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Agent Skill · 开发

axiom-ai(公理艾)

axiom-ai 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

axiom-ai 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 9,402。

总安装 2,728周安装 116GitHub Stars 873下载 956核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:axiom-ai(公理艾)
来源仓库:https://github.com/charleswiltgen/axiom
仓库路径:skills/axiom-ai
安装命令:
npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ai
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ai

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

处理 Apple Intelligence 和 Foundation Models 相关工作,作为 AI 路由核心技能。支持 LanguageModelSession 和 @Generable 结构化输出。区分设备端生成与自定义 ML 模型部署路径。

安装命令:npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ai axiom-ai 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/axiom-ai

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ai;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

axiom-ai 怎么安装?

在终端执行 npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ai。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

axiom-ai 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

axiom-ai 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

axiom-ai 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

axiom-ai 用的人多吗?

公开数据是累计安装 2,728 次,近一周安装 116 次,下载 956 次,GitHub 873 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

axiom-ai 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
axiom-ai

公理艾 · 本页

2,728873需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Apple Intelligence & AI

You MUST use this skill for ANY Apple Intelligence or Foundation Models work.

When to Use

Use this router when:

  • Implementing Apple Intelligence features
  • Using Foundation Models
  • Working with LanguageModelSession
  • Generating structured output with @Generable
  • Debugging AI generation issues
  • iOS 26 on-device AI

AI Approach Triage

First, determine which kind of AI the developer needs:

Developer IntentRoute To
On-device text generation (Apple Intelligence)Stay here → Foundation Models skills
Custom ML model deployment (PyTorch, TensorFlow)See skills/ios-ml.md → CoreML conversion, compression
Computer vision (image analysis, OCR, segmentation)/skill axiom-vision → Vision framework
Cloud API integration (OpenAI, generic HTTP)/skill axiom-networking → URLSession patterns
Cloud Claude integration (Anthropic SDK, Messages API, Claude Agent SDK)See claude-api skill (external) → includes automated Opus 4.6 → 4.7 migration
System AI features (Writing Tools, Genmoji)No custom code needed — these are system-provided

Key boundary: Foundation Models vs ML (custom models)

  • Foundation Models = Apple's on-device LLM framework (LanguageModelSession, @Generable)
  • ML = Custom model deployment (CoreML conversion, quantization, MLTensor, speech-to-text)
  • If developer says "run my own model" → skills/ios-ml.md. If "use Apple Intelligence" → stay here.

Cross-Domain Routing

Foundation Models + concurrency (session blocking main thread, UI freezes):

  • Foundation Models sessions are async — blocking likely means missing await or running on @MainActor
  • Fix here first using async session patterns in foundation-models skill
  • If concurrency issue is broader than Foundation Models → also invoke axiom-concurrency

Foundation Models + data (@Generable decoding errors, structured output issues):

  • @Generable output problems are Foundation Models-specific, NOT generic Codable issues
  • Stay here → foundation-models-diag handles structured output debugging
  • If developer also has general Codable/serialization questions → also invoke axiom-data

Routing Logic

Foundation Models Work

Implementation patterns → skills/foundation-models.md

  • LanguageModelSession basics
  • @Generable structured output
  • Tool protocol integration
  • Streaming with PartiallyGenerated
  • Dynamic schemas
  • 26 WWDC code examples

API reference → skills/foundation-models-ref.md

  • Complete API documentation
  • All @Generable examples
  • Tool protocol patterns
  • Streaming generation patterns

Diagnostics → skills/foundation-models-diag.md

  • AI response blocked
  • Generation slow
  • Guardrail violations
  • Context limits exceeded
  • Model unavailable

Automated scanning → Launch foundation-models-auditor agent or /axiom:audit foundation-models (missing availability checks, main thread blocking, manual JSON parsing, session lifecycle issues)

Decision Tree

  1. Custom ML model / CoreML / PyTorch conversion? → See skills/ios-ml.md
  2. Computer vision / image analysis / OCR? → /skill axiom-vision
  3. Cloud AI API integration? → /skill axiom-networking
  4. Implementing Foundation Models / @Generable / Tool protocol? → foundation-models
  5. Need API reference / code examples? → foundation-models-ref
  6. Debugging AI issues (blocked, slow, guardrails)? → foundation-models-diag
  7. Foundation Models + UI freezing? → foundation-models (async patterns) + also invoke axiom-concurrency if needed
  8. Want automated Foundation Models code scan? → foundation-models-auditor (Agent)

Anti-Rationalization

ThoughtReality
"Foundation Models is just LanguageModelSession"Foundation Models has @Generable, Tool protocol, streaming, and guardrails. foundation-models covers all.
"I'll figure out the AI patterns as I go"AI APIs have specific error handling and fallback requirements. foundation-models prevents runtime failures.
"I've used LLMs before, this is similar"Apple's on-device models have unique constraints (guardrails, context limits). foundation-models is Apple-specific.
"I know the Anthropic SDK already"Opus 4.7 removed temperature, top_p, top_k, and prefill from the Messages API. Code that worked on 4.6 returns HTTP 400 at runtime. Read claude-api (external) before changing model IDs.

External Resources

Cloud Claude integration (claude-api skill, ships outside Axiom). Opus 4.7 removed temperature, top_p, top_k, and prefill from the Messages API — code that built successfully on 4.6 returns HTTP 400 at runtime, not compile time. The claude-api skill automates the migration (model ID swap, sampling-param removal, prefill replacement) and enforces prompt caching from day one. Skipping it costs an afternoon of production debugging when the first 400s arrive.

Apple's on-device Foundation Models and Anthropic's cloud Claude are unrelated stacks; use both in parallel when an app needs both, and treat claude-api as mandatory reading before any Claude model-ID change ships.

Critical Patterns

foundation-models:

  • LanguageModelSession setup
  • @Generable for structured output
  • Tool protocol for function calling
  • Streaming generation
  • Dynamic schema evolution

foundation-models-diag:

  • Blocked response handling
  • Performance optimization
  • Guardrail violations
  • Context management

Example Invocations

User: "How do I use Apple Intelligence to generate structured data?" → Read: skills/foundation-models.md

User: "My AI generation is being blocked" → Read: skills/foundation-models-diag.md

User: "Show me @Generable examples" → Read: skills/foundation-models-ref.md

User: "Implement streaming AI generation" → Read: skills/foundation-models.md

User: "I want to add AI to my app" → First ask: Apple Intelligence (Foundation Models) or custom ML model? Route accordingly.

User: "My Foundation Models session is blocking the UI" → Read: skills/foundation-models.md (async patterns) + also invoke axiom-concurrency if needed

User: "Review my Foundation Models code for issues" → Invoke: foundation-models-auditor agent

User: "I want to run my PyTorch model on device" → Read: skills/ios-ml.md (CoreML conversion, not Foundation Models)

平台分布

Codex

34.27%
按下载量换算328

Claude

30.25%
按下载量换算289

Cursor

19.51%
按下载量换算187

Gemini CLI

9.52%
按下载量换算91

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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