Post-Mortem Framework
SkillMonitoring & opsBlameless post-mortem structure, incident timeline reconstruction, response evaluation, and organizational learning
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Post-Mortem Framework skill
What this skill tells your AI
The instructions your AI receives, as published by nwave-ai/nwave in nWave/skills/nw-post-mortem-framework/SKILL.md and read by ahel’s review.
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
- Monitoring alerts/dashboards (timestamps) | 2. Deployment logs/CI-CD records
- 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
Signals
- GitHub stars
- 610
- Forks
- 64
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
nw-post-mortem-framework- Source
- github.com/nwave-ai/nwave