post-mortem-learning

Analyze delivery failures to identify root causes and extract raw lessons.

157|28|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/Fr-e-d/GAAI-framework --skill post-mortem-learning-fr-e-d
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: post-mortem-learning
Source: https://github.com/Fr-e-d/GAAI-framework/tree/main/.gaai/core/skills/cross/post-mortem-learning
Command: npx skills add https://github.com/Fr-e-d/GAAI-framework --skill post-mortem-learning-fr-e-d

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps understand why deliveries fail or degrade by analyzing failures to identify root causes and extract actionable lessons.

Core Features & Use Cases

  • Failure Analysis: Reconstructs delivery events to pinpoint technical causes, contextual gaps, and decision errors.
  • Lesson Extraction: Extracts specific, raw lessons from failures to drive systemic improvements.
  • Use Case: After a critical feature delivery repeatedly fails QA due to overlooked edge cases, activate this skill to analyze the failure timeline, identify the specific acceptance criteria that were missed, and extract raw lessons about improving test coverage for similar scenarios.

Quick Start

Analyze the recent delivery failure for story 'STORY-123' and identify the root causes and raw lessons learned.

Frequently Asked Questions about post-mortem-learning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze delivery failures to identify root causes and contributing factors?

To analyze delivery failures and identify root causes, reconstruct delivery events using failed story results, QA reports, and decision memory. This pinpoints technical causes, contextual gaps, and decision errors to map rule gaps and extract raw lessons.

When should I perform a post-mortem analysis on a failed software delivery?

Perform a post-mortem analysis after significant delivery failures, repeated QA failures, or when patterns of issues need to be understood. Activating during these events captures the necessary context to map rule gaps and drive continuous improvement.

How do I extract actionable lessons learned from repeated QA failures?

Extract actionable lessons learned from repeated QA failures by analyzing the failure timeline to identify specific missed acceptance criteria. This reconstructs events to produce raw lessons that drive systemic improvements for similar scenarios.

What data do I need to reconstruct events for a post-mortem failure analysis?

Reconstructing events for a post-mortem failure analysis requires access to failed story results, QA reports, delivered artifacts, decision memory, and applied rules. These inputs map rule gaps and identify contextual failures.

Can I use failure analysis to map rule gaps and prevent future continuous improvement issues?

Yes, failure analysis maps rule gaps by reconstructing delivery events and comparing them against applied rules. Identifying where rules failed to prevent contextual gaps and decision errors provides raw lessons for continuous improvement.