Borrowing it
Nothing to install: this file belongs to zish-rob-crur/nucleus-apple-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zish-rob-crur/nucleus-apple-mcp/main/.agents/skills/swiftui-performance/SKILL.mdgit clone --depth 1 https://github.com/zish-rob-crur/nucleus-apple-mcpWrote 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/zish-rob-crur/nucleus-apple-mcp/swiftui-performance)<a href="https://agentmods.dev/skills/zish-rob-crur/nucleus-apple-mcp/swiftui-performance"><img src="https://agentmods.dev/badge/skills/zish-rob-crur/nucleus-apple-mcp/swiftui-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zish-rob-crur/nucleus-apple-mcp/swiftui-performance"><img src="https://agentmods.dev/badge/skills/zish-rob-crur/nucleus-apple-mcp/swiftui-performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00059 | $0.03924 |
| Opus 5 | $0.00030 | $0.01962 |
| Sonnet 5 | $0.00012 | $0.00785 |
| Haiku 4.5 | $0.00006 | $0.00392 |
Grade A, and why
swiftui-performance 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SwiftUI Performance
Audit SwiftUI view performance end-to-end, from instrumentation and baselining to root-cause analysis and concrete remediation steps.
Contents
- Workflow Decision Tree
- 1. Code-First Review
- 2. Guide the User to Profile
- 3. Analyze and Diagnose
- 4. Remediate
- Common Code Smells (and Fixes)
- 5. Verify
- Outputs
- Instruments Profiling
- Identity and Lifetime
- Lazy Loading Patterns
- State and Observation Optimization
- Common Mistakes
- Review Checklist
- References
Workflow Decision Tree
- If the user provides code, start with "Code-First Review."
- If the user only describes symptoms, ask for minimal code/context, then do "Code-First Review."
- If code review is inconclusive, go to "Guide the User to Profile" and ask for a trace or screenshots.
1. Code-First Review
Collect:
- Target view/feature code.
- Data flow: state, environment, observable models.
- Symptoms and reproduction steps.
Focus on:
- View invalidation storms from broad state changes.
- Unstable identity in lists (
idchurn,UUID()per render). - Top-level conditional view swapping (
if/elsereturning different root branches). - Heavy work in
body(formatting, sorting, image decoding). - Layout thrash (deep stacks,
GeometryReader, preference chains). - Large images without downsampling or resizing.
- Over-animated hierarchies (implicit animations on large trees).
Provide:
- Likely root causes with code references.
- Suggested fixes and refactors.
- If needed, a minimal repro or instrumentation suggestion.
2. Guide the User to Profile
Explain how to collect data with Instruments:
- Use the SwiftUI template in Instruments.
- Profile a Release build on a real device when possible.
- Reproduce the exact interaction (scroll, navigation, animation).
- Capture SwiftUI timeline and Time Profiler.
- Export or screenshot the relevant lanes and the call tree.
What ships with it
5 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.
- 12d ago First seen · 476 lines · 59 tokens per session scan A 17b4548865e8
swiftui-performance is a skill published in the GitHub repository zish-rob-crur/nucleus-apple-mcp (20 stars, last pushed 7d ago), licensed MIT. It adds 59 tokens to every session and 3,924 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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