Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working code feels heavier than it needs to be — deeply nested logic, long functions, unclear names, duplication after a merge, or a review that flagged complexity. Reduces complexity while preserving behavior exactly. Not for code you do not yet understand, or code you are about to rewrite.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Attribution: Sourced from addyosmani/agent-skills by Addy Osmani, licensed MIT. Modified for this collection.
Inspired by the Claude Code Simplifier plugin. Adapted here as a model-agnostic, process-driven skill for any AI coding agent.
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 NOT to use:
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?
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.
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);
}
Simplification has a failure mode: over-simplification. Watch for these traps:
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.
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.
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 |
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.
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.
// 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;
}
# 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)
// 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.
| 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. |
After completing a simplification pass:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
日本語の概要は準備中です。原文の説明を表示しています。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
日本語の概要は準備中です。原文の説明を表示しています。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
日本語の概要は準備中です。原文の説明を表示しています。