# Tech Stack From Job Posts Scraper (`coregent/tech-stack-from-job-posts-scraper`) Actor Extract public job posts from Greenhouse, Lever, Ashby, and public career pages and detect the technologies, tools, and cloud platforms companies are hiring for - no login or cookies. - **URL**: https://apify.com/coregent/tech-stack-from-job-posts-scraper.md - **Developed by:** [Delowar Munna](https://apify.com/coregent) (community) - **Categories:** Jobs, 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 job-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 ## Tech Stack From Job Posts Scraper ![Tech Stack From Job Posts Scraper](https://raw.githubusercontent.com/coregentdevspace/tech-stack-from-job-posts-scraper-assets/main/thumbnail-tech-stack-from-job-posts-scraper.png) Turn **public job posts into technology-demand data**. Point this actor at a **Greenhouse**, **Lever**, or **Ashby** board (or a public career page) and it returns clean, flat, CSV-ready rows — each enriched with the **technologies, tools, cloud platforms, frameworks, databases, and AI/ML stacks** the company is hiring for, plus a transparent `tech_signal_score`. **No login, no cookies, no session tokens.** The actor reads each ATS's **public JSON API** over HTTP, so it stays fast and cost-predictable. You pay one flat event per unique job row that passes your filters. ### ✨ Why this scraper - **Hiring-intent, not just job rows** — generic job scrapers return listings; this turns each description into structured technology signals for B2B sales, GTM research, and recruiting. - **JSON-first, no browser** — reads Greenhouse / Lever / Ashby public APIs directly. One request per board returns every posting _with its description_. - **Transparent detection** — a local, in-code keyword dictionary (no AI, no paid API). You can see and extend exactly what is matched. - **Flat 29-field output** — no nested objects; drops straight into Sheets, Excel, or a CRM. - **Pay-Per-Event** — one flat `job-result` event per saved unique job. Duplicates and filtered rows are never charged. --- ### 🚀 Quick start — sample inputs #### Example 1 — ATS boards + technology filter ```json { "startUrls": ["https://jobs.lever.co/example-company", "https://boards.greenhouse.io/examplecompany"], "maxResults": 200, "technologyKeywords": ["Snowflake", "dbt", "Kubernetes"], "requireTechnologyMatch": true, "technologyCategories": ["cloud", "data", "devops"], "keywordFilter": "data engineer", "locationFilter": "Australia", "remoteFilter": "any", "minTechSignalScore": 30, "includeDescriptionText": true, "dedupe": true, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Example 2 — single Ashby board, all jobs, custom residential proxy via your own provider ```json { "startUrls": ["https://jobs.ashbyhq.com/example"], "maxResults": 500, "includeDescriptionText": true, "dedupe": true, "proxyConfiguration": { "useApifyProxy": false, "proxyUrls": ["http://user:pass@proxy.iproyal.com:12321"] } } ``` > Supported ATS URLs use each platform's public JSON API. Other career pages are read via `schema.org/JobPosting` structured data when present, or by following an embedded Greenhouse/Lever/Ashby board. Pages with neither are reported as `unsupported_inputs`. > Apify Residential proxy is blocked; if you need residential routing, supply your own provider via `proxyConfiguration.proxyUrls`. See **🚦 Proxy policy** below. *** ### 📦 Output The dataset has one view: **Jobs & detected tech stack** — a 29-column flat table. ![Tech Stack From Job Posts Scraper — Jobs & detected tech stack table view](https://raw.githubusercontent.com/coregentdevspace/tech-stack-from-job-posts-scraper-assets/main/tech-stack-from-job-posts-scraper-output-jobs-n-detected-tech-stack-table-view.png) #### Output fields (29) `source_input`, `source_platform`, `company_name`, `company_domain`, `job_id`, `job_title`, `department`, `location`, `remote_type`, `employment_type`, `posted_at`, `job_url`, `apply_url`, `description_text`, `detected_technologies`, `technology_categories`, `languages`, `frameworks`, `cloud_platforms`, `databases`, `devops_tools`, `data_ai_tools`, `business_tools`, `tech_signal_score`, `tech_signal_label`, `reason_tags`, `matched_user_keywords`, `raw_description_length`, `scraped_at`. #### Sample record — Jobs & detected tech stack (Real run output; `description_text` is truncated here for readability.) ```json { "source_input": "https://jobs.ashbyhq.com/ramp", "source_platform": "ashby", "company_name": "Ramp", "company_domain": "ramp.com", "job_id": "41696f51-7b29-4e12-b528-46c2f6c4f5f7", "job_title": "Senior Data Scientist, Growth", "department": "Data", "location": "New York, NY (HQ)", "remote_type": "remote", "employment_type": "Full-time", "posted_at": "2026-02-02T14:46:45.488Z", "job_url": "https://jobs.ashbyhq.com/ramp/41696f51-7b29-4e12-b528-46c2f6c4f5f7", "apply_url": "https://jobs.ashbyhq.com/ramp/41696f51-7b29-4e12-b528-46c2f6c4f5f7/application", "description_text": "About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over...", "detected_technologies": "Airflow, BigQuery, Dagster, dbt, Fivetran, Git, Looker, NumPy, Pandas, Prefect, Python, Redshift, scikit-learn, Snowflake, SQL", "technology_categories": "ai_ml, analytics, data, database, language, other", "languages": "Python, SQL", "frameworks": "", "cloud_platforms": "", "databases": "BigQuery, Redshift, Snowflake", "devops_tools": "", "data_ai_tools": "Airflow, Dagster, dbt, Fivetran, NumPy, Pandas, Prefect, scikit-learn", "business_tools": "Looker", "tech_signal_score": 90, "tech_signal_label": "high", "reason_tags": "multiple_technologies, data_stack, ai_ml_stack, crm_or_marketing_stack, user_keyword_match, engineering_role", "matched_user_keywords": "dbt, Snowflake", "raw_description_length": 6118, "scraped_at": "2026-06-05T00:50:33.198Z" } ``` *** ### 🎯 Tech signal score Transparent rule-based score (0–100) computed from the detected technologies and the role title — no AI, no external enrichment. | Signal | Points | | ------------------------------------------------------- | -----------------: | | Each unique detected technology | +10 (capped at 50) | | At least two technology categories detected | +10 | | Cloud platform or DevOps tool detected | +10 | | Database / data / AI-ML tool detected | +10 | | Engineering / data / IT / security / product role title | +10 | | A user-supplied `technologyKeywords` term matched | +10 | Score is capped at 100. **Labels**: `high` (60–100) · `medium` (30–59) · `low` (1–29) · `none` (0). `reason_tags` explains the score — e.g. `multiple_technologies`, `cloud_mentioned`, `devops_stack`, `data_stack`, `ai_ml_stack`, `crm_or_marketing_stack`, `user_keyword_match`, `engineering_role`. *** ### ⚙️ Filters | Filter | Effect | | ------------------------ | ------------------------------------------------------------------------------------ | | `keywordFilter` | Case-insensitive substring on title + department + description. | | `requireTechnologyMatch` | Keep only rows with at least one detected technology. | | `technologyCategories` | Keep rows with at least one detected technology in the selected categories. | | `locationFilter` | Case-insensitive substring on location; jobs with no location are excluded when set. | | `remoteFilter` | `any` / `remote` / `hybrid` / `onsite` against the derived `remote_type`. | | `minTechSignalScore` | Keep rows with `tech_signal_score` ≥ threshold. | | `dedupe` | Drop duplicates by platform job ID, canonical job URL, and title/company/location. | Filters are applied **before** any dataset push or event charge. *** ### 💰 Pricing **Pay-Per-Event**. One flat event per saved row (the per-event price is configured on the Apify console): | Event | Charged when | | ------------ | -------------------------------------------------------------------------------------------- | | `job-result` | Once per unique job row that passed all filters and was successfully written to the dataset. | Your bill is simply `results_saved × price_per_event`. The actor honors the user-configured per-run spending cap (Apify `eventChargeLimitReached`): it caps how many results it collects up-front to what the limit can pay for, and stops cleanly the moment the cap is reached during charging. Not charged: duplicates, rows filtered out, rows missing a title/durable identifier, and failed or blocked requests. #### 🚦 Proxy policy Use **Apify Datacenter** proxy or **no proxy** for normal runs — both work reliably for the public ATS JSON APIs at this actor's conservative concurrency. **Apify Residential proxy is not supported.** The actor will fail at startup if `proxyConfiguration.apifyProxyGroups` includes `RESIDENTIAL`. Reason: in pay-per-event actors, residential bandwidth (~/GB) is billed to the developer, not the run user, so a single bandwidth-heavy run could exceed the per-result event revenue. If you genuinely need residential routing, supply your own residential provider via the proxy editor's **Custom proxy URLs** field — that traffic goes through your provider, not Apify, and is unaffected: ``` http://user:pass@proxy.iproyal.com:12321 http://user:pass@proxy.brightdata.com:22225 http://user:pass@proxy.oxylabs.io:7777 ``` *** ### 📊 Run summary After each run, a `RUN_SUMMARY` entry is written to the key-value store: ```json { "inputs_total": 3, "successful_inputs": 3, "failed_inputs": 0, "unsupported_inputs": 0, "raw_results_found": 420, "results_saved": 200, "duplicates_removed": 12, "filtered_out": 208, "charged_events": 200, "blocked_requests": 0, "retry_count": 1, "source_platform_counts": { "greenhouse": 120, "lever": 50, "ashby": 30 }, "technology_counts": { "Python": 88, "AWS": 61, "Kubernetes": 44 }, "category_counts": { "language": 180, "cloud": 110, "devops": 70 }, "runtime_seconds": 41, "scraped_at": "2026-06-04T06:00:00.000Z" } ``` `charged_events` equals the number of successfully saved unique rows. *** ### 🚧 Limitations (V1) - **Public data only**: no login, cookies, or member-only content. The actor reads each ATS's public JSON board API. - **Supported sources**: Greenhouse, Lever, and Ashby via public APIs; other career pages only when they expose `schema.org/JobPosting` JSON-LD or embed one of those boards. - **Technology detection** uses a transparent local dictionary — it detects common languages, frameworks, cloud platforms, databases, DevOps, data/AI, analytics, CRM, security, and mobile tooling, not every niche tool. - **`company_domain`** is best-effort and is often `null` for ATS-hosted boards (no company website is exposed). - **No** recruiter/contact extraction, email enrichment, company-site crawling, or AI scoring. *** ### ❓ FAQ **Do I need an account or cookies?