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Scoped Change

Community
scarletkc
scoped-change

Judgment rules for locating the correct boundary of a requested change: staying inert outside it while completing every required site inside it. Use when scope is ambiguous, a diff touches neighboring surfaces, required dependent edits are unclear, or a proposed compatibility layer, migration, fallback, flag, abstraction, or parallel implementation may exceed the request.

Overview

Publisherscarletkc
Repositoryagents
Skill namescoped-change
Stars
225
Forks
12
Bundled files
Instructions only
LicenseApache-2.0
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 scarletkc on GitHub. Read the source before you install it.

Installation

Install the Scoped Change 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/scarletkc/agents/tree/main/skills/scoped-change
  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/scarletkc/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/scoped-change .claude/skills/scoped-change
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scoped Change 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 Scoped Change 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 Scoped Change 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.

Scoped Change

A change has one correct size and the request defines it. Overshooting and undershooting are the same failure — a boundary that was never located — and they fail differently: unrequested edits surface in review or in production, missing edits surface as the user finding them. Locate the boundary first, then be exhaustive inside it and inert outside it.

When the boundary is corrected mid-task, the result reads as though the corrected scope were the only one there had ever been. A title, comment, or rationale that keeps the dropped option alive re-imports the overshoot the correction just removed, and the reader pays for it twice; what belongs in a deliverable is ux-writing.

Outside the boundary

  • Edit only the surfaces the request names. A request that names one layer is a request about that layer, and the neighboring layers stay untouched even when they look wrong. Report what you noticed instead of fixing it; a separate observation is cheap, an entangled diff is not. Counter-example: asked to adapt a backend endpoint to an existing signup form, an agent redesigned the form too, and the reviewer had to pull the two apart before either could ship.
  • Compatibility is a cost, and someone must already be paying it. Migrations, dual-read paths, deprecated aliases, and version shims are correct only when real users hold the old state. Establish that they exist before writing one; when they don't, a clean break is the smaller change. This is a question of fact, not caution. Counter-example: an unreleased product with no installed base got a save-format migration path for data no user had ever written, and the dead branch still had to be maintained and tested.
  • Reuse the existing implementation; never add a second one beside it. If the project already renders this panel, formats this message, or awards this promotion, extend that path. A parallel implementation that looks correct is worse than an obvious gap, because it diverges silently and nothing fails when it does. Counter-example: a new promotion banner was written from scratch next to the existing level-up banner; a release later only one of them had the new animation.
  • Prefer changing an existing rule to adding one next to it. A new constant, flag, or branch that coexists with the rule it was meant to replace leaves two sources of truth and doubles the states to reason about. Counter-example: a request to restrict upgrades to a single branch arrived as a new second rule constant; narrowing the meaning of the original constant was the entire change.
  • Solve the stated problem, not its general form. Extension points, plugin layers, and configuration surfaces are worth building when a second caller exists, not when one is imagined. The simple implementation is the deliverable unless the request asked for the general one.

Inside the boundary

  • Apply the change at every site it implies. The requested behavior is the unit, not the file you happened to open. Before declaring it done, grep for the siblings of what you edited — the other cards of that type, the other localized strings of that key, the other callers of that helper — and confirm each one either changed or correctly didn't. Counter-example: a new card type shipped without the category prefix that every other card's label carries, so it read as a different kind of object in the UI.
  • Matching the siblings is part of the request, not extra scope. A new item of an existing kind inherits that kind's required fields, naming convention, rarity or tier metadata, and text conventions without being told. Ask when the value is genuinely undetermined; don't ship the field missing. Counter-example: a new entity landed without the rarity field every sibling declares, and only surfaced when a loader that groups by rarity silently dropped it.

When the boundary is unclear

  • Ask only for consequential boundary decisions. If the requested boundary cannot be determined without making a consequential choice the user has not already made, ask before crossing it. Routine implementation decisions stay with the agent and must not become confirmation gates.

Order of work

  • Lock behavior in tests once it is settled, not while it is moving. Every behavior change still ships with happy-path and failure coverage; the point is sequence. Tests and docs written against logic that is still under discussion pin the wrong behavior and then argue for it, and the rewrite costs more than the wait. Get the logic confirmed, then cover it.

Frequently asked questions

What does the Scoped Change AI skill do?

Judgment rules for locating the correct boundary of a requested change: staying inert outside it while completing every required site inside it. Use when scope is ambiguous, a diff touches neighboring surfaces, required dependent edits are unclear, or a proposed compatibility layer, migration, fallback, flag, abstraction, or parallel implementation may exceed the request.

Why use Scoped Change on TypingMind?

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

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

Which AI models can use Scoped Change?

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 Scoped Change?

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

Is the Scoped Change AI skill free?

Yes. It is published on GitHub by scarletkc under the Apache-2.0 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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