fetch `/api/v1/media/{id}/info/`. 4. **Normalize** — extract a stable set of fields from whatever shape Instagram returned. 5. **Derive** — compute hook text, hook style, CTA type, hashtags/mentions, cadence, ratios, and sample benchmarks. 6. **Export** — push one flat row per Reel to the dataset. ### Inputs | Field | Type | Default | Description | |---|---|---|---| | `startUrls` | `string[]` | required | Profile URLs, bare usernames, or Reel URLs (mix freely) | | `maxReelsPerProfile` | int | `20` | Cap Reels per profile input | | `dateFrom` | ISO date | — | Only include Reels published on/after this date | | `dateTo` | ISO date | — | Only include Reels published on/before this date | | `skipPinnedReels` | bool | `true` | Skip pinned Reels (they distort benchmarks) | | `includeProfileContext` | bool | `true` | Emit `profile*` fields on every row | | `includeDerivedFields` | bool | `true` | Emit `hookStyle`, `ctaType`, ratios, benchmarks | | `includeHashtagsArray` | bool | `true` | Emit `hashtags` as array | | `includeMentionsArray` | bool | `true` | Emit `mentions` as array | | `includeAudioFields` | bool | `true` | Emit `audioTitle`, `audioArtist`, `audioType`, `audioOriginal` | | `includeVideoUrl` | bool | `false` | Emit best-effort signed video URL | | `requestTimeoutSecs` | int | `300` | Per-request timeout | | `maxConcurrency` | int | `1` | Concurrent inputs | | `debugMode` | bool | `false` | Verbose logging | ### Sample inputs #### Example 1 — Multi-account competitor analysis Compare Reel output across three competitor accounts, newest 30 each, skipping pinned Reels so benchmarks aren't distorted. ```json { "startUrls": [ "https://www.instagram.com/nike/", "https://www.instagram.com/adidas/", "lululemon" ], "maxReelsPerProfile": 30, "skipPinnedReels": true, "includeProfileContext": true, "includeDerivedFields": true } ```` #### Example 2 — Single Reel deep dive Analyze one specific Reel with full profile context, audio metadata, and the optional signed video URL. ```json { "startUrls": [ "https://www.instagram.com/reel/DWicOUHDvp5/" ], "includeProfileContext": true, "includeAudioFields": true, "includeVideoUrl": true } ``` #### Example 3 — Date-windowed profile scan Pull only Reels published in Q1 2026 from a single brand profile, trim output to scalar fields only (no arrays) for pure CSV workflows. ```json { "startUrls": [ "https://www.instagram.com/teslaaunz/" ], "maxReelsPerProfile": 50, "dateFrom": "2026-01-01", "dateTo": "2026-03-31", "skipPinnedReels": true, "includeHashtagsArray": false, "includeMentionsArray": false, "includeAudioFields": true } ``` ### Output The run produces a single dataset view — **Reel Rows** — with one flat, CSV-friendly row per Reel. ![Reel Rows table view](https://raw.githubusercontent.com/coregentdevspace/instagram-reel-hooks-competitor-analyzer-assets/main/instagram-reel-hooks-competitor-analyzer-output-all-fields-table-view.png) #### Sample record (one row per Reel) Real output from analyzing `https://www.instagram.com/teslaaunz/`: ```json { "inputUrl": "https://www.instagram.com/teslaaunz/?hl=en", "sourceType": "profile", "profileUsername": "teslaaunz", "profileFullName": "Tesla Australia and New Zealand", "profileUrl": "https://www.instagram.com/teslaaunz/", "profileId": "59601257554", "profileIsVerified": false, "profileCategoryName": null, "profileBiography": "We design and build products that people love.", "profileExternalUrl": "https://www.tesla.com/en_au", "profileFollowersCount": 11350, "profileFollowingCount": 6, "profilePostsCount": 151, "reelId": "3864775559205550713", "reelShortcode": "DWicOUHDvp5", "reelUrl": "https://www.instagram.com/reel/DWicOUHDvp5/", "reelType": "reel", "isPinned": false, "publishedAt": "2026-03-31T06:06:55.000Z", "publishedDate": "2026-03-31", "publishedTimestamp": 1774937215, "caption": "Reliable back up power and up to AUD $1,500/ NZD $1,700 back on your install. T&Cs apply", "hookText": "Reliable back up power and up to AUD $1,500/ NZD $1,700 back on your install. T&Cs apply", "hookLine1": "Reliable back up power and up to AUD $1,500/ NZD $1,700 back on your install. T&Cs apply", "hookLengthChars": 88, "hookLengthWords": 17, "hookStyle": "other", "ctaType": "none_detected", "hashtags": [], "mentions": [], "taggedUsers": [], "hashtagCount": 0, "mentionCount": 0, "languageHint": "en", "audioTitle": null, "audioArtist": null, "audioType": null, "audioOriginal": null, "durationSeconds": null, "likesCount": 54, "commentsCount": 1, "viewsCount": 688, "playsCount": null, "sharesCount": null, "engagementTotal": 55, "engagementRateByFollowers": 0.0048, "viewsToFollowersRatio": 0.0606, "likesToViewsRatio": 0.0785, "commentsToViewsRatio": 0.0015, "sharesToViewsRatio": null, "daysSincePublished": 24, "postingMonth": 3, "postingWeekday": "Tuesday", "postingHourLocal": 6, "dateBucket": "last_30d", "accountReelsSampleSize": 40, "accountAvgViewsSample": 6002.93, "accountAvgLikesSample": 176.47, "accountAvgCommentsSample": 8.65, "relativePerformanceVsAccountAvg": 0.115, "performanceBucket": "low", "contentTopicHint": null, "hasQuestionInHook": false, "hasNumberInHook": true, "hasEmojiInHook": false, "hasCTA": false, "captionLengthChars": 88, "captionLengthWords": 17, "thumbnailUrl": "https://scontent-iad3-2.cdninstagram.com/v/t51.82787-15/658797984_17985064196985555_7087428163089197254_n.jpg?...", "videoUrl": null, "scrapedAt": "2026-04-24T08:38:16.237Z", "runId": "Y9RGT5VKVzOJPL7fa" } ``` > The `thumbnailUrl` is a signed Instagram CDN URL (time-limited). The full URL is returned in the dataset — it is truncated above only for readability. #### Output field groups All fields are scalar or simple arrays; missing values are `null`. **Source / account** — `inputUrl`, `sourceType`, `profileUsername`, `profileFullName`, `profileUrl`, `profileId`, `profileIsVerified`, `profileCategoryName`, `profileBiography`, `profileExternalUrl`, `profileFollowersCount`, `profileFollowingCount`, `profilePostsCount` **Reel identity** — `reelId`, `reelShortcode`, `reelUrl`, `reelType`, `isPinned`, `publishedAt`, `publishedDate`, `publishedTimestamp` **Content** — `caption`, `hookText`, `hookLine1`, `hookLengthChars`, `hookLengthWords`, `hookStyle`, `ctaType`, `hashtags[]`, `mentions[]`, `taggedUsers[]`, `hashtagCount`, `mentionCount`, `languageHint`, `audioTitle`, `audioArtist`, `audioType`, `audioOriginal`, `durationSeconds` **Performance** — `likesCount`, `commentsCount`, `viewsCount`, `playsCount`, `sharesCount`, `engagementTotal`, `engagementRateByFollowers`, `viewsToFollowersRatio`, `likesToViewsRatio`, `commentsToViewsRatio`, `sharesToViewsRatio` **Timing / cadence** — `daysSincePublished`, `postingMonth`, `postingWeekday`, `postingHourLocal`, `dateBucket` **Benchmarks** — `accountReelsSampleSize`, `accountAvgViewsSample`, `accountAvgLikesSample`, `accountAvgCommentsSample`, `relativePerformanceVsAccountAvg`, `performanceBucket` (`high` / `avg` / `low` / `unknown`) **Hook flags + misc** — `contentTopicHint`, `hasQuestionInHook`, `hasNumberInHook`, `hasEmojiInHook`, `hasCTA`, `captionLengthChars`, `captionLengthWords`, `thumbnailUrl`, `videoUrl`, `scrapedAt`, `runId` #### Hook style values `question`, `how_to`, `list`, `mistake`, `warning`, `story`, `opinion`, `direct_cta`, `other`, `unknown`. #### CTA type values `follow`, `comment`, `dm`, `link_in_bio`, `save`, `share`, `buy`, `watch_more`, `none_detected`, `unknown`. ### Benchmarks For profile inputs, the actor computes the sample average of views / likes / comments across the Reels it collected, then annotates each row with: - `relativePerformanceVsAccountAvg` = `viewsCount ÷ accountAvgViewsSample` (falls back to likes when views are unavailable) - `performanceBucket` — `high` (>1.5×), `avg` (0.5–1.5×), `low` (<0.5×) In the sample record above, the Reel got 688 views against an account sample average of ~6,003 views (40-Reel sample), giving a `relativePerformanceVsAccountAvg` of `0.115` and bucketing it as `low`. For single-Reel inputs, `accountReelsSampleSize = 1` and benchmarks are `null`. ### Pricing Pay-per-event: **`reel-result`** — charged once per Reel row pushed to the dataset. The actor respects your configured spending limit: if the limit is reached mid-run, the run stops gracefully — no further rows pushed, no further events charged. ### Limitations - **Public data only.