图像分析
- 作者仓库星标 977
- 叉子 74
- 许可证 MIT
- 作者更新于 2026年6月15日 03:09
- 作者仓库 Axiom
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 94 / 100 · 已通过审计
- 作者 / 版本 / 许可
- @CharlesWiltgen · MIT
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- macOS · Linux
- 底层运行要求
- Python
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。
需要注意： 未限定 allowed-tools，默认拥有全部工具权限。
---
name: axiom-ai
description: Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models…
category: AI 智能
runtime: Python
---
# axiom-ai 输出预览
## PART A: 任务判断
- 适用问题：提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求：目标材料、限制条件、期望输出和验收方式。
- 证据边界：围绕“When to Use / AI Approach Triage / Training Path Boundaries”读取原文规则，不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断：确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理，并标出输入、限制和预期结果。
- **02** 执行计划：优先按“When to Use / AI Approach Triage / Training Path Boundaries”拆成步骤，说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果：给出可复制的命令、文件改动、检查清单或内容草稿，并说明如何继续迭代。
- **04** 风险边界：结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先小样例验证，再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。原文出现了 `/skill`、`/axiom` 这类斜杠命令；如果你的 Agent 支持命令触发，优先用命令开场，再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是：读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“When to Use / AI Approach Triage / Training Path Boundaries”工作；涉及文件或命令时，先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作；如果输出泛泛而谈，就补充输入、边界和验收标准后重跑。
---
name: axiom-ai
description: Use when implementing ANY Apple Intelligence or on-device AI feature. Covers Foundation Models…
category: AI 智能
source: CharlesWiltgen/Axiom
---
# axiom-ai
## 什么时候使用
- axiom-ai 是 AI 智能方向的技能，主要扩展 Agent 在调模型、改提示词、跑评测这类场景下的能力 适合处理AI Agent、提示词、模型评估与自动化推理，核心价值是把输入、判断、执行、验证和交付边界固定下来，避免 Agent…
- 面向提示词、Agent 工作流、模型评估或自动化推理，优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「When to Use / AI Approach Triage / Training Path Boundaries」组织步骤，不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令；主要在本地完成；通常不需要额外 API Key。
- 先跑小样例，确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown，不编造命令、平台或依赖。作者原文负责流程事实；仓库文件负责来源和命令；流狐只补充适用场景、限制和质量判断。
skill "axiom-ai" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> When to Use / AI Approach Triage / Training Path Boundaries
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
}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 Intent | Route To |
|---|---|
| On-device text generation (Apple Intelligence) | Stay here → Foundation Models skills |
| Custom ML model deployment (PyTorch, TensorFlow) — classic Core ML | See skills/ios-ml.md (hub) → conversion / compression / training files |
| Custom LLM-scale / transformer model on-device (27-cycle) | See skills/core-ai.md → Core AI conversion, runtime, specialization |
| 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.
Training Path Boundaries
When developers say "I need to train / fine-tune / personalize a model," four distinct paths exist. They are often conflated; each has different output, lifecycle, and runtime compatibility.
| Path | Trains | Output | Lifecycle | Routes to |
|---|---|---|---|---|
| FM custom adapter (26-cycle only — runtime obsoleted in 27.0) | Apple's frozen on-device 3B LLM (rank-32 LoRA) | .fmadapter package, ~160 MB |
Build-time per OS version, delivered via Background Assets | skills/foundation-models-adapters.md (discipline) + skills/foundation-models-adapters-ref.md (toolkit + runtime) + skills/foundation-models-adapters-diag.md (failure modes); delivery via axiom-integration (skills/background-assets.md) |
Core ML MLUpdateTask |
Your NN-spec model's fully-connected and convolutional layers | Updated .mlmodelc saved to disk |
Runtime, per-user (on-device personalization) | skills/coreml-training.md |
| Create ML | A new Core ML model from scratch / transfer learning | .mlmodel |
Build-time, on Mac or iOS (per type) | skills/coreml-training.md |
MLX LM (mlx_lm.lora) |
Open-source LLMs on Apple silicon | adapters/adapters.safetensors — NOT loadable by Foundation Models |
Build-time; not an iOS distribution path | External — outside Axiom scope; treat as adjacent research tool |
| Server LLM fine-tune | Cloud-hosted model (e.g., vendor fine-tunes) | Cloud artifact, accessed via API | Build-time; runs in cloud | /skill axiom-networking for the API integration; the fine-tune workflow is the vendor's domain |
Critical distinctions:
- MLX LM output (
.safetensors) cannot be loaded into aLanguageModelSession. Different toolchain, different deployment target. MLUpdateTaskis NN-spec only — does not support ML Program (.mlpackage) models from modern PyTorch / TensorFlow conversion. This is the main reason it's rarely used in new projects.- FM custom adapters are pinned per-base-model version (per-OS). One adapter does NOT serve every device in your install base — see the Approach Triage section in
skills/foundation-models.mdfor the deflection ladder.
For the full "which path applies to me?" disambiguation (decision tree, the three week-costing mistakes, per-path routing) → skills/training-paths.md.
