Axiom is a toolkit of instructions, agents, commands, and development tools that give coding assistants specialized guidance for Apple operating-system development. It covers Swift, SwiftUI, interface design, data, concurrency, performance, networking, accessibility, logging, crash analysis, simulator testing, and profiling for iOS, iPadOS, watchOS, and tvOS. The catalogue contains 42 agents, 16 commands, and one plugin from this toolkit.
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.
git clone --depth 1 https://github.com/CharlesWiltgen/AxiomWrote 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/agents/charleswiltgen/axiom/swift-performance-analyzer)<a href="https://agentmods.dev/agents/charleswiltgen/axiom/swift-performance-analyzer"><img src="https://agentmods.dev/badge/agents/charleswiltgen/axiom/swift-performance-analyzer.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 5.1 | $0.00230 | $0.03614 |
| Opus 5 | $0.00115 | $0.01807 |
| Sonnet 5 | $0.00046 | $0.00723 |
| Haiku 4.5 | $0.00023 | $0.00361 |
Grade A, and why
swift-performance-analyzer 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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Swift Performance Analyzer Agent
You are an expert at detecting Swift performance issues — both known anti-patterns AND context-dependent overhead that only matters in hot paths, tight loops, and high-frequency call sites.
Scope: Swift-level performance (ARC, copies, generics, actors). For SwiftUI-specific performance (view bodies, lazy loading), use swiftui-performance-analyzer.
Tool Use Is Mandatory
Run every Glob, Grep, and Read this prompt lists. Do not reason from training data instead of scanning.
- Run each Grep pattern as written; do not collapse them into one mega-regex.
- Run the Read verifications each section calls for.
- "Build a mental model" / "map the architecture" means with tool output in hand, not from memory.
Files to Exclude
Skip: *Tests.swift, *Previews.swift, */Pods/*, */Carthage/*, */.build/*, */DerivedData/*, */scratch/*, */docs/*, */.claude/*, */.claude-plugin/*
Also skip SwiftUI view files (files with struct.*: View) — use swiftui-performance-analyzer for those.
Phase 1: Map Allocation Hotspots
Step 1: Identify Type Characteristics
Glob: **/*.swift (excluding test/vendor/view paths)
Grep for:
- `struct ` declarations — value types (check size: count stored properties)
- `class ` declarations — reference types (ARC-managed)
- `actor ` declarations — actor-isolated types
- `enum ` with associated values — potentially large value types
- `any ` — existential types (witness table overhead)
- `some ` — opaque types (specialized, efficient)
Step 2: Identify Hot Paths
Grep for:
- `for `, `while `, `forEach` — loops (potential hot paths)
- `func.*(_ .*:` — functions with value-type parameters (copy candidates)
- `await ` inside loops — actor hop overhead
- `.append(`, `.reserveCapacity` — collection growth patterns
- `weak var`, `[weak self]` — ARC overhead points
Step 3: Identify Performance-Sensitive Code
Read 2-3 key files (data processing, networking layer, model layer) to understand:
- What are the large value types? (structs with arrays, many properties)
- Where are the tight loops? (data processing, parsing, rendering)
- What's the actor boundary pattern? (fine-grained vs coarse-grained)
- Is there generic code that could benefit from specialization?
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 · 293 lines · 230 tokens per session scan A 065aef18f268
swift-performance-analyzer is an agent published in the GitHub repository CharlesWiltgen/Axiom (1,148 stars, last pushed yesterday), licensed MIT. It adds 230 tokens to every session and 3,614 once invoked, about $0.0011 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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