# Bitbucket Email Scraper Fast Advanced And Cheapest (`solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest`) Actor 📩 Extract targeted emails from Bitbucket fast with the Fast Advanced Cheapest scraper—advanced filters, bulk export, and reliable results for outreach, sales, and lead generation. 🚀💼 Get leads faster, save costs, scale growth! - **URL**: https://apify.com/solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest.md - **Developed by:** [SolidScraper](https://apify.com/solid-scraper) (community) - **Categories:** Lead generation, Automation, Developer tools - **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 ### Bitbucket Email Scraper - Fast, Advanced and Cheapest 📬 **Bitbucket Email Scraper - Fast, Advanced and Cheapest** is an actor that **extracts email addresses from Bitbucket** based on the **keywords** and **email-domain filters** you choose—so you can quickly build a contact list without manual searching. It’s designed for teams looking for a *Bitbucket email extractor*, *Git hosting email scraper tool*, or a *fast email scraping tool* to automate **bulk email extraction from Bitbucket**. Whether you’re a marketer, recruiter, data analyst, or researcher, this actor helps you turn publicly available Bitbucket data into outreach-ready leads—saving you hours of manual work. --- ### Why choose Bitbucket Email Scraper - Fast, Advanced and Cheapest? | Feature | Benefit | | --- | --- | | ✅ All-in-one email extraction | Extract emails from Bitbucket using your `keywords` and `customDomains` in a single run | | ✅ Built-in reliability & progress resume | Saves progress so you can resume runs without losing already collected emails | | ✅ Controlled collection limits | Use `maxEmails` to cap scraping time and cost predictability (no guarantee the exact number is reached) | | ✅ Structured dataset output | Pushes consistent records into the **Bitbucket Emails Dataset** with fields like `keyword`, `url`, and `email` | | ✅ Scale-ready automation | Iterates across your keyword × domain combinations to expand coverage for **Bitbucket to email list** workflows | | ✅ Uses your filters for relevance | Improves results by matching target email domains like `@gmail.com` with your keyword intent | --- ### Key features - 🔍 **Accurate Bitbucket email extraction**: Extracts email addresses from Bitbucket content and pushes them into the dataset ✅ - 🧩 **Keyword-led targeting**: Uses your `keywords` list to drive lead discovery (great for *Bitbucket profile email finder* use cases) - 🌐 **Custom email-domain filtering**: Lets you focus on specific domains via `customDomains` (e.g., `@gmail.com`, `@yahoo.com`) - 💾 **Real-time saving to the dataset**: Each discovered lead is pushed incrementally so you don’t lose results if a run stops - 🛡️ **Resume-ready progress tracking**: Persists cursor state and already-seen emails so long runs can continue where they left off - 🎯 **Max results control**: Stops when `maxEmails` is reached (helpful for building a **cheapest email scraper for Bitbucket** workflow) - 🔄 **Robust stop conditions**: Includes logic to stop when repeated pages yield no new emails, helping keep runs efficient --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["manager", "founder"], "location": "", "customDomains": ["@gmail.com", "@yahoo.com"], "maxEmails": 20 } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `keywords` | ✅ Yes | A list of keywords (or queries) used to find relevant Bitbucket leads and email addresses. | | `location` | ❌ No | Location to filter results. Leave empty (`""`) if you don’t want a location constraint. | | `customDomains` | ❌ No | List of email domains to extract (for example `@gmail.com`, `@yahoo.com`). Using targeted domains typically improves result quality for a **Bitbucket users email scraper** workflow. | | `maxEmails` | ❌ No | Maximum number of emails to collect. The scraper stops once it reaches this limit. Higher limits can take longer and don’t guarantee the exact number will be reached. Default is `20`. | *** ### Output The actor saves results to the **Bitbucket Emails Dataset** in JSON records (one row per pushed lead). Example output record: ```json { "network": "Bitbucket.com", "keyword": "manager", "title": "No title", "description": "No data", "url": "No URL", "email": "example@gmail.com" } ``` #### Output Fields | Field | Type | Description | | --- | --- | --- | | `network` | string | The source network label. In this actor it is pushed as `Bitbucket.com`. | | `keyword` | string | The keyword value used for this lead discovery. Useful for building a **Bitbucket data mining email extractor** dataset you can analyze later. | | `title` | string | Title text associated with the lead page/result (or `No title` when not available). | | `description` | string | Extracted text content used for email detection (may be `No data` / `No data`-like when empty). | | `url` | string | URL associated with the lead (or `No URL` when not available). | | `email` | string | The extracted email address pushed to the dataset. | **Export note:** You can export the dataset from the Apify Console in common formats like JSON/CSV depending on your chosen export options. *** ### How to use Bitbucket Email Scraper - Fast, Advanced and Cheapest (via Apify Console) 1. **Open Apify Console** 🔐\ Log in at https://console.apify.com and go to the **Actors** section. 