Sprint logo

Sprint

Community
Houseofmvps
sprint

Sprint workflow pipeline — chains plan → build → test → review → ship skills into a structured sprint. Use when starting a new feature or project iteration to follow the full lifecycle.

Overview

PublisherHouseofmvps
Repositoryultraship
Skill namesprint
Stars
122
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • 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 Sprint AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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/sprint .claude/skills/sprint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sprint 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 Sprint 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 Sprint 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 Workflow Pipeline

A sprint is the full lifecycle of shipping a feature — from planning through deployment. This skill chains Ultraship skills into a structured pipeline where each phase produces artifacts that feed the next.

Announce at start: "I'm using the sprint workflow to guide this feature from plan to ship."

The Pipeline

┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
│  PLAN    │───▶│  BUILD   │───▶│  TEST    │───▶│  REVIEW  │───▶│  SHIP    │───▶│  VERIFY  │
│          │    │          │    │          │    │          │    │          │    │          │
│ /write-  │    │ /execute-│    │ /tdd     │    │ /review  │    │ /deploy  │    │ /canary  │
│  plan    │    │  plan    │    │          │    │          │    │ /ship    │    │ /retro   │
└──────────┘    └──────────┘    └──────────┘    └──────────┘    └──────────┘    └──────────┘
     │                │               │               │               │               │
     ▼                ▼               ▼               ▼               ▼               ▼
  Plan doc       Working code    Tests pass     Issues fixed    Deployed         Verified

Phase 1: Plan

Trigger: User describes a feature, requirement, or bug to fix.

  1. If the scope is large, use ultraship:brainstorming first to explore the idea space
  2. Use ultraship:writing-plans to create an implementation plan
  3. Plan is saved to docs/ultraship/plans/YYYY-MM-DD-<feature>.md

Artifacts produced: Implementation plan with file map, task list, test strategy

Gate: Plan must be reviewed and approved by user before proceeding to Build.

Phase 2: Build

Trigger: Plan is approved.

  1. Use ultraship:executing-plans to implement the plan task by task
  2. If tasks are independent, use ultraship:dispatching-parallel-agents for parallel execution
  3. If working in isolation, use ultraship:using-git-worktrees for a clean workspace

Scaling to large work: Subagents can now nest up to 5 levels deep — a dispatched implementer can fan out its own helpers for sub-steps. For codebase-wide changes (a migration, a cross-cutting refactor, an audit of every route), prefer a dynamic Workflow: describe the task naturally and include the word "workflow" so Claude generates an orchestration script that pipelines the work across many background subagents with verification built in. Use a single controller for a handful of tasks; escalate to a Workflow when the work-list is large or unknown in size.

Artifacts produced: Working code, committed to a feature branch

Gate: All planned tasks are implemented. Code compiles/runs without errors.

Phase 3: Test

Trigger: Implementation is complete.

  1. Use ultraship:test-driven-development to write tests for new code
  2. Run the full test suite to catch regressions
  3. If bugs are found, use ultraship:systematic-debugging to diagnose (not guess-and-fix)

Artifacts produced: Passing test suite, test coverage for new code

Gate: All tests pass. No known bugs in new code.

Phase 4: Review

Trigger: Tests pass.

  1. Use ultraship:requesting-code-review for self-review
  2. Run /review for automated code review with confidence scoring
  3. Run /secure for security scanning
  4. Fix any high-confidence issues found

Artifacts produced: Review report, security scan results, fixes committed

Gate: No critical or high-severity issues remaining.

Phase 5: Ship

Trigger: Review is clean.

  1. Use ultraship:verification-before-completion for final verification
  2. Run /ship for the full pre-deploy scorecard (SEO, security, code quality, bundle)
  3. If score is acceptable, run /deploy for the deploy pipeline
  4. Use ultraship:finishing-a-development-branch to merge/PR

Artifacts produced: Deploy to production, PR/merge to main

Gate: /ship scorecard is READY TO SHIP. Deploy succeeds.

Phase 6: Verify

Trigger: Deploy completes.

  1. Run /canary to verify production health
  2. If canary detects issues, escalate to /rescue
  3. Save any deployment learnings via /learn
  4. Run /retro at the end of the sprint to review overall progress

Artifacts produced: Canary report, learnings, retrospective

Workflow Rules

  1. Never skip phases. Each phase exists because skipping it causes problems.
  2. Gates are mandatory. Don't proceed to the next phase until the gate criteria are met.
  3. Artifacts chain forward. Each phase's output is the next phase's input.
  4. The user decides pace. Some sprints complete in an hour. Some take a week. The pipeline adapts.
  5. Small batches. Prefer shipping small features frequently over large features infrequently.

Quick Sprint (for small changes)

For bug fixes or small features (< 50 lines of code), compress the pipeline:

  1. Investigate → Use /investigate to find root cause
  2. Fix + Test → Fix the bug, write a test
  3. Verify → Run /review, check tests pass
  4. Ship → Push and run /canary

Skip the full planning phase for changes that don't need it.

Sprint Status

Track sprint progress using tasks. At any point, the user can ask "where are we?" and get a clear answer:

Sprint: Add webhook retry logic
Phase: 3/6 — TEST
Status: 2 tests written, 1 failing (timeout issue in retry delay)
Next: Fix failing test, then proceed to REVIEW

Frequently asked questions

What does the Sprint AI skill do?

Sprint workflow pipeline — chains plan → build → test → review → ship skills into a structured sprint. Use when starting a new feature or project iteration to follow the full lifecycle.

Why use Sprint on TypingMind?

Because you install it once and use it with any model. Sprint 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 Sprint in TypingMind?

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

Which AI models can use Sprint?

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 Sprint?

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

Is the Sprint 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