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/nw-post-mortem-framework

Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning

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nwave
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Install
$ npx -y skills add nWave-ai/nWave --skill nw-post-mortem-framework --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • 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/nw-post-mortem-framework

Context preview

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Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning

SKILL.md

nw-post-mortem-framework.SKILL.md
name: nw-post-mortem-framework
description: Blameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning
user-invocable: false
disable-model-invocation: true

Post-Mortem Framework

Principles

  • **Blameless**: focus on systems/processes, not individuals. People make reasonable decisions given available info.
  • **Evidence-based**: every finding backed by logs, metrics, or documented actions
  • **Action-oriented**: every finding produces concrete, assigned action item
  • **Learning-focused**: capture what worked alongside what failed

Post-Mortem Document Structure

# Post-Mortem: [Incident Title]

**Date**: [incident date]
**Duration**: [start to resolution]
**Severity**: [P0-P3]
**Author**: [analyst]

## Summary
[2-3 sentence overview: what happened, impact, resolution]

## Timeline
| Time | Event | Source |
|------|-------|--------|
| HH:MM | [event] | [log/metric/report] |

## Impact
- Users affected: [number/percentage]
- Duration of impact: [time]
- Business impact: [quantified if possible]
- Systems affected: [list]

## Root Cause Analysis
[5 Whys analysis with evidence at each level]

## Detection and Response
- Time to detect: [duration] -- [how detected]
- Time to respond: [duration] -- [first action]
- Time to mitigate: [duration] -- [mitigation applied]
- Time to resolve: [duration] -- [permanent fix]

## What Went Well
- [positive observations about detection, response, recovery]

## What Could Be Improved
- [areas where detection, response, recovery fell short]

## Action Items
| ID | Action | Owner | Priority | Due Date |
|----|--------|-------|----------|----------|
| 1 | [specific action] | [team/person] | [P0-P3] | [date] |

## Lessons Learned
- [key takeaways for the organization]

Incident Timeline Reconstruction

Sources

1. Monitoring alerts/dashboards (timestamps) | 2. Deployment logs/CI-CD records 3. Communication channels (Slack, email, incident) | 4. VCS (commits, merges, deploys) | 5. User reports/support tickets

Quality Checks

Events chronological with verified timestamps | gaps >5 min noted/explained | decision points identified with available info | causal relationships noted

Response Effectiveness Evaluation

Detection

Detected by monitoring or users? | Duration onset-to-detection? | Existing alerts relevant? Missing?

Escalation

Right team at right time? | Procedures followed? | Communication clear to stakeholders?

Resolution

Mitigation effective? | Rollback considered/viable? | Duration mitigation-to-permanent-fix?

Organizational Learning

Knowledge Capture

Document root causes as reusable patterns | update runbooks | share in retrospectives

Process Improvements

Update monitoring/alerting per detection gaps | revise deployment per rollback effectiveness | strengthen testing for failure scenario

Action Item Tracking

Every item has owner + due date | track in standups/sprint reviews | verify effectiveness post-deployment

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AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).

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