2. **Find your actor**\ Search for **Bitbucket Email Scraper - Fast, Advanced and Cheapest** and open the actor page to view inputs and output. 3. **Go to the INPUT panel** 🧠\ Enter your settings using the built-in form or provide an `input.json` payload. 4. **Set your targeting keywords** 🔍\ Provide one or more `keywords` values (e.g., `manager`, `founder`). This drives the **Bitbucket email extractor** discovery logic. 5. **Choose email domains (recommended)** ✉️\ Add `customDomains` like `@gmail.com` and/or `@yahoo.com` to focus harvesting and improve relevance for **Bitbucket users email scraper** use cases. 6. **Optional: add location + cap results** 🎯\ Set `location` if you want a location filter. Optionally set `maxEmails` to control how many emails you want to collect. 7. **Click Run and monitor logs** ▶️\ Watch the runtime logs for progress and see how many unique emails are collected during the run. 8. **Open OUTPUT → Dataset and export** 📊\ After completion, open the dataset (**Scraped Leads**) and export your JSON/CSV for downstream enrichment, CRM imports, or analysis. *No coding required—get accurate results in minutes.* *** ### Advanced features & SEO optimization - 🚀 **Engineered for “Bitbucket email extractor” lead workflows**: Built specifically to turn Bitbucket content into an email list using your **keywords** and `customDomains`, ideal for *Bitbucket to email list* automation. - 🔄 **Progress resume for long runs**: Saves a cursor and already-seen emails so you can continue without restarting—perfect for **bulk email extraction from Bitbucket**. - ⏱️ **Timeout-friendly design via run options**: The actor description notes you can extend the timeout in **Run Options** (default: 3600 seconds / 1 hour) if needed. - 🧼 **Stop when results stall**: Uses stop conditions to avoid wasting time after repeated pages yield no new emails. - 📚 **Structured output for analysis**: Each pushed record includes `keyword`, `url`, and `email`, making it easy to filter and analyze in spreadsheets or BI tools. *** ### Best use cases - 📈 **Marketing lead generation**: Build a **Bitbucket commit author email** list quickly for outreach campaigns. - 🧑‍💼 **Recruiters and talent sourcers**: Extract emails tied to roles like “manager” or “founder” to speed up candidate outreach. - 🧪 **Data analysts**: Study which keywords produce the most leads by analyzing `keyword` and `email` coverage in the dataset. - 🏢 **B2B enrichment teams**: Create a *Git hosting email scraper tool* pipeline that merges scraped emails with your CRM records. - ✉️ **Sales development teams**: Generate a **Bitbucket profile email finder** output you can segment by domain for better personalization. - 🧾 **Researchers and investigators**: Collect contacts from publicly available Bitbucket data for investigative or reporting workflows. - ⚙️ **Automation engineers**: Feed dataset exports into your automation pipeline for ongoing **Bitbucket email harvesting automation**. *** ### Technical specifications - **Supported Input Formats** - ✅ `input.json` object matching the schema fields: `keywords`, `location`, `customDomains`, `maxEmails` - **Proxy Support** - ✅ Includes proxy support for reliable scraping (handled internally) - **Retry Mechanism** - ✅ Includes retry logic and fallback behavior to improve resilience - **Dataset Structure** - ✅ Dataset title: **Bitbucket Emails Dataset** - ✅ Default view name: **Scraped Leads** - ✅ Pushed fields include: `network`, `keyword`, `title`, `description`, `url`, `email` - **Rate Limits & Performance** - ✅ Uses pauses between requests and stops when `maxEmails` is reached or when results stall - ✅ Bigger searches / higher limits can take longer (timeout can be extended in **Run Options**) - **Limitations** - ❌ If matching emails are scarce, you may receive fewer results than `maxEmails` - ❌ Extraction depends on emails being present in publicly available sources *** ### FAQ #### Does Bitbucket Email Scraper - Fast, Advanced and Cheapest extract emails only from Bitbucket? ✅ Yes. The actor is designed to