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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.

63

Quality

75%

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tessl review fix ./.claude-plugin/plugins/axiom/skills/axiom-ai/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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: triage-first structure, crisp decision tree, and consistently signaled one-level-deep references per subdomain. Its main cost is token weight — the Example Invocations section, Critical Patterns, and Anti-Rationalization table restate routing already covered elsewhere, and OS-version specifics are scattered rather than isolated in a deprecation section.

Suggestions

Cut or collapse the Example Invocations section into the Decision Tree: roughly 15 of its 17 entries repeat a decision-tree branch verbatim, removing ~50 lines with no routing information lost.

Delete the Critical Patterns section — it restates the Foundation Models Work and Diagnostics bullet lists from earlier in the same file.

Consolidate time-sensitive version facts (26-cycle adapter obsoletion, Opus 4.6 → 4.7 migration, FB18924722) into a single 'Version-boundary notes' or deprecation section so they stop penalizing the routing body's conciseness.

DimensionReasoningScore

Conciseness

The routing tables are dense and useful, but the ~50-line Example Invocations section substantially duplicates the 13-step Decision Tree, Critical Patterns repeats the earlier Foundation Models Work listing, and time-sensitive version pins ("26-cycle", "27.0", "Opus 4.6 → 4.7", "FB18924722") are scattered rather than confined to a deprecated/old-patterns section. This fits the mostly-efficient-but-needs-tightening anchor rather than the minor-trimming level 4.

3 / 5

Actionability

Every scenario ends in a concrete destination — named files ("Read: skills/foundation-models.md"), agents ("Launch foundation-models-auditor agent"), or commands ("/axiom:audit foundation-models") — satisfying the instruction-only guidance standard. Not a 5 because the routed-to files are not part of this bundle and several entries are prose-heavy conditional paragraphs rather than crisp directives.

4 / 5

Workflow Clarity

The triage-first flow ("First, determine which kind of AI the developer needs" → triage table → training-path boundaries → numbered Decision Tree) is clearly sequenced and unambiguous. It stops short of level 5 because there are no checkpoints or recovery guidance (e.g., handling a route target that does not exist), though no destructive or batch operations exist to trigger the validation cap.

4 / 5

Progressive Disclosure

References are consistently one level deep and well signaled, with per-file content previews organized by topic (foundation-models, -ref, -diag, -evaluations, -adapters) — a genuinely good hub design. Scored against the body alone since no bundle files ship with this skill; it misses level 5 because substantial inline material (the Anti-Rationalization table, External Resources, duplicated sections) is weight that belongs in the referenced files.

4 / 5

Total

15

/

20

Passed

Description

83%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 with an explicit use-when trigger clause and a concrete technology enumeration that clearly delimits the Apple on-device AI domain. Its weaknesses are minor: a few natural synonyms are absent and the CoreML mention blurs the boundary with the custom-ML skill the body routes to separately.

DimensionReasoningScore

Specificity

Enumerates concrete capabilities and technologies ("implementing, testing, or evaluating", "@Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML"), matching the several-specific-actions anchor with minor gaps (adapters and suggested-actions from the body are absent). Not a 5 because coverage of the skill's full surface is incomplete; not a 3 because far more than 1-2 actions are named.

4 / 5

Completeness

"Use when implementing, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature" is an explicit when-clause with concrete triggers, and "Covers Foundation Models, @Generable, ..." explicitly states what the skill does — both anchors of the top level are met, exceeding the merely-adequate level 4.

5 / 5

Trigger Term Quality

"Apple Intelligence", "on-device AI", and "speech-to-text" are natural phrases users would say, backed by technical triggers (Foundation Models, SpeechTranscriber). A few natural terms are missing ("transcription", "Siri", "Writing Tools", "Genmoji"), which blocks the comprehensive-synonyms anchor at 5.

4 / 5

Distinctiveness Conflict Risk

The Apple on-device niche has distinct triggers (@Generable, LanguageModelSession, SpeechTranscriber), but "on-device AI" and "CoreML" overlap with the custom-ML/Core AI skills this very router defers to elsewhere, creating minor overlap risk rather than the minimal-conflict level 5.

4 / 5

Total

17

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

15

/

16

Passed

Repository
CharlesWiltgen/Axiom
Reviewed

Table of Contents

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