Agent skill

Refactoring

by seb1n in seb1n/awesome-ai-agent-skills

Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns.

MITAuto-check passedDevelopment

Install Refactoring

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill refactoring -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills refactoring --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/code-and-development/refactoring .claude/skills/refactoring && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
refactoring
GitHub stars
206
Token cost
~2.1k tokens
SKILL.md length
687 words
Files
1
Skills in repo
92
Repo updated
First seen
Licence
MIT

At a glance

Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns.

  • Works in 6 steps: Identify Code Smells: Scan the target… → Select Refactoring Patterns: For every… → Plan the Change Order: Determine a safe… → …
  • The user requests refactoring
  • SKILL.md covers Workflow, Supported Languages, Usage and Examples, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Refactoring is an agent skill from seb1n/awesome-ai-agent-skills. 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.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Refactoring. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests refactoring
  • Provides relevant inputs for this workflow

Example prompts

  • “/refactoring”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Identify Code Smells: Scan the target code for common quality issues — long functions, deeply nested conditionals, duplicated logic…
  2. Select Refactoring Patterns: For every identified smell, choose the most appropriate refactoring pattern. Common patterns include Extract…
  3. Plan the Change Order: Determine a safe sequence for applying refactorings. Prefer small, independent changes that can each be verified in…
  4. Apply Refactorings: Transform the code one pattern at a time. Preserve the original public API and behavior. Use language-idiomatic…
  5. Run Tests and Verify: Execute the existing test suite after each transformation. If no tests exist, generate lightweight unit tests…
  6. Document Changes: Summarize each refactoring applied, the smell it addressed, and any follow-up improvements that are now possible. This…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Refactoring loads about 2.1k tokens when it runs. Until then it costs about 59 tokens; SKILL.md has 687 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 687 words, ~2,093 tokens.

Download SKILL.mdSave it as .claude/skills/refactoring/SKILL.md (or your agent's skills folder).
name
refactoring
description
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.
license
MIT
metadata.author
awesome-ai-agent-skills contributors
metadata.version
1.1.0

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).

Show full SKILL.md (284 more words)Show less
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.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in code-and-development/refactoring of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

Refactoring next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Refactoring compared with similar skills
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Refactoring this skillseb1n/awesome-ai-agent-skills206—~2.1kAutomated safety check: PassMIT
Guidelinesakash-network/node1.1k22 repos~577Automated safety check: PassMIT
Component Refactoringlangflow-ai/langflow156k—~3.5kAutomated safety check: PassMIT
Migrate Core Code to Submodulestinyhumansai/openhuman41k—~2.6kAutomated safety check: PassGPL-3.0
ast-grep Structural Searchcode-yeongyu/oh-my-openagent70k—~3.3kAutomated safety check: PassMIT
Systematic Code Refactoringluongnv89/claude-howto42k—~3kAutomated safety check: PassMIT

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Categories

Questions about Refactoring

What does Refactoring do?

Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Refactoring is an agent skill from seb1n/awesome-ai-agent-skills. Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns.

When should I use Refactoring?

Refactoring fits situations like: the user requests refactoring; provides relevant inputs for this workflow.

How do I install Refactoring in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill refactoring -a claude-code`. Or copy the skill folder (code-and-development/refactoring in seb1n/awesome-ai-agent-skills) into .claude/skills/refactoring in your project. Claude Code loads it when a task matches its description.

How do I install Refactoring in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill refactoring -a codex`. Or copy the skill folder (code-and-development/refactoring in seb1n/awesome-ai-agent-skills) into .agents/skills/refactoring in your project. Codex loads it when a task matches its description.

Can I use Refactoring in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill refactoring -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/refactoring, .gemini/skills/refactoring, .github/skills/refactoring and .opencode/skills/refactoring in your project.

What does Refactoring need to run?

SKILL.md names no scripts, command-line tools or credentials: Refactoring is instructions for the agent only. Our summary lists: Python 3.

Does Refactoring access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Refactoring safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Refactoring use?

Refactoring is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Refactoring use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Refactoring?

Skills that share tags, products or a category with Refactoring: Guidelines (akash-network/node, 1.1k stars), Component Refactoring (langflow-ai/langflow, 156k stars), Migrate Core Code to Submodules (tinyhumansai/openhuman, 41k stars) and ast-grep Structural Search (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Refactoring?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.