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- Update outdated comments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 wi