Cross-Domain Routing
Foundation Models + concurrency (session blocking main thread, UI freezes):
- Foundation Models sessions are async — blocking likely means missing
awaitor 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
Foundation Models + security (prompt injection, securing agent tools, confirmation gating):
- Threat modeling and mitigations for agentic features (
.onToolCallconfirmation,.historyTransformspotlighting/redaction, lock-screen intent policy) → axiom-security (skills/agentic-security.md) - Stay here for the API surface itself (DynamicProfile, tools, sessions)
Routing Logic
Custom Core ML Work (your own models, not Apple's LLM)
skills/ios-ml.md is the hub (deployment, runtime, speech-to-text). The lifecycle stages have dedicated files:
- Convert a trained PyTorch/TF/Keras model →
skills/coreml-conversion.md(coremltools.convert, ML Program vs NN-spec, parity validation) - Compress it →
skills/coreml-compression.md(the PTQ-vs-QAT decision, palettization/quantization/pruning) - Train from scratch / personalize on-device →
skills/coreml-training.md(Create ML;MLUpdateTaskand its NN-spec-only limitation)
Core AI — the 27-cycle path for LLM-scale on-device models (OS27)
skills/core-ai.md covers Core AI, the on-device inference framework that powers Apple Intelligence and is now open to your apps. Route here (not skills/ios-ml.md) when the model is LLM-scale / a transformer, or when the developer needs custom Metal kernels, multi-function assets, ahead-of-time compilation, KV-cache states, or the specialization/caching deployment model. Covers the Python toolchain (coreai-torch/coreai-opt), the Swift runtime (import CoreAI → AIModel/InferenceFunction/NDArray), specialization discipline, and the Foundation Models bridge (CoreAILanguageModel from the open-source coreai-models package — not a system-framework type).
Foundation Models Work
Implementation patterns → skills/foundation-models.md
- LanguageModelSession basics
- @Generable structured output
- Tool protocol integration
- Streaming with PartiallyGenerated
- Dynamic schemas
- Private Cloud Compute model + multimodal image input (
OS27) - WWDC 2025 + 2026 code examples
API reference → skills/foundation-models-ref.md
- Complete API documentation
- All @Generable examples
- Tool protocol patterns
- Streaming generation patterns
OS27: Private Cloud Compute, multimodalAttachment+ImageReferencetool args,LanguageModelprotocol + capabilities, reasoning + token usage, Dynamic Profiles (full modifier surface +@SessionProperty), Dynamic Instructions, custom model providers (LanguageModelExecutor),LanguageModelErrormigration, built-in system tools, improved Foundation Models Instrument
Diagnostics → skills/foundation-models-diag.md
- AI response blocked
- Generation slow
- Guardrail violations
- Context limits exceeded
- Model unavailable
Guardrails & safety decisions → skills/foundation-models-guardrails.md
- When to use
permissiveContentTransformationsvs.default - False-positive triage (correct refusal vs over-restrictive)
- Custom safety eval / red-team methodology
- Adapter × guardrail interaction (safety erosion)
Measuring feature quality (Evaluations framework, OS27) → skills/foundation-models-evaluations-ref.md
- Building a regression suite for an AI feature (
Evaluation,Metric,Evaluator, run via Swift Testing.evaluates) - Datasets (
ModelSample/ArrayLoader) + synthesizing more (makeSamples/SampleGenerator) - Model-as-judge for open-ended output (
ModelJudgeEvaluator,ScoringScale) - Agentic tool-call/trajectory evaluation (
ToolCallEvaluator,TrajectoryExpectation) - Hill-climbing a prompt/instruction change against an optimization-target metric
Custom adapter training (after Approach Triage rungs 1-4) → skills/foundation-models-adapters.md
- Decision discipline (when adapter training is justified vs. rungs 1-4)
- Maintenance contract (per-OS retrain burden, four-axis eval)
- Per-OS variant strategy and runtime fallback
- Dataset construction discipline
- HIG disclosure for adapter-enhanced features
Adapter toolkit & runtime API → skills/foundation-models-adapters-ref.md
- Python toolkit setup (3.11, 32 GB Apple silicon Mac or Linux GPU)
- Dataset JSONL schema (chat-turn + tool-calling extension)
examples.train_adapter,examples.train_draft_model,examples.generate,export.export_fmadapterSystemLanguageModel.Adapterruntime API andAssetErrorcases- Per-base-model-version compatibility matrix
com.apple.developer.foundation-model-adapterentitlement
Adapter-specific diagnostics → skills/foundation-models-adapters-diag.md
compatibleAdapterNotFound,invalidAdapterName,invalidAsset- Tool calls don't fire from adapter
- Adapter consumes context window with trivial prompts
- Accuracy drops after OS update (FB18924722)
coremltools.libmilstoragepythonmissing on export
Automated scanning → Launch foundation-models-auditor agent or /axiom:audit foundation-models
Detects anti-patterns AND architectural gaps:
- Missing availability checks, main-thread
