# Charity Email Scraper (`solid-scraper/charity-email-scraper`) Actor 📧 Charity Email Scraper finds verified donor/volunteer organization contacts fast using keywords and locations. Ideal for nonprofits, outreach, and fundraising teams—save time, boost reach, and grow your mission with targeted emails. 🚀 - **URL**: https://apify.com/solid-scraper/charity-email-scraper.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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. 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 ### Charity Email Scraper 📬 **Charity Email Scraper** is an actor that helps you **find charity email addresses** by scraping **publicly available data** and then extracting contact information from the charities’ websites. It’s designed to solve a common outreach bottleneck: turning a list of relevant charities into usable leads—quickly and at scale—without manually opening every website one by one. Whether you’re using a **charity email scraper**, a **nonprofit email scraper**, or a **prospecting email scraper for nonprofits**, this tool streamlines discovery and enrichment so you can build a **bulk email list for fundraising** and outreach campaigns faster. ### Section 01 **What is Charity Email Scraper?** **Charity Email Scraper** searches for charities using your **search term** and one or more **locations**, then collects business listings and scrapes contact details from their websites. In practice, it’s a charity contact email extractor that helps you **scrape nonprofit contact emails** and enrich your dataset with supporting fields like phone numbers and social links. Whether you’re a marketer running **email lead generation tool for charities**, a researcher building a dataset for **data enrichment for nonprofit email leads**, or a data analyst automating outreach list prep, this actor helps you gather the contacts you need—saving you hours of manual work. ### Section 02 **Why choose Charity Email Scraper?** | Feature | Benefit | | --- | --- | | ✅ **All-in-one lead collection** | Combines discovery and website scraping to build a usable nonprofit outreach dataset | | ✅ **Built-in proxy support** | Helps scraping stay reliable at scale and reduces the chance of rate-limit disruptions | | ✅ **Resilient scraping behavior** | Includes retries and fallbacks for resilience when pages don’t behave as expected | | ✅ **Structured output for leads** | Produces consistent dataset rows with email, phones, and social media fields | | ✅ **Scalable limits** | Lets you control how many businesses you want via `maxBusinesses` and per-location handling | | ✅ **Automation-ready dataset** | Outputs clean records that you can export and feed into CRM or analytics workflows | ### Section 03 **Key features** - 🔍 **Charity-focused business discovery**: Uses your `googleMapsSearchTerm` and `googleMapsLocation` values to target relevant charity/business listings. - 🌐 **Website contact extraction**: Scrapes the websites it finds to populate `scraped_emails`, `scraped_phones`, and `scraped_social_media`. - 🧾 **Structured lead records**: Saves results with clear fields like `name`, `website`, `full_address`, `emails_found`, and `scrape_status`. - 🛡️ **Reliability with proxy configuration**: Supports proxy settings for more dependable scraping runs when you scale up. - 🔄 **Resilience for messy sites**: Designed with retries and fallbacks so you get more usable results across real-world websites. - 💾 **Real-time dataset saving**: Pushes results to the Apify dataset as the run progresses (including flat one-row-per-email output). - 📊 **Useful quality metrics**: Includes `avg_rating` and `total_reviews` from the discovered listing data. - 🧠 **Email-only behavior option**: Supports saving only businesses that produce emails (via email-only mode logic). ### Section 04 **Input** Provide input via an `input.json` file. Example structure: ```json { "googleMapsSearchTerm": "Charity", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `googleMapsSearchTerm` | Yes | The business type or niche you want the scraper to target (for example, `Charity` or `dentists`). | | `googleMapsLocation` | Yes | One or more target locations for your lead search (for example, `["Miami, Florida"]` style values—use what you need for your outreach geography). | | `maxBusinesses` | No | The target number of businesses to find **with emails**. Valid range is `1-1000`. The run stops when this target is reached. | | `scrapeMaxBusinessesPerLocation` | No | Controls how the `maxBusinesses` limit is applied. If enabled, the scraper aims for up to `maxBusinesses` results per location; if disabled, it combines results across locations up to a single total limit. | | `proxyConfiguration` | No | Proxy settings for web scraping. Recommended for larger-scale runs to help avoid blocks and rate-limit issues. | | `proxyConfiguration.proxy support` | No (within object) | When set to `true`, enables Apify Proxy (recommended). | ### Section 05 **Output** The actor saves results to the **Apify dataset** as individual JSON objects (with flattened lead rows where an email is found). Example output row (one lead email row): ```json { "name": "Example Charity Name", "website": "https://example.org", "phone": "+1 555 123 4567", "full_address": "123 Main St New York NY 10001 US", "city": "New York", "state": "NY", "zip": "10001", "country_code": "US", "avg_rating": 4.6, "total_reviews": 120, "lat": 40.7128, "long": -74.006, "place_id": "PLACE_ID_VALUE", "scraped_emails": [], "scraped_phones": [], "scraped_social_media": [], "emails_found": 3, "pages_scraped": 8, "scrape_status": "success", "email_found": "contact@example.org" } ``` > Note: When emails are found, the actor pushes **flat rows**—one dataset row per extracted email—using `email_found`. The