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Refactoring

Community
seb1n
refactoring

Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Use when the user requests refactoring or provides relevant inputs for this workflow.

Overview

Publisherseb1n
Repositoryawesome-ai-agent-skills
Skill namerefactoring
Stars
188
Forks
35
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 seb1n on GitHub. Read the source before you install it.

Installation

Install the Refactoring 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/seb1n/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/code-and-development/refactoring .claude/skills/refactoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refactoring 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 Refactoring 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 Refactoring 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 Refactoring

This skill guides an AI agent through the disciplined process of restructuring existing code without changing its external behavior. Refactoring improves readability, reduces complexity, and makes the codebase easier to extend and maintain. The agent identifies code smells, proposes targeted refactoring patterns, applies transformations safely, and verifies correctness through tests.

Workflow

  1. Identify Code Smells: Scan the target code for common quality issues — long functions, deeply nested conditionals, duplicated logic, overly broad variable scoping, magic numbers, dead code, and large parameter lists. Flag each smell with its location and a brief explanation of why it harms the codebase.

  2. Select Refactoring Patterns: For every identified smell, choose the most appropriate refactoring pattern. Common patterns include Extract Method, Rename Symbol, Simplify Conditional, Inline Variable, Replace Magic Number with Named Constant, Remove Dead Code, and Introduce Parameter Object. Explain the trade-offs and expected improvement for each proposed change.

  3. Plan the Change Order: Determine a safe sequence for applying refactorings. Prefer small, independent changes that can each be verified in isolation. Group related changes (e.g., extracting a helper then renaming it) and avoid interleaving unrelated transformations that make rollback difficult.

  4. Apply Refactorings: Transform the code one pattern at a time. Preserve the original public API and behavior. Use language-idiomatic constructs — list comprehensions in Python, destructuring in JavaScript, pattern matching in Rust, etc.

  5. Run Tests and Verify: Execute the existing test suite after each transformation. If no tests exist, generate lightweight unit tests covering the refactored paths before and after the change. Confirm that all tests pass and that no regressions have been introduced.

  6. Document Changes: Summarize each refactoring applied, the smell it addressed, and any follow-up improvements that are now possible. This summary serves as a commit message or PR description.

Supported Languages

  • Python
  • JavaScript / TypeScript
  • Java
  • Go
  • Rust
  • C / C++
  • Ruby

Usage

Provide the code you want refactored along with an optional goal such as "reduce complexity," "improve naming," or "break this into smaller functions." The agent will analyze the code, present a refactoring plan, and apply the changes upon approval. You can also point the agent at an entire file or module and ask it to perform a general quality pass.

Examples

Example 1 — Extract Method and Simplify Conditionals (Python)

User Request: "This function is too long and the nested ifs are hard to follow. Refactor it."

Before:

python
def register_user(payload):
    if payload.get("email"):
        if "@" in payload["email"]:
            if payload.get("password") and len(payload["password"]) >= 8:
                user = {"email": payload["email"], "active": True}
                db.save(user)
                send_welcome_email(user["email"])
                log.info(f"User {user['email']} registered")
                return user
            else:
                raise ValueError("Password must be at least 8 characters")
        else:
            raise ValueError("Invalid email format")
    else:
        raise ValueError("Email is required")

After:

python
def register_user(payload):
    _validate_payload(payload)
    user = _create_user(payload["email"])
    _notify_and_log(user)
    return user

def _validate_payload(payload):
    if not payload.get("email"):
        raise ValueError("Email is required")
    if "@" not in payload["email"]:
        raise ValueError("Invalid email format")
    if not payload.get("password") or len(payload["password"]) < 8:
        raise ValueError("Password must be at least 8 characters")

def _create_user(email):
    user = {"email": email, "active": True}
    db.save(user)
    return user

def _notify_and_log(user):
    send_welcome_email(user["email"])
    log.info(f"User {user['email']} registered")

Patterns applied: Extract Method, Flatten Nested Conditionals (guard clauses).

Example 2 — Remove Duplication and Introduce Constants (TypeScript)

User Request: "Clean up this Express route handler. There's a lot of repetition."

Before:

typescript
app.post("/orders", async (req, res) => {
  if (!req.body.items || req.body.items.length === 0) {
    return res.status(400).json({ error: "Items are required" });
  }
  if (req.body.items.length > 50) {
    return res.status(400).json({ error: "Too many items" });
  }
  let total = 0;
  for (const item of req.body.items) {
    total += item.price * item.quantity;
  }
  if (total > 10000) {
    return res.status(400).json({ error: "Order exceeds maximum total" });
  }
  const order = { items: req.body.items, total: total, status: "pending" };
  await db.orders.insert(order);
  return res.status(201).json(order);
});

After:

typescript
const MAX_ITEMS = 50;
const MAX_ORDER_TOTAL = 10_000;

app.post("/orders", async (req, res) => {
  const { items } = req.body;
  const validationError = validateOrder(items);
  if (validationError) {
    return res.status(400).json({ error: validationError });
  }

  const total = calculateTotal(items);
  if (total > MAX_ORDER_TOTAL) {
    return res.status(400).json({ error: "Order exceeds maximum total" });
  }

  const order = await createOrder(items, total);
  return res.status(201).json(order);
});

function validateOrder(items?: OrderItem[]): string | null {
  if (!items || items.length === 0) return "Items are required";
  if (items.length > MAX_ITEMS) return "Too many items";
  return null;
}

function calculateTotal(items: OrderItem[]): number {
  return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}

async function createOrder(items: OrderItem[], total: number) {
  const order = { items, total, status: "pending" as const };
  await db.orders.insert(order);
  return order;
}

Patterns applied: Extract Method, Replace Magic Number with Named Constant, Destructuring.

Best Practices

  • Keep refactorings small and atomic. Each change should be independently verifiable and easy to revert if something breaks.
  • Never refactor and add features at the same time. Mixing behavior changes with structural changes makes bugs nearly impossible to trace.
  • Preserve the public API. Internal restructuring should not force callers to change unless the user explicitly requests an API redesign.
  • Lean on the test suite. If test coverage is low, write characterization tests that capture current behavior before refactoring.
  • Use guard clauses to flatten deeply nested conditionals. Early returns improve readability far more than adding comments to nested branches.
  • Rename aggressively. Clear names eliminate the need for comments. A function called validate_email is better than check with a comment explaining what it checks.

Edge Cases

  • No existing tests: Generate minimal tests that capture current input/output behavior before applying any transformations. Warn the user that confidence in correctness depends on test coverage.
  • Tightly coupled modules: When refactoring one module would break imports or contracts in another, map the dependency graph first and propose an interface boundary before extracting logic.
  • Generated or vendored code: Do not refactor auto-generated files (e.g., protobuf stubs, ORM migrations). Flag them and skip.
  • Performance-critical hot paths: Some "ugly" code is intentionally optimized. Verify with the user before replacing hand-tuned loops with higher-level abstractions that may regress performance.
  • Mixed formatting styles: If the file has inconsistent style (tabs vs. spaces, quote styles), run the project's formatter after refactoring rather than manually fixing style during the refactoring pass.

Frequently asked questions

What does the Refactoring AI skill do?

Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Use when the user requests refactoring or provides relevant inputs for this workflow.

Why use Refactoring on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/seb1n/awesome-ai-agent-skills/tree/main/code-and-development/refactoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Refactoring?

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 Refactoring?

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

Is the Refactoring AI skill free?

Yes. It is published on GitHub by seb1n 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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