kaizen

SKILL.md

Kaizen: Continuous Improvement

Apply continuous improvement mindset - suggest small iterative improvements, error-proof designs, follow established patterns, avoid over-engineering; automatically applied to guide quality and simplicity

Overview

Small improvements, continuously. Error-proof by design. Follow what works. Build only what's needed.

Core principle: Many small improvements beat one big change. Prevent errors at design time, not with fixes.

When to Use

Always applied for:

Code implementation and refactoring

Architecture and design decisions

Process and workflow improvements

Error handling and validation

Philosophy: Quality through incremental progress and prevention, not perfection through massive effort.

The Four Pillars

1. Continuous Improvement (Kaizen)

Small, frequent improvements compound into major gains.

Principles

Incremental over revolutionary:

Make smallest viable change that improves quality

One improvement at a time

Verify each change before next

Build momentum through small wins

Always leave code better:

Fix small issues as you encounter them

Refactor while you work (within scope)

Update outdated comments

Remove dead code when you see it

Iterative refinement:

First version: make it work

Second pass: make it clear

Third pass: make it efficient

Don't try all three at once

// Iteration 2: Make it clear (refactor)
const calculateTotal = (items: Item[]): number => {
return items.reduce((total, item) => {
return total + (item.price * item.quantity);
}, 0);
};

// Iteration 3: Make it robust (add validation)
const calculateTotal = (items: Item[]): number => {
if (!items?.length) return 0;

return items.reduce((total, item) => {
if (item.price < 0 || item.quantity < 0) {
throw new Error('Price and quantity must be non-negative');
}
return total + (item.price * item.quantity);
}, 0);
};

Each step is complete, tested, and working
</Good>

<Bad>
```typescript
// Trying to do everything at once
const calculateTotal = (items: Item[]): number => {
  // Validate, optimize, add features, handle edge cases all together
  if (!items?.length) return 0;
  const validItems = items.filter(item => {
    if (item.price < 0) throw new Error('Negative price');
    if (item.quantity < 0) throw new Error('Negative quantity');
    return item.quantity > 0; // Also filtering zero quantities
  });
  // Plus caching, plus logging, plus currency conversion...
  return validItems.reduce(...); // Too many concerns at once
};

Overwhelming, error-prone, hard to verify

In Practice

When implementing features:

Start with simplest version that works

Add one improvement (error handling, validation, etc.)

Test and verify

Repeat if time permits

Don't try to make it perfect immediately

When refactoring:

Fix one smell at a time

Commit after each improvement

Keep tests passing throughout

Stop when "good enough" (diminishing returns)

When reviewing code:

Suggest incremental improvements (not rewrites)

Prioritize: critical → important → nice-to-have

Focus on highest-impact changes first

Accept "better than before" even if not perfect

2. Poka-Yoke (Error Proofing)

Design systems that prevent errors at compile/design time, not runtime.

Principles

Make errors impossible:

Type system catches mistakes

Compiler enforces contracts

Invalid states unrepresentable

Errors caught early (left of production)

Design for safety:

Fail fast and loudly

Provide helpful error messages

Make correct path obvious

Make incorrect path difficult

Defense in layers:

Type system (compile time)

Validation (runtime, early)

Guards (preconditions)

Error boundaries (graceful degradation)

Type System Error Proofing

// Good: Only valid states possible
type OrderStatus = 'pending' | 'processing' | 'shipped' | 'delivered';
type Order = {
status: OrderStatus;
total: number;
};

// Better: States with associated data
type Order =
| { status: 'pending'; createdAt: Date }
| { status: 'processing'; startedAt: Date; estimatedCompletion: Date }
| { status: 'shipped'; trackingNumber: string; shippedAt: Date }
| { status: 'delivered'; deliveredAt: Date; signature: string };

// Now impossible to have shipped without trackingNumber

Type system prevents entire classes of errors
</Good>

<Good>
```typescript
// Make invalid states unrepresentable
type NonEmptyArray<T> = [T, ...T[]];

const firstItem = <T>(items: NonEmptyArray<T>): T => {
  return items[0]; // Always safe, never undefined!
};

// Caller must prove array is non-empty
const items: number[] = [1, 2, 3];
if (items.length > 0) {
  firstItem(items as NonEmptyArray<number>); // Safe
}

Function signature guarantees safety

Validation Error Proofing

// Good: Validate immediately
const processPayment = (amount: number) => {
if (amount <= 0) {
throw new Error('Payment amount must be positive');
}
if (amount > 10000) {
throw new Error('Payment exceeds maximum allowed');
}

const fee = amount * 0.03;
// ... now safe to use
};

