type.inbound
and sender.email.email == 'noreply@github.com'
and length(attachments) == 0
and (
any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "callback_scam" and .confidence != "low"
)
or (
regex.icontains(body.current_thread.text,
(
"mcafee|n[o0]rt[o0]n|geek.{0,5}squad|paypal|ebay|symantec|best buy|lifel[o0]ck"
)
)
and (
3 of (
strings.ilike(body.current_thread.text, '*purchase*'),
strings.ilike(body.current_thread.text, '*payment*'),
strings.ilike(body.current_thread.text, '*transaction*'),
strings.ilike(body.current_thread.text, '*subscription*'),
strings.ilike(body.current_thread.text, '*antivirus*'),
strings.ilike(body.current_thread.text, '*order*'),
strings.ilike(body.current_thread.text, '*support*'),
strings.ilike(body.current_thread.text, '*receipt*'),
strings.ilike(body.current_thread.text, '*invoice*'),
strings.ilike(body.current_thread.text, '*call*'),
strings.ilike(body.current_thread.text, '*cancel*'),
strings.ilike(body.current_thread.text, '*renew*'),
strings.ilike(body.current_thread.text, '*refund*'),
strings.ilike(body.current_thread.text, '*host key*')
)
)
// phone number regex
and any([body.current_thread.text, subject.subject],
regex.icontains(.,
'\+?([ilo0-9]{1}.)?\(?[ilo0-9]{3}?\)?.[ilo0-9]{3}.?[ilo0-9]{4}',
'\+?([ilo0-9]{1,2})?\s?\(?\d{3}\)?[\s\.\-⋅]{0,5}[ilo0-9]{3}[\s\.\-⋅]{0,5}[ilo0-9]{4}'
)
)
)
)
Playground
Test against your own EMLs or sample data.