Retro

Community

Houseofmvps

retro

Sprint retrospective — analyzes git velocity, commit patterns, test health, and shipping cadence. Use after a sprint, at the end of the week, or when the user wants to reflect on progress.

Overview

Publisher	Houseofmvps
Repository	`ultraship`
Skill name	`retro`
Stars	122
Forks	14
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by Houseofmvps on GitHub. Read the source before you install it.

Installation

Install the Retro AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Houseofmvps/ultraship.git /tmp/ultraship
mkdir -p .claude/skills
cp -r /tmp/ultraship/skills/retro .claude/skills/retro

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Retro in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Retro on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Retro is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sprint Retrospective

Retrospectives turn shipping data into actionable insights. This skill analyzes your git history, commit patterns, test health, and shipping velocity to surface what's working and what needs attention.

Announce at start: "I'm running a sprint retrospective on this project."

Process

Step 1: Gather Data

Run the retro analyzer:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/retro-analyzer.mjs <project-directory> --days 7

For custom date ranges:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/retro-analyzer.mjs <project-directory> --since 2026-03-24

Step 2: Present the Retrospective

Format the data as a clear, actionable report:

Velocity Report Card


+===========================================+
|     S P R I N T   R E T R O              |
+===========================================+
|  Period        Mar 24 – Mar 31           |
|  Commits       47                         |
|  Lines Added   2,341                      |
|  Lines Removed 892                        |
|  Net Change    +1,449                     |
|  Authors       1                          |
+===========================================+

Commit Breakdown

Show the distribution of commit types (features, fixes, refactors, tests, docs, chores) as a visual breakdown. Highlight the feature-to-fix ratio — a healthy project ships more features than fixes.

Hot Files

List the most-changed files. Files with excessive churn may need to be split into smaller modules.

Test Health

Number of test files
Passing / failing tests
Whether a test script exists
Recommendation if tests are absent or failing

Shipping Cadence

Peak day of week (when do you ship most?)
Peak hour (when are you most productive?)
Tags/releases in this period

Step 3: Insights & Recommendations

Based on the data, surface:

What went well:

High velocity periods
Good deletion-to-addition ratio (code hygiene)
Consistent shipping cadence
Test coverage improvements

What needs attention:

Fix-heavy sprints (more fixes than features = quality debt)
Hot files that churn excessively
Missing or failing tests
Uneven shipping cadence (feast-or-famine pattern)

Action items:

Concrete, specific recommendations with clear next steps
Link to relevant Ultraship skills (e.g., "Use /tdd for the untested module")

Step 4: Cross-Reference with Learnings (Optional)

If the project has learnings (from /learn), search for relevant context:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/learnings-manager.mjs search --query "sprint"
node ${CLAUDE_PLUGIN_ROOT}/tools/learnings-manager.mjs search --query "debugging"

Incorporate past learnings into recommendations.

Multi-Project Mode

For founders running multiple projects, run retros across all of them:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/retro-analyzer.mjs ~/project-a --days 7
node ${CLAUDE_PLUGIN_ROOT}/tools/retro-analyzer.mjs ~/project-b --days 7

Compare velocity across projects to understand where time is being spent.

Output

The retrospective should be concise and actionable — not a data dump. Lead with insights, support with data. The goal is to help the user ship better next week.

Frequently asked questions

What does the Retro AI skill do?

Sprint retrospective — analyzes git velocity, commit patterns, test health, and shipping cadence. Use after a sprint, at the end of the week, or when the user wants to reflect on progress.

Why use Retro on TypingMind?

Because you install it once and use it with any model. Retro is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Retro in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Houseofmvps/ultraship/tree/main/skills/retro. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Retro?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Retro?

As many as you like. As long as a model supports skills, you can use Retro with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Retro AI skill free?

Yes. It is published on GitHub by Houseofmvps under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Sprint Retrospective

Process

Step 1: Gather Data

Step 2: Present the Retrospective

Velocity Report Card

Commit Breakdown

Hot Files

Test Health

Shipping Cadence

Step 3: Insights & Recommendations

Step 4: Cross-Reference with Learnings (Optional)

Multi-Project Mode

Output

Frequently asked questions

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