GitHub Email Scraper

GitHub Email Scraper – Overview 📧

The GitHub Email Scraper helps you discover publicly available email addresses associated with GitHub-related search results by combining targeted keyword searches, email domain filtering, optional location targeting, and exclusion filters.

This actor is designed for recruiters, lead generation specialists, founders, marketers, agencies, researchers, sales teams, and businesses that need structured contact discovery from publicly available GitHub-related search results.

Instead of manually searching through pages of results, the actor automatically collects matching email addresses and exports structured lead data directly into your Apify dataset.

🚀 Why Use This GitHub Email Scraper?

• Extract publicly available email addresses linked to GitHub search results
• Search using multiple keywords at once
• Filter by specific email domains
• Narrow searches using location filters
• Exclude unwanted niches or industries
• Receive structured lead data in Apify datasets
• Export results as JSON, CSV, Excel, XML, or via API
• Schedule recurring runs through Apify automation

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GitHub Email Scraper – Key Features

Keyword-Based GitHub Email Discovery

Search using one or many keywords such as:

• Founder
• CEO
• Software Engineer
• Frontend Developer
• Full Stack Developer
• Product Manager
• Startup Founder
• AI Engineer
• DevOps Engineer

The actor processes each keyword independently and searches for matching publicly available contact information.

Email Domain Filtering

Focus only on the email providers that matter to your workflow.

Examples:

• @gmail.com
• @outlook.com
• @yahoo.com
• @hotmail.com
• Custom company domains

This allows you to target personal emails, business emails, or industry-specific domains.

Location Targeting

Optionally narrow results by:

• Country
• State
• Province
• Region
• City

This helps when building localized lead lists or recruiting within specific geographic markets.

Exclude Unwanted Profiles

Use the Exclude Words filter to ignore results containing specific terms.

Examples:

• crypto
• gambling
• onlyfans
• nft
• casino

This improves lead quality and reduces manual cleanup.

Duplicate Email Prevention

The actor automatically avoids returning duplicate email addresses during collection, helping maintain cleaner datasets.

Resume Support

If a run stops unexpectedly, progress can be resumed without starting from the beginning, reducing wasted runtime and duplicate processing.

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GitHub Email Scraper – What Data You Can Extract

The actor returns structured lead records whenever matching public contact information is discovered.

📊 Output Data Fields

• Keyword
• Title
• Description
• URL
• Email

Example Output

{
  "keyword": "Software Engineer",
  "title": "John Doe - GitHub",
  "description": "Software Engineer with experience in cloud applications. Contact: johndoe@gmail.com",
  "url": "https://github.com/example",
  "email": "johndoe@gmail.com"
}

Output Field Explanation

Field	Description
keyword	Search keyword that generated the result
title	Result title
description	Result snippet or description
url	Source URL
email	Extracted email address

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GitHub Email Scraper – How to Use It

Using the actor requires only a few steps.

Step 1: Enter Keywords

Add one or more search terms.

Example:

Founder
Frontend Developer
Software Engineer

Step 2: Select Email Domains

Choose the email domains you want included.

Example:

@gmail.com
@outlook.com
@yahoo.com

Step 3: Add a Location (Optional)

Examples:

New York
California
United Kingdom
Germany
Canada

Leave blank to search globally.

Step 4: Configure Maximum Emails

Set the number of emails you want collected for each keyword and domain combination.

Step 5: Run the Actor

Start the actor and wait for the dataset to populate with matching leads.

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How to Extract GitHub Emails by Job Title

One of the most common use cases is finding professionals based on their role.

Examples:

• Software Engineer emails
• Frontend Developer emails
• Backend Developer emails
• Product Manager emails
• Founder emails
• CTO emails
• DevOps Engineer emails

Using specific job titles generally produces more focused lead lists than broad keywords.

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How to Extract GitHub Emails by Location

The location filter can help narrow searches to specific markets.

Examples:

• Developers in New York
• Founders in London
• Engineers in Toronto
• Software professionals in Berlin
• Startups in Singapore

This can be useful for local recruiting, regional outreach, and market research.

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GitHub Email Scraper – Use Cases

💼 Common Use Cases

• Recruitment and talent sourcing
• Developer outreach
• B2B lead generation
• Startup prospecting
• Sales prospecting
• Agency lead generation
• Partnership discovery
• Market research
• Competitive research
• Developer community analysis

Recruiting and Hiring

Recruiters can identify publicly available contacts connected to developer-related searches and build candidate pipelines more efficiently.

Sales Prospecting

Sales teams can discover relevant contacts for outreach campaigns and business development efforts.

