code-simplification

name	code-simplification
description	Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.

Code Simplification

Inspired by the Claude Code Simplifier plugin. Adapted here as a model-agnostic, process-driven skill for any AI coding agent.

Overview

Complexity is the silent killer of software projects. It doesn't announce itself with a crash — it accumulates one clever abstraction at a time, one unnecessary indirection at a time, until the codebase becomes a maze that even its authors fear to touch. Simplification is the deliberate act of reversing this entropy.

The simplification contract: Every change must preserve exact behavior while reducing cognitive load. The goal is not fewer lines — it's code that is easier to read, understand, modify, and debug. Before simplifying, understand why the complexity exists (Chesterton's Fence). Then remove what doesn't earn its keep.

Real-world impact: A study of 700+ codebases found that complexity metrics predict bug density better than any other factor. Code that's hard to read is code that's hard to fix — and code that's hard to fix accumulates workarounds until it becomes unmaintainable.

When to Use

• After a feature is working and tests pass, but the implementation feels heavier than it needs to be
• During code review when readability or complexity issues are flagged
• When you encounter deeply nested logic, long functions, or unclear names
• When refactoring code written under time pressure
• When consolidating related logic scattered across files
• After merging changes that introduced duplication or inconsistency

When NOT to use:

• Code is already clean and readable — don't simplify for the sake of it
• You don't understand what the code does yet — comprehend before you simplify
• The code is performance-critical and the "simpler" version would be measurably slower
• You're about to rewrite the module entirely — simplifying throwaway code wastes effort

The Five Principles

1. Preserve Behavior Exactly

Don't change what the code does — only how it expresses it. All inputs, outputs, side effects, error behavior, and edge cases must remain identical. If you're not sure a simplification preserves behavior, don't make it.

ASK BEFORE EVERY CHANGE:
→ Does this produce the same output for every input?
→ Does this maintain the same error behavior?
→ Does this preserve the same side effects and ordering?
→ Do all existing tests still pass without modification?

2. Follow Project Conventions

Simplification means making code more consistent with the codebase, not imposing external preferences. Before simplifying:

1. Read CLAUDE.md / project conventions
2. Study how neighboring code handles similar patterns
3. Match the project's style for:
   - Import ordering and module system
   - Function declaration style
   - Naming conventions
   - Error handling patterns
   - Type annotation depth

Simplification that breaks project consistency is not simplification — it's churn.

3. Prefer Clarity Over Cleverness

Explicit code is better than compact code when the compact version requires a mental pause to parse.

// UNCLEAR: Dense ternary chain
const label = isNew ? 'New' : isUpdated ? 'Updated' : isArchived ? 'Archived' : 'Active';

// CLEAR: Readable mapping
function getStatusLabel(item: Item): string {
  if (item.isNew) return 'New';
  if (item.isUpdated) return 'Updated';
  if (item.isArchived) return 'Archived';
  return 'Active';
}

// UNCLEAR: Chained reduces with inline logic
const result = items.reduce((acc, item) => ({
  ...acc,
  [item.id]: { ...acc[item.id], count: (acc[item.id]?.count ?? 0) + 1 }
}), {});

// CLEAR: Named intermediate step
const countById = new Map<string, number>();
for (const item of items) {
  countById.set(item.id, (countById.get(item.id) ?? 0) + 1);
}

4. Maintain Balance

Simplification has a failure mode: over-simplification. Watch for these traps:

• Inlining too aggressively — removing a helper that gave a concept a name makes the call site harder to read
• Combining unrelated logic — two simple functions merged into one complex function is not simpler
• Removing "unnecessary" abstraction — some abstractions exist for extensibility or testability, not complexity
• Optimizing for line count — fewer lines is not the goal; easier comprehension is

5. Scope to What Changed

Default to simplifying recently modified code. Avoid drive-by refactors of unrelated code unless explicitly asked to broaden scope. Unscoped simplification creates noise in diffs and risks unintended regressions.

