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AI for Slack Difficulty: Intermediate ClaudeChatGPT

Slack DevOps Onboarding Bot Prompt

Design a Slack onboarding bot for new DevOps / SRE hires — Day-1 checklist, account provisioning verification, runbook tour, on-call shadow scheduling, and 30/60/90-day check-ins.

Target user
Engineering managers reducing onboarding time and ramp friction for SRE hires
Difficulty
Intermediate
Tools
Claude, ChatGPT

The prompt

You are a senior engineering manager who has built Slack-driven onboarding for SRE hires that cut their time-to-first-on-call from 12 weeks to 6 weeks with measurable confidence.

I will provide:
- Team size + on-call rotation cadence
- Tools the team uses (Kubernetes, Terraform, Datadog, PagerDuty, etc.)
- Existing onboarding artifacts (docs, runbooks, learning paths)
- SSO + provisioning automation in place

Your job:

1. **Day 1 sequence** — bot DMs the new hire on first login:
   - Welcome message with manager + buddy named
   - Account provisioning check (does Okta show all groups assigned?)
   - "Reply DONE when:" checklist:
     - Cloned the main monorepo
     - Joined the team channel
     - Set up local dev env (link to setup guide)
     - Read the team's "How we operate" doc
     - Took the laptop-hardening photo for IT

2. **Week 1 sequence** — daily nudge per workday:
   - Day 2: meet 3 services — bot links to one-pager per service + asks new hire to summarize back
   - Day 3: tools tour — Datadog dashboards, Grafana, PagerDuty schedule, ArgoCD UI
   - Day 4: read 3 recent postmortems; reply with one question per
   - Day 5: shadow first on-call session via Zoom; bot schedules it

3. **Weeks 2-4 sequence** — increasing autonomy:
   - Week 2: pair on a small infra change PR
   - Week 3: handle one low-severity alert with buddy backup
   - Week 4: present the team's architecture to the bot (recorded for buddy review)

4. **Knowledge check-ins** — bot quizzes the new hire on key facts:
   - "What does our SLO target on the auth service mean in real terms?"
   - "Where do you go to roll back a deployment in service X?"
   - "Who's the secondary on-call this week?"
   - Multiple choice + free text; results route to manager + buddy for follow-up

5. **Runbook tour** — bot walks through 5 most-run runbooks:
   - Shows the runbook
   - Asks the new hire to articulate when they'd use it
   - Records their answer for buddy review

6. **30/60/90-day check-ins**:
   - 30d: how confident are you on (services / tools / on-call)? Bot asks via 5-point Likert with comment field
   - 60d: what's confusing? What docs are wrong/missing?
   - 90d: ready for primary on-call? Manager + buddy review the answer

7. **Bot-to-human handoff** — if the new hire is stuck:
   - Replies like "I don't know what X means" → bot routes to buddy
   - No reply in 48h → manager pinged
   - Low confidence on 30d check → manager + buddy convene

8. **Cohort comparison** — for managers, aggregated dashboard:
   - Where do new hires consistently get stuck?
   - Which runbooks need better docs (high stuck-rate)?
   - Average time-to-first-solo-on-call

9. **Data minimization** — bot stores: completion state, question responses (for manager review), confidence scores. Bot does NOT store: keystroke logs, private chats, personal opinions on coworkers.

10. **Anti-patterns to avoid** — bot as surveillance, bot replaces 1:1 human contact, bot quizzes that don't track to real-world readiness, "completion" that doesn't translate to actual capability.

Output as: (a) Day 1 / Week 1 / Weeks 2-4 message scripts, (b) knowledge-check question bank, (c) runbook tour structure, (d) 30/60/90 questionnaire, (e) bot-to-human escalation rules, (f) manager dashboard schema, (g) data retention + privacy policy.

Bias toward: human contact + bot scaffolding, measurable outcomes, killing the bot's prompts once the hire's ramped (not perpetual nagging).
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