extract email addresses from Bitbucket using the keywords and domain filters you provide. #### What happens if I set `maxEmails` higher than the available results? ❌ The actor stops when it reaches `maxEmails`, but it does not guarantee the exact number will be collected. If the available publicly discoverable leads are limited, you may get fewer than requested. #### What should I put in `customDomains`? Use `customDomains` to limit extraction to specific email domains such as `@gmail.com` or `@yahoo.com`. This helps improve relevance, especially for workflows like *Bitbucket profile email finder* and *Bitbucket email extractor* lead sourcing. #### Can I resume a run if it stops early? ✅ Yes. The actor persists progress (including a cursor and the set of already-seen emails), so you can resume long runs without starting from scratch. #### Do I need coding to use this actor? ✅ No coding required. You can run it directly from Apify Console by configuring inputs and then exporting the dataset. #### Is this tool meant for bulk email extraction? ✅ Yes. It’s built for scaling contact discovery by iterating across your `keywords` and `customDomains`, producing a dataset you can export for bulk workflows like *bulk email extraction from Bitbucket*. #### Where can I request help or propose improvements? 💡 For help or feature requests related to Bitbucket Email Scraper - Fast, Advanced and Cheapest, email . *** ### Support & feature requests Want to improve your Bitbucket Email Scraper - Fast, Advanced and Cheapest results or request a new feature? 🚀 - 💡 **Feature Requests**: Examples include CSV-friendly formatting, additional output fields, or enhancements to extraction targeting based on your needs. - 📧 **Contact**: For support and ideas, reach us at . Your feedback directly shapes the roadmap for this Bitbucket email harvesting automation. *** ### *Bitbucket Email Scraper - Fast, Advanced and Cheapest* — get more leads faster. If you’re looking for the most comprehensive **Bitbucket email extractor** experience—built for speed, reliability, and clean dataset output—this is your tool. # Actor input Schema ## `keywords` (type: `array`): A list of keywords or queries to search for. ## `location` (type: `string`): Location to filter search results. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## Actor input object example ```json { "keywords": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ], "maxEmails": 20 } ``` # 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 = { "keywords": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest").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 = { "keywords": [ "manager", "founder", ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com", ], } # Run the Actor and wait for it to finish run = client.actor("solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest").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 '{ "keywords": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }' | apify call solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=solid-scraper/bitbucket-email-scraper-fast-advanced-and-cheapest", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Bitbucket Email Scraper Fast Advanced And Cheapest", "description": "📩 Extract targeted emails from Bitbucket fast with the Fast Advanced Cheapest scraper—advanced filters, bulk export, and reliable results for outreach, sales, and lead generation. 🚀💼 Get leads faster, save costs, scale growth!", "version": "1.0", "x-build-id": "1Jfg0EfogIOvYjb7Z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/solid-scraper~bitbucket-email-scraper-fast-advanced-and-cheapest/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-solid-scraper-bitbucket-email-scraper-fast-advanced-and-cheapest", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/solid-scraper~bitbucket-email-scraper-fast-advanced-and-cheapest/runs": { "post": { "operationId": "runs-sync-solid-scraper-bitbucket-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/solid-scraper~bitbucket-email-scraper-fast-advanced-and-cheapest/run-sync": { "post": { "operationId": "run-sync-solid-scraper-bitbucket-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Keywords or Queries", "type": "array", "description": "A list of keywords or queries to search for.", "default": [ "manager", "founder" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "customDomains": { "title": "Enter Custom Email Domains (e.g. @gmail.com, @yahoo.com)", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com", "@yahoo.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Enter Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 } } }, "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 } } } } } } } } } } ```