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JingyiWng
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bmad-retrospective

Conduct post-epic retrospectives from story files and sprint-status.yaml.

Updated Mar 25, 2026
Search Tags:#workflow#documentation#analysis#ai#retrospective#scrum#epic
One-click install
npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill bmad-retrospective-jingyiwng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bmad-retrospective
Source: https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher/tree/main/_bmad/bmm/workflows/4-implementation/bmad-retrospective
Command: npx skills add https://github.com/JingyiWng/databricks_ai_dev_kit_price_watcher --skill bmad-retrospective-jingyiwng

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Teams often struggle to conduct thorough post‑epic retrospectives, missing valuable lessons and actionable insights that could improve future work.

Core Features & Use Cases

  • Automated Epic Discovery: Detects the most recent completed epic using sprint status and story files.
  • Deep Story Analysis: Extracts challenges, successes, technical debt, and testing outcomes from all stories in the epic.
  • Previous Retro Integration: Reviews past retrospectives to track action‑item follow‑through and apply learned lessons.
  • Next Epic Preview: Provides a concise look at the upcoming epic, highlighting dependencies and preparation needs.
  • Interactive AI‑Facilitated Dialogue: Guides the user through a structured, role‑play conversation that ensures psychological safety and comprehensive coverage.

Quick Start

Run the bmad‑retrospective skill and say, “Start a retrospective for the latest completed epic.”

Frequently Asked Questions about bmad-retrospective

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

FAQPage Schema
How do I conduct an epic retrospective for completed Scrum stories?▼

An epic retrospective analyzes completed Scrum stories to extract lessons, identify technical debt, and assess success. It reviews story files, sprint-status, and project artifacts to generate actionable insights for future work.

What is needed to run an AI-guided retrospective on my project?▼

Running an AI-guided retrospective requires access to project configuration, story markdown files, sprint-status.yaml, and optional previous retrospective documents to accurately detect completed epics and track action-item follow-through.

How does automated epic discovery work for sprint retrospectives?▼

Automated epic discovery works by analyzing your sprint-status.yaml and story files to detect the most recent completed epic. This ensures your sprint retrospective focuses on relevant, finished work without manual epic selection.

Can I review previous retrospective action items during a new sprint analysis?▼

Yes, you can review previous retrospective action items during a new sprint analysis. The system integrates past retrospective documents to track action-item follow-through and apply learned lessons to the current epic evaluation.

What is the best way to analyze technical debt and testing outcomes from story files?▼

The best way to analyze technical debt and testing outcomes is through deep story analysis, which automatically extracts challenges, successes, and testing results from all markdown story files within a completed epic.

Does this retrospective tool provide insights for the next upcoming epic?▼

Yes, the retrospective tool provides a next epic preview that highlights dependencies and preparation needs. This gives your team a concise look at upcoming work immediately after analyzing the completed epic.

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