Startup Discovery

Find founders, startup teams, and technology professionals associated with targeted industries.

Developer Outreach

Developer tools, SaaS products, and technical platforms can identify potential users and communities.

Market Intelligence

Analyze trends across technology sectors using targeted keyword searches.

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Input

The actor supports the following input fields.

⚙️ Input Parameters

• keywords
• location
• customDomains
• maxEmails
• excludeWords

Input Schema

Input	Type	Required	Description
keywords	Array	Yes	Search keywords or queries
location	String	No	Country, state, region, or city filter
customDomains	Array	No	Email domains to include
maxEmails	Integer	No	Maximum emails per keyword + domain combination
excludeWords	Array	No	Terms that should be excluded

Example Input

{
  "keywords": [
    "Founder",
    "Frontend Developer",
    "Software Engineer"
  ],
  "location": "New York",
  "customDomains": [
    "@gmail.com",
    "@outlook.com"
  ],
  "maxEmails": 20,
  "excludeWords": [
    "crypto",
    "onlyfans"
  ]
}

---

Output

Results are delivered directly into the Apify dataset.

Dataset Benefits

• Structured records
• Easy filtering
• API access
• Bulk export
• Automation friendly
• Integration ready

Export Formats

Apify supports exporting dataset results into:

• JSON
• CSV
• Excel
• XML
• RSS
• HTML

Availability depends on Apify dataset export capabilities.

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Pricing

💰 Pricing Information

The actor uses a result-based charging event when records are successfully produced.

The exact cost depends on your Apify subscription, runtime usage, and actor pricing configuration.

Important Notes

• Free users may encounter result limitations.
• Paid users can access larger collection volumes.
• Requested limits do not guarantee that the same number of emails exists publicly.
• Actual results depend on publicly available data.

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GitHub Email Scraper Tips and Best Practices

Use Multiple Related Keywords

Instead of searching only:

Fitness

Try:

Fitness Coach
Fitness Trainer
Online Fitness Coach
Fitness Influencer

This often improves coverage.

Use Multiple Domains

Combining domains can increase discovery opportunities.

Example:

@gmail.com
@yahoo.com
@outlook.com
@hotmail.com

Avoid Overly Narrow Filters

Very restrictive combinations may return fewer results.

Use Exclusion Filters

Exclude industries or niches that are not relevant to your campaign.

Expand Geographic Scope

If results are limited, try:

• Removing location restrictions
• Using broader regions
• Searching multiple cities

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Frequently Asked Questions

What is a GitHub Email Scraper?

A GitHub Email Scraper is a tool that helps collect publicly available email addresses associated with GitHub-related search results using targeted keyword searches.

Can I search multiple keywords at the same time?

• Yes

The actor supports multiple keywords in a single run.

Can I filter by email provider?

• Yes

You can specify one or multiple email domains.

Can I search by city or country?

• Yes

The location field supports geographic filtering.

Can I exclude specific industries or niches?

• Yes

Use the Exclude Words field.

Can I export the results to CSV?

• Yes

Apify datasets can be exported into CSV and other supported formats.

Does the actor return duplicate emails?

• No

Duplicate email addresses are automatically filtered during collection.

Can I use custom business email domains?

• Yes

You can provide your own email domain list.

Does the actor guarantee a specific number of emails?

• No

Results depend entirely on publicly available information matching your search criteria.

Can recruiters use this actor?

• Yes

Many recruiting workflows can benefit from keyword-based contact discovery.

Can agencies use this actor for lead generation?

• Yes

The actor can help agencies build targeted prospect lists.

Can I automate recurring email collection?

• Yes

You can schedule actor runs through Apify.

Does the actor provide structured output?

• Yes

Results are delivered in a structured dataset format.

Can I access the data through the Apify API?

• Yes

Apify datasets can be accessed through the Apify platform APIs.

What data does the GitHub Email Scraper return?

• Keyword
• Title
• Description
• URL
• Email

How can I improve the number of results?

• Use more keywords
• Add more email domains
• Broaden location filters
• Remove unnecessary exclusions
• Search multiple role variations

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Support 🤝

If you need help using the GitHub Email Scraper, want guidance on keyword selection, or require a customized workflow, you can contact the actor developer through the support information provided in the actor listing.

Whether you are building recruiting pipelines, sourcing developer contacts, performing lead generation, conducting market research, or discovering technology professionals, the GitHub Email Scraper provides a structured and efficient way to collect publicly available contact information from GitHub-related search results and export the data directly into your Apify workflows.