postmortem-writing

Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.

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Repository: oimiragieo/agent-studio
Letzte Quellaktivität: 2. Juli 2026 um 16:49
Erkannte Sprache von SKILL.md: Englisch
Sterne: 36
Forks: 6

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name	postmortem-writing
description	Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.
version	1.1.0
model	sonnet
invoked_by	both
user_invocable	true
tools	["Read","Write","Bash"]
best_practices	["Start immediately after incidents","Be specific with times and errors","Include graphs and visual evidence","Assign owners to action items"]
error_handling	graceful
streaming	supported
verified	true
lastVerifiedAt	"2026-02-22T00:00:00.000Z"
source	builtin
trust_score	100
provenance_sha	2fac8d24eced739f

Mode: Cognitive/Prompt-Driven — No standalone utility script; use via agent context.

Postmortem Writing

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

When to Use This Skill

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.

:

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



| Time  | Event                                           |
| ----- | ----------------------------------------------- |
| 14:23 | Deployment v2.3.4 completed to production       |
| 14:31 | First alert:           |
| 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      |





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.



 : Code change in  replaced pooled  with direct  calls.

 :
 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%)

 :
 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


## 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

## Lessons Learned

### What Went Well
1. Alerting detected the issue before customer reports
2. Team collaborated effectively under pressure
3. Rollback procedure worked smoothly
4. Communication was clear and timely

### What Went Wrong
1. Code review missed critical change
2. Test coverage gap for connection pooling
3. Staging environment doesn't reflect production traffic
4. Alert thresholds were not tuned properly

### Where We Got Lucky
1. Incident occurred during business hours with full team available
2. Database handled the load without failing completely
3. No other incidents occurred simultaneously

## Action Items

| Priority | Action | Owner | Due Date | Ticket |
|----------|--------|-------|----------|--------|
| P0 | Add integration test for connection pool behavior | @alice | 2024-01-22 | ENG-1234 |
| P0 | Lower database connection alert threshold to 70% | @bob | 2024-01-17 | OPS-567 |
| P1 | Document connection management patterns | @alice | 2024-01-29 | DOC-89 |
| P1 | Implement deployment-correlated alerting | @bob | 2024-02-05 | OPS-568 |
| P2 | Evaluate canary deployment strategy | @charlie | 2024-02-15 | ENG-1235 |
| P2 | Load test staging with production-like traffic | @dave | 2024-02-28 | QA-123 |

## Appendix

### Supporting Data

#### Error Rate Graph
[Link to Grafana dashboard snapshot]

#### Database Connection Graph
[Link to metrics]

### Related Incidents
- 2023-11-02: Similar connection issue in User Service (POSTMORTEM-42)

### References
- [Connection Pool Best Practices](internal-wiki/connection-pools)
- [Deployment Runbook](internal-wiki/deployment-runbook)

# 5 Whys Analysis: [Incident]

## Problem Statement

Payment service experienced 47-minute outage due to database connection exhaustion.

## Analysis

### Why #1: Why did the service fail?

**Answer**: Database connections were exhausted, causing all new requests to fail.

**Evidence**: Metrics showed connection count at 100/100 (max), with 500+ pending requests.

---

### Why #2: Why were database connections exhausted?

**Answer**: Each incoming request opened a new database connection instead of using the connection pool.

**Evidence**: Code diff shows direct `DriverManager.getConnection()` instead of pooled `DataSource`.

---

### Why #3: Why did the code bypass the connection pool?

**Answer**: A developer refactored the repository class and inadvertently changed the connection acquisition method.

**Evidence**: PR #1234 shows the change, made while fixing a different bug.

---

### Why #4: Why wasn't this caught in code review?

**Answer**: The reviewer focused on the functional change (the bug fix) and didn't notice the infrastructure change.

**Evidence**: Review comments only discuss business logic.

---

### Why #5: Why isn't there a safety net for this type of change?

**Answer**: We lack automated tests that verify connection pool behavior and lack documentation about our connection patterns.

**Evidence**: Test suite has no tests for connection handling; wiki has no article on database connections.

## Root Causes Identified

1. **Primary**: Missing automated tests for infrastructure behavior
2. **Secondary**: Insufficient documentation of architectural patterns
3. **Tertiary**: Code review checklist doesn't include infrastructure considerations

## Systemic Improvements

| Root Cause    | Improvement                       | Type       |
| ------------- | --------------------------------- | ---------- |
| Missing tests | Add infrastructure behavior tests | Prevention |
| Missing docs  | Document connection patterns      | Prevention |
| Review gaps   | Update review checklist           | Detection  |
| No canary     | Implement canary deployments      | Mitigation |

# Quick Postmortem: [Brief Title]

**Date**: 2024-01-15 | **Duration**: 12 min | **Severity**: SEV3

## What Happened

API latency spiked to 5s due to cache miss storm after cache flush.

## Timeline

- 10:00 - Cache flush initiated for config update
- 10:02 - Latency alerts fire
- 10:05 - Identified as cache miss storm
- 10:08 - Enabled cache warming
- 10:12 - Latency normalized

## Root Cause

Full cache flush for minor config update caused thundering herd.

## Fix

- Immediate: Enabled cache warming
- Long-term: Implement partial cache invalidation (ENG-999)

## Lessons

Don't full-flush cache in production; use targeted invalidation.

## Meeting Structure (60 minutes)

### 1. Opening (5 min)

- Remind everyone of blameless culture
- "We're here to learn, not to blame"
- Review meeting norms

### 2. Timeline Review (15 min)

- Walk through events chronologically
- Ask clarifying questions
- Identify gaps in timeline

### 3. Analysis Discussion (20 min)

- What failed?
- Why did it fail?
- What conditions allowed this?
- What would have prevented it?

### 4. Action Items (15 min)

- Brainstorm improvements
- Prioritize by impact and effort
- Assign owners and due dates

### 5. Closing (5 min)

- Summarize key learnings
- Confirm action item owners
- Schedule follow-up if needed

## Facilitation Tips

- Keep discussion on track
- Redirect blame to systems
- Encourage quiet participants
- Document dissenting views
- Time-box tangents

Anti-Pattern	Problem	Better Approach
Blame game	Shuts down learning	Focus on systems
Shallow analysis	Doesn't prevent recurrence	Ask "why" 5 times
No action items	Waste of time	Always have concrete next steps
Unrealistic actions	Never completed	Scope to achievable tasks
No follow-up	Actions forgotten	Track in ticketing system