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language-detector-api

Implements and debugs browser Language Detector API integrations in JavaScript or TypeScript web apps. Use when adding LanguageDetector support checks, availability and model download flows, session creation, detect() calls, input-usage measurement, permissions-policy handling, or compatibility fallbacks for built-in language detection. Don't use for server-side language detection SDKs, cloud translation services, or generic NLP pipelines.

80

Quality

100%

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SKILL.md
Quality
Evals
Security

Quality

Content

100%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 tightly written procedural skill: five sequenced steps with stop-and-ask checkpoints, explicit validation, and an exception-to-recovery error-handling section, with details correctly pushed into condition-signaled reference and asset files that all exist. There is no filler, no over-explanation, and no deep reference nesting.

DimensionReasoningScore

Conciseness

The 70-line body is pure procedural directives with no explanation of concepts Claude already knows (no 'what is the Language Detector API' preamble) and no padded sections; even the non-obvious notes ('Treat availability() as a capability check, not a guarantee...') carry real domain knowledge. It matches the 5 anchor (lean, assumes Claude's competence, every token earns its place) rather than the 4 anchor, since nothing needs trimming.

5 / 5

Actionability

Guidance is fully concrete throughout: an executable command ('node scripts/find-language-detector-targets.mjs .'), exact file reads ('Read references/language-detector-reference.md', 'Read assets/language-detector-session.template.ts'), and exact API calls (LanguageDetector.availability(), create(), detect(), measureInputUsage(), destroy(), AbortController). Per the rubric's scoring note, the absence of inline code in this instruction-only skill is not penalized because the guidance is specific and actionable, placing it at the 5 anchor rather than the 4 anchor.

5 / 5

Workflow Clarity

Five clearly sequenced steps with stop-and-ask checkpoints ('stop and ask which app should receive...'), an explicit validation step (Step 5 re-runs the inventory script, tests a create()+detect() flow, and runs build/typecheck/tests), and an Error Handling section that maps specific exceptions (NotAllowedError, InvalidStateError, QuotaExceededError) to concrete recovery actions — a feedback loop. This matches the 5 anchor (explicit validation steps, feedback loops for error recovery) rather than the 4 anchor.

5 / 5

Progressive Disclosure

The body is a lean 70-line overview; the four references, one script, and one asset template all exist on disk, are referenced with clear condition signals ('Read references/examples.md when the feature needs...'), and are one level deep (the only cross-reference is a lateral troubleshooting.md → compatibility.md pointer). This matches the 5 anchor (clear overview, well-signaled one-level-deep references, easy navigation) rather than the 4 anchor.

5 / 5

Total

20

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20

Passed

Description

100%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.

An exemplary description: third-person voice, a concrete multi-action capability statement, an explicit 'Use when' trigger clause, and explicit exclusions that delimit the skill from adjacent APIs. It closely matches the rubric's good-overall examples with no padding or over-claims.

DimensionReasoningScore

Specificity

The description names the domain ('browser Language Detector API integrations in JavaScript or TypeScript web apps') and lists many concrete actions — 'support checks, availability and model download flows, session creation, detect() calls, input-usage measurement, permissions-policy handling, or compatibility fallbacks' — covering both implementing and debugging. This matches the 5 anchor (multiple specific concrete actions, comprehensive coverage); it is above the 4 anchor because there are no minor gaps in capability coverage.

5 / 5

Completeness

It explicitly answers both parts: the 'what' is the first sentence ('Implements and debugs browser Language Detector API integrations...') and the 'when' is an explicit 'Use when adding...' clause with concrete trigger phrases, plus a 'Don't use for' exclusion. This matches the 5 anchor exactly; the 4 anchor requires a 'when' that could be more explicit, which does not apply here.

5 / 5

Trigger Term Quality

Natural trigger terms a user would say are comprehensively covered: 'LanguageDetector', 'detect() calls', 'built-in language detection', 'model download', 'availability', 'compatibility fallbacks'. No file extensions are relevant to a browser API, and no common synonym or variation of the trigger phrasing is missing, so it sits at the 5 anchor rather than the 4 anchor ('a few natural terms missing').

5 / 5

Distinctiveness Conflict Risk

The explicit exclusion list — 'Don't use for server-side language detection SDKs, cloud translation services, or generic NLP pipelines' — fences off the nearest confusable skills, giving a clear browser-API niche with distinct triggers and minimal conflict risk, matching the 5 anchor rather than the 4 anchor ('minor overlap risk with closely related skills').

5 / 5

Total

20

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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
webmaxru/web-ai-agent-skills
Reviewed

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