vertical-slice-postmortem

Analyze post-delivery code slices for defects, recipes, tests, and resource spend.

5|3|Updated Mar 20, 2026
One-click install
npx skills add https://github.com/leifericf/agentic-sdk --skill vertical-slice-postmortem
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vertical-slice-postmortem
Source: https://github.com/leifericf/agentic-sdk/tree/main/skills/vertical-slice-postmortem
Command: npx skills add https://github.com/leifericf/agentic-sdk --skill vertical-slice-postmortem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill ensures that post-major code slice deliveries are reviewed and optimized for both quality and efficiency, preventing costly mistakes in future deliveries.

Core Features & Use Cases

  • Post-mortem Analysis: Review the root causes of defects in landed slices.
  • Recipe Hardening: Improve the source code and tests that missed defects.
  • Cost Reduction: Analyze and trim unnecessary token spend and verification time.
  • Retrospective Reporting: Generate a detailed report for review and improvement.
  • Use Case: After a new feature has been landed, use this Skill to analyze its post-mortem review, improve the recipes and tests that failed, and optimize the workflow to ensure the next feature slice is more efficient.

Quick Start

Run the vertical-slice-postmortem skill after landing a new major feature slice.

Frequently Asked Questions about vertical-slice-postmortem

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

FAQPage Schema
How do I automate post-mortem analysis after landing a major feature slice?

Post-mortem analysis reviews root causes of defects in landed slices, improves source code and tests, and evaluates campaign resource spend to reduce future costs. This Skill automates root-cause analysis and generates retrospective reports for optimization.

What is code slice optimization for quality, cost, and speed?

Code slice optimization reviews major feature deliveries to improve code quality, reduce unnecessary token spend, and trim verification time. It hardens write-time recipes and tests to ensure future feature slices are delivered more efficiently.

How do I perform root-cause analysis on defects in landed feature slices?

Root-cause analysis on landed slices requires project-specific context and state to identify defect origins and improve the write-time recipes and tests that failed. The Skill automates this analysis and outputs a detailed retrospective report for review.

Does this post-mortem analysis require any specific project context?

Yes, performing post-mortem improvements requires project-specific context and state to analyze defects and optimize code slices effectively. Without this context, the Skill cannot accurately evaluate resource spend or harden recipes.

What's the best way to reduce token spend and verification time after feature delivery?

The best way to reduce token spend is to analyze and trim unnecessary resource consumption during the post-delivery review phase. This Skill evaluates campaign resource spend and optimizes workflows to ensure the next feature slice is more efficient.

When should I run a post-mortem review on my code slices?

You should run a post-mortem review immediately after landing a new major feature slice. Running it at this stage allows you to analyze defects, improve failed recipes and tests, and optimize the workflow before starting the next delivery.