clean-code-skill

name	clean-code-skill
description	Write clean, human-readable code with proper naming, small functions, self-documenting patterns, and object calisthenics - language-agnostic
license	Apache-2.0
compatibility	opencode
metadata	{"audience":"developers","workflow":"code-quality","languages":["language-agnostic"],"protocol":"autoresearch-opt-in"}

What I do

I help you write clean, maintainable code that is easy to understand and change:

1. Improve Naming: Apply consistent, understandable, specific, and searchable naming conventions
2. Reduce Function Size: Keep functions small (under 10 lines) with single responsibility
3. Apply Object Calisthenics: Follow the 9 rules for better object-oriented design
4. Eliminate Comments: Replace comments with self-documenting code
5. Improve Formatting: Apply consistent code structure and organization
6. Reduce Cognitive Load: Write code that tells a story

When to use me

Use this skill when:

• Code is hard to understand even though it works
• Functions are long and do multiple things
• Names are vague, inconsistent, or misleading
• Code requires extensive comments to explain what it does
• Reviewing code for readability and maintainability
• Onboarding new developers who struggle with the codebase
• Setting up coding standards for a team
• Refactoring legacy code for better maintainability

Prerequisites

• Code to analyze or write
• Understanding of basic programming concepts
• Willingness to refactor for readability
• Knowledge of the domain/business context for naming

What is Clean Code?

Code that is:

• Easy to understand - reveals intent clearly
• Easy to change - modifications are localized
• Easy to test - dependencies are injectable
• Simple - no unnecessary complexity

The Human-Centered Approach

Code has THREE consumers:

1. Users - get their needs met
2. Customers - make or save money
3. Developers - must maintain it

Design for all three, but remember: developers read code 10x more than they write it.

---

Naming Principles

Priority Order for Naming

1. Consistency & Uniqueness (HIGHEST PRIORITY)
2. Understandability
3. Specificity
4. Brevity
5. Searchability
6. Pronounceability
7. Austerity

1. Consistency & Uniqueness

Same concept = same name everywhere. One name per concept.

# BAD: Inconsistent names for same concept
get_user_by_id(id)
fetch_customer_by_id(id)
retrieve_client_by_id(id)

# GOOD: Consistent
get_user(id)
get_order(id)
get_product(id)

2. Understandability

Use domain language, not technical jargon.

// BAD: Technical
const arr = users.filter(u => u.isActive);

// GOOD: Domain language
const activeCustomers = users.filter(user => user.isActive);

3. Specificity

Avoid vague names: data, info, manager, handler, processor, utils

# BAD: Vague
class DataManager:
    pass

def process_info(data):
    pass

# GOOD: Specific
class OrderRepository:
    pass

def validate_payment(payment):
    pass

4. Brevity (but not at cost of clarity)

Short names are good only if meaning is preserved.

// BAD: Too cryptic
const usrLst = getUsrs();

// BAD: Unnecessarily long
const listOfAllActiveUsersInTheSystem = getActiveUsers();

// GOOD: Brief but clear
const activeUsers = getActiveUsers();

5. Searchability

Names should be unique enough to grep/search.

# BAD: Common word, hard to search
data = fetch()

# GOOD: Unique, searchable
order_summary = fetch_order_summary()

6. Pronounceability

You should be able to say it in conversation.

// BAD
const genymdhms = generateYearMonthDayHourMinuteSecond();

// GOOD
const timestamp = generateTimestamp();

7. Austerity

Avoid unnecessary filler words.

# BAD: Redundant
user_data = user  # 'Data' adds nothing
class UserClass:  # 'Class' adds nothing
    pass

# GOOD
user = user
class User:
    pass

Learning: method-name-reuse-different-semantics

A method name reused across classes with DIFFERENT semantics violates the principle of least surprise — process() on OrderService mutates state, process() on PaymentGateway makes a network call, and process() on ReportBuilder returns a new immutable value. The reader assumes one meaning; the code does another. Before naming a method, grep for the proposed name across the codebase. If it exists elsewhere, either verify the semantic meaning is IDENTICAL, or include the specific condition/context in the name (process_for_refund, build_draft).

