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 skills add ComeOnOliver/skillshub --skill axiom-swift-performancegit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote 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/comeonoliver/skillshub/axiom-swift-performance)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/axiom-swift-performance"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-swift-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/comeonoliver/skillshub/axiom-swift-performance"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-swift-performance.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00036 | $0.08345 |
| Opus 5 | $0.00018 | $0.04172 |
| Sonnet 5 | $0.00007 | $0.01669 |
| Haiku 4.5 | $0.00004 | $0.00834 |
Grade A, and why
axiom-swift-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 7d 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 — 1,238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swift Performance Optimization
Purpose
Core Principle: Optimize Swift code by understanding language-level performance characteristics—value semantics, ARC behavior, generic specialization, and memory layout—to write fast, efficient code without premature micro-optimization.
Swift Version: Swift 6.2+ (for InlineArray, Span, @concurrent)
Xcode: 16+
Platforms: iOS 18+, macOS 15+
Related Skills:
axiom-performance-profiling— Use Instruments to measure (do this first!)axiom-swiftui-performance— SwiftUI-specific optimizationsaxiom-build-performance— Compilation speedaxiom-swift-concurrency— Correctness-focused concurrency patterns
When to Use This Skill
✅ Use this skill when
- App profiling shows Swift code as the bottleneck (Time Profiler hotspots)
- Excessive memory allocations or retain/release traffic
- Implementing performance-critical algorithms or data structures
- Writing framework or library code with performance requirements
- Optimizing tight loops or frequently called methods
- Dealing with large data structures or collections
- Code review identifying performance anti-patterns
Quick Decision Tree
Performance issue identified?
│
├─ Profiler shows excessive copying?
│ └─ → Part 1: Noncopyable Types
│ └─ → Part 2: Copy-on-Write
│
├─ Retain/release overhead in Time Profiler?
│ └─ → Part 4: ARC Optimization
│
├─ Generic code in hot path?
│ └─ → Part 5: Generics & Specialization
│
├─ Collection operations slow?
│ └─ → Part 7: Collection Performance
│
├─ Async/await overhead visible?
│ └─ → Part 8: Concurrency Performance
│
├─ Struct vs class decision?
│ └─ → Part 3: Value vs Reference
│
└─ Memory layout concerns?
└─ → Part 9: Memory Layout
The Four Principles of Swift Performance
From WWDC 2024-10217: Swift's low-level performance characteristics come down to four areas. Each maps to a Part in this skill.
| Principle | What It Costs | Skill Coverage |
|---|---|---|
| Function Calls | Dispatch overhead, optimization barriers | Part 5 (Generics), Part 6 (Inlining) |
| Memory Allocation | Stack vs heap, allocation frequency | Part 3 (Value vs Reference), Part 7 (Collections) |
| Memory Layout | Cache locality, padding, contiguity | Part 9 (Memory Layout), Part 11 (Span) |
| Value Copying | COW triggers, defensive copies, ARC traffic | Part 1 (Noncopyable), Part 2 (COW), Part 4 (ARC) |
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.
- 7d ago First seen · 1,238 lines · 36 tokens per session scan A 1b0ea92ff63f
axiom-swift-performance is a skill published in the GitHub repository ComeOnOliver/skillshub (63 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 8,345 once invoked, about $0.0002 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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