The Simplification Process

Step 1: Understand Before Touching (Chesterton's Fence)

Before changing or removing anything, understand why it exists. This is Chesterton's Fence: if you see a fence across a road and don't understand why it's there, don't tear it down. First understand the reason, then decide if the reason still applies.

BEFORE SIMPLIFYING, ANSWER:
- What is this code's responsibility?
- What calls it? What does it call?
- What are the edge cases and error paths?
- Are there tests that define the expected behavior?
- Why might it have been written this way? (Performance? Platform constraint? Historical reason?)
- Check git blame: what was the original context for this code?

If you can't answer these, you're not ready to simplify. Read more context first.

Step 2: Identify Simplification Opportunities

Scan for these patterns — each one is a concrete signal, not a vague smell:

Structural complexity:

Pattern	Signal	Simplification
Deep nesting (3+ levels)	Hard to follow control flow	Extract conditions into guard clauses or helper functions
Long functions (50+ lines)	Multiple responsibilities	Split into focused functions with descriptive names
Nested ternaries	Requires mental stack to parse	Replace with if/else chains, switch, or lookup objects
Boolean parameter flags	doThing(true, false, true)	Replace with options objects or separate functions
Repeated conditionals	Same if check in multiple places	Extract to a well-named predicate function

Naming and readability:

Pattern	Signal	Simplification
Generic names	data, result, temp, val, item	Rename to describe the content: userProfile, validationErrors
Abbreviated names	usr, cfg, btn, evt	Use full words unless the abbreviation is universal (id, url, api)
Misleading names	Function named get that also mutates state	Rename to reflect actual behavior
Comments explaining "what"	// increment counter above count++	Delete the comment — the code is clear enough
Comments explaining "why"	// Retry because the API is flaky under load	Keep these — they carry intent the code can't express

Redundancy:

Pattern	Signal	Simplification
Duplicated logic	Same 5+ lines in multiple places	Extract to a shared function
Dead code	Unreachable branches, unused variables, commented-out blocks	Remove (after confirming it's truly dead)
Unnecessary abstractions	Wrapper that adds no value	Inline the wrapper, call the underlying function directly
Over-engineered patterns	Factory-for-a-factory, strategy-with-one-strategy	Replace with the simple direct approach
Redundant type assertions	Casting to a type that's already inferred	Remove the assertion

Step 3: Apply Changes Incrementally

Make one simplification at a time. Run tests after each change. Submit refactoring changes separately from feature or bug fix changes. A PR that refactors and adds a feature is two PRs — split them.

FOR EACH SIMPLIFICATION:
1. Make the change
2. Run the test suite
3. If tests pass → commit (or continue to next simplification)
4. If tests fail → revert and reconsider

Avoid batching multiple simplifications into a single untested change. If something breaks, you need to know which simplification caused it.

The Rule of 500: If a refactoring would touch more than 500 lines, invest in automation (codemods, sed scripts, AST transforms) rather than making the changes by hand. Manual edits at that scale are error-prone and exhausting to review.

Step 4: Verify the Result

After all simplifications, step back and evaluate the whole:

COMPARE BEFORE AND AFTER:
- Is the simplified version genuinely easier to understand?
- Did you introduce any new patterns inconsistent with the codebase?
- Is the diff clean and reviewable?
- Would a teammate approve this change?

If the "simplified" version is harder to understand or review, revert. Not every simplification attempt succeeds.

Language-Specific Guidance

TypeScript / JavaScript

// SIMPLIFY: Unnecessary async wrapper
// Before
async function getUser(id: string): Promise<User> {
  return await userService.findById(id);
}
// After
function getUser(id: string): Promise<User> {
  return userService.findById(id);
}

// SIMPLIFY: Verbose conditional assignment
// Before
let displayName: string;
if (user.nickname) {
  displayName = user.nickname;
} else {
  displayName = user.fullName;
}
// After
const displayName = user.nickname || user.fullName;

// SIMPLIFY: Manual array building
// Before
const activeUsers: User[] = [];
for (const user of users) {
  if (user.isActive) {
    activeUsers.push(user);
  }
}
// After
const activeUsers = users.filter((user) => user.isActive);

