functional
Write cleaner code using functional programming and immutable data patterns.
Install
mkdir -p .claude/skills/functional && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3587" && unzip -o skill.zip -d .claude/skills/functional && rm skill.zipInstalls to .claude/skills/functional
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Functional programming patterns with immutable data. Use when writing logic, data transformations, or encountering mutation bugs. Covers immutability violations catalog, pure functions, composition, early returns, and options objects. Do NOT over-apply heavy FP abstractions (monads, fp-ts) unless the project requires them.Key capabilities
- →Refactor loops into array methods
- →Implement immutable data structures
- →Compose small pure functions
- →Apply guard clauses for early returns
How it works
The skill guides the refactoring of imperative logic into pure functions and immutable data structures using declarative array methods and composition.
Inputs & outputs
When to use functional
- →Refactor loops into functional array methods
- →Eliminate state mutation bugs
- →Compose functions for data pipelines
- →Implement immutable data structures
About this skill
Functional Patterns
Deep-dive resources are in the resources/ directory. Load them on demand:
| Resource | Load when... |
|---|---|
immutability-catalog.md | Fixing mutation bugs, applying readonly/ReadonlyArray types, or looking up the immutable alternative to an array/object mutation |
composition-patterns.md | Composing small functions into pipelines, refactoring monolithic logic, or flattening deeply nested code |
Small pure functions are an implementation technique, not a mandate to publish one function per module. Keep related helpers private and colocated when they compose into one coherent responsibility; use codebase-design when choosing the stable caller-facing contract.
Core Principles
- Immutable domain data by default - keep local or boundary mutation encapsulated when it is clearer or required
- Pure functions wherever possible
- Composition over inheritance
- Self-documenting code first - keep comments that explain constraints or non-obvious reasons
- Array methods for transformations - use loops when control flow is clearer
- Options objects for parameter groups - keep simple positional APIs simple
Why Immutability Matters
Immutable data is a foundation of functional programming. It makes code predictable (same input → same output, no hidden state changes), debuggable (state does not change underneath a reader), testable (less hidden mutable state), and React-friendly (reconciliation and memoization can rely on reference changes). It also reduces shared-state concurrency hazards, but does not by itself prevent races in I/O or coordination.
// ❌ WRONG - Mutation creates unpredictable behavior
const user = { name: 'Alice', permissions: ['read'] };
grantPermission(user, 'write'); // Mutates user.permissions internally
console.log(user.permissions); // ['read', 'write'] - SURPRISE! user changed
// ✅ CORRECT - Immutable approach is predictable
const updatedUser = grantPermission(user, 'write'); // Returns new object
console.log(user.permissions); // ['read'] - original unchanged
console.log(updatedUser.permissions); // ['read', 'write'] - new version
Use readonly on data that is intended to be immutable and ReadonlyArray<T> for immutable arrays so the compiler enforces that contract. Encapsulated mutable accumulators, caches, and adapter state are acceptable when they do not leak mutation into the domain contract. For common mutations and immutable alternatives, load resources/immutability-catalog.md.
Functional Light
Follow "Functional Light" principles - practical functional patterns without heavy abstractions:
- DO: pure functions, immutable data, composition, declarative code, array methods,
readonlytype safety - DON'T: category theory, monads, heavy FP libraries (fp-ts, Ramda), over-engineering, functional for its own sake
Why: The goal is maintainable, testable code - not academic purity. If a functional pattern makes code harder to understand, don't use it.
// ✅ GOOD - Simple, clear, functional
const activeUsers = users.filter(u => u.active);
const userNames = activeUsers.map(u => u.name);
// ❌ OVER-ENGINEERED - Unnecessary abstraction
const compose = <T>(...fns: Array<(arg: T) => T>) => (x: T) =>
fns.reduceRight((v, f) => f(v), x);
const withoutInactive = compose(
(users: readonly User[]): readonly User[] => users.filter(u => u.active),
(users: readonly User[]): readonly User[] => users.filter(u => !u.suspended),
)(users);
Self-Documenting Code and Useful Comments
Code should be clear through naming and structure. Prefer refactoring comments that merely narrate syntax, but keep comments that explain a non-obvious decision or constraint.
Comments worth keeping:
- JSDoc for public APIs when generating documentation
- "Why"-comments required by other skills: characterisation test file headers and SUSPICIOUS behavior markers (see the
characterisation-testsskill) - Constraints the code cannot express (e.g. a workaround pinned to an upstream bug, an ordering requirement imposed by an external system)
❌ WRONG - Comments explaining unclear code
// Get the user and check if active and has permission
function check(u: any) {
// Check user exists, then active, then permission
if (u) {
if (u.a) {
if (u.p) return true;
}
}
return false;
}
✅ CORRECT - Self-documenting code
function canUserAccessResource(user: User | undefined): boolean {
if (!user) return false;
if (!user.isActive) return false;
if (!user.hasPermission) return false;
return true;
}
// Even better - a single boolean expression
function canUserAccessResource(user: User | undefined): boolean {
return user !== undefined && user.isActive && user.hasPermission;
}
Check undefined explicitly in the boolean form: optional chaining (user?.isActive && user?.hasPermission) yields boolean | undefined and fails to compile under strict mode.
