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Reduce MTTR with AI Difficulty: Advanced ClaudeChatGPTCursor

Game-Day Scenario Generator Prompt

Generate realistic failure-injection scenarios and a facilitation script to rehearse incident response on your actual stack — so the team's detection, triage, and mitigation muscles are warm before a real outage, cutting MTTR when it counts.

Target user
SRE leads and reliability engineers running game days
Difficulty
Advanced
Tools
Claude, ChatGPT, Cursor

The prompt

You are a reliability engineer designing a game day to rehearse incident response and expose MTTR weaknesses before a real outage does. Produce realistic scenarios for OUR stack plus a facilitation plan.

Give me:
- The system: [KEY SERVICES, DEPENDENCIES, INFRA — enough to make scenarios realistic]
- Recent real incidents or known fragilities: [WHAT HAS HURT US, OR WHERE WE FEEL BLIND]
- Maturity to test: [DETECTION / TRIAGE / MITIGATION / COMMS — where do we most want to find gaps?]
- Constraints: [STAGING vs PROD, ALLOWED BLAST RADIUS, DURATION]

Work through this:

1. **Propose 3-5 scenarios of graded difficulty.** Each should inject a realistic failure (dependency slowdown, partial region loss, cert expiry, cache stampede, poison message, silent data corruption). Bias toward failures that would be hard to DETECT, since detection gaps cost the most MTTR.

2. **For each scenario, specify the mechanics.** The fault to inject, the safe way to inject it, the intended blast-radius limit, and the abort/rollback switch. Flag clearly if a scenario is only safe in staging.

3. **Define success as a rehearsal.** For each, the observable outcomes to measure: time-to-detect, time-to-triage, time-to-mitigate, and which runbook or signal SHOULD have caught it. The point is to find the gap, not to "pass."

4. **Plant learning objectives.** For each scenario, the specific MTTR weakness it is designed to expose (e.g. "no alert covers stale-cache reads," "on-call doesn't know the failover command").

5. **Provide a facilitation script.** Roles, how to inject without tipping off responders, what to observe silently, and a debrief template that converts findings into concrete follow-up actions.

Output format: a "GAME DAY PLAN" — a scenario table (SCENARIO, FAULT, INJECTION METHOD, BLAST-RADIUS LIMIT, ABORT SWITCH, STAGING-ONLY?, METRICS, WEAKNESS TARGETED) — then a facilitation script and a debrief-to-actions template. Mark every scenario as a draft requiring safety review before execution.

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Why this prompt works

The cheapest minute of MTTR to remove is the one you remove before the incident happens. Teams that rehearse — game days, chaos exercises — respond to real outages faster because detection, triage, and mitigation are practiced reflexes rather than first-time improvisation. But most teams don’t run game days regularly, and the ones that try often stall on the blank page: inventing realistic, safe, instructive scenarios for their specific stack is real work.

This prompt removes that friction by generating graded scenarios tailored to the system and — importantly — biased toward failures that are hard to detect, because detection gaps are where the largest and least-visible MTTR losses hide. Each scenario names the exact weakness it is meant to expose and defines success as finding the gap, not passing the test, which is the mindset that makes a game day worth running.

The guardrails are non-negotiable for this class of work: failure injection can become a real outage. The prompt requires a blast-radius limit, a tested abort switch, staging-only flags, and human safety review on every scenario before anything runs. Used safely, it converts the vague intention to “do a game day sometime” into a concrete, reviewable plan whose findings feed straight into the runbooks and alerts that shrink MTTR on the day it is real.

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