type.inbound
and (
(
any(ml.nlu_classifier(body.current_thread.text).entities,
.name == "sender"
and regex.icontains(.text,
'(?:U\.?S\.?|United States)\s*(?:District\s+)?Courts?\b'
)
)
and any(ml.nlu_classifier(body.current_thread.text).intents,
.name == "cred_theft" and .confidence != "low"
)
)
or 2 of (
strings.icontains(body.current_thread.text, "PACER"),
// real PACER Service Center phone number
strings.icontains(body.current_thread.text, "1-800-676-6856"),
strings.icontains(body.current_thread.text, "contempt charges"),
strings.icontains(body.current_thread.text, "Official Court Notice"),
regex.icontains(body.current_thread.text, '(?:jury summons|juror number)'),
strings.icontains(body.current_thread.text,
'complaint has been filed against you'
)
)
)
and not any(ml.nlu_classifier(body.current_thread.text).topics,
.name == "Newsletters and Digests" and .confidence == "high"
)
// negate the actual federal judiciary unless it fails DMARC authentication
and not (
sender.email.domain.tld == "gov"
and coalesce(headers.auth_summary.dmarc.pass, false)
)
// negate highly trusted sender domains unless they fail DMARC authentication
and not (
sender.email.domain.root_domain in $high_trust_sender_root_domains
and coalesce(headers.auth_summary.dmarc.pass, false)
)
Playground
Test against your own EMLs or sample data.