If a comment only restates what the code does, refactor instead: extract functions with descriptive names, use meaningful variable names, break complex logic into steps, or use type aliases for domain concepts.
✅ Acceptable JSDoc for public APIs
/**
* Registers a scenario for runtime switching.
* @throws {ValidationError} if scenario ID is duplicate
*/
export function registerScenario(definition: ScenaristScenario): void {
Choosing Array Methods and Loops
Prefer map, filter, reduce for transformations. They're declarative (what, not how) and naturally immutable.
✅ CORRECT - map, filter, reduce, and chaining
const scenarioIds = scenarios.map(s => s.id);
const activeScenarios = scenarios.filter(s => s.active);
const totalActiveMinutes = sessions
.filter(session => session.active)
.map(session => session.durationMinutes * session.repetitions)
.reduce((sum, minutes) => sum + minutes, 0);
When Loops Are Acceptable
Imperative loops are fine when:
- Early termination is essential (use
for...ofwithbreak) - Performance critical (measure first!)
- Side effects are necessary (logging, DOM manipulation)
Choose Array.find(), Array.some(), or Array.every() when those operations express the intent more directly; do not replace a clear loop merely to satisfy a style rule.
When to Use Options Objects
Use an options object when parameters form a meaningful group, several values share the same type, or optional arguments make ordering unclear. A small, stable function with obvious positional parameters can remain positional.
✅ CORRECT - Options object
type CreateReportOptions = {
reportId: string;
format: 'pdf' | 'csv';
locale: string;
timeZone: string;
includeCharts?: boolean;
sendEmail?: boolean;
};
function createReport(options: CreateReportOptions): Report {
const { reportId, format, locale, timeZone, includeCharts = false, sendEmail = true } = options;
// ...
}
// Call site - crystal clear
createReport({ reportId: 'report_123', format: 'pdf', locale: 'en-GB', timeZone: 'Europe/London', includeCharts: true });
Use positional parameters when the order is obvious, as in add(a, b), or a familiar high-frequency utility would become noisier with an options object. Switch to named options when same-typed or optional arguments make a call ambiguous; parameter count is a signal, not a fixed limit.
Pure Functions
Pure functions have no side effects and always return the same output for the same input:
- No side effects - doesn't mutate external state, modify arguments, or perform I/O
- Deterministic - same input → same output; no dependency on
Date.now(),Math.random(), or globals - Referentially transparent - can replace the call with its return value
Pure functions are testable (no setup/teardown), composable, predictable, cacheable, and parallelizable.
When Impurity Is Necessary
Some functions must be impure (I/O, randomness, side effects). Isolate them:
// ✅ CORRECT - Isolate impure functions at edges
// Pure core
function calculateTotalWeightGrams(parcels: ReadonlyArray<Parcel>): number {
return parcels.reduce((sum, parcel) => sum + parcel.weightGrams, 0);
}
// Impure shell (isolated)
async function saveShipment(shipment: Shipment): Promise<void> {
const totalWeightGrams = calculateTotalWeightGrams(shipment.parcels); // Pure
await database.save({ ...shipment, totalWeightGrams }); // Impure (I/O)
}
Pattern: Keep impure functions at system boundaries (adapters, ports). Keep core domain logic pure.
Early Returns Over Nesting
Treat deep nesting as a readability signal, not a numeric rule. When nested control flow obscures the main path, extract functions or flatten it with guard clauses. For worked examples, load resources/composition-patterns.md.
// ❌ WRONG - Nested conditions
if (user) {
if (user.isActive) {
if (user.hasPermission) {
// do something
}
}
}
// ✅ CORRECT - Early returns (guard clauses)
if (!user) return;
if (!user.isActive) return;
if (!user.hasPermission) return;
// do something
Result Type for Error Handling
Use a Result type when expected failures are part of the caller-facing contract and callers must handle both branches. Preserve an established exception, nullable-value, or framework error convention when it communicates the contract more clearly.
type Result<T, E = Error> =
| { readonly success: true; readonly data: T }
| { readonly success: false; readonly error: E };
// Usage
function processBatch(batch: Batch): Result<BatchRun> {
if (batch.itemCount <= 0) {
return { success: false, error: new Error('Batch must contain an item') };
}
const run = executeBatch(batch);
return { success: true, data: run };
}
// Caller handles bo
---
*Content truncated.*
When not to use it
- →When functional patterns make code harder to understand
- →Over-applying heavy FP abstractions like monads
Limitations
- →Avoid heavy FP libraries unless the project requires them
How it compares
It focuses on practical 'Functional Light' patterns to improve maintainability rather than academic category theory abstractions.
Compared to similar skills
functional side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| functional (this skill) | 3 | 2mo | No flags | Intermediate |
| typescript-expert | 10 | 6mo | Review | Advanced |
| agent-coder | 3 | 6mo | No flags | Intermediate |
| modern-javascript-patterns | 2 | 4mo | No flags | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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