Conciseness

The body is lean and assumes Claude's competence — no explanation of known concepts, dense reference-style lines ("Response: output.choices[0].message.content[0].image (URL, valid ~24h)"). It falls short of 5 only because the asterisk size-format gotcha ("1024*1024" not "1024x1024") is repeated in three sections (Current API, Parameters, Troubleshooting) and the filetrans-vs-sync model distinction twice, which could be consolidated; it is well above 3 since there is no padded or unnecessary explanation anywhere.

Actionability

Fully executable guidance throughout: copy-paste Python for all three tools (ImageSelector, TTSSelector, DashscopeAsr) with exact argument dicts, exact endpoints and headers, full request body schemas, and precise response paths ("output.audio.url", "transcripts[].sentences[].words[]"). This matches the score-5 anchor — specific examples cover the common cases.

Workflow Clarity

The Recommended Workflow is a numbered sequence with explicit validation checkpoints and feedback loops ("Generate a sample first", "Approve voice and pacing before committing to full generation", "Audio must be at a publicly accessible URL — Local paths are rejected"), the ASR flow is sequenced end-to-end (submit → poll until SUCCEEDED → download → parse), and the Troubleshooting section maps errors to fixes. This fits the score-5 anchor (clear sequence, explicit validation, error recovery); the destructive/batch cap does not apply.

Progressive Disclosure

The skill is a single well-organized file with clear sections (API, Usage, Workflow, Parameters, Troubleshooting) and no bundle files, so navigation is easy. It does not reach 5 because at ~130 lines it exceeds the simple-skill threshold and the Parameters/Troubleshooting detail could be split into a reference file; it is above 3 because the structure is good and the inline content is core operational detail, not misplaced bulk.

Specificity

The description lists multiple concrete actions with model names — "image generation (qwen-image-2.0-pro), text-to-speech (qwen3-tts-flash), and ASR with word-level timestamps (qwen3-asr-flash-filetrans)" — comprehensively covering the skill's three capabilities. It matches the score-5 anchor (multiple specific concrete actions, comprehensive) rather than 4, since there are no coverage gaps relative to what the skill does.

Completeness

Both halves are explicit: the 'what' is the three named capabilities, and the 'when' is a concrete trigger clause — "Use when generating images via Qwen-Image, narrating via Qwen-TTS, or transcribing with word-level timestamps via Qwen-ASR." This directly matches the score-5 anchor (clearly answers both what AND when with concrete trigger phrases); a 4 would require the 'when' to be less explicit, which it is not.

Trigger Term Quality

Natural trigger phrases are present ("generating images", "narrating via Qwen-TTS", "transcribing with word-level timestamps", "text-to-speech") alongside provider names ("DashScope", "Alibaba Cloud Bailian"). Not 5 because common user synonyms like "speech-to-text", "voiceover", or "subtitles" (a primary use case per the body) are missing; not 3 because coverage is good, not merely 'some relevant keywords'.

Distinctiveness Conflict Risk

The description carves a clear provider niche ("DashScope (Alibaba Cloud Bailian / 阿里云百炼)") with model-qualified triggers ("via Qwen-Image", "via Qwen-TTS"), so it is unlikely to fire for other image/TTS/ASR skills. This fits the score-5 anchor (clear niche, distinct triggers, minimal conflict risk) rather than 4, since the generic verbs are all qualified by provider or model.