// SIMPLIFY: Redundant boolean return
// Before
function isValid(input: string): boolean {
  if (input.length > 0 && input.length < 100) {
    return true;
  }
  return false;
}
// After
function isValid(input: string): boolean {
  return input.length > 0 && input.length < 100;
}

Python

# SIMPLIFY: Verbose dictionary building
# Before
result = {}
for item in items:
    result[item.id] = item.name
# After
result = {item.id: item.name for item in items}

# SIMPLIFY: Nested conditionals with early return
# Before
def process(data):
    if data is not None:
        if data.is_valid():
            if data.has_permission():
                return do_work(data)
            else:
                raise PermissionError("No permission")
        else:
            raise ValueError("Invalid data")
    else:
        raise TypeError("Data is None")
# After
def process(data):
    if data is None:
        raise TypeError("Data is None")
    if not data.is_valid():
        raise ValueError("Invalid data")
    if not data.has_permission():
        raise PermissionError("No permission")
    return do_work(data)

React / JSX

// SIMPLIFY: Verbose conditional rendering
// Before
function UserBadge({ user }: Props) {
  if (user.isAdmin) {
    return <Badge variant="admin">Admin</Badge>;
  } else {
    return <Badge variant="default">User</Badge>;
  }
}
// After
function UserBadge({ user }: Props) {
  const variant = user.isAdmin ? 'admin' : 'default';
  const label = user.isAdmin ? 'Admin' : 'User';
  return <Badge variant={variant}>{label}</Badge>;
}

// SIMPLIFY: Prop drilling through intermediate components
// Before — consider whether context or composition solves this better.
// This is a judgment call — flag it, don't auto-refactor.

Common Rationalizations

Rationalization	Reality
"It's working, no need to touch it"	Working code that's hard to read will be hard to fix when it breaks. Simplifying now saves time on every future change.
"Fewer lines is always simpler"	A 1-line nested ternary is not simpler than a 5-line if/else. Simplicity is about comprehension speed, not line count.
"I'll just quickly simplify this unrelated code too"	Unscoped simplification creates noisy diffs and risks regressions in code you didn't intend to change. Stay focused.
"The types make it self-documenting"	Types document structure, not intent. A well-named function explains why better than a type signature explains what.
"This abstraction might be useful later"	Don't preserve speculative abstractions. If it's not used now, it's complexity without value. Remove it and re-add when needed.
"The original author must have had a reason"	Maybe. Check git blame — apply Chesterton's Fence. But accumulated complexity often has no reason; it's just the residue of iteration under pressure.
"I'll refactor while adding this feature"	Separate refactoring from feature work. Mixed changes are harder to review, revert, and understand in history.

Red Flags

• Simplifying without understanding why the complexity existed

• Removing "unused" code that is actually part of a feature flag path

• Replacing explicit code with "clever" one-liners

• Simplifying for fewer lines at the cost of readability

• Removing tests because "the code is simpler now"

• Simplification that requires modifying tests to pass (you likely changed behavior)

• "Simplified" code that is longer and harder to follow than the original

• Renaming things to match your preferences rather than project conventions

• Removing error handling because "it makes the code cleaner"

• Simplifying code you don't fully understand

• Batching many simplifications into one large, hard-to-review commit

• Refactoring code outside the scope of the current task without being asked

See Also

• code-review-and-quality
• test-driven-development
• incremental-implementation

Verification

• All existing tests still pass after simplification
• New team member understands the simplified code faster than the original
• No behavior changes (verified by tests and/or diff review)
• Simplification is justified (what complexity was removed and why)

After completing a simplification pass:

• All existing tests pass without modification
• Build succeeds with no new warnings
• Linter/formatter passes (no style regressions)
• Each simplification is a reviewable, incremental change
• The diff is clean — no unrelated changes mixed in
• Simplified code follows project conventions (checked against CLAUDE.md or equivalent)
• No error handling was removed or weakened
• No dead code was left behind (unused imports, unreachable branches)
• A teammate or review agent would approve the change as a net improvement