// Better: Validation at boundary with branded type
type PositiveNumber = number & { readonly __brand: 'PositiveNumber' };

const validatePositive = (n: number): PositiveNumber => {
if (n <= 0) throw new Error('Must be positive');
return n as PositiveNumber;
};

const processPayment = (amount: PositiveNumber) => {
// amount is guaranteed positive, no need to check
const fee = amount * 0.03;
};

// Validate at system boundary
const handlePaymentRequest = (req: Request) => {
const amount = validatePositive(req.body.amount); // Validate once
processPayment(amount); // Use everywhere safely
};

Validate once at boundary, safe everywhere else
</Good>

#### Guards and Preconditions

<Good>
```typescript
// Early returns prevent deeply nested code
const processUser = (user: User | null) => {
  if (!user) {
    logger.error('User not found');
    return;
  }
  
  if (!user.email) {
    logger.error('User email missing');
    return;
  }
  
  if (!user.isActive) {
    logger.info('User inactive, skipping');
    return;
  }
  
  // Main logic here, guaranteed user is valid and active
  sendEmail(user.email, 'Welcome!');
};

Guards make assumptions explicit and enforced

Configuration Error Proofing

const client = new APIClient({ timeout: 5000 }); // apiKey missing!

// Good: Required config, fails early
type Config = {
apiKey: string;
timeout: number;
};

const loadConfig = (): Config => {
const apiKey = process.env.API_KEY;
if (!apiKey) {
throw new Error('API_KEY environment variable required');
}

return {
apiKey,
timeout: 5000,
};
};

// App fails at startup if config invalid, not during request
const config = loadConfig();
const client = new APIClient(config);

Fail at startup, not in production
</Good>

#### In Practice

**When designing APIs:**
- Use types to constrain inputs
- Make invalid states unrepresentable
- Return Result<T, E> instead of throwing
- Document preconditions in types

**When handling errors:**
- Validate at system boundaries
- Use guards for preconditions
- Fail fast with clear messages
- Log context for debugging

**When configuring:**
- Required over optional with defaults
- Validate all config at startup
- Fail deployment if config invalid
- Don't allow partial configurations

### 3. Standardized Work

Follow established patterns. Document what works. Make good practices easy to follow.

#### Principles

**Consistency over cleverness:**
- Follow existing codebase patterns
- Don't reinvent solved problems
- New pattern only if significantly better
- Team agreement on new patterns

**Documentation lives with code:**
- README for setup and architecture
- CLAUDE.md for AI coding conventions
- Comments for "why", not "what"
- Examples for complex patterns

**Automate standards:**
- Linters enforce style
- Type checks enforce contracts
- Tests verify behavior
- CI/CD enforces quality gates

#### Following Patterns

<Good>
```typescript
// Existing codebase pattern for API clients
class UserAPIClient {
  async getUser(id: string): Promise<User> {
    return this.fetch(`/users/${id}`);
  }
}

// New code follows the same pattern
class OrderAPIClient {
  async getOrder(id: string): Promise<Order> {
    return this.fetch(`/orders/${id}`);
  }
}

Consistency makes codebase predictable

// New code introduces different pattern without discussion
const getOrder = async (id: string): Promise => {
// Breaking consistency "because I prefer functions"
};