** No login, no private accounts, no login-walled data. - Some fields (`viewsCount`, `playsCount`, `sharesCount`, `audioArtist`) are best-effort and may be `null` for any given Reel. - `postingHourLocal` reflects UTC hour, not the viewer's timezone. - All derived fields (`hookStyle`, `ctaType`, benchmarks) are transparent, rule-based heuristics — not machine-learning classifications. ### Local development ```bash cd actor npm install npm start ## Run with INPUT.json npm run dev ## Run with local storage ``` Deployment to Apify is manual (`apify push`). This actor does not auto-deploy. # Actor input Schema ## `startUrls` (type: `array`): Public Instagram profile URLs (e.g. https://www.instagram.com/nike/), bare usernames (e.g. nike), or direct Reel URLs (e.g. https://www.instagram.com/reel/ABC123/). Mix and match. ## `maxReelsPerProfile` (type: `integer`): Maximum number of Reels to collect from each profile input. Ignored for direct Reel URL inputs. ## `dateFrom` (type: `string`): Only include Reels published on or after this date (ISO 8601, e.g. 2025-01-01). Leave empty for no lower bound. ## `dateTo` (type: `string`): Only include Reels published on or before this date (ISO 8601). Leave empty for no upper bound. ## `skipPinnedReels` (type: `boolean`): Skip pinned Reels when pinning is detectable. Pinned Reels often distort benchmarks because profiles pin their best-performing content. ## `includeProfileContext` (type: `boolean`): Emit profile-level fields (followers, bio, category, verified) on every Reel row. Useful for normalization and downstream joins. ## `includeDerivedFields` (type: `boolean`): Emit derived analytics: hookStyle, ctaType, engagement ratios, and account-level benchmarks. ## `includeHashtagsArray` (type: `boolean`): Emit hashtags as an array field. Disable to keep CSV exports purely scalar. ## `includeMentionsArray` (type: `boolean`): Emit @mentions as an array field. Disable to keep CSV exports purely scalar. ## `includeAudioFields` (type: `boolean`): Emit audio metadata (audioTitle, audioArtist, audioType, audioOriginal) when available. ## `includeVideoUrl` (type: `boolean`): Emit a best-effort direct video URL. These URLs are short-lived and signed; they are provided for convenience only. ## `requestTimeoutSecs` (type: `integer`): Maximum time in seconds to process each input entry. ## `maxConcurrency` (type: `integer`): Maximum number of inputs to process concurrently. Keep low for Instagram stability. ## `debugMode` (type: `boolean`): Enable verbose logging for debugging. ## Actor input object example ```json { "startUrls": [ "https://www.instagram.com/garyvee/" ], "maxReelsPerProfile": 20, "dateFrom": "", "dateTo": "", "skipPinnedReels": true, "includeProfileContext": true, "includeDerivedFields": true, "includeHashtagsArray": true, "includeMentionsArray": true, "includeAudioFields": true, "includeVideoUrl": false, "requestTimeoutSecs": 300, "maxConcurrency": 1, "debugMode": false } ``` # Actor output Schema ## `reels` (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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("coregent/instagram-reel-hooks-competitor-analyzer").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 = {} # Run the Actor and wait for it to finish run = client.actor("coregent/instagram-reel-hooks-competitor-analyzer").