# BAD — same name, three different contracts
class OrderService:
    def process(self, order):           # mutates order.status in DB
        order.status = "processed"
        db.commit()

class PaymentGateway:
    def process(self, amount):          # network call, returns bool
        return self.client.charge(amount)

class ReportBuilder:
    def process(self, rows):            # pure, returns new Report
        return Report(rows)

# GOOD — names encode the distinguishing condition
class OrderService:
    def mark_processed(self, order): ...
class PaymentGateway:
    def charge_card(self, amount) -> bool: ...
class ReportBuilder:
    def build_from_rows(self, rows) -> Report: ...

Detection:

# find method names defined on more than one class
rg 'def (\w+)\(self' --type py --type ts -o --no-filename | sort | uniq -c | sort -rn | head -30

Rule: Before naming a method, grep the codebase for the proposed name. If it exists elsewhere, confirm the semantic contract is identical, or qualify the name with the specific condition. Never let one name mean two different things.

---

Object Calisthenics (9 Rules)

Exercises to improve OO design. Follow strictly during practice, relax slightly in production.

Rule 1: One Level of Indentation per Method

# BAD: Multiple levels
def process(orders):
    for order in orders:
        if order.is_valid():
            for item in order.items:
                if item.in_stock:
                    # process...

# GOOD: Extract methods
def process(orders):
    for order in orders:
        if order.is_valid():
            process_order(order)

def process_order(order):
    for item in order.items:
        if item.in_stock:
            process_item(item)

Rule 2: Don't Use the ELSE Keyword

Use early returns, guard clauses, or polymorphism.

Learning: duplicate-service-account-check

Don't call the same expensive method twice in one function. Extract to a local variable — single call, clear intent, no redundant work.

# BAD: two calls, two network round-trips
def process_payment(self, order):
    if self.auth_service.get_service_account() is None:
        raise NoServiceAccount()
    account = self.auth_service.get_service_account()
    self.charge(account, order.total)

# GOOD: single call, local variable
def process_payment(self, order):
    account = self.auth_service.get_service_account()
    if account is None:
        raise NoServiceAccount()
    self.charge(account, order.total)

Rule 2 continued: Don't Use the ELSE Keyword

// BAD: else
function getDiscount(user: User): number {
    if (user.isPremium) {
        return 20;
    } else {
        return 0;
    }
}

// GOOD: Early return
function getDiscount(user: User): number {
    if (user.isPremium) return 20;
    return 0;
}

Rule 3: Wrap All Primitives and Strings

Primitives should be wrapped in domain objects when they have meaning.

# BAD: Primitive obsession
def create_user(email: str, age: int):
    pass

# GOOD: Value objects
class Email:
    def __init__(self, value: str):
        if not self._is_valid(value):
            raise InvalidEmail()
        self._value = value
    
    def _is_valid(self, email: str) -> bool:
        return '@' in email

class Age:
    def __init__(self, value: int):
        if value < 0 or value > 150:
            raise InvalidAge()
        self._value = value

def create_user(email: Email, age: Age):
    pass

Rule 4: First-Class Collections

Any class with a collection should have no other instance variables.

// BAD: Collection mixed with other state
class Order {
    items: Item[] = [];
    customerId: string;
    total: number;
}

// GOOD: Collection is its own class
class OrderItems {
    constructor(private items: Item[] = []) {}
    
    add(item: Item): void { /* ... */ }
    total(): Money { /* ... */ }
    isEmpty(): boolean { /* ... */ }
}

class Order {
    constructor(
        private items: OrderItems,
        private customerId: CustomerId
    ) {}
}

Rule 5: One Dot per Line (Law of Demeter)

Don't chain through object graphs.

# BAD: Train wreck
city = order.customer.address.city

# GOOD: Tell, don't ask
city = order.get_shipping_city()

Rule 6: Don't Abbreviate

If a name is too long to type, the class is doing too much.

// BAD
const custRepo = new CustRepo();
const ord = new Ord();

// GOOD
const customerRepository = new CustomerRepository();
const order = new Order();

Rule 7: Keep All Entities Small

• Classes: < 50 lines
• Methods: < 10 lines
• Files: < 100 lines

If larger, it's probably doing too much. Split it.

Rule 8: No Classes with More Than Two Instance Variables

Forces small, focused classes.

