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/postmortem-writing

Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence.

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lihongwei-cn
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$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill postmortem-writing --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/postmortem-writing

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Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence.

SKILL.md

postmortem-writing.SKILL.md
name: postmortem-writing
description: "Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence."
risk: unknown
source: community
date_added: "2026-02-27"

Postmortem Writing

Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence.

Do not use this skill when

  • The task is unrelated to postmortem writing
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open `resources/implementation-playbook.md`.

Use this skill when

  • Conducting post-incident reviews
  • Writing postmortem documents
  • Facilitating blameless postmortem meetings
  • Identifying root causes and contributing factors
  • Creating actionable follow-up items
  • Building organizational learning culture

Core Concepts

1. Blameless Culture

| Blame-Focused | Blameless | |---------------|-----------| | "Who caused this?" | "What conditions allowed this?" | | "Someone made a mistake" | "The system allowed this mistake" | | Punish individuals | Improve systems | | Hide information | Share learnings | | Fear of speaking up | Psychological safety |

2. Postmortem Triggers

  • SEV1 or SEV2 incidents
  • Customer-facing outages > 15 minutes
  • Data loss or security incidents
  • Near-misses that could have been severe
  • Novel failure modes
  • Incidents requiring unusual intervention

Quick Start

Postmortem Timeline

Day 0: Incident occurs
Day 1-2: Draft postmortem document
Day 3-5: Postmortem meeting
Day 5-7: Finalize document, create tickets
Week 2+: Action item completion
Quarterly: Review patterns across incidents

Templates

Template 1: Standard Postmortem

# Postmortem: [Incident Title]

**Date**: 2024-01-15
**Authors**: @alice, @bob
**Status**: Draft | In Review | Final
**Incident Severity**: SEV2
**Incident Duration**: 47 minutes

## Executive Summary

On January 15, 2024, the payment processing service experienced a 47-minute outage affecting approximately 12,000 customers. The root cause was a database connection pool exhaustion triggered by a configuration change in deployment v2.3.4. The incident was resolved by rolling back to v2.3.3 and increasing connection pool limits.

**Impact**:
- 12,000 customers unable to complete purchases
- Estimated revenue loss: $45,000
- 847 support tickets created
- No data loss or security implications

## Timeline (All times UTC)

| Time | Event |
|------|-------|
| 14:23 | Deployment v2.3.4 completed to production |
| 14:31 | First alert: `payment_error_rate > 5%` |
| 14:33 | On-call engineer @alice acknowledges alert |
| 14:35 | Initial investigation begins, error rate at 23% |
| 14:41 | Incident declared SEV2, @bob joins |
| 14:45 | Database connection exhaustion identified |
| 14:52 | Decision to rollback deployment |
| 14:58 | Rollback to v2.3.3 initiated |
| 15:10 | Rollback complete, error rate dropping |
| 15:18 | Service fully recovered, incident resolved |

## Root Cause Analysis

### What Happened

The v2.3.4 deployment included a change to the database query pattern that inadvertently removed connection pooling for a frequently-called endpoint. Each request opened a new database connection instead of reusing pooled connections.

### Why It Happened

1. **Proximate Cause**: Code change in `PaymentRepository.java` replaced pooled `DataSource` with direct `DriverManager.getConnection()` calls.

2. **Contributing Factors**:
   - Code review did not catch the connection handling change
   - No integration tests specifically for connection pool behavior
   - Staging environment has lower traffic, masking the issue
   - Database connection metrics alert threshold was too high (90%)

3. **5 Whys Analysis**:
   - Why did the service fail? → Database connections exhausted
   - Why were connections exhausted? → Each request opened new connection
   - Why did each request open new connection? → Code bypassed connection pool
   - Why did code bypass connection pool? → Developer unfamiliar with codebase patterns
   - Why was developer unfamiliar? → No documentation on connection management patterns

### System Diagram

[Client] → [Load Balancer] → [Payment Service] → [Database] ↓ Connection Pool (broken) ↓ Direct connections (cause)


## Detection

### What Worked
- Error rate alert fired within 8 minutes of deployment
- Grafana dashboard clearly showed connection spike
- On-call response was swift (2 minute acknowledgment)

### What Didn't Work
- Database connection metric alert threshold too high
- No deployment-correlated alerting
- Canary deployment would have caught this earlier

### Detection Gap
The deployment completed at 14:23, but the first alert didn't fire until 14:31 (8 minutes). A deployment-aware alert could have detected the issue faster.

## Response

### What Worked
- On-call engineer quickly identified database as the issue
- Rollback decision was made decisively
- Clear communication in incident channel

### What Could Be Improved
- Took 10 minutes to correlate issue with recent deployment
- Had to manually check deployment history
- Rollback took 12 minutes (could be faster)

## Impact

### Customer Impact
- 12,000 unique customers affected
- Average impact duration: 35 minutes
- 847 support tickets (23% of affected users)
- Customer satisfaction score dropped 12 points

### Business Impact
- Estimated revenue loss: $45,000
- Support cost: ~$2,500 (agent time)
- Engineering time: ~8 person-hours

### Technical Impact
- Database primary experienced elevated load
- Some replica lag during incident
- No permanent damage to systems

## Les
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