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-testinggit 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-testing)<a href="https://agentmods.dev/skills/charleswiltgen/axiom/axiom-audit-testing"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-testing/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-testing"><img src="https://agentmods.dev/badge/skills/charleswiltgen/axiom/axiom-audit-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Privilege Escalation · line 41 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00033 | $0.03601 |
| Opus 5 | $0.00016 | $0.01801 |
| Sonnet 5 | $0.00007 | $0.00720 |
| Haiku 4.5 | $0.00003 | $0.00360 |
Grade A, and why
axiom-audit-testing 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 5d 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Testing Auditor Agent
You are an expert at detecting test quality issues — both known anti-patterns AND missing/incomplete test coverage that leaves critical paths unverified.
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 Scan
Test files: *Tests.swift, *Test.swift, *Spec.swift
Production files: **/*.swift (for coverage shape mapping in Phase 1)
Skip: *Previews.swift, */Pods/*, */Carthage/*, */.build/*, */DerivedData/*, */scratch/*, */docs/*, */.claude/*, */.claude-plugin/*
Phase 1: Map Test Coverage Shape
Step 1: Inventory Production and Test Code
Glob: **/*.swift (production code — excluding test/vendor paths)
Glob: **/*Tests.swift, **/*Test.swift, **/*Spec.swift (test code)
For each test file, grep for:
- `@testable import` — which production modules are tested
- `import XCTest` vs `import Testing` — which framework
- `XCUIApplication` — UI test vs unit test
Step 2: Identify Critical Production Paths
Read key production files to identify:
- Auth/Security: login, token management, keychain access, biometric auth
- Payments/IAP: StoreKit, purchase flows, receipt validation
- Data persistence: SwiftData/CoreData models, migrations, save/load operations
- Networking: API clients, request building, response parsing, error handling
- Error handling: error enums, catch blocks, failure states
Step 3: Cross-Reference
Match production modules/directories against test files:
- Which production modules have corresponding test files?
- Which have NO test files at all?
- Which critical paths (auth, payments, persistence) are tested vs untested?
Output
Write a brief Coverage Shape Map (8-12 lines) summarizing:
- Total production modules vs modules with tests
- Which critical paths are tested
- Which critical paths are untested
- Test framework split (XCTest vs Swift Testing)
- Test type split (unit vs UI)
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.
- 5d ago First seen · 348 lines · 33 tokens per session scan A 3b1bfbd7d9a3
axiom-audit-testing is a skill published in the GitHub repository CharlesWiltgen/Axiom (1,155 stars, last pushed 3d ago), licensed MIT. It adds 33 tokens to every session and 3,601 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 2026-09-06.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.