Swift Performance Optimization Skill

Swift Performance Optimization Skill is a skill for Claude Code, Codex from termio-sh/termio. It costs 68 tokens per session (1,439 once invoked), scanned A, original, MIT.

A guide to investigating and improving the performance of Swift apps and other Apple-platform software. It uses measurements from real release builds to examine issues such as slow launches, high memory use, scrolling problems, energy use, networking, and concurrency.

In plain words
What is it for?
It is for designing Instruments experiments, comparing before-and-after traces, locating bottlenecks, and adding performance regression thresholds where repeatable tests are possible.
Why use it?
It prevents speculative optimisations by requiring a reproducible baseline, a targeted measurement, and checks that changes do not harm correctness, accessibility, or memory and energy use.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/termio-sh/termio/swift-performance-optimization-skill
Any agent
npx skills add termio-sh/termio --skill swift-performance-optimization-skill
Clone the repo
git clone --depth 1 https://github.com/termio-sh/termio

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for Swift Performance Optimization Skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/termio-sh/termio/swift-performance-optimization-skill.svg)](https://agentmods.dev/skills/termio-sh/termio/swift-performance-optimization-skill)
Your own site
<a href="https://agentmods.dev/skills/termio-sh/termio/swift-performance-optimization-skill"><img src="https://agentmods.dev/badge/skills/termio-sh/termio/swift-performance-optimization-skill.svg" alt="Measured on agentmods" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,439 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00068 $0.01439
Opus 5 $0.00034 $0.00720
Sonnet 5 $0.00014 $0.00288
Haiku 4.5 $0.00007 $0.00144

Measured 4d ago against content hash f6e43a81a161, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Swift Performance Optimization Skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/swift-performance-optimization-skill/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Swift Performance Optimization Skill

Optimize from evidence. Preserve behavior, accessibility, data correctness, and lifecycle safety while changing performance characteristics.

Baseline

The copy-ready examples use Xcode 16, Swift 6 language mode with strict concurrency, and iOS 17. Most principles apply to older targets; verify each API against the app's actual minimum. Material from the OS 27 development cycle is beta relative to stable Xcode 26.6 and requires an explicit request, beta labeling, stable fallback, and availability gate.

Measurement workflow

  1. Define a user-visible symptom and a reproducible scenario.
  2. Record device, OS, build configuration, dataset, thermal state, and network conditions.
  3. Measure an optimized Release build on representative hardware. Simulator-only timing is not release evidence.
  4. Select the instrument that answers the hypothesis.
  5. Save a baseline trace or metric, change one variable, then repeat the same scenario.
  6. Confirm that the bottleneck moved and no memory, energy, correctness, or accessibility regression appeared.
  7. Add a regression threshold where the workload is stable enough to automate.

Useful tools include:

Symptom First evidence source
CPU-bound work Time Profiler; inspect heavy stacks and self time
SwiftUI update cost SwiftUI instrument plus Time Profiler
Scroll or animation stalls Animation Hitches, Core Animation, signposts
Growth or leaks Allocations, Leaks, Memory Graph, memgraphs
Slow launch App Launch template and launch signposts
Battery or thermal issues Energy Log and device testing
Field regressions MetricKit payloads and app-specific telemetry

Use os_signpost or signposter intervals around important operations so traces answer product questions rather than only showing raw symbols.

Lifetime and memory safety

  • A capture is a cycle only when the closure is retained along a path back to its owner. Do not add [weak self] mechanically to every closure.
  • Use weak when the owner may legitimately disappear before the callback. Use unowned only when the lifetime invariant is proven and documented; a wrong assumption traps.
  • Prefer structured async functions over storing completion closures.
  • Cancel owned tasks and invalidate repeating timers when the owner stops needing them and during teardown.
  • Never use [unowned self] in a repeating timer merely to silence a cycle.

Read the full file on GitHub · 137 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 137 lines · 68 tokens per session scan A f6e43a81a161

Subscribe to this mod's changes

Swift Performance Optimization Skill is a skill published in the GitHub repository termio-sh/termio (359 stars, last pushed 4d ago), licensed MIT. It adds 68 tokens to every session and 1,439 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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