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Refactor

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codeaholicguy
refactor

AI DevKit · Systematic structural or multi-file refactors across any stack while preserving behavior and public contracts. Use for reorganizing modules, boundaries, naming, APIs/contracts, staged refactor plans, or refactor risk review.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namerefactor
Stars
1.6K
Forks
252
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by codeaholicguy on GitHub. Read the source before you install it.

Installation

Install the Refactor 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/refactor .claude/skills/refactor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refactor 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 Refactor 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 Refactor 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.

Refactor

Use for structural refactors. Use simplify-implementation for local readability, dead code, or small logic cleanup.

Rules

  • Preserve behavior and public contracts unless changes are explicit.
  • Classify first: small = local/no public movement; medium = multi-file/extraction/boundary/export touch; large = package/cross-package/staged migration/broad consumers.
  • For medium/large refactors, write a brief before editing: evidence, pressure, remaining delta, do/defer/avoid ranking, non-goals, contracts, target shape, validation, compatibility.
  • Do not propose target trees without current-code evidence: tree, file size/mixed concerns, imports/exports, consumers, validation commands.
  • Separate moves/renames from logic changes and design/API behavior questions.
  • Prefer existing conventions, provider locality, and the smallest structure that solves observed pressure.
  • Subtract before adding: remove dead wrappers, redundant validators, stale exports, and unused paths before introducing new structure.
  • Avoid taste refactors, premature abstractions, and thin one-file directories unless staged or conventional.
  • Validate with fresh command output.

Workflow

  1. Discover stack, configs, entry points, exact validation commands, and prior decisions when available.
  2. Map contracts: exports, APIs, routes, CLI, config, schemas, events, files, docs, examples, consumers.
  3. Map structure: directories, naming, boundaries, dependency direction, cycles, mixed concerns, duplication.
  4. Check reader load: can a new reader find where key state comes from and what can change it quickly? Collapse pass-through layers that do not hide policy, adaptation, or real complexity.
  5. If continuing work, compare current state and list only remaining delta.
  6. Choose refactor type and shape:
    • extraction, reorganization, or design refactor
    • flat/internal, feature-first, domain-first, layer-first, core/adapters/entrypoints, service/repository
    • adapter-heavy: provider-specific stays provider-local; shared pure logic -> shared/core/formatting; SDK/client code -> adapter/entrypoint/delivery
  7. Check boundary discipline: validate at CLI/config/network/external API edges; keep internal logic typed, domain-shaped, and pure where practical.
  8. Prefer domain structure over repeated conditionals: state machine, typed model, registry/map, reducer, or ownership-focused module when it deletes branches or invalid states.
  9. Rank moves as do now, defer, or avoid.
  10. Stage: baseline -> delete dead paths -> move/rename -> imports/call sites -> split/merge -> simplify -> exports/docs/tests.
  11. For internal API changes, inventory callers, migrate them, and delete the legacy API in the same wave when no external contract requires compatibility.
  12. Preserve or explain compatibility re-exports/wrappers/barrels like types.ts plus types/.
  13. Validate: tests, compile/typecheck, lint, build, public/downstream smoke checks, diff review.

Stop

Pause when contracts are unclear, baseline cannot be checked and no narrower validation exists, breaking changes need migration decisions, ownership/product decisions are required, or the work is becoming a rewrite.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Refactor AI skill do?

AI DevKit · Systematic structural or multi-file refactors across any stack while preserving behavior and public contracts. Use for reorganizing modules, boundaries, naming, APIs/contracts, staged refactor plans, or refactor risk review.

Why use Refactor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/refactor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Refactor?

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 Refactor?

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

Is the Refactor AI skill free?

Yes. It is published on GitHub by codeaholicguy 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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