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 Kasempiternal/axiom-v2 --skill ax-swift-perfgit clone --depth 1 https://github.com/Kasempiternal/axiom-v2Wrote 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/kasempiternal/axiom-v2/ax-swift-perf)<a href="https://agentmods.dev/skills/kasempiternal/axiom-v2/ax-swift-perf"><img src="https://agentmods.dev/badge/skills/kasempiternal/axiom-v2/ax-swift-perf/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/kasempiternal/axiom-v2/ax-swift-perf"><img src="https://agentmods.dev/badge/skills/kasempiternal/axiom-v2/ax-swift-perf.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 | $0.00040 | $0.03306 |
| Opus 5 | $0.00020 | $0.01653 |
| Sonnet 5 | $0.00008 | $0.00661 |
| Haiku 4.5 | $0.00004 | $0.00331 |
Grade A, and why
ax-swift-perf 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 11d 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 — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swift Performance
Quick Patterns
Reserve Capacity
// BAD: ~14 reallocations for 10000 appends
var array: [Int] = []
for i in 0..<10000 { array.append(i) }
// GOOD: Single allocation
var array: [Int] = []
array.reserveCapacity(10000)
for i in 0..<10000 { array.append(i) }
ContiguousArray (15% faster)
// For pure Swift (no ObjC bridging)
var array: ContiguousArray<Int> = []
array.reserveCapacity(count)
Avoid Defensive Copies
class DataStore { var items: [Item] = [] }
// BAD: Swift may defensively copy store.items each iteration
func process(_ store: DataStore) {
for item in store.items { handle(item) }
}
// GOOD: One explicit copy
func process(_ store: DataStore) {
let items = store.items
for item in items { handle(item) }
}
Unowned vs Weak (2x faster)
// Use unowned when child lifetime < parent lifetime
class Child {
unowned let parent: Parent // No atomic overhead
}
Generic over Existential (10x faster)
// BAD: Existential container + dynamic dispatch
func drawAll(shapes: [any Drawable]) { for s in shapes { s.draw() } }
// GOOD: Specializable, static dispatch
func drawAll<T: Drawable>(shapes: [T]) { for s in shapes { s.draw() } }
Batch Actor Calls
// BAD: 10000 actor hops
for _ in 0..<10000 { await counter.increment() }
// GOOD: Single actor hop
await counter.incrementBatch(10000)
Lazy Evaluation
// BAD: Processes entire array
let result = array.map { expensive($0) }.filter { $0 > 0 }.first
// GOOD: Stops at first match
let result = array.lazy.map { expensive($0) }.filter { $0 > 0 }.first
Decision Tree
Performance issue identified?
|
|-- Profiler shows excessive copying?
| |-- Large types copied repeatedly -> Noncopyable Types
| +-- COW triggers in loop -> Copy-on-Write optimization
|
|-- Retain/release overhead in Time Profiler?
| +-- ARC Optimization (unowned, capture only what's needed)
|
|-- Generic code in hot path?
| |-- Using `any Protocol`? -> Switch to `some` or generic <T>
| +-- Cross-module? -> @inlinable, @_specialize
|
|-- Collection operations slow?
| |-- Many reallocations? -> reserveCapacity
| |-- Pure Swift? -> ContiguousArray
| |-- Fixed size? -> InlineArray
| +-- Short-circuit needed? -> .lazy
|
|-- Async/await overhead visible?
| |-- Many actor hops? -> Batch operations
| |-- Task per item? -> TaskGroup instead
| +-- Sync func marked async? -> Remove async
|
|-- Struct vs class decision?
| |-- <= 64 bytes, no identity -> Struct
| |-- > 64 bytes or shared -> Class
| +-- Large + value semantics -> COW wrapper
|
+-- Memory layout concerns?
|-- Struct padding -> Reorder fields (largest first)
|-- Cache misses -> ContiguousArray (not linked list)
+-- Runtime exclusivity checks -> Move to struct
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.
- 11d ago First seen · 472 lines · 40 tokens per session scan A 9865e55a4bd4
ax-swift-perf is a skill published in the GitHub repository Kasempiternal/axiom-v2 (4 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 3,306 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 2026-08-31.
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