Request Refactor Plan

Community

AvdLee

request-refactor-plan

Create a detailed refactor plan with tiny commits via user interview, then file it as a GitHub issue. Use when user wants to plan a refactor, create a refactoring RFC, or break a refactor into safe incremental steps.

Overview

Publisher	AvdLee
Repository	`RocketSimApp`
Skill name	`request-refactor-plan`
Stars	805
Forks	37
Bundled files	Instructions only
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 AvdLee on GitHub. Read the source before you install it.

Installation

Install the Request Refactor Plan 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.
https://github.com/AvdLee/RocketSimApp/tree/master/docs/.agents/skills/request-refactor-plan
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/AvdLee/RocketSimApp/tree/master/docs/.agents/skills/request-refactor-plan

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/AvdLee/RocketSimApp.git /tmp/RocketSimApp
mkdir -p .claude/skills
cp -r /tmp/RocketSimApp/docs/.agents/skills/request-refactor-plan .claude/skills/request-refactor-plan

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

Use it in TypingMind

Enable Request Refactor Plan 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 Request Refactor Plan 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 Request Refactor Plan 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.

This skill will be invoked when the user wants to create a refactor request. You should go through the steps below. You may skip steps if you don't consider them necessary.

Ask the user for a long, detailed description of the problem they want to solve and any potential ideas for solutions.
Explore the repo to verify their assertions and understand the current state of the codebase.
Ask whether they have considered other options, and present other options to them.
Interview the user about the implementation. Be extremely detailed and thorough.
Hammer out the exact scope of the implementation. Work out what you plan to change and what you plan not to change.
Look in the codebase to check for test coverage of this area of the codebase. If there is insufficient test coverage, ask the user what their plans for testing are.
Break the implementation into a plan of tiny commits. Remember Martin Fowler's advice to "make each refactoring step as small as possible, so that you can always see the program working."
Create a GitHub issue with the refactor plan. Use the following template for the issue description:

Problem Statement

The problem that the developer is facing, from the developer's perspective.

Solution

The solution to the problem, from the developer's perspective.

Commits

A LONG, detailed implementation plan. Write the plan in plain English, breaking down the implementation into the tiniest commits possible. Each commit should leave the codebase in a working state.

Decision Document

A list of implementation decisions that were made. This can include:

The modules that will be built/modified
The interfaces of those modules that will be modified
Technical clarifications from the developer
Architectural decisions
Schema changes
API contracts
Specific interactions

Do NOT include specific file paths or code snippets. They may end up being outdated very quickly.

Testing Decisions

A list of testing decisions that were made. Include:

A description of what makes a good test (only test external behavior, not implementation details)
Which modules will be tested
Prior art for the tests (i.e. similar types of tests in the codebase)

Out of Scope

A description of the things that are out of scope for this refactor.

Further Notes (optional)

Any further notes about the refactor.

Frequently asked questions

What does the Request Refactor Plan AI skill do?

Why use Request Refactor Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AvdLee/RocketSimApp/tree/master/docs/.agents/skills/request-refactor-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Request Refactor Plan?

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 Request Refactor Plan?

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

Is the Request Refactor Plan AI skill free?

It is published on GitHub by AvdLee. Check the repository for licensing terms. 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

Openclaw Debugging

openclaw

OrganizationPopular

Debug OpenClaw model, provider, tool-surface, code-mode, streaming, and live/Crabbox behavior by choosing the right logs, probes, and proof path before changing code, including fetching stored…

Openclaw Live Updater

openclaw

OrganizationPopular

Maintain the canonical live OpenClaw main checkout, macOS LaunchAgent-managed Gateway, local macOS app, exact-head main CI, and recurring full release validation. Use for fast-forward update…

Openclaw Pr Maintainer

openclaw

OrganizationPopular

Review, triage, repair, or land OpenClaw issues and pull requests with current-source evidence and the native maintainer workflow.

Openclaw Refactor Docs

openclaw

OrganizationPopular

Refactor an existing OpenClaw docs page with source-audited preservation, restructuring, and verification.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Problem Statement

Solution

Commits

Decision Document

Testing Decisions

Out of Scope

Further Notes (optional)

Frequently asked questions

Related AI Skills

Set up your own AI workspace now