Conciseness

The body is dense, package-specific facts with no padding of concepts Claude already knows ('speak() returns TypeScript's speech result keys: data (base64 audio), format, mimeType, transcript'). However, the 'Relevant API Surface' list inlines ~30 symbols including many non-audio ones (AxBalancer, AxMeter, set_tracer) that do not earn their place in an audio-specific skill, keeping it below a 5.

Actionability

The Core Pattern is executable ('llm = ai("openai", api_key=os.environ["OPENAI_API_KEY"])'), but the skill's core purpose — speech, audio fields, realtime event folding — is covered only in descriptive prose with defaults and key names ('OpenAI defaults to gpt-4o-mini-tts with the alloy voice') rather than any executable speak() or audio-input example. This is more than a minor gap for a code skill, so it sits above the pseudocode anchor at 3 rather than 4.

Workflow Clarity

The Guardrails provide an implicit sequence with a validation-style checkpoint: 'Start from package examples for exact native syntax before inventing a new call shape' and 'Use no-key examples for deterministic local checks and provider request mapping'. There are no numbered steps or explicit error-recovery loops, but no destructive or batch operations are involved, so the simple-skill guidance applies and this lands at 4 rather than 5.

Progressive Disclosure

Sections are well organized (When To Use, Package Facts, Core Pattern, Speech And Audio Fields, Guardrails) and package artifacts are clearly signaled ('Package API docs: API.md and axir-api.json', 'Runnable examples: examples/'). No bundle files exist alongside SKILL.md to verify navigation against, and the large inline API-symbol list is content that could live in a separate reference file — minor organization gaps typical of a 4 rather than 5.

Specificity

The description lists several concrete capability areas: 'audio input/output, OpenAI Responses audio mapping, realtime event folding, and generated package audio examples'. This matches the several-specific-actions anchor, but it omits speech synthesis and transcription (which the body covers), so coverage has minor gaps rather than being comprehensive at 5.

Completeness

An explicit trigger clause is present ('Use when writing Python code with axllm for...') and the what is embedded via the concrete task list, so both what and when are answered. But the 'when' clause doubles as the 'what' — there is no separate action statement (e.g., 'Maps speech and realtime events through the generated provider surface') — keeping it at 4 rather than the fully explicit both-parts anchor at 5.

Trigger Term Quality

Good keyword coverage exists: 'Python', 'audio input/output', 'realtime', 'audio examples', plus the package name 'axllm' for users who know it. However, the most natural user terms for this domain — speech, transcription, voice, TTS — are absent, and 'realtime event folding' is compiler jargon users would not say, so it falls just below the comprehensive-synonyms anchor at 5.

Distinctiveness Conflict Risk

The description names a specific generated package ('axllm') and a narrow niche (OpenAI Responses audio mapping, realtime event folding), giving it distinct triggers with minimal overlap risk against generic Python or audio skills. It clearly matches the clear-niche anchor.