flattened output removes the list field `scraped_emails` and the internal `_detailed_emails` (if present). #### Output Fields | Field | Type | Description | | --- | --- | --- | | `name` | string | Business/charity name as found in the discovery step. | | `website` | string | Website URL extracted from the listing. | | `phone` | string | Phone number from the listing data (not necessarily the scraped website contacts). | | `full_address` | string | Full constructed address string. | | `city` | string | City name from listing data. | | `state` | string | State/region from listing data. | | `zip` | string | ZIP/postal code from listing data. | | `country_code` | string | Country code from listing data. | | `scraped_emails` | array | Extracted emails from the charity website (present in non-flattened business rows; removed in flattened email rows). | | `scraped_phones` | array | Extracted phone numbers from the charity website. | | `scraped_social_media` | array | Extracted social media links from the charity website. | | `emails_found` | number | Total number of emails found on the website scraping step. | | `pages_scraped` | number | How many pages were processed for the website scraping. | | `avg_rating` | number | Rating associated with the discovered listing. | | `total_reviews` | number | Total number of reviews associated with the discovered listing. | | `lat` | number | Latitude from the discovery data. | | `long` | number | Longitude from the discovery data. | | `place_id` | string | Listing place identifier from the discovery data. | | `scrape_status` | string | Scraping status (e.g., `success`, `failed`, `no_website`, `error`). | | `email_found` | string | The specific email value for that dataset row (used in the flat one-row-per-email output). | After the run, you can export the dataset to JSON or CSV directly from the Apify UI (depending on your workflow). ### Section 06 **How to use Charity Email Scraper (via Apify Console)** 1. **Open Apify Console** Go to the Apify Console: [console.apify.com](https://console.apify.com) and log in. 2. **Find the actor** Search for **Charity Email Scraper** in the Actors marketplace and open the actor page. 3. **Add your inputs** In the **INPUT** section, provide: - `googleMapsSearchTerm` (required) - `googleMapsLocation` (required; can be multiple) 4. **Set your limits** Adjust: - `maxBusinesses` (target number of businesses with emails) - `scrapeMaxBusinessesPerLocation` (whether the limit applies per location or globally) 5. **Configure proxy (recommended)** In **Proxy configuration**, set `proxyConfiguration.proxy support` to `true` for Apify Proxy-backed runs. 6. **Run the actor** Click **Run**. During execution, you’ll see logs for the discovery and website scraping stages, including progress updates and success/failure outcomes. 7. **Review results** After completion, open the **dataset output** to see extracted contacts. If emails are found, you’ll see one row per extracted email via `email_found`. 8. **Export** Export your results as JSON/CSV and load into your CRM, spreadsheet, or analysis pipeline. No coding required—get accurate charity email leads in minutes with **Charity Email Scraper**. 💪 ### Section 07 **Advanced features & SEO optimization** - 💡 **Engineered for nonprofit email lead generation**: Built specifically for the “find charity email addresses + enrich contact details” workflow—ideal for **email lead generation tool for charities** use cases. - 🧠 **Email-only mode logic**: Supports a mode where only businesses with emails are saved, which helps keep your outreach list clean. - 🌐 **Website-first contact enrichment**: Extracts emails plus supporting `scraped_phones` and `scraped_social_media` fields to support **charity contact email extractor** workflows. - 🛡️ **GDPR-minded workflow support (practical)**: You control the geography, niche, and volume via inputs like `googleMapsLocation` and `maxBusinesses` to help you target only what you need for compliant outreach. - 📊 **Consistent dataset schema**: Output is structured with fields such as `scrape_status`, `pages_scraped`, and `emails_found`, which makes **scrape emails from charity websites** easier to operationalize. ### Section 08 **Best use cases** - 📈 **Fundraising teams building donor email lists**: Generate a **bulk email list for fundraising** with charity emails plus supporting contact context. - 🧭 **Nonprofit marketers running outreach sequences**: Prepare targeted lists from charity website contacts to improve campaign speed. - 🔬 **Researchers collecting nonprofit contact datasets**: Build structured samples for analysis using fields like rating, reviews, and location metadata. - 🏢 **Partnership managers sourcing charity collaborators**: Quickly compile charities and their contact channels for co-marketing or sponsorship outreach. - 🧾 **Data analysts enriching lead spreadsheets**: Feed clean rows into BI pipelines, using `emails_found`, `pages_scraped`, and `scrape_status`. - 💻 **CRM operators automating prospecting email scraper for nonprofits**: Automate list updates and reduce manual copy-paste from websites. - ⚙️ **Email ops teams improving list coverage**: Compare website-derived contacts across multiple charities and track scraping success rates. ### Section 09 **Technical specifications** - **Supported Input Formats** - ✅ `googleMapsSearchTerm` (string) - ✅ `googleMapsLocation` (array of strings) - ✅ `proxyConfiguration` (object with `proxy support`) - **Proxy Support** - ✅ `proxyConfiguration.proxy support` supported via the Apify proxy configuration object. - **Retry Mechanism / Resilience** - ✅ Includes retries and fallbacks for resilience during scraping (behavior designed to improve real-world success). - **Dataset Structure** - ✅ Uses the Apify dataset named by