Swift Runtime Performance

Swift Runtime Performance

Purpose

Use this skill to review Swift code for runtime-level performance costs without turning every abstraction into a problem. Focus on concrete costs such as allocation, ARC traffic, dispatch, specialization, copying, optimizer visibility, and unsafe memory boundaries.

This skill should help the agent distinguish real hot-path runtime costs from theoretical micro-optimizations.

When to use this skill

Use this skill when the task involves Swift runtime behavior such as:

• heap allocation, stack vs heap storage, object layout, boxed values, or closure contexts;
• ARC retain/release traffic, ownership, lifetime, weak/unowned references, or closure captures;
• direct dispatch, class dispatch, Objective-C dispatch, witness dispatch, or dynamic dispatch in hot paths;
• any Protocol, some Protocol, generics, type erasure, specialization, or unspecialized hot code;
• copy-on-write collections, large values, custom COW storage, or repeated copies;
• optimized SIL inspection, compiler optimization, inlining, devirtualization, or specialization evidence;
• unsafe Swift, pointer lifetime, memory binding, aliasing, buffer mutation, or safe wrappers around unsafe regions;
• module boundaries that affect optimizer visibility, @inlinable, @usableFromInline, @frozen, or public API resilience trade-offs.

When not to use this skill

Do not use this skill for:

• general Swift syntax or API usage questions with no runtime performance concern;
• app launch investigations where the main issue is pre-main, dyld, static initializers, SDK startup, first frame, or first interaction;
• SwiftUI performance issues where the main issue is identity, invalidation, state scope, layout, drawing, animation, or scrolling;
• Swift Concurrency issues where the main issue is task lifetime, actor isolation, MainActor responsiveness, cancellation, AsyncSequence cleanup, reentrancy, or executor behavior;
• profiling workflow questions where the main task is choosing tools, interpreting traces, designing signposts, XCTest metrics, MetricKit, or production signals;
• broad architecture questions unless there is a specific runtime cost in a hot path.

If another skill is more specific, route there first and use this skill only for the runtime subproblem.

Neighbor skill boundaries

Use these boundaries before applying runtime advice:

• Use swift-concurrency-performance when the task centers on actors, tasks, MainActor, cancellation, AsyncSequence, continuations, task groups, executor behavior, or responsiveness under async work.
• Use ios-launch-performance when the task centers on cold launch, warm launch, pre-main, dyld, framework loading, static initializers, AppDelegate, SwiftUI App, first frame, first interaction, or launch metrics.
• Use swiftui-performance when the task centers on SwiftUI body evaluation, invalidation, identity, state ownership, dependency scope, List, LazyVStack, layout, drawing, animation, or lifecycle work in views.
• Use ios-performance-profiling when the task centers on choosing Instruments templates, interpreting traces, XCTest metrics, MetricKit, signposts, memory graphs, hangs, hitches, CPU, allocations, disk I/O, networking, or production telemetry.
• Use this skill when a neighboring task reveals a Swift runtime-level cost such as allocation churn, ARC traffic, existential boxing, witness dispatch, unspecialized generics, repeated COW copies, or unsafe memory boundaries.

Core principle

Do not optimize based only on how the source code looks.

First identify:

1. whether the code is on a hot path;
2. what runtime cost is suspected;
3. whether the cost is visible in measurement, compiler output, or a small benchmark;
4. whether the proposed change preserves semantics and improves the measured path;
5. what trade-off the change introduces.

A runtime optimization is useful only when it reduces cost in a path that matters.

Runtime cost taxonomy

Classify the suspected issue before recommending a change.

