axiom-ai

Implement Apple Intelligence features with LanguageModelSession and @Generable structured output.

34|2|Updated Mar 16, 2026
One-click install
npx skills add https://github.com/DengNaichen/Stet --skill axiom-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: axiom-ai
Source: https://github.com/DengNaichen/Stet/tree/main/.agents/skills/axiom-ai
Command: npx skills add https://github.com/DengNaichen/Stet --skill axiom-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides the definitive framework for implementing, testing, and evaluating Apple Intelligence and on-device AI features, preventing common architectural pitfalls and runtime failures.

Core Features & Use Cases

  • Foundation Models Integration: Standardizes the use of LanguageModelSession, @Generable structured output, and Tool protocol integration.
  • Evaluation-Driven Development: Provides a robust suite for designing datasets, calibrating model-as-judge evaluators, and measuring AI performance.
  • Use Case: Use this skill when building a messaging app that requires on-device suggested replies or when implementing custom LLM-scale features that must adhere to Apple's strict guardrails and context limits.

Quick Start

Use the axiom-ai skill to implement a LanguageModelSession for structured data generation using the @Generable protocol.

Frequently Asked Questions about axiom-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement structured output with Apple Intelligence Foundation Models?▼

To implement structured output with Apple Intelligence Foundation Models, use the @Generable protocol within a LanguageModelSession to define and generate typed data structures directly on-device.

What is the Tool protocol in Apple's on-device AI framework?▼

The Tool protocol in on-device AI frameworks is an integration interface that allows the Foundation Model to invoke external functions and interact with app-specific logic during a LanguageModelSession.

How do I evaluate and calibrate LLM performance for on-device AI features?▼

To evaluate and calibrate LLM performance for on-device AI features, implement evaluation-driven development by designing datasets and calibrating model-as-judge evaluators to measure accuracy against expected outputs.

Does Apple Intelligence support custom guardrails and context window management?▼

Yes, Apple Intelligence supports custom guardrails and context window management by requiring strict adherence to on-device AI framework constraints during LanguageModelSession implementation and model refinement.

Why does my @Generable model output fail to match the expected schema during on-device inference?▼

@Generable model output fails during on-device inference when the LanguageModelSession does not properly enforce schema constraints or when context window limits truncate the Foundation Model's response generation.