How do I install the Retro Analysis skill?

Run npx skillmds add majiayu000/retro-analysis in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Retro Analysis safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Retro Analysis?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Retro Analysis free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Retro Analysis?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Retro Analysis by majiayu000 — Agent Skill

Name: Retro Analysis
Rating: 4.666666666666667 (3 reviews)
Author: majiayu000

Retro Analysis

Analyse sprint delivery data and produce a structured retrospective brief. Use when asked to run a retrospective, analyse sprint data, prepare a retro brief, or turn sprint metrics into discussion prompts. Produces a data-grounded retrospective brief with completion stats, pattern analysis, Start/Stop/Continue prompts, and one concrete experiment for next sprint.

majiayu000 512e545 2 files · 3.4 KB Updated Sep 9, 2026 567 repo stars

File contents

Retrospective Analysis Skill

Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened.

Required Inputs

Ask the user for these if not provided:

Sprint tickets: planned vs. completed
Carry-over tickets and reasons (if known)
Tickets reopened after closing (quality signal)
Any incidents or unplanned work (scope creep signal)
Sprint velocity vs. historical average (trend context)

Process

Calculate: completion rate, carry-over rate, unplanned work percentage
Identify patterns: which ticket types were most likely to carry over? Which caused blockers?
Note any process or communication breakdowns visible in the data
Prepare 3 "Start / Stop / Continue" prompts based on the data — not generic, specific to this sprint
Suggest 1 concrete experiment for the next sprint based on the biggest friction point
Validate — Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable

Output Structure

Sprint [Number] Retrospective Brief

By the Numbers:

Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%]
Unplanned work: [n] tickets ([%] of capacity)
Velocity: [points] vs. [average] average

What the Data Suggests: [2-3 observations grounded in the numbers above]

Discussion Prompts:

Start: [specific prompt based on this sprint's data]
Stop: [specific prompt based on this sprint's data]
Continue: [specific prompt based on this sprint's data]

Suggested Experiment for Next Sprint: [One concrete, testable process change — with a specific success metric]

Quality Checks

Each Start/Stop/Continue prompt names a specific behaviour, not a vague category
The recommended experiment is testable in one sprint
Carry-over analysis identifies the ticket type or cause, not just the count
Data observations don't assign blame — they describe patterns
Velocity trend is mentioned in context (is this a one-off or a pattern?)

---name: retro-analysisdescription: Analyse sprint delivery data and produce a structured retrospective brief. Use when asked to run a retrospective, analyse sprint data, prepare a retro brief, or turn sprint metrics into discussion prompts. Produces a data-grounded retrospective brief with completion stats, pattern analysis, Start/Stop/Continue prompts, and one concrete experiment for next sprint.--- # Retrospective Analysis Skill Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened. ## Required Inputs Ask the user for these if not provided: - **Sprint tickets: planned vs. completed** - **Carry-over tickets and reasons** (if known) - **Tickets reopened after closing** (quality signal) - **Any incidents or unplanned work** (scope creep signal) - **Sprint velocity vs. historical average** (trend context) ## Process 1. Calculate: completion rate, carry-over rate, unplanned work percentage 2. Identify patterns: which ticket types were most likely to carry over? Which caused blockers? 3. Note any process or communication breakdowns visible in the data 4. Prepare 3 "Start / Stop / Continue" prompts based on the data — not generic, specific to this sprint 5. Suggest 1 concrete experiment for the next sprint based on the biggest friction point 6. **Validate** — Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable ## Output Structure ### Sprint [Number] Retrospective Brief **By the Numbers:** - Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%] - Unplanned work: [n] tickets ([%] of capacity) - Velocity: [points] vs. [average] average **What the Data Suggests:** [2-3 observations grounded in the numbers above] **Discussion Prompts:** - Start: [specific prompt based on this sprint's data] - Stop: [specific prompt based on this sprint's data] - Continue: [specific prompt based on this sprint's data] **Suggested Experiment for Next Sprint:** [One concrete, testable process change — with a specific success metric] ## Quality Checks - [ ] Each Start/Stop/Continue prompt names a specific behaviour, not a vague category - [ ] The recommended experiment is testable in one sprint - [ ] Carry-over analysis identifies the ticket type or cause, not just the count - [ ] Data observations don't assign blame — they describe patterns - [ ] Velocity trend is mentioned in context (is this a one-off or a pattern?)

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