** No. The actor only uses public ATS JSON endpoints. **Which ATS platforms are supported?** Greenhouse, Lever, and Ashby via their public APIs. Generic career pages work when they expose JSON-LD `JobPosting` data or embed one of those boards. **How are technologies detected?** A local, transparent keyword dictionary with aliases and word-boundary rules runs against the job title, department, and description. Add your own terms with `technologyKeywords`. **Can I export to CSV?** Yes — every field is flat (no nested objects). Use Apify's CSV / Excel export, or call the dataset API with `format=csv`. *** ### 🛠️ Technical notes - **Stack**: Node.js 22 · Apify SDK 3 · Crawlee `CheerioCrawler` (HTTP + JSON) · native fetch. No browser. - **Endpoints**: Greenhouse `boards-api`, Lever `v0/postings`, Ashby `posting-api/job-board` (all public, no auth). - **Concurrency**: `min=1`, `max=5` (conservative; tune after real runs). - **Memory**: 1 GB min · 2 GB default · 4 GB max. - **Proxy**: Apify Proxy enabled by default; custom configs accepted; Apify Residential rejected at startup. # Actor input Schema ## `startUrls` (type: `array`): Public job, company career, or ATS board URLs to process (Greenhouse, Lever, Ashby, or a public career page). Example: https://jobs.lever.co/example or https://boards.greenhouse.io/example. No login, cookies, or tokens. ## `maxResults` (type: `integer`): Maximum number of saved unique job rows across the whole run (not per URL). Range 1-10000. ## `technologyKeywords` (type: `array`): Optional extra technology terms to detect (and optionally require). Added to the built-in dictionary. Example: Snowflake, dbt, Kubernetes. Max 100 terms. ## `requireTechnologyMatch` (type: `boolean`): Keep only jobs where at least one technology (built-in or user-supplied) was detected. Rows with no detected technology are filtered out and not charged. ## `technologyCategories` (type: `array`): Keep only jobs with at least one detected technology in these categories. Leave empty for all. ## `keywordFilter` (type: `string`): Optional text filter applied to job title + department + description (case-insensitive). Leave empty for no keyword filter. Max 200 chars. ## `locationFilter` (type: `string`): Optional location filter applied to the job's location text (case-insensitive). Jobs with no location are excluded when this is set. Max 100 chars. ## `remoteFilter` (type: `string`): Keep only jobs of this work mode (derived from title/location/description). 'Any' keeps all. ## `minTechSignalScore` (type: `integer`): Keep only jobs whose tech\_signal\_score is greater than or equal to this threshold (0-100). 0 keeps all. ## `includeDescriptionText` (type: `boolean`): Save the full visible job description in description\_text. Detection always uses the description internally; turning this off only reduces dataset size. ## `dedupe` (type: `boolean`): Remove duplicate job posts by platform job ID, canonical job URL, and title/company/location so you are not charged for duplicates. ## `proxyConfiguration` (type: `object`): Apify Proxy configuration. Defaults to Apify Proxy enabled. Apify Residential is NOT supported and will fail the run at startup; if you need residential routing, supply your own provider via Custom proxy URLs (proxyUrls). ## Actor input object example ```json { "startUrls": [ "https://jobs.lever.co/mistral", "https://boards.greenhouse.io/gitlab", "https://jobs.ashbyhq.com/ramp" ], "maxResults": 100, "technologyKeywords": [ "Snowflake", "dbt", "Kubernetes" ], "requireTechnologyMatch": false, "technologyCategories": [], "keywordFilter": "", "locationFilter": "", "remoteFilter": "any", "minTechSignalScore": 0, "includeDescriptionText": true, "dedupe": true, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `overview` (type: `string`): Flat 29-field table of every job row pushed to the dataset, including provenance, company/job fields, the detected technologies and category columns, and the tech-signal score. # 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 = { "startUrls": [ "https://jobs.lever.co/mistral", "https://boards.greenhouse.io/gitlab", "https://jobs.ashbyhq.com/ramp" ], "technologyKeywords": [ "Snowflake", "dbt", "Kubernetes" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("coregent/tech-stack-from-job-posts-scraper").