Spam Detection
Never waste time sorting through junk. Every inbound email is automatically scored for spam, so your team can focus on real customers. Suspicious messages are labeled but never silently dropped — legitimate emails are never lost.
Spam score labels
Each email receives a spam score from the email provider, which Scitor maps to a label on the created issue:
| Score | Label | Meaning |
|---|---|---|
| Below 2 | spam:clean |
Very low spam likelihood — legitimate email |
| 2 – 5 | spam:low |
Some spam indicators, but likely legitimate |
| 5 – 8 | spam:medium |
Multiple spam indicators — review recommended |
| 8+ | spam:high |
Strong spam indicators — likely spam |
Info
Lower scores are better. A negative score (e.g., -0.1) indicates a very clean email.
What happens with spam
Scitor does not automatically block or discard emails based on spam score. All emails are delivered as issues, with the spam label applied for your team’s review. This approach ensures legitimate emails are never lost.
For persistent spam from specific senders, use the /block-sender command to prevent future emails from that address.
Automating spam handling
You can use GitHub Actions to automatically handle spam:
name: Auto-close high spam
on:
issues:
types: [labeled]
jobs:
close-spam:
if: github.event.label.name == 'spam:high'
runs-on: ubuntu-latest
steps:
- uses: actions/github-script@v7
with:
script: |
await github.rest.issues.update({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
state: 'closed',
state_reason: 'not_planned'
})
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.issue.number,
body: '🤖 Automatically closed — high spam score detected.'
})
Tip
Combine spam labels with the /block-sender command for a manual-but-effective spam management workflow. Review spam:high issues periodically and block repeat offenders.