# Clinical Trials Scraper for Pipeline Intel (`skootle/clinical-trials-intel`) Actor Scrape ClinicalTrials.gov by condition, sponsor, drug, phase, or location. Get sponsors, phases, enrollment, geo-coded sites, and a pipeline summary of the competitive landscape in every run. - **URL**: https://apify.com/skootle/clinical-trials-intel.md - **Developed by:** [Skootle](https://apify.com/skootle) (community) - **Categories:** AI, Agents, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.10 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Fast answer: what this Actor is for Pull every trial matching a drug, disease, sponsor, or city from ClinicalTrials.gov, and get a read on the competitive landscape in the same run. Built for pharma and biotech teams tracking rival pipelines, investors sizing a therapeutic area, CROs and site networks hunting enrollment, and AI agents that need structured trial data instead of scraped HTML. - Run it from the Apify UI for a one-off pull. - Schedule it or call it by API to watch a competitor's pipeline week over week. - Feed the dataset straight into spreadsheets, dashboards, and LLM workflows. ![Clinical Trials Pipeline Intelligence](https://raw.githubusercontent.com/kesjam/skootle-actors-assets/main/heroes/clinical-trials-intel.png) ### TL;DR Search ClinicalTrials.gov by term, condition, sponsor, intervention, or location. Every trial comes back as a clean row: `nctId`, `briefTitle`, `status` and `phase` as normalized enums, `leadSponsor` with `leadSponsorClass` (INDUSTRY, NIH, ACADEMIC, and so on), `conditions[]`, `interventions[]` with type, `enrollmentCount` as a real number, `siteCount` and `countryCount`, ISO 8601 `startDate` and `primaryCompletionDate`, `durationDays`, eligibility (`minimumAge`, `sex`, `healthyVolunteers`), `primaryOutcomes[]`, and `sites[]` with facility, city, country, and lat/lon coordinates. Then the part nobody else ships: one `pipeline_summary` record per run that turns those rows into a market view. Sponsor competition ranked by trial count, late-phase count, and total enrollment. Phase mix and status mix with percentages. Industry-sponsored share. Median enrollment. Site geography. Every record also carries an `agentMarkdown` field, and each run writes an `AGENT_BRIEFING` you can hand straight to an LLM. > Try it on a small query, then let us know what you think in a [review](https://apify.com/skootle/clinical-trials-intel/reviews). ### What does this Actor do? It queries the official ClinicalTrials.gov v2 API, normalizes every study into a typed record, and computes a landscape summary across the result set. No browser, no proxy, no scraping of rendered HTML, which is why it runs in seconds and costs a fraction of a cent. Two record types land in one dataset: 1. **`trial`**, one per study, with the fields listed above. Dates are ISO 8601 even though the source mixes `2019`, `2019-10`, and `2019-10-05` formats. Phases are collapsed into a single enum, including combined phases like `PHASE1_PHASE2`. Enrollment and site counts are numbers, not strings, so you can sum and sort them without cleaning. 2. **`pipeline_summary`**, one per run, with `topSponsors[]` (each with `trialCount`, `latePhaseCount`, `totalEnrollment`), `byPhase[]` and `byStatus[]` with counts and percentages, `topConditions[]`, `topInterventionTypes[]`, `topCountries[]` by site count, plus `industrySponsoredPct`, `latePhasePct`, `recruitingCount`, `medianEnrollment`, and `totalEnrollment`. ### Why scrape clinical trial data? ClinicalTrials.gov is the single registry where drug developers are legally required to disclose what they are testing, on whom, where, and when. That makes it the closest thing to a public map of the pharma pipeline. The problem is the site is built for reading one study at a time. The export gives you flat CSV with no normalization, dates in three formats, phases as ragged arrays, and no way to ask "who is winning in this indication". Teams end up doing this by hand: search, click, copy sponsor names into a sheet, eyeball which competitors are in phase 3, guess at enrollment totals. That is a day of work that goes stale in a week. This Actor collapses that into one call. You get the rows and the read on the room together. - **Competitive intelligence.** Point it at a drug class and see which sponsors own the late-stage trials, how many patients they have enrolled, and who just started recruiting. - **Investor diligence.** Size a therapeutic area before a term sheet: how crowded is it, how much of it is industry money versus academic, what is the median trial size. - **Site and patient recruitment.** Filter to `RECRUITING` and pull geo-coded sites, then map which facilities are actively enrolling near your catchment. - **AI agents and RAG.