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axiom-ai

Use when implementing, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature. Covers Foundation Models, @Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML.

Quality

77%

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tessl review fix ./axiom-cursor/skills/axiom-ai/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

An information-dense, highly actionable router skill with exemplary progressive disclosure — every routing decision names a specific file, API, or error, and detail is uniformly delegated to clearly-labeled one-level-deep references. The main cost is token efficiency: the same routing knowledge is repeated in up to four different sections, and one placeholder section (Cursor Subagent Routing) is too vague to act on.

Suggestions

Consolidate the redundant routing sections: the Decision Tree, Example Invocations, and "Critical Patterns" largely restate the Approach Triage table and Routing Logic — keep the triage table plus one canonical decision tree and trim the rest to reclaim context-window budget.

Either flesh out the "Cursor Subagent Routing" section (name the available subagents and when each is called) or delete it — "Delegate to the appropriate Cursor subagent" with no named options is the body's only unactionable instruction.

Add a terminal fallback branch to the Decision Tree (what to do when a query matches no entry) to close the last workflow-clarity gap.

DimensionReasoningScore

Conciseness

Nearly every line carries non-obvious, version-specific Apple facts (kappa thresholds, entitlement names, per-OS adapter pinning) rather than concepts Claude already knows, but the same routing decisions are restated across four sections — the Approach Triage table, Routing Logic, the Decision Tree, and Example Invocations — and the "Critical Patterns" section near-duplicates the Foundation Models Work listing. Could be tightened by consolidating; not 2 because there is no padding or generic explanation.

3 / 5

Actionability

Routing is concrete throughout: named files to read per situation, named APIs, error cases, entitlements, and ~20 User-query → Read `skills/X.md` example invocations covering the common cases. Not 5 because of the near-empty "Cursor Subagent Routing" section — "Delegate to the appropriate Cursor subagent when this router calls for a specialized auditor" names no subagents beyond foundation-models-auditor, leaving that instruction un-executable as written.

4 / 5

Workflow Clarity

The triage workflow is clearly sequenced ("First, determine which kind of AI the developer needs" → intent table → boundary clarifications → decision tree) and includes a clarification checkpoint ("First ask: Apple Intelligence or custom ML model?") plus an Anti-Rationalization table guarding wrong turns. Not 5 because the routing process itself has no verification/feedback loop (e.g., what to do when a query matches none of the tree entries or when a routed file is missing).

4 / 5

Progressive Disclosure

A pure router/overview: virtually all detail is delegated to one-level-deep, clearly signaled `skills/*.md` references, each annotated with what it contains, and navigation is served three ways (triage table, decision tree, example invocations). No reference material is inlined in the body; only two external references are marked "See `claude-api` skill (external)" so nesting never goes past one level.

5 / 5

Total

16

/

20

Passed

Description

71%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 well-formed router description: an explicit, specific trigger clause paired with a concrete, framework-named coverage list. Its weaknesses are the missing what-does-it-do verb, absent common synonyms (transcription, dictation, "Core ML"), and slight over-reach into custom-ML territory via "ANY on-device AI" and "CoreML".

Suggestions

Add one verb phrase stating what the skill does with the covered technologies (e.g., "Routes and guides..." or "Implements and debugs...") so the what half is an action, not just a topic list.

Narrow the conflict surface: either drop "CoreML" from the description (the body already routes custom Core ML work to skills/ios-ml.md) or reword "ANY ... on-device AI" so it clearly means Apple Intelligence rather than any custom on-device model, and add the natural synonyms "transcription" and "dictation".

DimensionReasoningScore

Specificity

"implementing, testing, or evaluating" names three concrete actions and "Foundation Models, @Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML" pins a specific technology surface. Falls short of 5 because "Covers ..." is a topic list rather than full action-and-object statements of what the skill does with each technology.

4 / 5

Completeness

"Use when implementing, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature" is an explicit and specific when-clause, and "Covers Foundation Models, @Generable, ..." supplies the what. Not 5 because the what is a coverage list with no verbs describing what the skill actually does with those technologies (route? guide? audit?).

4 / 5

Trigger Term Quality

Strong natural trigger phrases — "Apple Intelligence", "on-device AI", "speech-to-text" — alongside framework names a developer would actually say ("Foundation Models", "LanguageModelSession", "SpeechTranscriber"). Not 5 because common synonyms are missing: "transcription", "dictation", "SFSpeechRecognizer", and "Core ML" (the spaced form users type, vs the "CoreML" token used).

4 / 5

Distinctiveness Conflict Risk

Terms like Foundation Models, @Generable, and LanguageModelSession carve a distinctive niche, but "ANY ... on-device AI" plus the "CoreML" token reaches into custom-Core-ML-model territory that a dedicated Core ML / ML-hub skill would own — a user asking to convert or deploy their own model would naturally trigger this description. More than the 'minor overlap' of 4, far less broad than 2.

3 / 5

Total

15

/

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

CharlesWiltgen/Axiom

Path

axiom-cursor/skills/axiom-ai/SKILL.md

Reviewed

Table of Contents

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