bmad-retrospective

Name: bmad-retrospective
Availability: InStock
Author: ThuanTan2060

Discovers completed epics and analyzes story evidence to guide a no-blame retrospective.

Updated May 17, 2026

Search Tags:#retrospective #technical debt #bmad #epic review #team facilitation #lesson extraction #planning continuity

Ready to run

npx skills add https://github.com/thuantan2060/facebook-sale-tracking --skill bmad-retrospective-thuantan2060

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bmad-retrospective
Source: https://github.com/thuantan2060/facebook-sale-tracking/tree/main/.agents/skills/bmad-retrospective
Command: npx skills add https://github.com/thuantan2060/facebook-sale-tracking --skill bmad-retrospective-thuantan2060

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, and includes scripts (resource) components.

What problem does it solve?

This Skill helps teams run a structured retrospective after an epic finishes so they can extract lessons, assess outcomes, and prepare improvements for the next epic.

Core Features & Use Cases

Epic Discovery & Readiness Check: Detects the most relevant completed epic (primarily from sprint status) and verifies whether stories are actually marked done.
Deep Evidence-Based Analysis: Scans story records to synthesize recurring struggles, review feedback patterns, breakthroughs, technical debt, and quality/test insights.
Continuity & Forward-Looking Planning: Loads the previous epic’s retrospective when available, cross-references commitments and lessons, and previews the next epic’s dependencies and risks.
Facilitated Team Dialogue: Guides a psychologically safe discussion with explicit “what went well / what didn’t” sections and clear, ownerable action items.

Quick Start

Run the bmad-retrospective skill to generate an epic retrospective with lessons learned and next-epic preparation for the selected epic number.

Frequently Asked Questions about bmad-retrospective

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

FAQPage Schema

How do I run a structured retrospective after completing a BMAD epic?▼

To run a structured retrospective, the skill discovers completed epics from sprint status, analyzes story evidence for lessons, and guides a no-blame team discussion. It requires YAML-frontmatter metadata and project configuration resolution to generate actionable next steps for the next epic.

How does evidence-based analysis work for technical debt and story artifacts?▼

Evidence-based analysis works by scanning story records to synthesize recurring struggles, review feedback patterns, breakthroughs, and technical debt. It extracts quality and test insights from story artifacts to ensure continuity from the previous epic and preparation for the next epic.

Do I need Python3 to facilitate team discussions and extract lessons from completed epics?▼

Yes, you need Python3 installed as a dependency to run the scripts that facilitate team discussions and extract lessons. The skill uses deterministic discovery logic and safeguarded facilitation outputs to analyze sprint status and story artifacts.

What is the best way to prepare continuity and forward-looking planning for the next epic?▼

The best way to prepare continuity is by loading the previous epic's retrospective when available, cross-referencing commitments and lessons, and previewing the next epic's dependencies and risks. This ensures forward-looking planning is grounded in evidence from completed story artifacts.

Can I use this retrospective skill if my BMAD stories are not marked done in the sprint status?▼

You cannot use this skill effectively if stories are not marked done, because epic discovery and readiness checks verify whether stories are actually completed in the sprint status. The skill relies on completed story artifacts to synthesize recurring struggles and technical debt accurately.

What is epic discovery and readiness check in a post-epic retrospective?▼

Epic discovery and readiness check is the process of detecting the most relevant completed epic primarily from sprint status and verifying whether stories are actually marked done. It applies deterministic discovery logic to project configuration resolution and YAML-frontmatter metadata to ensure valid retrospective facilitation.