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Code Refactor

Community
mhattingpete
code-refactor

Perform bulk code refactoring operations like renaming variables/functions across files, replacing patterns, and updating API calls. Use when users request renaming identifiers, replacing deprecated code patterns, updating method calls, or making consistent changes across multiple locations.

Overview

Publishermhattingpete
Repositoryclaude-skills-marketplace
Skill namecode-refactor
Stars
675
Forks
96
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 mhattingpete on GitHub. Read the source before you install it.

Installation

Install the Code 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/mhattingpete/claude-skills-marketplace.git /tmp/claude-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/claude-skills-marketplace/code-operations-plugin/skills/code-refactor .claude/skills/code-refactor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code 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 Code 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 Code 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.

Code Refactor

Systematic code refactoring across files. Auto-switches to execution mode for 10+ files (90% token savings).

Mode Selection

  • 1-9 files: Use native tools (Grep + Edit with replace_all)
  • 10+ files: Automatically use code-execution skill

Execution example (50 files):

python
from api.code_transform import rename_identifier
result = rename_identifier('.', 'oldName', 'newName', '**/*.py')
# Returns: {'files_modified': 50, 'total_replacements': 247}
# ~500 tokens vs ~25,000 tokens traditional

When to Use

  • "rename [identifier] to [new_name]"
  • "replace all [pattern] with [replacement]"
  • "refactor to use [new_pattern]"
  • "update all calls to [function/API]"
  • "convert [old_pattern] to [new_pattern]"

Core Workflow (Native Mode)

1. Find All Occurrences

Grep(pattern="getUserData", output_mode="files_with_matches")     # Find files
Grep(pattern="getUserData", output_mode="content", -n=true, -B=2, -A=2)  # Verify with context

2. Replace All Instances

Edit(
  file_path="src/api.js",
  old_string="getUserData",
  new_string="fetchUserData",
  replace_all=true
)

3. Verify Changes

Grep(pattern="getUserData", output_mode="files_with_matches")  # Should return none

Workflow Examples

Rename Function

  1. Find: Grep(pattern="getUserData", output_mode="files_with_matches")
  2. Count: "Found 15 occurrences in 5 files"
  3. Replace in each file with replace_all=true
  4. Verify: Re-run Grep
  5. Suggest: Run tests

Replace Deprecated Pattern

  1. Find: Grep(pattern="\\bvar\\s+\\w+", output_mode="content", -n=true)
  2. Analyze: Check if reassigned (let) or constant (const)
  3. Replace: Edit(old_string="var count = 0", new_string="let count = 0")
  4. Verify: npm run lint

Update API Calls

  1. Find: Grep(pattern="/api/auth/login", output_mode="content", -n=true)
  2. Replace: Edit(old_string="'/api/auth/login'", new_string="'/api/v2/authentication/login'", replace_all=true)
  3. Test: Recommend integration tests

Best Practices

Planning:

  • Find all instances first
  • Review context of each match
  • Inform user of scope
  • Consider edge cases (strings, comments)

Safe Process:

  1. Search → Find all
  2. Analyze → Verify appropriate
  3. Inform → Tell user scope
  4. Execute → Make changes
  5. Verify → Confirm applied
  6. Test → Suggest running tests

Edge Cases:

  • Strings/comments: Ask if should update
  • Exported APIs: Warn of breaking changes
  • Case sensitivity: Be explicit

Tool Reference

Edit with replace_all:

  • replace_all=true: Replace all occurrences
  • replace_all=false: Replace only first (or fail if multiple)
  • Must match EXACTLY (whitespace, quotes)

Grep patterns:

  • -n=true: Show line numbers
  • -B=N, -A=N: Context lines
  • -i=true: Case-insensitive
  • type="py": Filter by file type

Integration

  • test-fixing: Fix broken tests after refactoring
  • code-transfer: Move refactored code
  • feature-planning: Plan large refactorings

Frequently asked questions

What does the Code Refactor AI skill do?

Perform bulk code refactoring operations like renaming variables/functions across files, replacing patterns, and updating API calls. Use when users request renaming identifiers, replacing deprecated code patterns, updating method calls, or making consistent changes across multiple locations.

Why use Code Refactor on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mhattingpete/claude-skills-marketplace/tree/main/code-operations-plugin/skills/code-refactor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code 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 Code Refactor?

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

Is the Code Refactor AI skill free?

Yes. It is published on GitHub by mhattingpete 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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