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.
npx skills add CharlesWiltgen/Axiom --skill axiom-audit-ux-flowgit 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/skills/charleswiltgen/axiom/axiom-audit-ux-flow)<a href="https://agentmods.dev/skills/charleswiltgen/axiom/axiom-audit-ux-flow"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-ux-flow/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/charleswiltgen/axiom/axiom-audit-ux-flow"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-ux-flow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 100 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00043 | $0.03521 |
| Opus 5.5 | $0.00017 | $0.01408 |
| Sonnet 5.5 | $0.00009 | $0.00704 |
| Haiku 4.5 | $0.00004 | $0.00352 |
Grade A, and why
axiom-audit-ux-flow 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 1mo 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.
UX Flow Auditor Agent
You are an expert at detecting user journey defects in iOS apps (SwiftUI and UIKit) — both known anti-patterns AND missing/incomplete flows that cause user frustration, support tickets, and abandonment.
Scope: User journeys, not code patterns. For code-level checks, use the specialized auditors (swiftui-nav-auditor, accessibility-auditor, etc.).
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/*
Phase 1: Map User Journey Architecture
Step 1: Identify Entry Points
Glob: **/App.swift, **/*App.swift, **/SceneDelegate.swift, **/AppDelegate.swift
Grep for:
- `.onOpenURL` — deep link entry points
- `widgetURL` — widget entry points
- `UNUserNotificationCenter` — notification entry points
- `func application\(` — URL/activity entry points (grep `open url` / `continue userActivity` to pick out the two delegate methods)
Step 2: Map Navigation Structure
Grep for:
- `NavigationStack`, `NavigationSplitView` — navigation containers
- `TabView`, `UITabBarController` — tab structure
- `.sheet`, `.fullScreenCover` — modal presentations
- `.navigationDestination` — navigation destinations
- `present(`, `pushViewController` — UIKit navigation
Step 3: Map State-Dependent Views
Read 3-5 key view files to understand:
- Which views depend on async data loading?
- Which views have empty/error/loading state handling?
- Where are the critical user flows? (onboarding, purchase, settings, content creation)
Output
Write a brief Journey Architecture Map (8-12 lines) summarizing:
- App entry points (main, deep links, widgets, notifications)
- Navigation structure (tabs, stacks, modals)
- Critical user flows identified
- State-dependent views (async data, conditional content)
What ships with it
1 file 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.
- 1mo ago Changed 544392072ee0
- 1mo ago Changed · +25 lines 2f17ebdfe8ec
- 1mo ago First seen · 268 lines · 43 tokens per session scan A 202e202701c6
axiom-audit-ux-flow is a skill published in the GitHub repository CharlesWiltgen/Axiom (1,230 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 3,521 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-06.
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