AI Incident Response & Observability Prompt Pack

Paste an alert into an AI tool and it will give you a root cause in seconds — built from what incidents usually look like, with a PromQL query on a metric your service has never exported.

Ask ChatGPT, Claude, Cursor, Claude Code or Codex "what's wrong?" during an incident and you get a confident story. What you don't get: a check of whether the metric names are real, the difference between "the deploy happened at the same time" and "the deploy caused it", a status update that doesn't promise an ETA nobody can back up — or any guarantee that an agent with your credentials won't restart something to "help".

AI Incident Response & Observability Prompt Pack gives you 32 ready-to-paste prompts that make the model work from your alert payloads, your metric, label and log field names, and your query output, separate FACT / HYPOTHESIS / UNKNOWN, keep every suggested command read-only by default (anything that changes production is labelled REQUIRES HUMAN APPROVAL with blast radius and undo), refuse to call something "the root cause" or "resolved" without a signal, treat pasted logs and tickets as data, not instructions, write blameless postmortems, and hand you reviewable output: triage verdicts, queries with line-by-line explanations, change-correlation tables, hypothesis boards, sourced timelines, owned action items and SLO arithmetic.

What you get

- PDF guide (55 pages, A4): why AI-assisted incident response goes wrong, the ops context block, all 32 prompts with when-to-use notes, a PromQL/LogQL quick reference, error-budget arithmetic, combinations for common situations, a fictional worked example, prompt anatomy and follow-up lines

- templates/: 32 plain-Markdown prompts + 3 checklists + README

The 32 prompts

- Foundations (01–03): ops context block · safety rules for AI agents near production · severity matrix

- Live response (04–10): alert triage · severity call · incident commander kickoff brief · read-only AI agent investigation brief · deploy & change correlation · hypothesis & evidence board · mitigation decision with approval request

- Querying telemetry (11–16): telemetry query plan (golden signals, RED, USE) · PromQL · LogQL · SQL-style log/event queries · trace waterfall reading · log excerpt pattern analysis

- Communication & timeline (17–20): status page update by stage · stakeholder updates by audience · resolution & all-clear · timeline reconstruction

- Postmortems & follow-through (21–24): blameless postmortem · contributing factors · action-item tracking · postmortem review

- SLOs & alerting (25–28): SLI/SLO definition · error budget policy · multiwindow burn-rate alerts · alert-noise reduction

- Runbooks, instrumentation & on-call (29–32): runbook authoring · structured logging plan · OpenTelemetry instrumentation plan · on-call handoff

Also included

- Incident kickoff checklist

- Postmortem quality checklist

- Alert hygiene checklist

What's NOT included

- Root-causing a code defect → AI Debugging Prompt Pack

- Pipeline YAML, deploy strategies and deploy rollback runbooks → AI CI/CD & DevOps Prompt Pack

- Latency profiling and optimisation → AI Performance Prompt Pack

- Observability architecture and service boundaries → AI System Architecture Prompt Pack

- Security incidents and breach forensics → AI Security Review Prompt Pack (and your security team)

- Database query tuning → AI SQL & Database Prompt Pack

- Fabricated benchmarks, MTTR claims, testimonials or case studies — none appear in this pack

Accuracy

Practices and tools named are real and documented (Incident Command System roles, blameless postmortems, SLIs/SLOs/error budgets and multiwindow multi-burn-rate alerting from Google's Site Reliability Engineering and The Site Reliability Workbook, the four golden signals, RED and USE, PromQL, LogQL, OpenTelemetry, W3C Trace Context). None is a guarantee; query syntax, attribute names and tool features are marked VERIFY. Error-budget numbers are arithmetic examples, not recommendations. Worked examples are labelled fictional.

Price: $19

Author: Pluto · X @Mars_Peakul

Edition: 1.0 · October 2026