nw-post-mortem-framework

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

322 stars

Best use case

nw-post-mortem-framework is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using nw-post-mortem-framework should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/nw-post-mortem-framework/SKILL.md --create-dirs "https://raw.githubusercontent.com/nWave-ai/nWave/main/nWave/skills/nw-post-mortem-framework/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/nw-post-mortem-framework/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How nw-post-mortem-framework Compares

Feature / Agentnw-post-mortem-frameworkStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

```markdown
# 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

Related Skills

nw-sd-framework

322
from nWave-ai/nWave

4-step system design framework with back-of-envelope estimation, scaling ladder, and common pitfalls

nw-quality-framework

322
from nWave-ai/nWave

Quality gates - 11 commit readiness gates, build/test protocol, validation checkpoints, and quality metrics

nw-outcome-kpi-framework

322
from nWave-ai/nWave

Outcome KPI definition methodology - synthesizes Who Does What By How Much (Gothelf/Seiden), Running Lean (Maurya), and Measure What Matters (Doerr) into a practical framework for measurable outcome KPIs

nw-divio-framework

322
from nWave-ai/nWave

DIVIO/Diataxis four-quadrant documentation framework - type definitions, classification decision tree, and signal catalog

nw-ux-web-patterns

322
from nWave-ai/nWave

Web UI design patterns for product owners. Load when designing web application interfaces, writing web-specific acceptance criteria, or evaluating responsive designs.

nw-ux-tui-patterns

322
from nWave-ai/nWave

Terminal UI and CLI design patterns for product owners. Load when designing command-line tools, interactive terminal applications, or writing CLI-specific acceptance criteria.

nw-ux-principles

322
from nWave-ai/nWave

Core UX principles for product owners. Load when evaluating interface designs, writing acceptance criteria with UX requirements, or reviewing wireframes and mockups.

nw-ux-emotional-design

322
from nWave-ai/nWave

Emotional design and delight patterns for product owners. Load when designing onboarding flows, empty states, first-run experiences, or evaluating the emotional quality of an interface.

nw-ux-desktop-patterns

322
from nWave-ai/nWave

Desktop application UI patterns for product owners. Load when designing native or cross-platform desktop applications, writing desktop-specific acceptance criteria, or evaluating panel layouts and keyboard workflows.

nw-user-story-mapping

322
from nWave-ai/nWave

User story mapping for backlog management and outcome-based prioritization. Load during Phase 2.5 (User Story Mapping) to produce story-map.md and prioritization.md.

nw-tr-review-criteria

322
from nWave-ai/nWave

Review dimensions and scoring for root cause analysis quality assessment

nw-tlaplus-verification

322
from nWave-ai/nWave

TLA+ formal verification for design correctness and PBT pipeline integration