respond(), manual JSON parsing, missing specific error catches (guardrail / contextWindow), session created per-tap, no streaming for long output, missing@Guideconstraints, nested non-@Generabletypes, no fallback UI - Prompt-injection risk from direct user-text interpolation,
@Generableenums without@frozen(future-case crash), missing Cancel UX, missing transcript trimming, stale availability cache after Settings toggle, partial-output validation gaps, Tool errors indistinguishable from session errors, no retry on transient errors
Scores: PRODUCTION-READY / NEEDS HARDENING / FRAGILE
Decision Tree
- Custom ML model / CoreML? → skills/ios-ml.md hub → convert (
coreml-conversion.md), compress (coreml-compression.md), or train/personalize (coreml-training.md). LLM-scale / transformer / 27-cycle custom model? → skills/core-ai.md (Core AI) - Computer vision / image analysis / OCR? → /skill axiom-vision
- Cloud AI API integration? → /skill axiom-networking
- Implementing Foundation Models / @Generable / Tool protocol? → foundation-models
- Need API reference / code examples? → foundation-models-ref
- Debugging AI issues (blocked, slow, guardrails)? → foundation-models-diag
- Foundation Models + UI freezing? → foundation-models (async patterns) + also invoke axiom-concurrency if needed
- Considering training a custom adapter? → foundation-models Approach Triage (rungs 1-4) FIRST; only after documented rung-1-4 failures → foundation-models-adapters
- Implementing adapter loading, training pipeline, or runtime selection? → foundation-models-adapters + foundation-models-adapters-ref + axiom-integration (skills/background-assets.md) for delivery
- Debugging adapter-specific failures (compatibleAdapterNotFound, tool calls don't fire from adapter, accuracy regression after OS update)? → foundation-models-adapters-diag
- Want automated Foundation Models code scan? → foundation-models-auditor (Agent — detects 10 anti-patterns AND completeness gaps including prompt injection, frozen-enum discipline, transcript trimming, Cancel UX; scores PRODUCTION-READY / NEEDS HARDENING / FRAGILE)
- Measuring whether an AI feature improved/regressed, or building an eval/regression suite (incl. agentic tool-call eval)? → foundation-models-evaluations-ref (
OS27Evaluations framework)
Anti-Rationalization
| Thought | Reality |
|---|---|
| "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. |
| "We need to train a custom adapter to fix the model's outputs" | Most "we need an adapter" requests resolve via rungs 1-4 of the Approach Triage (prompt engineering, @Generable/@Guide, tool calling, built-in content-tagging adapter). foundation-models has the ladder; foundation-models-adapters is only justified after each rung's failure is documented. |
| "We trained one adapter, ship it for all our users" | Each .fmadapter pins to one base-model version; one adapter does not cover a multi-OS install base. foundation-models-adapters covers per-OS variant strategy and compatibleAdapterIdentifiers(name:) runtime selection. |
| "Skip locale-specific eval, our users are mostly English-speaking" | Apple's 2025 tech report groups eval as English-US / English-outside-US / PFIGSCJK. English-only eval against a multi-locale app ships invisible non-English regressions. foundation-models-adapters covers the four-axis eval requirement. |
| "Just bundle the .fmadapter file in the app" | Apple's docs explicitly prohibit this. Adapters ship via Background Assets onDemand policy. axiom-integration (skills/background-assets.md) covers the delivery half. |
| "We'll add a custom adapter for our iOS 27 app" | The custom-adapter runtime (SystemLanguageModel.Adapter) is obsoleted in 27.0 and does not compile on a 27 deployment target — no replacement in the 27 SDK. foundation-models-adapters covers the pivot: rungs 1-4 or a custom provider (LanguageModelExecutor). |
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 (classic Core ML conversion, not Foundation Models)
User: "I want to run my own LLM / SAM segmentation model on device" / "convert a PyTorch transformer with Core AI" / "my Core AI model stalls on first launch"
→ Read: skills/core-ai.md (Core AI conversion, runtime, specialization & caching)
User: "How do I train a custom adapter for our app's summarization?"
→ Read: skills/foundation-models.md (Approach Triage rungs 1-4 FIRST), then skills/foundation-models-adapters.md only if rung-1-4 failures are documented
User: "Our adapter loaded fine on iOS 26.0 but throws compatibleAdapterNotFound on 26.1"
→ Read: skills/foundation-models-adapters-diag.md (Pattern 1)
User: "What's the toolkit setup for adapter training?"
→ Read: skills/foundation-models-adapters-ref.md (Toolkit Setup)
User: "How do we ship a custom adapter to users?"
→ Read: skills/foundation-models-adapters.md (runtime lifecycle) + axiom-integration (skills/background-assets.md) (delivery)
User: "How do I measure if my prompt change made the tagging feature better?" / "Write an eval suite for my AI feature"
→ Read: skills/foundation-models-evaluations-ref.md (Evaluations framework — Metrics, Swift Testing .evaluates, model-as-judge, tool-call eval)
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核