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foundation-models-on-device

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

62

Quality

72%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./skills/foundation-models-on-device/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The content is highly actionable with complete, executable Swift examples and clearly sequenced patterns including validation checkpoints. It loses points for redundant "When to" sections and a monolithic structure with no progressive disclosure to reference files.

Suggestions

Merge the redundant "When to Activate" and "When to Use" sections into a single trigger list to remove overlapping bullets.

Consolidate "Key Design Decisions", "Best Practices", and "Anti-Patterns to Avoid" — much of the guidance (availability checks, response.content, token limits) is repeated across all three.

Move the detailed @Generable constraint reference and tool-calling deep dive into a separate reference file (e.g. references/generable-guide.md) and link to it, keeping SKILL.md as a concise overview.

DimensionReasoningScore

Conciseness

The body is mostly efficient code and concrete patterns, but "When to Activate" and "When to Use" overlap heavily, and "Key Design Decisions", "Best Practices", and "Anti-Patterns" restate overlapping guidance that could be tightened.

2 / 3

Actionability

Every pattern ships complete, executable Swift code — availability checks, sessions, @Generable types, tool definitions, and SwiftUI streaming — that is copy-paste ready with specific API usage.

3 / 3

Workflow Clarity

Patterns are sequenced as explicit numbered steps, with the availability check acting as a validation checkpoint and dedicated tool-error handling providing a feedback loop; no destructive/batch operations require additional checkpoints.

3 / 3

Progressive Disclosure

Sections are well-organized, but at ~240 lines with no bundle files the content is monolithic — the detailed @Generable, tool-calling, and API-reference material could be split into one-level-deep reference files.

2 / 3

Total

10

/

12

Passed

Description

67%

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

The description is specific and clearly distinctive, naming four concrete capabilities within a well-scoped Apple on-device niche. Its main weakness is the missing explicit trigger guidance and reliance on framework jargon over natural user terms.

Suggestions

Add an explicit 'Use when...' clause (e.g., 'Use when building on-device AI features with Apple Intelligence, generating or summarizing text offline, or extracting structured data from natural language').

Include the user-facing term "Apple Intelligence" alongside "on-device LLM" to match how users actually phrase requests.

Consider leading with verb-driven third-person phrasing ('Generates text, extracts structured output, and calls custom tools on-device via Apple FoundationModels') for clearer action framing.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — "text generation, guided generation with @Generable, tool calling, and snapshot streaming" — comparable to the anchor listing several specific actions.

3 / 3

Completeness

It clearly states what the skill does, but there is no "Use when..." clause or equivalent explicit trigger guidance, which per the guidelines caps completeness at 2.

2 / 3

Trigger Term Quality

Relevant terms like "on-device LLM", "text generation", and "tool calling" are present, but it leans on technical jargon (@Generable, snapshot streaming) and omits the common user-facing term "Apple Intelligence" and natural phrasing variations.

2 / 3

Distinctiveness Conflict Risk

The niche is sharply defined — "Apple FoundationModels framework for on-device LLM ... in iOS 26+" — making it unlikely to trigger for unrelated skills.

3 / 3

Total

10

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
affaan-m/ECC
Reviewed

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