Agent Academy is a free, source-linked library of skills, prompts, MCP servers and bot templates, organised into courses by profession, for people who hand real work to AI agents: 1,467 live resources, each with its original link, license and safety tags.
Skill
postmortem-writing
Original text · EnglishWrite effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.
Republished in full under its license, with attribution. The source is authoritative.
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.
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 and detailed worked examples
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
Template 2: 5 Whys Analysis
# 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 |
Template 3: Quick Postmortem (Minor Incidents)
# 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.
Facilitation Guide
Running a Postmortem Meeting
## 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-Patterns to Avoid
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
Best Practices
Do's
Start immediately - Memory fades fast
Be specific - Exact times, exact errors
Include graphs - Visual evidence
Assign owners - No orphan action items
Share widely - Organizational learning
Don'ts
Don't name and shame - Ever
Don't skip small incidents - They reveal patterns
Don't make it a blame doc - That kills learning
Don't create busywork - Actions should be meaningful
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`,…
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar…
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code works but feels brittle, when error handling seems thin, when orphaned resources or missing cleanup are suspected, or when the agent claims done but hidden debt may exist. Catches the specific failure patterns AI agents produce that humans would not.