AI Analysis
Triage every support ticket instantly. Scitor’s AI automatically reads every inbound email and form submission, then labels it with sentiment, category, priority, and a brief summary — so your team can see what matters at a glance, without reading every message.
AI analysis is available on the Pro plan.
What gets analyzed
When an email or form submission arrives, Scitor’s AI engine produces four outputs:
Sentiment detection
Determines the emotional tone of the message:
| Label | Description | Color |
|---|---|---|
sentiment:positive |
Customer is happy or grateful | 🟢 Green |
sentiment:neutral |
Neutral or informational tone | 🔵 Light blue |
sentiment:negative |
Customer is frustrated or upset | 🔴 Red |
Category classification
Classifies the message into a support category:
| Label | Description | Color |
|---|---|---|
category:bug-report |
Bug or error report | 🔴 Dark red |
category:feature-request |
Feature or enhancement request | 🔵 Cyan |
category:question |
General question | 🟣 Purple |
category:account |
Account-related inquiry | 🟠 Peach |
category:billing |
Billing or payment issue | 🟡 Yellow |
category:other |
Doesn’t fit other categories | ⚪ Gray |
Summary
A brief 1-2 sentence summary (max 50 words) of the email content. This appears in the issue body below the email content:
🤖 AI analysis — positive · question
Summary: Customer is asking about the availability of the API
documentation and whether there are code samples for the webhook
integration.
Priority assignment
The AI assigns a priority level based on urgency, impact, and content:
| Label | Description |
|---|---|
priority:urgent |
System down, security breach, data loss |
priority:high |
Major feature broken, blocking issue |
priority:medium |
Bugs with workarounds, partial issues |
priority:low |
Questions, feature requests, general inquiries |
See Ticket Priority for full details on priority configuration and the /priority command.
How it works
Scitor analyzes emails using a dedicated AI model. The analysis runs in an isolated environment — your email content is not stored after analysis and is not used for model training.
Privacy
- Email content is only used for the current analysis request
- Content is truncated to 16,000 characters before analysis
- Control characters and potential prompt injections are sanitized
- No data is sent to third-party AI services
Enabling AI analysis
AI analysis requires:
- Pro plan — Available through the GitHub Marketplace
- AI enabled in configuration — Enabled by default, but can be toggled:
# .github/scitor.yaml
ai: true # or false to disable
Using AI labels for automation
Since AI analysis applies standard GitHub labels, you can use them with GitHub’s built-in features:
Filtering issues
label:sentiment:negative label:category:bug-report
GitHub Actions automation
on:
issues:
types: [labeled]
jobs:
escalate:
if: contains(github.event.label.name, 'sentiment:negative')
runs-on: ubuntu-latest
steps:
- name: Notify team
uses: slackapi/slack-github-action@v2
with:
payload: |
{"text": "⚠️ Negative sentiment detected on ${{ github.event.issue.html_url }}"}
Project board rules
Use GitHub Projects’ built-in automation to automatically move issues to specific columns based on their labels.
Tip
Combine sentiment and category labels for powerful filtering. For example, sentiment:negative category:billing surfaces frustrated customers with billing issues — a high-priority combination.