the actor storage configuration: **Business Contact Data** - ✅ Includes fields such as `scraped_emails`, `scraped_phones`, `scraped_social_media`, `emails_found`, `pages_scraped`, and `scrape_status`. - ✅ When emails are extracted, outputs **flattened lead rows** with `email_found` (one row per email). - **Limit Controls** - ✅ `maxBusinesses` stops discovery once the target is reached. - ✅ `scrapeMaxBusinessesPerLocation` controls whether limits apply per location or globally. - **Limitations** - ❌ If a listing has no website, website scraping cannot occur and the result will reflect `scrape_status` such as `no_website`. - ❌ Not every charity listing will produce emails; you control volume using `maxBusinesses` and can use email-only behavior to keep results focused. ### Section 10 **FAQ** #### Does Charity Email Scraper work with multiple locations? ✅ Yes. You can pass `googleMapsLocation` as an array of locations. If you enable `scrapeMaxBusinessesPerLocation`, it targets up to `maxBusinesses` per location; otherwise it combines results across locations up to a single overall limit. #### What kind of data does Charity Email Scraper extract? ✅ It scrapes charity/business websites to extract contact details, including `scraped_emails`, `scraped_phones`, and `scraped_social_media`. It also saves discovery fields like `name`, `website`, `full_address`, `avg_rating`, and `total_reviews`. #### How is the output stored in the dataset? ✅ The actor pushes results to the Apify dataset as JSON objects. When emails are found, it also pushes **flat rows** so you get one dataset row per email via `email_found`. #### Can I limit how many charities with emails I get? ✅ Yes. Use `maxBusinesses` to set the target number of businesses with emails. The run stops once the target is reached, including in email-only behavior logic. #### Do I need proxies to run the actor? ❌ Not required for correctness, but proxy support is available and recommended for larger runs through `proxyConfiguration` (notably `proxyConfiguration.proxy support`). #### Can it validate emails? ✅ There is support for email validation via the `validate_emails` setting passed into the website scraping configuration. In the current input schema, this validation toggle is not exposed directly, but validation is controlled in the scraping config flow. #### Can I integrate the output into my CRM or analysis workflow? 💻 Yes. The dataset is structured with consistent fields like `email_found`, `emails_found`, `pages_scraped`, and `scrape_status`, which makes it straightforward to export to JSON/CSV and import into common tools. #### Is Charity Email Scraper compliant for legal use? ✅ The actor extracts from **publicly accessible sources**, but legal compliance (including GDPR, CCPA, and anti-spam regulations) is your responsibility. Use the data ethically and only for legitimate outreach. ### Section 11 **Support & feature requests** Want to improve Charity Email Scraper or request enhancements for charity email scraper workflows? We’d love to hear from you. 💬 - 💡 **Feature Requests:** For example, adding a dedicated CSV export option, expanding output fields, or improving email filtering for charity mailing list scraper use cases. - 📧 **Contact:** Reach out at . Your feedback helps shape the roadmap for the **Charity Email Scraper** actor. ### Section 12 **Closing CTA / Final thoughts** *If you’re looking for an SEO-optimized charity email scraper that turns public charity info into structured outreach leads, Charity Email Scraper delivers a practical, scalable workflow from discovery to dataset output.*\ *Run it once, export the dataset, and spend your time on outreach—not manual searching.* ### Section 13 **Disclaimer** **This tool accesses publicly accessible sources only.** It does not access private profiles, authenticated data, or password-protected pages. You are responsible for ensuring your use complies with applicable laws and regulations (including GDPR/CCPA where relevant), spam regulations, and each source website’s terms of service. For any data removal requests, contact . Please use **Charity Email Scraper** responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "Charity", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": 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 = { "googleMapsSearchTerm": "Charity", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/charity-email-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 = { "googleMapsSearchTerm": "Charity", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/charity-email-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 '{ "googleMapsSearchTerm": "Charity", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call solid-scraper/charity-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/charity-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Charity Email Scraper", "description": "📧 Charity Email Scraper finds verified donor/volunteer organization contacts fast using keywords and locations. Ideal for nonprofits, outreach, and fundraising teams—save time, boost reach, and grow your mission with targeted emails. 🚀", "version": "1.0", "x-build-id": "hdGhVbfEQ1eNSCOGD" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~charity-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-charity-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~charity-email-scraper/runs": { "post": { "operationId": "runs-sync-solid-scraper-charity-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~charity-email-scraper/run-sync": { "post": { "operationId": "run-sync-solid-scraper-charity-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "Charity" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "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 } } } } } } } } } } ```