Use these categories:

• Allocation — heap objects, boxes, closure contexts, existential containers, temporary objects, intermediate collections.
• ARC — retain/release traffic, closure captures, weak/unowned access, bridged object lifetime, reference-backed value storage.
• Dispatch — dynamic dispatch, witness dispatch, Objective-C dispatch, closure calls, missed devirtualization.
• Existentials and generics — any, some, type erasure, generic specialization, unspecialized hot paths, boxing.
• Copying — COW storage, large values, repeated collection mutation, defensive copies, bridging copies.
• Compiler optimization — inlining, specialization, devirtualization, module visibility, resilience boundaries, optimized SIL output.
• Unsafe boundary — pointer lifetime, binding, alignment, aliasing, mutation, escaping buffers, safe wrappers.
• Module boundary — public API visibility, @inlinable, @usableFromInline, @frozen, ABI resilience, optimizer visibility.

Core workflow

1. Locate the user-visible symptom. Identify whether the concern is latency, scrolling, repeated work, memory growth, CPU use, binary size, launch impact, or theoretical code review risk.
2. Confirm the hot path. Ask whether the code runs frequently, touches many elements, blocks interaction, runs during startup, or appears in measurements.
3. Classify the suspected cost. Use the runtime cost taxonomy instead of saying the code is vaguely “slow.”
4. Look for evidence. Prefer Instruments, Allocations, Time Profiler, optimized SIL, benchmark output, XCTest performance tests, or production signals.
5. Separate semantics from mechanics. Do not remove an abstraction only because it has a possible cost. Check what design purpose it serves.
6. Propose the smallest safe change. Prefer local changes that reduce allocation, ARC traffic, dispatch, copying, or missed specialization without damaging API clarity.
7. Explain trade-offs. Mention readability, API flexibility, testability, binary size, ABI stability, build time, or maintenance cost.
8. Validate the result. Do not call the optimization successful without a before/after validation path.

Decision rules

If the code is not on a hot path

Avoid low-level rewrites.

Explain that the concern may be theoretically valid but unlikely to matter without evidence. Suggest measurement only if the path is suspected to affect user-visible performance.

If the issue is allocation

Check whether allocation comes from:

• class instances;
• closure contexts;
• boxed variables;
• existential storage;
• type erasure wrappers;
• intermediate arrays, dictionaries, sets, strings, or data buffers;
• bridging between Swift and Objective-C/Foundation types.

Prefer reducing repeated allocation over replacing every reference type.

If the issue is ARC

Check ownership and lifetime before recommending changes.

Look for repeated retain/release traffic in loops, closure captures of large owners, unnecessary weak access in hot paths, and reference-backed value storage. Do not treat weak and unowned as performance fixes. They are ownership tools first.

If the issue is dispatch

Ask whether dynamic dispatch is intentional.

Prefer final for app-level classes that are not designed for inheritance. Use generics, concrete types, or internal implementation details only when they preserve the intended abstraction and matter in the measured path.

Do not flatten useful polymorphism without evidence.

If the issue is any Protocol

Ask whether runtime heterogeneity is required.

any Protocol is not automatically wrong. It is appropriate when values of different concrete types must be stored or passed uniformly. Consider generics or opaque types when the hot path can remain statically typed.

If the issue is generics

Check whether specialization actually happens.

Generics help most when the optimizer can see enough implementation detail to specialize the hot path. Module boundaries, public resilience, large functions, or type erasure can limit that.

If the issue is copy-on-write

Check mutation patterns and uniqueness boundaries.

Repeated mutation of COW values can be cheap when storage is uniquely referenced and expensive when it repeatedly copies. Custom COW must preserve value semantics and document thread-safety assumptions.

If the issue is SIL or compiler output

Use optimized SIL, not Debug SIL, for performance conclusions.

Look for evidence such as allocation instructions, retain/release traffic, witness dispatch, existential opening, closure creation, missed specialization, and missed devirtualization. Treat SIL as evidence, not as an excuse to overfit source code to one compiler version.

If the issue is unsafe Swift

Do not recommend unsafe code as a first step.

Use unsafe APIs only when safe APIs cannot express the operation efficiently enough, measurement shows the abstraction cost matters, and the unsafe region can be kept small behind a safe wrapper.

If the issue is module boundaries

Separate runtime optimization from architecture and build-time concerns.

Use @inlinable, @usableFromInline, and @frozen only when the API commitment is acceptable. These attributes are not generic “make it faster” switches.