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 '{}' | apify call coregent/instagram-reel-hooks-competitor-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=coregent/instagram-reel-hooks-competitor-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Reel Hooks & Competitor Analyzer", "description": "Analyze Instagram Reels from competitor accounts. Extract hooks, captions, hashtags, audio, posting cadence, and engagement metrics in flat CSV-friendly output for benchmarking, content research, and creative strategy.", "version": "1.0", "x-build-id": "gWtiOL7YPIVuQYIX6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/coregent~instagram-reel-hooks-competitor-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-coregent-instagram-reel-hooks-competitor-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/coregent~instagram-reel-hooks-competitor-analyzer/runs": { "post": { "operationId": "runs-sync-coregent-instagram-reel-hooks-competitor-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/coregent~instagram-reel-hooks-competitor-analyzer/run-sync": { "post": { "operationId": "run-sync-coregent-instagram-reel-hooks-competitor-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Profiles or Reel URLs", "type": "array", "description": "Public Instagram profile URLs (e.g. https://www.instagram.com/nike/), bare usernames (e.g. nike), or direct Reel URLs (e.g. https://www.instagram.com/reel/ABC123/). Mix and match.", "items": { "type": "string" }, "default": [ "https://www.instagram.com/garyvee/" ] }, "maxReelsPerProfile": { "title": "Max Reels Per Profile", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of Reels to collect from each profile input. Ignored for direct Reel URL inputs.", "default": 20 }, "dateFrom": { "title": "Date From", "type": "string", "description": "Only include Reels published on or after this date (ISO 8601, e.g. 2025-01-01). Leave empty for no lower bound.", "default": "" }, "dateTo": { "title": "Date To", "type": "string", "description": "Only include Reels published on or before this date (ISO 8601). Leave empty for no upper bound.", "default": "" }, "skipPinnedReels": { "title": "Skip Pinned Reels", "type": "boolean", "description": "Skip pinned Reels when pinning is detectable. Pinned Reels often distort benchmarks because profiles pin their best-performing content.", "default": true }, "includeProfileContext": { "title": "Include Profile Context", "type": "boolean", "description": "Emit profile-level fields (followers, bio, category, verified) on every Reel row. Useful for normalization and downstream joins.", "default": true }, "includeDerivedFields": { "title": "Include Derived Fields", "type": "boolean", "description": "Emit derived analytics: hookStyle, ctaType, engagement ratios, and account-level benchmarks.", "default": true }, "includeHashtagsArray": { "title": "Include Hashtags Array", "type": "boolean", "description": "Emit hashtags as an array field. Disable to keep CSV exports purely scalar.", "default": true }, "includeMentionsArray": { "title": "Include Mentions Array", "type": "boolean", "description": "Emit @mentions as an array field. Disable to keep CSV exports purely scalar.", "default": true }, "includeAudioFields": { "title": "Include Audio Fields", "type": "boolean", "description": "Emit audio metadata (audioTitle, audioArtist, audioType, audioOriginal) when available.", "default": true }, "includeVideoUrl": { "title": "Include Video URL", "type": "boolean", "description": "Emit a best-effort direct video URL. These URLs are short-lived and signed; they are provided for convenience only.", "default": false }, "requestTimeoutSecs": { "title": "Request Timeout (seconds)", "minimum": 30, "maximum": 900, "type": "integer", "description": "Maximum time in seconds to process each input entry.", "default": 300 }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 5, "type": "integer", "description": "Maximum number of inputs to process concurrently. Keep low for Instagram stability.", "default": 1 }, "debugMode": { "title": "Debug Mode", "type": "boolean", "description": "Enable verbose logging for debugging.", "default": false } } }, "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 } } } } } } } } } } ```