Code Simplification
Inspired by the Claude Code Simplifier plugin. Adapted here as a model-agnostic, process-driven skill for any AI coding agent.
Overview
Simplify code by reducing complexity while preserving exact behavior. The goal is not fewer lines — it's code that is easier to read, understand, modify, and debug. Every simplification must pass a simple test: "Would a new team member understand this faster than the original?"
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