01
Paste source text
Start with copied text, HTML fragments, CRM notes, CSV exports, or any mixed content that may contain email addresses.
How MailSniper works
How to extract emails from text, remove duplicates, and export a clean list.
MailSniper is designed around a narrow, practical workflow: paste text, scan for email addresses, remove duplicate emails, sort the list, review the counts, then copy or download the cleaned result. The page explains what happens, what stays local, and where manual review still matters.
02
Scan in the browser
The current extractor runs locally in the page. The text does not need to be uploaded to a MailSniper server for matching.
03
Match email patterns
MailSniper looks for common modern email forms, including subdomains, plus addressing, long TLDs, and punycode TLDs.
04
Remove duplicates
Repeated addresses are collapsed into one readable output row while total and unique counts remain visible.
05
Sort or keep order
You can keep the first-seen order or sort the output A-Z when reviewing a larger list.
06
Copy or download output
Copy the active output, download a TXT file, or export a structured CSV for spreadsheets, CRMs, and review workflows.
The matching pipeline
The extractor uses a focused email matching pattern, then stores a case-insensitive key for each match so repeated addresses can be removed from the output. The visible result preserves the readable address from the source text while keeping counts clear.
This is useful for cleanup and review, but it is not the same thing as mailbox verification. A syntactically valid address can still be inactive, outdated, or inappropriate to contact.
Currently supported
- Standard addresses such as name@example.com.
- Subdomains such as team@mail.example.co.uk.
- Plus addressing such as first.last+tag@example.com.
- Hyphenated domains and long top-level domains.
- Duplicate detection that treats casing consistently.
Important limits
- It does not verify whether an inbox exists or can receive mail.
- It does not send validation pings to external mail servers.
- It may miss uncommon or intentionally obfuscated formats.
- It may need manual review when source text is badly broken.
- It does not decide whether you have permission to use an address.
Trust boundary
The current tool keeps the core work inside the browser.
For the main extractor, pasted text is handled by the page code running on your device. That design is intentional because email lists can be sensitive. Future features should keep this privacy boundary clear whenever possible.
No upload needed
The matching, deduplication, sorting, and copy workflow can run without sending pasted text to a backend.
Visible review
Counts and output remain on screen so users can inspect the result before copying it into another system.
Plain output
The cleaned list is newline-separated text, which keeps export simple and avoids locking users into a format.
Reset anytime
Clear removes the pasted text and output from the active page state so users can start over quickly.
What users should still review
A cleaned list is easier to inspect, but it is not automatically a permission-based contact list. Review source quality, consent, outdated domains, role inboxes, and the purpose of any follow-up before using extracted addresses.
Role addresses
Addresses like info@, support@, sales@, and admin@ may need separate handling depending on the workflow.
Duplicates
Duplicates are removed from output, but repeated appearances can still tell you something about the source data.
Invalid source lines
Future cleaner features should expose invalid lines separately instead of silently hiding every non-match.
Responsible use
MailSniper helps review and clean text. It does not grant permission to contact people or bypass legal requirements.
Next product direction
The strongest next step is to evolve the extractor into a fuller email list cleaner: valid and invalid rows, role-address separation, domain extraction, grouped results, and export controls while keeping the user in control.