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#Uncomment the following lines and use tail -f procmail.log to debug #LOGFILE=$HOME/procmail.log #VERBOSE=yes #LOGABSTRACT=all # Feed redirected spam to sa-learn, and also store a copy in a folder called spam. # This folder of false negatives could be useful if we needed to rebuild our Bayes # database in the future. :0 * ^To:.*spam@example.com { * < 256000 :0c: spamassassin.spamlock | sa-learn --spam :0: spamassassin.filelock spam } # Send all other mail through SpamAssassin :0fw: spamassassin.lock * < 256000 | spamassassin # Mail that is very likely spam (>15) can be saved on the server # (not forwarded), or by moving the # down one line, even dropped # on the floor. Note that dropping mail on the floor is a *bad* # idea unless you really, really believe no false positives will # have a score greater than 15. If you want all mail forwarded, # just add #'s in front of each of these lines: :0: spamassassin.filelock2 * ^X-Spam-Level: \*\*\*\*\*\*\*\*\*\*\*\*\*\*\* #/dev/null almost-certainly-spam # Forward all mail with a score less than 15 to my non-publicized address :0 ! privateaddress@example.net |
This file is available \[^procmailrcprocmailrc.forward.txt here\]. If you don't currently have a procmail file, you can import this one by entering: Wiki Markup
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wget http://wiki.apache.org/spamassassin-data/attachments/ProcmailToForwardMail/attachments/procmailrc.forward.txt mv procmailrc.forward.txt .procmailrc |
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Way more detail on how to do this is at SingleUserUnixInstall.
Other training options
An even easier form of mistake-based training is to use IMAP and create a Learn{{`As}}`Spam folder, as described in the
\[wiki:Self:SingleUserUnixInstall#head-bea6b8dc4f219edd3b9976e8f922a8f1c0603125 IMAP section of SingleUserUnixInstall\]. Wiki Markup
Contributors
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