** Every record ships with `agentMarkdown` and every run writes an `AGENT_BRIEFING`, so an LLM can reason over the landscape without you writing a formatter. - **Regulatory and medical affairs.** Track status changes (a trial flipping to `TERMINATED` or `COMPLETED`) by scheduling this weekly and diffing the output. ### Worked example: the GLP-1 obesity landscape Input: ```json { "query": "GLP-1 obesity", "maxItems": 50 } ```` That run finished in 6 seconds. Real rows it returned: | NCT ID | Title | Status | Phase | Sponsor | Type | Enrollment | Sites | Countries | |---|---|---|---|---|---|---|---|---| | NCT07284901 | Efficacy and Safety of KAI-9531 Once Weekly in Obesity or Overweight and Diabetes | RECRUITING | PHASE3 | Kailera | INDUSTRY | 1700 | 58 | 4 | | NCT06603571 | Weight Management With LY3841136 and Tirzepatide, Alone or in Combination | ACTIVE\_NOT\_RECRUITING | PHASE2 | Eli Lilly and Company | INDUSTRY | 350 | 52 | 2 | | NCT06857942 | Tirzepatide Following Ixekizumab in Plaque Psoriasis and Obesity (TOGETHER AMPLIFY-PsO) | RECRUITING | PHASE4 | Eli Lilly and Company | INDUSTRY | 200 | 40 | 1 | | NCT05671653 | Effect of PF-07081532 on Blood Levels of Common Birth Control Pills | TERMINATED | PHASE1 | Pfizer | INDUSTRY | 32 | 1 | 1 | | NCT06517212 | Tirzepatide Weight Loss for MRD+ Early Breast Cancer | RECRUITING | PHASE2 | Baylor Research Institute | OTHER | 48 | 1 | 1 | And the `pipeline_summary` record from the same run, rendered from its `agentMarkdown`: ``` ### Trial landscape: GLP-1 obesity 50 trials. 10 recruiting or about to. 18% industry sponsored. 22% in late phase (P3, P2/P3, P4). Median enrollment 49, 7820 participants across the set. #### Phase mix - NA: 23 (46%) - PHASE4: 9 (18%) - PHASE2: 5 (10%) - PHASE1: 3 (6%) - PHASE3: 2 (4%) #### Top sponsors - University of Aarhus (OTHER): 2 trials, 0 late phase, 38 enrolled - Eli Lilly and Company (INDUSTRY): 2 trials, 1 late phase, 550 enrolled - Hvidovre University Hospital (OTHER): 2 trials, 0 late phase, 20 enrolled ``` In one call you learn that the space is mostly investigator-initiated mechanism studies, that Lilly is the industry player with late-stage weight, and that Kailera just put 1,700 patients into a phase 3 across 58 sites. That is the read a competitive intelligence analyst would spend a morning assembling. ### Which inputs should I use? Every field is optional, but give it at least one. They combine. - **`query`** searches titles, conditions, and interventions together. Best starting point. Example: `CAR-T lymphoma`. - **`condition`** narrows to a disease area. Example: `non-small cell lung cancer`. - **`sponsor`** watches one company. Example: `Eli Lilly`. Pair it with nothing else to get that sponsor's whole registered pipeline. - **`intervention`** tracks a molecule or device. Example: `semaglutide`. - **`location`** filters by where trial sites sit. Example: `Texas`. - **`statuses`** limits recruitment status. Pick `RECRUITING` to find trials enrolling right now. - **`phases`** limits phase. `PHASE3` plus `PHASE4` gives you the late-stage picture only. - **`maxItems`** caps how many trial records come back, up to 1,000. The pipeline summary is computed from exactly the trials you pull, so a bigger pull gives a more representative landscape. - **`emitPipelineSummary`** defaults to true. Turn it off if you only want raw rows. ### Three recipes that pay for themselves **Watch a rival's whole pipeline.** ```json { "sponsor": "Novo Nordisk", "maxItems": 200 } ``` The summary tells you how much of their registered work is late phase, how many patients they have committed, and which conditions they are pushing into. Schedule it weekly and the diff is your early warning system. **Find sites that are actively enrolling near you.** ```json { "condition": "pancreatic cancer", "statuses": ["RECRUITING"], "location": "Texas", "maxItems": 100 } ``` Every returned trial carries `sites[]` with facility names and lat/lon, so this drops straight into a map. **Size a therapeutic area before you invest.** ```json { "condition": "ALS", "phases": ["PHASE2", "PHASE3"], "maxItems": 300 } ``` `industrySponsoredPct` tells you whether real money is in the space or whether it is still academic. `medianEnrollment` tells you what a serious trial costs to run. `topSponsors` tells you who you would be competing with, and `latePhaseCount` per sponsor tells you how close they are. ### Field reference **Trial record** | Field | Type | Notes | |---|---|---| | `nctId` | string | Registry identifier, stable and idempotent | | `url` | string | Direct link to the study page | | `briefTitle`, `officialTitle` | string | Official title is null when the sponsor did not post one | | `status` | enum | RECRUITING, COMPLETED, TERMINATED, WITHDRAWN, and others; UNKNOWN if absent | | `phase` | enum | PHASE1 through PHASE4, plus PHASE1\_PHASE2, PHASE2\_PHASE3, NA, UNKNOWN | | `leadSponsor`, `leadSponsorClass` | string, enum | INDUSTRY, NIH, FED, ACADEMIC, OTHER\_GOV, NETWORK, OTHER | | `isIndustrySponsored` | boolean | Convenience flag for filtering commercial work | | `collaborators` | string\[] | Co-sponsors, often where partnerships surface | | `conditions` | string\[] | Diseases under study | | `interventions` | object\[] | `type` (DRUG, BIOLOGICAL, DEVICE, PROCEDURE, RADIATION) and `name` | | `enrollmentCount`, `enrollmentType` | number, string | ACTUAL or ESTIMATED; null when not posted | | `siteCount`, `countryCount` | number | Numeric mirrors, sortable without cleaning | | `startDate`, `primaryCompletionDate`, `completionDate`, `lastUpdateDate` | ISO 8601 | Normalized from the registry's mixed formats | | `durationDays` | number | Computed start to completion | | `minimumAge`, `maximumAge`, `sex`, `healthyVolunteers` | mixed | Eligibility signals | | `sites` | object\[] | facility, city, state, country, zip, lat, lon | | `primaryOutcomes` | string\[] | The endpoints the trial is actually measuring | | `fieldCompletenessScore` | number | 0 to 100, weighted by what buyers use | | `agentMarkdown` | string | One-line LLM-ready summary of the record | **Pipeline summary record** `trialCount`, `byPhase[]`, `byStatus[]`, `topSponsors[]`, `topConditions[]`, `topInterventionTypes[]`, `topCountries[]`, `industrySponsoredPct`, `recruitingCount`, `medianEnrollment`, `totalEnrollment`, `latePhasePct`, `agentMarkdown`. ### How much does it cost? Pay per event. You pay for the trial records and the summary you receive, nothing else. There is no proxy surcharge because this Actor does not need a proxy: it talks to the official ClinicalTrials.gov API directly. A 50-trial run finishes in about 6 seconds. Because the underlying data is a public API rather than a defended website, this is one of the cheapest data sources in the Skootle catalog, and that is reflected in the price. ### FAQ **Is this legal?** Yes. ClinicalTrials.gov is a public registry operated by the U.S. National Library of Medicine, and it publishes an official public API specifically so tools can query it. This Actor uses that API as intended. It reads only public registry data. It does not touch patient records, and no protected health information exists in the output. **How fresh is the data?** As fresh as the registry. Sponsors update their entries and this Actor reads the live API on every run, so you see what ClinicalTrials.gov shows today. The `lastUpdateDate` field on each record tells you when the sponsor last touched that study. **Why do some trials show phase NA or UNKNOWN?** Because the registry does. Observational studies and many investigator-initiated studies have no assigned phase, which the registry records as `NA`. Where a sponsor left the field blank entirely, this Actor normalizes it to `UNKNOWN` rather than guessing. Both are honest reflections of the source. **Why is enrollment sometimes missing?** Sponsors are not always required to post it, particularly for very early studies. The field comes back `null` rather than zero, so your averages are not silently wrong. Every record carries a `fieldCompletenessScore` so you can filter to only the well-populated rows if you want. **Can I track a competitor over time?** Yes, and this is the highest-value way to use it. Schedule the Actor weekly with a `sponsor` filter, then diff the datasets. New NCT IDs are new programs. Status flips to `TERMINATED` are failures. Status flips to `RECRUITING` on a phase 3 mean a program just got serious. **What happens if my query matches nothing?** The run fails with a clear message instead of quietly reporting success with an empty dataset. You are never billed for a run that gave you nothing. This is a deliberate design choice across every Skootle Actor. **Can I get results as CSV or Excel?** Yes. Every Apify dataset exports to CSV, Excel, JSON, XML, and RSS from the UI or the API. The dataset also ships with two prepared views, one for trials and one for the pipeline summary. ### Your feedback Found a bug, a missing field, or a query that behaves oddly? Please open it on the [Issues tab](https://apify.com/skootle/clinical-trials-intel/issues) rather than the reviews page. Issues get a response within 48 hours and I use every Actor I publish, so breakages get fixed the same week. Reviews are best saved for whether the tool actually earned its keep. ### More from Skootle Production scrapers for niches that move money: - [SAM.gov Federal Contracts Scraper](https://apify.com/skootle/sam-gov-federal-contracts) for federal opportunity intelligence, same HTTP-only architecture, same agent-grade output. - [SEC EDGAR Filings Scraper](https://apify.com/skootle/sec-edgar-filings) to pair a sponsor's trial pipeline with its filings. - [arXiv Papers Monitor](https://apify.com/skootle/arxiv-papers) for the