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 = { "startUrls": [ "https://jobs.lever.co/mistral", "https://boards.greenhouse.io/gitlab", "https://jobs.ashbyhq.com/ramp", ], "technologyKeywords": [ "Snowflake", "dbt", "Kubernetes", ], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("coregent/tech-stack-from-job-posts-scraper").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 '{ "startUrls": [ "https://jobs.lever.co/mistral", "https://boards.greenhouse.io/gitlab", "https://jobs.ashbyhq.com/ramp" ], "technologyKeywords": [ "Snowflake", "dbt", "Kubernetes" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call coregent/tech-stack-from-job-posts-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=coregent/tech-stack-from-job-posts-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tech Stack From Job Posts Scraper", "description": "Extract public job posts from Greenhouse, Lever, Ashby, and public career pages and detect the technologies, tools, and cloud platforms companies are hiring for - no login or cookies.", "version": "1.0", "x-build-id": "705MSbe1HT0yslclx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/coregent~tech-stack-from-job-posts-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-coregent-tech-stack-from-job-posts-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/coregent~tech-stack-from-job-posts-scraper/runs": { "post": { "operationId": "runs-sync-coregent-tech-stack-from-job-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/coregent~tech-stack-from-job-posts-scraper/run-sync": { "post": { "operationId": "run-sync-coregent-tech-stack-from-job-posts-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "startUrls": { "title": "Start URLs", "type": "array", "description": "Public job, company career, or ATS board URLs to process (Greenhouse, Lever, Ashby, or a public career page). Example: https://jobs.lever.co/example or https://boards.greenhouse.io/example. No login, cookies, or tokens.", "default": [], "items": { "type": "string" } }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of saved unique job rows across the whole run (not per URL). Range 1-10000.", "default": 100 }, "technologyKeywords": { "title": "Technology keywords", "type": "array", "description": "Optional extra technology terms to detect (and optionally require). Added to the built-in dictionary. Example: Snowflake, dbt, Kubernetes. Max 100 terms.", "default": [], "items": { "type": "string" } }, "requireTechnologyMatch": { "title": "Require a technology match", "type": "boolean", "description": "Keep only jobs where at least one technology (built-in or user-supplied) was detected. Rows with no detected technology are filtered out and not charged.", "default": false }, "technologyCategories": { "title": "Technology categories", "type": "array", "description": "Keep only jobs with at least one detected technology in these categories. Leave empty for all.", "items": { "type": "string", "enum": [ "language", "framework", "cloud", "database", "data", "devops", "ai_ml", "analytics", "crm", "security", "mobile", "other" ], "enumTitles": [ "Language", "Framework", "Cloud", "Database", "Data", "DevOps", "AI / ML", "Analytics", "CRM", "Security", "Mobile", "Other" ] }, "default": [] }, "keywordFilter": { "title": "Keyword filter", "type": "string", "description": "Optional text filter applied to job title + department + description (case-insensitive). Leave empty for no keyword filter. Max 200 chars.", "default": "" }, "locationFilter": { "title": "Location filter", "type": "string", "description": "Optional location filter applied to the job's location text (case-insensitive). Jobs with no location are excluded when this is set. Max 100 chars.", "default": "" }, "remoteFilter": { "title": "Remote type filter", "enum": [ "any", "remote", "hybrid", "onsite" ], "type": "string", "description": "Keep only jobs of this work mode (derived from title/location/description). 'Any' keeps all.", "default": "any" }, "minTechSignalScore": { "title": "Minimum tech signal score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Keep only jobs whose tech_signal_score is greater than or equal to this threshold (0-100). 0 keeps all.", "default": 0 }, "includeDescriptionText": { "title": "Include description text", "type": "boolean", "description": "Save the full visible job description in description_text. Detection always uses the description internally; turning this off only reduces dataset size.", "default": true }, "dedupe": { "title": "Deduplicate jobs", "type": "boolean", "description": "Remove duplicate job posts by platform job ID, canonical job URL, and title/company/location so you are not charged for duplicates.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy configuration. Defaults to Apify Proxy enabled. Apify Residential is NOT supported and will fail the run at startup; if you need residential routing, supply your own provider via Custom proxy URLs (proxyUrls).", "default": { "useApifyProxy": 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 } } } } } } } } } } ```