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.
npx agentmods add skills/gaelic-ghost/socket/swiftui-performance-auditnpx skills add gaelic-ghost/socket --skill swiftui-performance-auditgit clone --depth 1 https://github.com/gaelic-ghost/socketWrote 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.
[](https://agentmods.dev/skills/gaelic-ghost/socket/swiftui-performance-audit)<a href="https://agentmods.dev/skills/gaelic-ghost/socket/swiftui-performance-audit"><img src="https://agentmods.dev/badge/skills/gaelic-ghost/socket/swiftui-performance-audit.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00061 | $0.00760 |
| Opus 5 | $0.00030 | $0.00380 |
| Sonnet 5 | $0.00012 | $0.00152 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
swiftui-performance-audit 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SwiftUI Performance Audit
Purpose
Make performance work evidence-led: inspect the smallest relevant SwiftUI data-flow and view slice first, name code-level suspicion separately from runtime proof, then hand trace capture to the existing Xcode or SwiftPM testing workflow.
When To Use
- Use for slow rendering, dropped frames, broad view updates, unstable list identity, expensive body work, layout churn, image pressure, broad animation, high CPU, or memory growth in SwiftUI.
- Do not use this skill as a generic style review, a replacement for Instruments, or a reason to introduce caches, view models, stores, or coordinators.
Single-Path Workflow
- Apply the Apple docs gate through
explore-apple-swift-docs. Confirm current Observation and SwiftUI behavior before proposing a performance change. - Collect the target view, reproduction steps, data flow, device/simulator, build configuration, and the observed symptom.
- Classify the symptom as invalidation, identity churn, body computation, layout, image, animation, CPU, or retained-memory pressure.
- Read
references/code-smells-and-remediation.mdand identify only code-backed suspicions. Mark each finding as suspected until a capture proves it. - Prefer narrow ownership, stable identity, precomputation outside
body, downsampled images, localized state, and reduced layout complexity. Do not useequatable()or@Stateas an unmeasured cache. - When code inspection is insufficient, hand trace capture to
xcode-testing-workflowfor scheme/destination work orswift-package-testing-workflowfor package-first workloads. - Compare the same reproduction before and after the change. Report CPU, frame behavior, allocations, and view-update evidence only when captured.
Inputs
- target view/feature, smallest reproduction, and symptom
- state/observation/data-flow slice and relevant identities
- platform, device or simulator, build configuration, and any existing trace
Outputs
- findings labeled as code-level suspicion or trace-backed evidence
- smallest remediation proposal with ownership and behavior preserved
- profiling handoff or before/after validation result
What ships with it
3 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.
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.
- yesterday First seen · 57 lines · 61 tokens per session scan A 106c0d8b1ed4
swiftui-performance-audit is a skill published in the GitHub repository gaelic-ghost/socket (7 stars, last pushed 9d ago), licensed Apache-2.0. It adds 61 tokens to every session and 760 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 2026-09-03.
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