SkillsLib.ai

Postmortem Report Writer

Generate blameless postmortem reports following Google SRE culture

4.2(46 reviews)
100+ downloads
Updated Nov 2026
Verified SafeSecurity VerifiedThis skill was analyzed by our AI security scanner for harmful content including data exfiltration, system manipulation, credential theft, and prompt injection. No threats were detected.

What You Can Do

You can convert raw incident information—timelines, logs, metrics, and witness accounts—into professional postmortem reports that follow Google SRE blameless culture principles. The skill structures complex incidents into clear narratives covering what happened, why it happened, impact analysis, and actionable follow-up items, enabling your team to learn from failures and prevent recurrence.

Features

Blameless narrative writing

frames incidents as systemic failures, not individual mistakes

Timeline reconstruction

organizes events chronologically with exact timestamps and decision points

Impact quantification

documents customer-facing and internal effects with metrics

Root cause analysis

identifies underlying causes using 5-why methodology or equivalent

Contributing factors

surfaces process gaps, monitoring blind spots, and design limitations

Actionable follow-ups

generates specific, trackable remediation items with owners and deadlines

Google SRE compliance

follows industry-standard postmortem structure and cultural guidelines

Stakeholder-ready format

produces polished documents suitable for leadership review and team discussion

Example Output

Example 1: Database Failover Incident

Incident Title: Primary Database Failover Delay (45-minute customer impact)

Timeline:

  • 14:22 UTC: Disk IO saturation on primary database detected
  • 14:24 UTC: Automated health checks fail; failover initiated
  • 14:28 UTC: Secondary replica promoted; DNS update propagation begins
  • 14:67 UTC: 95% of traffic restored; final clients reconnect

Root Cause: Monitoring thresholds set too high to catch IO saturation early. Failover runbook was undocumented, causing 3-minute delay in decision-making.

Contributing Factors: No load testing in 6 months; cache invalidation strategy created unexpected query surge; secondary replica 45 seconds behind primary.

Follow-ups:

  • Implement predictive IO monitoring with earlier alerts (Owner: Platform team, Due: 2 weeks)
  • Automate failover decision at 80% IO utilization (Owner: SRE, Due: 3 weeks)

Example 2: API Rate Limiting Bug

Incident Title: Unintended 429 errors for legitimate traffic (72-minute outage)

Root Cause: Rate limiter configuration deployed with typo: per-user limits applied per-IP instead. Legitimate traffic from shared NAT hit limits immediately.

Contributing Factors: Rate limiter change deployed without load test; no staging environment matches production traffic patterns; missing validation in deployment pipeline.

Follow-ups:

  • Add integration tests for rate limiter edge cases (Owner: Backend team)
  • Require load test sign-off for traffic-facing changes (Owner: Release mgmt)

What's Included

  • SKILL.md instruction file with blameless culture principles and incident severity guidelines:
  • Postmortem template covering timeline, impact, root cause, contributing factors, and follow-ups:
  • Blameless writing checklist to catch blame language and reframe incidents systemically:
  • 5-Why analysis framework to dig deeper into root causes:
  • Follow-up action tracker with ownership, severity, and deadline fields:

Who It's For

  • SRE/DevOps Engineers — leading incident response and postmortem writing
  • Engineering Managers — reviewing postmortems and driving follow-up actions
  • Technical Leads — documenting incidents and building institutional knowledge
  • On-call Responders — capturing incident details immediately after resolution
  • Quality/Process Leads — tracking systemic improvements across incidents

Best For

  • Production incidents causing customer-facing downtime or data impact (>15 min)
  • Near-misses that could have caused major incidents without detection
  • Process failures requiring organizational learning and systemic change
  • Recurring incidents with similar root causes across different teams
  • High-complexity incidents involving multiple systems, teams, or contributing factors

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