Conciseness

The body is purely imperative directives with zero concept explanation and no padding — every line instructs ('Gate session creation behind LanguageDetector.availability()...', 'Use AbortController for cancelable create(), detect()... calls'), assuming Claude's competence, which matches the 'lean and efficient; every token earns its place' anchor.

Actionability

Concrete guidance throughout: an executable command ('node scripts/find-language-detector-targets.mjs .'), exact API surfaces (availability(), create(), detect(), measureInputUsage(), destroy()), and named exceptions (NotAllowedError, InvalidStateError, QuotaExceededError) with responses. However, the actual wrapper implementation is delegated to assets/language-detector-session.template.ts rather than shown, leaving minor gaps versus fully copy-paste-ready code.

Workflow Clarity

Five clearly sequenced steps culminate in a dedicated validation phase (Step 5: re-run the inventory script, test create()+detect() flows, confirm abort/destroy semantics, run build/typecheck/tests), and the Error Handling section provides feedback loops for each named failure — matching the 'explicit validation steps; feedback loops for error recovery' anchor.

Progressive Disclosure

The body is a concise procedural overview with conditional, well-signaled one-level-deep references ('Read references/examples.md when the feature needs a session wrapper, download-progress UI...'), and all referenced bundle files (4 references, 1 script, 1 asset template) exist with no nested reference chains, matching the top anchor.

Specificity

The description lists multiple concrete actions — 'support checks, availability and model download flows, session creation, detect() calls, input-usage measurement, permissions-policy handling, or compatibility fallbacks' — covering the domain comprehensively, matching the top anchor rather than the 'minor gaps' of a 4.

Completeness

It explicitly answers both what ('Implements and debugs browser Language Detector API integrations in JavaScript or TypeScript web apps') and when ('Use when adding LanguageDetector support checks, availability and model download flows...') with concrete trigger phrases, plus an explicit don't-use clause, matching the top anchor.

Trigger Term Quality

Good keyword coverage including 'Language Detector API', 'LanguageDetector', 'language detection', 'permissions-policy', and 'compatibility fallbacks', but common natural phrasings such as 'identify the language of text' or 'language identification' are missing, so it falls just short of the comprehensive synonym coverage of a 5.

Distinctiveness Conflict Risk

A clear niche (built-in browser Language Detector API) is reinforced by explicit exclusions — 'Don't use for server-side language detection SDKs, cloud translation services, or generic NLP pipelines' — minimizing conflict with adjacent Translator/NLP skills, which fits the 'clear niche with distinct triggers' anchor.