Inconsistency creates confusion
</Bad>

#### Error Handling Patterns

<Good>
```typescript
// Project standard: Result type for recoverable errors
type Result<T, E> = { ok: true; value: T } | { ok: false; error: E };

// All services follow this pattern
const fetchUser = async (id: string): Promise<Result<User, Error>> => {
  try {
    const user = await db.users.findById(id);
    if (!user) {
      return { ok: false, error: new Error('User not found') };
    }
    return { ok: true, value: user };
  } catch (err) {
    return { ok: false, error: err as Error };
  }
};

// Callers use consistent pattern
const result = await fetchUser('123');
if (!result.ok) {
  logger.error('Failed to fetch user', result.error);
  return;
}
const user = result.value; // Type-safe!

Standard pattern across codebase

Documentation Standards

In Practice

Before adding new patterns:

Search codebase for similar problems solved

Check CLAUDE.md for project conventions

Discuss with team if breaking from pattern

Update docs when introducing new pattern

When writing code:

Match existing file structure

Use same naming conventions

Follow same error handling approach

Import from same locations

When reviewing:

Check consistency with existing code

Point to examples in codebase

Suggest aligning with standards

Update CLAUDE.md if new standard emerges

4. Just-In-Time (JIT)

Build what's needed now. No more, no less. Avoid premature optimization and over-engineering.

Principles

YAGNI (You Aren't Gonna Need It):

Implement only current requirements

No "just in case" features

No "we might need this later" code

Delete speculation

Simplest thing that works:

Start with straightforward solution

Add complexity only when needed

Refactor when requirements change

Don't anticipate future needs

Optimize when measured:

No premature optimization

Profile before optimizing

Measure impact of changes

Accept "good enough" performance

YAGNI in Action

class ConsoleTransport implements LogTransport { /... / }
class FileTransport implements LogTransport { / ... / }
class RemoteTransport implements LogTransport { / .../ }

class Logger {
private transports: LogTransport[] = [];
private queue: LogEntry[] = [];
private rateLimiter: RateLimiter;
private formatter: LogFormatter;

// 200 lines of code for "maybe we'll need it"
}

const logError = (error: Error) => {
Logger.getInstance().log('error', error.message);
};

Building for imaginary future requirements
</Bad>

**When to add complexity:**
- Current requirement demands it
- Pain points identified through use
- Measured performance issues
- Multiple use cases emerged

<Good>
```typescript
// Start simple
const formatCurrency = (amount: number): string => {
  return `$${amount.toFixed(2)}`;
};

// Requirement evolves: support multiple currencies
const formatCurrency = (amount: number, currency: string): string => {
  const symbols = { USD: '$', EUR: '€', GBP: '£' };
  return `${symbols[currency]}${amount.toFixed(2)}`;
};

// Requirement evolves: support localization
const formatCurrency = (amount: number, locale: string): string => {
  return new Intl.NumberFormat(locale, {
    style: 'currency',
    currency: locale === 'en-US' ? 'USD' : 'EUR',
  }).format(amount);
};

Complexity added only when needed

Premature Abstraction

class GenericRepository { /300 lines / }
class QueryBuilder { / 200 lines/ }
// ... building entire ORM for single table

Massive abstraction for uncertain future
</Bad>

<Good>
```typescript
// Simple functions for current needs
const getUsers = async (): Promise<User[]> => {
  return db.query('SELECT * FROM users');
};

const getUserById = async (id: string): Promise<User | null> => {
  return db.query('SELECT * FROM users WHERE id = $1', [id]);
};

// When pattern emerges across multiple entities, then abstract

Abstract only when pattern proven across 3+ cases

Performance Optimization

// Benchmark shows: 50ms for 1000 users (acceptable)
// ✓ Ship it, no optimization needed

// Later: After profiling shows this is bottleneck
// Then optimize with indexed lookup or caching

Optimize based on measurement, not assumptions
</Good>

<Bad>
```typescript
// Premature optimization
const filterActiveUsers = (users: User[]): User[] => {
  // "This might be slow, so let's cache and index"
  const cache = new WeakMap();
  const indexed = buildBTreeIndex(users, 'isActive');
  // 100 lines of optimization code
  // Adds complexity, harder to maintain
  // No evidence it was needed
};

Complex solution for unmeasured problem

In Practice

When implementing:

Solve the immediate problem

Use straightforward approach

Resist "what if" thinking

Delete speculative code

When optimizing:

Profile first, optimize second

Measure before and after

Document why optimization needed

Keep simple version in tests

When abstracting:

Wait for 3+ similar cases (Rule of Three)

Make abstraction as simple as possible

Prefer duplication over wrong abstraction

Refactor when pattern clear

Integration with Commands

The Kaizen skill guides how you work. The commands provide structured analysis:

/why: Root cause analysis (5 Whys)

/cause-and-effect: Multi-factor analysis (Fishbone)

/plan-do-check-act: Iterative improvement cycles

/analyse-problem: Comprehensive documentation (A3)

/analyse: Smart method selection (Gemba/VSM/Muda)

Use commands for structured problem-solving. Apply skill for day-to-day development.

Red Flags

Violating Continuous Improvement:

"I'll refactor it later" (never happens)

Leaving code worse than you found it

Big bang rewrites instead of incremental

Violating Poka-Yoke:

"Users should just be careful"

Validation after use instead of before

Optional config with no validation

Violating Standardized Work:

"I prefer to do it my way"

Not checking existing patterns

Ignoring project conventions

Violating Just-In-Time:

"We might need this someday"

Building frameworks before using them

Optimizing without measuring

Remember

Kaizen is about:

Small improvements continuously

Preventing errors by design

Following proven patterns

Building only what's needed

Not about:

Perfection on first try

Massive refactoring projects

Clever abstractions

Premature optimization

Mindset: Good enough today, better tomorrow. Repeat.