Scope Cutting logo

Scope Cutting

Community
assimovt
scope-cutting

Cut scope ruthlessly using Shape Up's appetite-first approach. Use when asked to reduce scope, find the MVP, trim features, ship faster, or figure out what to cut. Applies fixed time variable scope thinking and scope hammering techniques.

Overview

Publisherassimovt
Repositoryproductskills
Skill namescope-cutting
Stars
71
Forks
6
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 assimovt on GitHub. Read the source before you install it.

Installation

Install the Scope Cutting 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.
    https://github.com/assimovt/productskills/tree/main/skills/scope-cutting
  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/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/scope-cutting .claude/skills/scope-cutting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scope Cutting 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 Scope Cutting 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 Scope Cutting 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.

Cut scope by flipping the question. Instead of "How long will this take?" ask "How much time is this problem WORTH?" Set the appetite first, then shape the solution to fit. This is Shape Up's core insight: fixed time, variable scope.

Set the Appetite

Before touching scope, declare the time budget:

  • Small batch: 1-2 weeks for 1-2 people
  • Big batch: 6 weeks max for a small team

If a project takes more than 6 weeks (or 3 weeks for 1-3 people per Linear's method), it's too big. Break it down or walk away.

The appetite is NOT an estimate. An estimate says "this will take X." An appetite says "this is WORTH X." If the shaped solution doesn't fit the appetite, cut scope until it does — or kill the project.

Scope Hammering

For every item in the spec, ask these questions in order:

  1. Must-have or nice-to-have? If the feature ships without it and users still get value, it's nice-to-have. Cut it.
  2. Shippable without it? Can you deploy a working version that doesn't include this? If yes, cut it from v1.
  3. Edge case or core path? If fewer than 20% of users will encounter this scenario, defer it.
  4. Compare to baseline. The baseline is NO feature — not the ideal version. "Is this better than nothing?" always wins over "Is this as good as it could be?"

Shape the Solution Down

When scope is still too big after hammering:

  • Cut breadth, not quality. Support 1 auth provider well instead of 3 badly. Handle the happy path completely instead of covering every error state.
  • Remove entire surfaces. Kill the settings page. Remove the dashboard. Ship with sensible defaults instead of configurability.
  • Time-bound the edge cases. "We'll handle CSV import of up to 1,000 rows. Above that, they email us." Solve it manually until scale demands automation.
  • Ask: "What's the simplest version a customer would thank us for?" That's your scope.

Circuit Breaker (Shape Up)

If it doesn't ship in one cycle, it's cancelled by default. No automatic rollover. The team must re-pitch for another cycle. This prevents zombie projects that drag on forever.

Guidelines

  • CRITICAL: NEVER cut quality — cut BREADTH. A polished feature that does one thing > a buggy feature that does five.
  • NEVER estimate. Set appetite. "This is worth 2 weeks" not "This will take 2 weeks."
  • NEVER let v1 have more than 5 must-haves. If it does, you haven't scoped hard enough.
  • ALWAYS compare to baseline (no feature at all), not to the ideal implementation.
  • ALWAYS ask "What can we ship in [appetite]?" not "How long until we ship everything?"
  • NEVER carry work over automatically. Unfinished work must be re-pitched.

Built on Shape Up (Basecamp) and the Linear Method. Skills from productskills.

Frequently asked questions

What does the Scope Cutting AI skill do?

Cut scope ruthlessly using Shape Up's appetite-first approach. Use when asked to reduce scope, find the MVP, trim features, ship faster, or figure out what to cut. Applies fixed time variable scope thinking and scope hammering techniques.

Why use Scope Cutting on TypingMind?

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

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

Which AI models can use Scope Cutting?

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 Scope Cutting?

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

Is the Scope Cutting AI skill free?

Yes. It is published on GitHub by assimovt 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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