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 Kasempiternal/axiom-v2 --skill ax-foundation-models-refgit clone --depth 1 https://github.com/Kasempiternal/axiom-v2Wrote 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/kasempiternal/axiom-v2/ax-foundation-models-ref)<a href="https://agentmods.dev/skills/kasempiternal/axiom-v2/ax-foundation-models-ref"><img src="https://agentmods.dev/badge/skills/kasempiternal/axiom-v2/ax-foundation-models-ref/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/kasempiternal/axiom-v2/ax-foundation-models-ref"><img src="https://agentmods.dev/badge/skills/kasempiternal/axiom-v2/ax-foundation-models-ref.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00046 | $0.03125 |
| Opus 5 | $0.00023 | $0.01563 |
| Sonnet 5 | $0.00009 | $0.00625 |
| Haiku 4.5 | $0.00005 | $0.00313 |
Grade A, and why
ax-foundation-models-ref 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 10d 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 — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Foundation Models Framework — API Reference
Quick Patterns
LanguageModelSession
import FoundationModels
// Basic
let session = LanguageModelSession()
// With instructions
let session = LanguageModelSession(instructions: "You are a helpful assistant.")
// With tools
let session = LanguageModelSession(tools: [GetWeatherTool()], instructions: "Help with weather.")
// With use case adapter
let session = LanguageModelSession(model: SystemLanguageModel(useCase: .contentTagging))
respond Methods
// Plain text
let response = try await session.respond(to: "Write a haiku")
response.content // String
// Structured output
let response = try await session.respond(to: "Generate a person", generating: Person.self)
response.content // Person
// With options
let response = try await session.respond(to: prompt, options: GenerationOptions(sampling: .greedy))
// Streaming
let stream = session.streamResponse(to: prompt, generating: Itinerary.self)
for try await partial in stream { self.itinerary = partial }
SystemLanguageModel
SystemLanguageModel.default.availability // .available | .unavailable(reason)
SystemLanguageModel.default.supportedLanguages // [Locale.Language]
SystemLanguageModel(useCase: .contentTagging) // Built-in adapter
Decision Tree
Looking up Foundation Models API?
|
+-- Session creation/config? -> LanguageModelSession section
+-- Structured output? -> @Generable + @Guide section
+-- Streaming? -> Streaming section
+-- External data integration? -> Tool Protocol section
+-- Runtime-defined schema? -> Dynamic Schemas section
+-- Sampling/temperature? -> Generation Options section
+-- Content tagging? -> Built-in Use Cases section
+-- Error handling? -> Error Handling section
+-- Performance tuning? -> Performance section
+-- Availability check? -> Availability section
Anti-Patterns
Skipping availability check before session creation
Always check SystemLanguageModel.default.availability first. Session creation on unsupported device crashes.
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.
- 10d ago First seen · 395 lines · 46 tokens per session scan A 9f1ca82ba0ee
ax-foundation-models-ref is a skill published in the GitHub repository Kasempiternal/axiom-v2 (4 stars, last pushed 6mo ago), licensed MIT. It adds 46 tokens to every session and 3,125 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 2026-08-31.
Other skills, from other repositories
coreml
Integrates and profiles Core ML models for on-device inference. Use for mlmodel/mlpackage loading, generated or feature-provider predictions, compute-unit selection, MLTensor, Vision integration, MLComputePlan, model pipelines, deployment, or performance analysis.
apple-on-device-ai
Designs private on-device AI for Apple platforms with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use for local LLM runtime selection, Apple Intelligence chat or tool use, Apple Silicon inference, model conversion/compression, or backend comparison; route Core ML prediction code to coreml.
vision-framework
Builds or reviews iOS computer-vision features with Vision and VisionKit, including OCR, barcode and document scanning, face/object detection, segmentation, tracking, and Core ML inference. Use for Vision requests, live DataScanner flows, or Vision/Core ML integration.
workflow-audit
Systematic UI workflow auditing for SwiftUI applications. Discovers entry points, traces user flows, detects dead ends and broken promises, audits data wiring, evaluates from user perspective. Triggers: "workflow audit", "audit flows", "find dead ends", "check navigation".
swift-expert
Expert-level Swift development for iOS, macOS with SwiftUI, Combine, and modern Swift 5.9+. Use when the user mentions iOS, macOS, SwiftUI, Combine, async await, or Apple platforms, or when the task involves Modern Swift Features, Basics and Optionals, Functions and Closures, or Structs and Classes.
ios-expert
Expert in iOS development with SwiftUI, UIKit, Combine, and Apple ecosystem integration. Use when the user mentions mobile, Swift, SwiftUI, UIKit, Apple platforms, or Xcode, or when the task involves iOS App Architecture, SwiftUI Fundamentals, UIKit Essentials, or Combine Framework.