# BAD: Too many variables
class Order:
    def __init__(self):
        self.id = None
        self.customer_id = None
        self.items = []
        self.total = 0
        self.status = None

# GOOD: Composed of smaller objects
class Order:
    def __init__(self, order_id, details):
        self._id = order_id
        self._details = details

class OrderDetails:
    def __init__(self, customer, line_items):
        self._customer = customer
        self._line_items = line_items

Rule 9: No Getters/Setters/Properties

Objects should have behavior, not just data. Tell objects what to do.

// BAD: Data bag with getters
class Account {
    getBalance(): number { return this.balance; }
    setBalance(value: number) { this.balance = value; }
}

// Caller does the work
if (account.getBalance() >= amount) {
    account.setBalance(account.getBalance() - amount);
}

// GOOD: Behavior-rich object
class Account {
    withdraw(amount: Money): WithdrawResult {
        if (!this.canWithdraw(amount)) {
            return WithdrawResult.insufficientFunds();
        }
        this.balance = this.balance.subtract(amount);
        return WithdrawResult.success();
    }
}

// Caller tells, object decides
const result = account.withdraw(amount);

Learning: two-phase-dataclass-initialization

A function returns an object with sentinel/placeholder values (0.0, None, "") that the caller must separately patch. If the second call is forgotten, the result is silently wrong — not an error, just incorrect data.

Rule: Every function must return a complete object. If a field cannot be computed at construction time, make it Optional[None] so the type system surfaces the incompleteness.

# BAD — caller must know to call compute_bounds() separately
def create_template(name: str) -> Template:
    return Template(name=name, mean=0.0, stddev=0.0, low=0.0, high=0.0)
# ... later ...
tpl = create_template("sensor")
tpl.compute_bounds(data)  # FORGOTTEN → silent wrong results, 0.0 bounds

# GOOD — split into separate complete-returning functions
def create_empty_template(name: str) -> Template:
    """Returns template with None bounds — caller MUST call populate()."""
    return Template(name=name, mean=None, stddev=None, low=None, high=None)

def populate_template(tpl: Template, data: list[float]) -> Template:
    """Returns a NEW template with computed bounds."""
    return Template(name=tpl.name, mean=mean(data), stddev=stdev(data), ...)

Detection: Look for functions returning objects with hardcoded 0.0, "", None, [] defaults that have companion compute_* or populate_* methods.

Learning: parallel-hierarchies-for-report-type-variants

When two container+hook+component trees are near-identical for different report types (e.g. FinancialReportContainer + useFinancialReport + FinancialReportTable vs OperationalReportContainer + useOperationalReport + OperationalReportTable), and the similarity is >70% (same fetch → transform → render lifecycle, same state machine, only the data shape differs), maintaining them as parallel trees is a duplication smell. Every bug fix must be applied twice and drifts silently on the second pass. Extract the shared container/hook structure and parameterize the report type via a strategy or a config object, so there is exactly one tree driven by a ReportType discriminator.

// BAD — two parallel trees, >70% identical, every fix applied twice
// features/financial-report/_containers/FinancialReport/index.tsx
// features/financial-report/hooks/useFinancialReport.ts        // identical fetch/transform
// features/operational-report/_containers/OperationalReport/index.tsx
// features/operational-report/hooks/useOperationalReport.ts    // identical except URL

// GOOD — one tree, report type drives the differences
type ReportType = 'financial' | 'operational'

const REPORT_CONFIG: Record<ReportType, {
  endpoint: string
  transform: (raw: unknown) => ReportRow[]
  columns: Column[]
}> = {
  financial:   { endpoint: '/api/financial-reports',   transform: toFinancialRows,   columns: FINANCIAL_COLUMNS   },
  operational: { endpoint: '/api/operational-reports', transform: toOperationalRows, columns: OPERATIONAL_COLUMNS },
}

function useReport(type: ReportType, id: string) {
  const { endpoint, transform } = REPORT_CONFIG[type]
  // single fetch/state/transform implementation
  return useFetchReport(endpoint, id, transform)
}

function ReportContainer({ type, id }: { type: ReportType; id: string }) {
  const { data, isLoading } = useReport(type, id)
  const { columns } = REPORT_CONFIG[type]
  return <ReportTable rows={data} columns={columns} loading={isLoading} />
}

Detection:

# find pairs of containers/hooks with >70% similarity
rg -l 'use[A-Z]\w+Report' --type ts --type tsx | xargs -I{} basename {} | sort
# or run a structural diff: jsdiff / difftastic on sibling feature folders

Rule: When two container+hook+component trees for different report types share >70% of their structure, extract a single parameterized tree driven by a ReportType discriminator and a config object. Maintaining parallel trees guarantees drift on the second copy.