research feeding the pipeline. - [Crexi Commercial Real Estate Scraper](https://apify.com/skootle/crexi-commercial-real-estate-scraper) if your interest is in the sites rather than the science. # Actor input Schema ## `query` (type: `string`): Free-text search across trial titles, conditions, and interventions. Example: CAR-T lymphoma ## `condition` (type: `string`): Narrow to a disease area. Example: non-small cell lung cancer ## `sponsor` (type: `string`): Watch one company's pipeline. Example: Eli Lilly ## `intervention` (type: `string`): Track a molecule or device. Example: semaglutide ## `location` (type: `string`): Country, state, or city where trial sites are located. Example: Texas ## `statuses` (type: `array`): Leave empty for all. Pick RECRUITING to find enrolling trials. ## `phases` (type: `array`): Leave empty for all phases. PHASE3 plus PHASE4 gives you the late-stage picture. ## `maxItems` (type: `integer`): How many trial records to return. The pipeline summary is computed from these. ## `emitPipelineSummary` (type: `boolean`): Adds one landscape record per run: sponsor competition, phase mix, enrollment scale, site geography. ## Actor input object example ```json { "query": "GLP-1 obesity", "statuses": [], "phases": [], "maxItems": 50, "emitPipelineSummary": true } ``` # 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 = { "query": "GLP-1 obesity" }; // Run the Actor and wait for it to finish const run = await client.actor("skootle/clinical-trials-intel").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 = { "query": "GLP-1 obesity" } # Run the Actor and wait for it to finish run = client.actor("skootle/clinical-trials-intel").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 '{ "query": "GLP-1 obesity" }' | apify call skootle/clinical-trials-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=skootle/clinical-trials-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Clinical Trials Scraper for Pipeline Intel", "description": "Scrape ClinicalTrials.gov by condition, sponsor, drug, phase, or location. Get sponsors, phases, enrollment, geo-coded sites, and a pipeline summary of the competitive landscape in every run.", "version": "0.0", "x-build-id": "b6ibpv6TdNSolWar1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/skootle~clinical-trials-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-skootle-clinical-trials-intel", "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/skootle~clinical-trials-intel/runs": { "post": { "operationId": "runs-sync-skootle-clinical-trials-intel", "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/skootle~clinical-trials-intel/run-sync": { "post": { "operationId": "run-sync-skootle-clinical-trials-intel", "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": { "query": { "title": "Search term", "type": "string", "description": "Free-text search across trial titles, conditions, and interventions. Example: CAR-T lymphoma" }, "condition": { "title": "Condition or disease", "type": "string", "description": "Narrow to a disease area. Example: non-small cell lung cancer" }, "sponsor": { "title": "Sponsor", "type": "string", "description": "Watch one company's pipeline. Example: Eli Lilly" }, "intervention": { "title": "Intervention or drug", "type": "string", "description": "Track a molecule or device. Example: semaglutide" }, "location": { "title": "Location", "type": "string", "description": "Country, state, or city where trial sites are located. Example: Texas" }, "statuses": { "title": "Recruitment status", "type": "array", "description": "Leave empty for all. Pick RECRUITING to find enrolling trials.", "items": { "type": "string", "enum": [ "NOT_YET_RECRUITING", "RECRUITING", "ENROLLING_BY_INVITATION", "ACTIVE_NOT_RECRUITING", "COMPLETED", "SUSPENDED", "TERMINATED", "WITHDRAWN" ], "enumTitles": [ "Not yet recruiting", "Recruiting", "Enrolling by invitation", "Active, not recruiting", "Completed", "Suspended", "Terminated", "Withdrawn" ] }, "default": [] }, "phases": { "title": "Phase", "type": "array", "description": "Leave empty for all phases. PHASE3 plus PHASE4 gives you the late-stage picture.", "items": { "type": "string", "enum": [ "EARLY_PHASE1", "PHASE1", "PHASE2", "PHASE3", "PHASE4" ], "enumTitles": [ "Early Phase 1", "Phase 1", "Phase 2", "Phase 3", "Phase 4" ] }, "default": [] }, "maxItems": { "title": "Max trials", "minimum": 1, "maximum": 1000, "type": "integer", "description": "How many trial records to return. The pipeline summary is computed from these.", "default": 50 }, "emitPipelineSummary": { "title": "Include pipeline summary record", "type": "boolean", "description": "Adds one landscape record per run: sponsor competition, phase mix, enrollment scale, site geography.", "default": true } } }, "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 } } } } } } } } } } ```