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apple-on-device-ai

Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml skill.

Quality

91%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is dpearson2699/swift-ios-skills

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-engineered router skill: executable code, a decision tree, a conversion pipeline with genuine validate/fix/retry loops, and clean one-level reference files. Weaknesses are limited to token cost from rules repeated across three sections and a few placeholder functions in the multi-backend example.

Suggestions

Deduplicate rules stated three times across 'Required guardrails', 'Common Mistakes', and the 'Review Checklist' — have the checklist reference the rules instead of restating them (e.g., items 1/3/4, 9/11).

Replace or label the placeholder helpers in the Multi-Backend Architecture example ('canLoadMLXModel()', 'foundationModelsRespond()') with concrete implementations or an explicit note that they are stubs.

Drop the 'Contents' table-of-contents block; the section headers plus reference links already provide navigation and it duplicates structure tokens.

DimensionReasoningScore

Conciseness

The body is dense tables and executable code with no concept explanations, but key rules (availability check, 60% RAM limit, cache clearing, one-request-per-session) are restated nearly verbatim across 'Required guardrails', 'Common Mistakes', and the 'Review Checklist'. Anchor 4, not 3: the rest is lean and the repetition is arguably navigational, but it could be tightened.

4 / 5

Actionability

Concrete, executable snippets throughout (availability switch, session creation, coremltools conversion, MLX load, exact memory limits and device/model tables), with minor gaps: the multi-backend example relies on placeholder helpers like 'canLoadMLXModel()' and 'foundationModelsRespond(prompt)' that are not defined.

4 / 5

Workflow Clarity

'Validate, Fix, and Reconvert' is a 5-step sequence with explicit validation checkpoints and feedback loops ('fix the conversion and rerun the fixtures', 'undo or tune changes that miss the threshold', 'repeat until... targets all pass'), the framework router gives a clear decision sequence, and the Review Checklist closes the loop.

5 / 5

Progressive Disclosure

The body stays at overview level with four one-level-deep references (all present in references/), each clearly signaled inline with a 'load when the task needs X' trigger, plus a References section. Content is appropriately split between router and detail files.

5 / 5

Total

18

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete actions, explicit what/when triggers in third person, and clear delineation from the sibling coreml skill. The only gap is a few missing natural synonyms a user might say when requesting this skill.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 'choosing an Apple-local model runtime', 'building an Apple Intelligence chatbot or tool-calling feature', 'running an LLM on Apple Silicon', 'converting or compressing a Python model for Core ML', 'comparing on-device inference backends' — comprehensively covering the four backends. Not 4: coverage spans the whole domain rather than having minor gaps.

5 / 5

Completeness

Explicitly answers 'what' ('Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp') and 'when' ('Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot...') with five concrete trigger phrases.

5 / 5

Trigger Term Quality

Good coverage of natural phrases users would say ('on-device AI', 'Apple Intelligence chatbot', 'running an LLM on Apple Silicon', 'Core ML', 'Foundation Models', 'MLX Swift', 'llama.cpp'). Not 5: a few natural synonyms are missing, e.g. 'quantize', 'GGUF', or 'offline/local inference'.

4 / 5

Distinctiveness Conflict Risk

Clear Apple on-device niche with distinct triggers and an explicit boundary to the sibling skill ('For Swift Core ML loading and prediction code, use the coreml skill'), minimizing conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

dpearson2699/swift-ios-skills

Path

skills/apple-on-device-ai/SKILL.md

Reviewed

Table of Contents

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