---

Comments

When to Write Comments

Only write comments to explain WHY, not WHAT or HOW.

Code explains what and how. Comments explain business reasons, non-obvious decisions, or warnings.

# BAD: Explains what (redundant)
# Add 1 to counter
counter += 1

# GOOD: Explains why
# Compensate for 0-based indexing in legacy API
counter += 1

Prefer Self-Documenting Code

Instead of commenting, rename to make intent clear.

// BAD: Comment needed
// Check if user can access premium features
if (user.subscriptionLevel >= 2 && !user.isBanned) { }

// GOOD: Self-documenting
if (user.canAccessPremiumFeatures()) { }

Learning: self-documented-duplication

Comments like # could be replaced by X, tracked as follow-up or # TODO: extract this into a shared helper are permanent confessions — the follow-up ticket is never filed and the comment ships to production as a badge of known debt. A self-documenting comment that describes duplication without eliminating it is worse than no comment at all: it makes the duplication feel deliberate. Convert every "could be replaced by" or "should extract" comment into a JIRA or GitHub ticket immediately, and either delete the comment or replace it with a reference to the ticket (# See PROJ-1234 for extraction plan).

# BAD — permanent confession, never lands
# This could be replaced by shared_validation.validate_email,
# tracked as follow-up (not done yet)
def validate_email(email: str) -> bool:
    return "@" in email

# Three other files paste the same comment — duplication now feels intentional

# GOOD — ticket created, comment references it, or just fix it now
# See PROJ-1234: consolidate email validation
def validate_email(email: str) -> bool:  # DELETE once PROJ-1234 lands
    return "@" in email

# BEST — just extract it and delete the comment entirely
from shared_validation import validate_email  # one source of truth

Detection:

rg "could be replaced|should be extracted|tracked as follow-up|TODO.*extract|FIXME.*duplicate" --type py --type ts --type tsx

Rule: "Could be replaced by" and "should extract" comments are debt that never ships. Either file a ticket and reference it in the comment, or eliminate the duplication now. Never let a confession comment ship as if the duplication were intentional.

---

Learning: brittle-single-strategy-data-extraction

Hardcoding a single extraction path silently returns null when it fails — the caller never knows why. Implement multi-strategy extraction with ordered fallbacks and a clear error when all strategies fail.

# BAD: single path, silent failure
def extract_report_id(response: requests.Response) -> str | None:
    try:
        return response.json()["data"]["report_id"]
    except (KeyError, ValueError):
        return None  # Caller has no idea what went wrong

# GOOD: multi-strategy with fallbacks
def extract_report_id(response: requests.Response) -> str:
    strategies = [
        lambda: response.json()["data"]["report_id"],
        lambda: response.headers["Location"].rstrip("/").split("/")[-1],
        lambda: response.json()["report_id"],
    ]
    for strategy in strategies:
        try:
            return strategy()
        except (KeyError, IndexError, ValueError, TypeError):
            continue
    raise ExtractionError(
        f"Could not extract report_id from response "
        f"(status={response.status_code}, body={response.text[:200]})"
    )

Learning: inline-imports-in-functions

Imports inside function bodies (lazy imports) hide the module's true dependencies from static analysis tools (mypy, pylint, IDE go-to-definition) and mask circular-import problems that should be fixed by restructuring the module graph. When from heavy_sdk import Client lives inside process(), the import graph looks clean in pydeps output but is actually broken. Fix the architecture — split the module, extract an interface, or move the import to module level. Lazy imports are acceptable ONLY for optional/heavy imports behind feature flags (e.g. import torch only when GPU inference is requested) where the dependency is genuinely conditional.

