Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Applies KISS, YAGNI, and SOLID principles for clean code with reduced complexity. Use when refactoring or reviewing code for over-engineering.
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Guidance on KISS, YAGNI, and SOLID principles with language-specific examples.
Principle: Avoid unnecessary complexity. Prefer obvious solutions over clever ones.
| Prefer | Avoid |
|---|---|
| Simple conditionals | Complex regex for simple checks |
| Explicit code | Magic numbers/strings |
| Standard patterns | Clever shortcuts |
| Direct solutions | Over-abstracted layers |
# Bad: Overly clever one-liner
users = [u for u in (db.get(id) for id in ids) if u and u.active and not u.banned]
# Good: Clear and readable
users = []
for user_id in ids:
user = db.get(user_id)
if user and user.active and not user.banned:
users.append(user)
// Bad: Unnecessary complexity
fn process(data: &[u8]) -> Result<Vec<u8>, Box<dyn std::error::Error>> {
data.iter()
.map(|&b| b.checked_add(1).ok_or("overflow"))
.collect::<Result<Vec<_>, _>>()
.map_err(|e| e.into())
}
// Good: Simple and clear
fn process(data: &[u8]) -> Result<Vec<u8>, &'static str> {
let mut result = Vec::with_capacity(data.len());
for &byte in data {
result.push(byte.checked_add(1).ok_or("overflow")?);
}
Ok(result)
}
Principle: Don't implement features until they are actually needed.
| Do | Don't |
|---|---|
| Solve current problem | Build for hypothetical futures |
| Add when 3rd use case appears | Create abstractions for 1 use case |
| Delete dead code | Keep "just in case" code |
| Minimal viable solution | Premature optimization |
# Bad: Premature abstraction for one use case
class AbstractDataProcessor:
def process(self, data): ...
def validate(self, data): ...
def transform(self, data): ...
class CSVProcessor(AbstractDataProcessor):
def process(self, data):
return self.transform(self.validate(data))
# Good: Simple function until more cases appear
def process_csv(data: list[str]) -> list[dict]:
return [parse_row(row) for row in data if row.strip()]
// Bad: Over-engineered config system
interface ConfigProvider<T> {
get<K extends keyof T>(key: K): T[K];
set<K extends keyof T>(key: K, value: T[K]): void;
watch<K extends keyof T>(key: K, callback: (v: T[K]) => void): void;
}
// Good: Simple config for current needs
const config = {
apiUrl: process.env.API_URL || 'http://localhost:3000',
timeout: 5000,
};
Each module/class should have one reason to change.
# Bad: Multiple responsibilities
class UserManager:
def create_user(self, data): ...
def send_welcome_email(self, user): ... # Email responsibility
def generate_report(self, users): ... # Reporting responsibility
# Good: Separated responsibilities
class UserRepository:
def create(self, data): ...
class EmailService:
def send_welcome(self, user): ...
class UserReportGenerator:
def generate(self, users): ...
Open for extension, closed for modification.
# Bad: Requires modification for new types
def calculate_area(shape):
if shape.type == "circle":
return 3.14 * shape.radius**2
elif shape.type == "rectangle":
return shape.width * shape.height
# Must modify to add new shapes
# Good: Extensible without modification
from abc import ABC, abstractmethod
class Shape(ABC):
@abstractmethod
def area(self) -> float: ...
class Circle(Shape):
def __init__(self, radius: float):
self.radius = radius
def area(self) -> float:
return 3.14 * self.radius**2
Subtypes must be substitutable for their base types.
# Bad: Violates LSP - Square changes Rectangle behavior
class Rectangle:
def set_width(self, w):
self.width = w
def set_height(self, h):
self.height = h
class Square(Rectangle): # Breaks when used as Rectangle
def set_width(self, w):
self.width = self.height = w # Unexpected side effect
# Good: Separate types with common interface
class Shape(ABC):
@abstractmethod
def area(self) -> float: ...
class Rectangle(Shape):
def __init__(self, width: float, height: float): ...
class Square(Shape):
def __init__(self, side: float): ...
Clients shouldn't depend on interfaces they don't use.
// Bad: Fat interface
interface Worker {
work(): void;
eat(): void;
sleep(): void;
}
// Good: Segregated interfaces
interface Workable {
work(): void;
}
interface Feedable {
eat(): void;
}
// Clients only implement what they need
class Robot implements Workable {
work(): void { /* ... */ }
}
Depend on abstractions, not concretions.
# Bad: Direct dependency on concrete class
class OrderService:
def __init__(self):
self.db = PostgresDatabase() # Tight coupling
# Good: Depend on abstraction
from abc import ABC, abstractmethod
class Database(ABC):
@abstractmethod
def save(self, data): ...
class OrderService:
def __init__(self, db: Database):
self.db = db # Injected abstraction
| Principle | Question to Ask | Red Flag |
|---|---|---|
| KISS | "Is there a simpler way?" | Complex solution for simple problem |
| YAGNI | "Do I need this right now?" | Building for hypothetical use cases |
| SRP | "What's the one reason to change?" | Class doing multiple jobs |
| OCP | "Can I extend without modifying?" | Switch statements for types |
| LSP | "Can subtypes replace base types?" | Overridden methods with side effects |
| ISP | "Does client need all methods?" | Empty method implementations |
| DIP | "Am I depending on abstractions?" | new keyword in business logic |
When reviewing code, check:
Verification: Run wc -l <file> to check line counts and rg -c "class " <file> (or grep -c "class " <file>) to count classes per file.
imbue:karpathy-principles - The "Simplicity First" principle wraps KISS, YAGNI, and SOLID into a four-principle synthesis derived from Karpathy's observations on LLM coding pitfallsdocs/quality-gates.md#skill-level-quality-gate-composition for the full gate-skill federation graphwc -l <file> run on any modified file and result noted if
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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