# CharlesWiltgen/Axiom

Battle-tested Claude Code skills for modern xOS (iOS, iPadOS, watchOS, tvOS) development

Repository: https://github.com/CharlesWiltgen/Axiom
Canonical: https://ross.abutalabs.com/products/charleswiltgen-axiom
Homepage: https://charleswiltgen.github.io/Axiom/
Language: Go
License: MIT
License Family: permissive
Last push: 2026-08-25T12:39:33+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 19
- inputs: {"age_days": 276, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1139, forks 83 (observed 2026-08-28T04:03:44.063905+00:00)

## What it is
Axiom is a collection of 273 battle-tested skills, 42 agents, 17 commands, and bundled developer tools (xclog, xcsym, xcui, xcprof) that give AI coding assistants deep Apple OS development expertise. It installs as a native plugin for Claude Code and Cursor, or via MCP, Pi, and Xcode integrations, covering Swift 6, SwiftUI, concurrency, accessibility, data migrations, and Apple Intelligence.

## Use cases
- give claude code expertise in swiftui and swift 6 development
- catch memory leaks and data races in my ios app before shipping
- symbolicate crash logs from metrickit and ips files automatically
- prevent data loss when migrating my app's database
- audit my app for voiceover and dynamic type accessibility issues
- capture simulator console logs for my ai coding assistant
- analyze xctrace performance traces without opening instruments
- implement apple intelligence foundation models with generable

## When to choose
- you build iOS, iPadOS, watchOS, or tvOS apps with an AI coding assistant like Claude Code, Cursor, or Codex
- you want curated, TDD-tested Apple platform guidance instead of generic AI answers
- you need bundled diagnostics tools for logs, crashes, UI testing, and performance profiling

## When to avoid
- you develop for Android, web, or non-Apple platforms
- you don't use AI coding assistants or IDEs that support skills/MCP
- you need a runtime library or framework to link into your app rather than assistant knowledge

## Facets
- artifact type: plugin
- maturity: active
- function: developer-tools, documentation, testing, monitoring, accessibility, machine-learning
- domain: developer-tools, mobile-development, apple-ecosystem, artificial-intelligence, education
- platform: cross-platform, cli, editor-plugin
- tags: claude-code, ai-coding-assistant, skills, swift, swiftui, apple-platforms, mcp, agents, diagnostics, xcode, macos, ios

## Member repositories
- CharlesWiltgen/Axiom (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.063905+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:35:49.735470+00:00, confidence not recorded.
  - readme: https://github.com/CharlesWiltgen/Axiom (fetched 2026-08-28T04:03:44.063905+00:00, sha 6bc62a398c84)
  - homepage: https://charleswiltgen.github.io/Axiom/ (fetched 2026-08-29T12:40:46.403792+00:00, sha 8fd262b70045)
- Data as of 2026-08-30T08:39:29.467469+00:00.