# BAD — hides a circular import; mypy can't see Client; IDE autocomplete broken
def process_payment(order):
    from services.audit import log_event   # circular: services.audit imports this module
    from heavy_payment_sdk import Client   # heavy SDK loaded on every cold call
    client = Client()
    log_event("payment_started")
    return client.charge(order.total)

# GOOD — module-level imports expose the dependency graph to static analysis
from services.audit import log_event
from heavy_payment_sdk import Client

def process_payment(order):
    client = Client()
    log_event("payment_started")
    return client.charge(order.total)

# ACCEPTABLE — genuinely conditional dependency behind a feature flag
def run_inference(model_path: str):
    if not settings.GPU_ENABLED:
        raise RuntimeError("GPU inference disabled")
    import torch  # heavy, optional, only loaded when actually needed
    return torch.load(model_path)

Detection:

rg "^\s+(import|from)\s" --type py

Rule: Module-level imports are the default. Inline imports hide dependencies from static analysis and mask circular-import bugs — fix the architecture instead. Exception: genuinely conditional/heavy dependencies behind feature flags.

---

Error Handling

Learning: broad-except-masks-bugs

Suggestion #1 + #8 merged — same root cause observed across 3 projects (Python async, Python DSP, TypeScript sequential async).

Narrow catches handle expected transport errors; broad except Exception masks programming bugs as service outages. Expected errors (ConnectError, TimeoutException, HTTPStatusError) are caught and degraded. Unexpected errors (AttributeError, KeyError, TypeError) must propagate as 500s so monitoring surfaces them — they are never "service unreachable."

# BAD — programming bugs (KeyError, AttributeError) are silently treated as "service down"
try:
    result = await client.call()
except Exception:
    logger.warning("Service unreachable, degrading...")
    return fallback  # Bug is now invisible — circuit breaker trips on a typo

# GOOD — narrow catch for expected transport errors; bugs propagate
try:
    result = await client.call()
except (ConnectError, TimeoutException, HTTPStatusError):
    logger.warning("Service unreachable, degrading...")
    return fallback
# AttributeError, KeyError, TypeError propagate as 500 → surfaces in monitoring

Detection:

# Find broad excepts in auth/transport/processing paths
rg 'except Exception\b|except:' --type py -l
# DSP/ML/processing modules are especially dangerous — wrong results with no error
rg 'except Exception' --type py -g '*dsp*' -g '*audio*' -g '*process*'

Rule: Define a domain exception hierarchy for expected failures. Never catch Exception or bare except: in code paths that process external data — you will mask genuine bugs as degraded results.

Learning: silent-failure-sequential-async

A critical async operation that catches its own failure and logs only console.error (or logger.error) prevents the caller from detecting the failure. The caller continues with stale or missing data, entering an inconsistent state with no feedback.

Rule: Sequential async operations must either:

1. Throw on failure — let the caller decide whether to degrade, or
2. Return a discriminated union — Result[T, E] type so the caller explicitly handles both paths.

# BAD — caller cannot detect failure; enters inconsistent state
async def sync_engine_to_cds():
    try:
        data = await engine.get_data()
        await cds.upload(data)
    except Exception:
        logger.error("Sync failed")  # Caller gets None, thinks everything is fine

# GOOD — throws; caller catches explicitly
async def sync_engine_to_cds() -> None:
    data = await engine.get_data()
    await cds.upload(data)  # Raises on failure — caller handles

// BAD — fire-and-forget with internal catch; caller has no signal
toast.promise(apiCall(), {
  error: "Failed",  // User sees toast, but caller's await never throws
})
await apiCall() // Unhandled rejection if no catch — see react-nextjs-antipatterns

// GOOD — single consumer; error propagates
try {
  const result = await apiCall()
  toast.success("Done")
} catch (e) {
  toast.error("Failed")
}

Detection:

# Functions that catch their own errors and only log
rg 'except.*:\s*\n\s*(logger|console)\.(error|warning)' --type py --type ts -U

---

Frontend Patterns

Learning: scattered-z-index-magic-numbers

Symptom: Z-index values hardcoded in 10+ CSS/TSX files, making layering impossible to reason about. Two developers adding modals both pick 9999; the second one wins; the first one's modal is now buried.

/* SMELL: scattered across 10+ files */
.modal-overlay { z-index: 9999; }
.dropdown-menu  { z-index: 1000; }
.toast          { z-index: 8000; }
.sidebar        { z-index: 500; }

/* REFACTORED: single source of truth */
:root {
  --z-sidebar:    100;
  --z-dropdown:   200;
  --z-sticky:     300;
  --z-modal-backdrop: 400;
  --z-modal:      500;
  --z-popover:    600;
  --z-toast:      700;
}

.modal-overlay { z-index: var(--z-modal-backdrop); }
.dropdown-menu  { z-index: var(--z-dropdown); }
.toast          { z-index: var(--z-toast); }
.sidebar        { z-index: var(--z-sidebar); }

Detection:

# Find hardcoded z-index values across the codebase
rg 'z-index:\s*\d+' --type css --type tsx -c | rg '[3-9]|[1-9][0-9]+'

Rule: Z-index values MUST be centralized as CSS custom properties (or a TypeScript constants file). Hardcoded numeric values drift across files and create layering races that are nearly impossible to debug after the fact.

---

Formatting

Vertical Spacing

• Related code together
• Blank lines between concepts
• Most important/public at top

Horizontal Spacing

• Consistent indentation
• Space around operators
• Max line length ~80-120 characters

Storytelling

Code should read top-to-bottom like a story. High-level at top, details below.

class OrderProcessor:
    # Public API first
    def process(self, order):
        self._validate(order)
        self._calculate_totals(order)
        return self._save(order)
    
    # Supporting methods below, in order of appearance
    def _validate(self, order):
        pass
    
    def _calculate_totals(self, order):
        pass
    
    def _save(self, order):
        pass

---

Steps

Step 1: Analyze Current Code

1. Read the code as if you're seeing it for the first time
2. Note areas that require mental effort to understand
3. Identify long functions (>10 lines)
4. Find vague or inconsistent names
5. Locate comments that explain WHAT

Step 2: Improve Naming

1. Apply the 7 naming principles in priority order
2. Use domain language, not technical jargon
3. Ensure consistency across the codebase
4. Make names searchable and pronounceable

Step 3: Reduce Function Size

1. Apply Rule 1 (one level of indentation)
2. Extract methods to achieve <10 lines per function
3. Ensure each function does one thing
4. Use early returns to avoid else

Step 4: Apply Object Calisthenics

1. Review against all 9 rules
2. Focus on the most impactful violations first
3. Refactor incrementally
4. Keep tests passing after each change

Step 5: Eliminate Unnecessary Comments

1. Replace WHAT comments with better naming
2. Keep WHY comments that explain business reasons
3. Add warnings for non-obvious edge cases
4. Document public APIs with proper documentation

---

Best Practices

General Clean Code Practices

• Meaningful Names: Spend time on naming, it pays off
• Small Functions: If you can't see the whole function, it's too long
• Do One Thing: Each function/class should have one purpose
• DRY (Don't Repeat Yourself): But wait for Rule of Three
• KISS (Keep It Simple): Simplest solution that works

Code Review Checklist

• Can I understand this code in 6 months?
• Are names consistent with the rest of the codebase?
• Does each function do one thing?
• Are functions shorter than 10 lines?
• Is there unnecessary complexity?
• Would a junior developer understand this?

---

Common Issues

Over-Abstraction

Issue: Creating too many small classes/methods for simple logic

Solution:

• Apply clean code when you feel pain
• Rule of Three for abstractions
• Keep related code together

Inconsistent Naming

Issue: Same concept has different names across codebase

Solution:

• Create a project glossary
• Use consistent naming conventions
• Review naming in code reviews

Too Many Small Functions

Issue: Code becomes hard to navigate with too many tiny functions

Solution:

• Group related functions in classes
• Use clear naming to show relationships
• Keep related code physically close

---

Verification Commands

After applying clean code principles:

# Check function/method lengths
find src -name "*.py" -exec grep -c "def " {} \;

# Check for common vague names
grep -r "data\|info\|manager\|handler\|utils" src/ --include="*.py"

# Count lines per file (should be < 100)
find src -name "*.py" -exec wc -l {} \; | sort -rn | head -20

# Check for TODO/FIXME that should be addressed
grep -r "TODO\|FIXME" src/ --include="*.py"

Clean Code Verification Checklist:

• All names are consistent, specific, and searchable
• Functions are under 10 lines
• Classes are under 50 lines
• No unnecessary comments (WHAT comments)
• Code reads top-to-bottom like a story
• No else keyword when early return works
• Primitives wrapped in domain objects
• Collections are first-class
• No getters/setters without behavior

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