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AC i18n-nextjs

Internationalization (i18n) guide for Next.js / Node.js web applications using the App Router. Covers translation file structure, locale routing, SEO metadata per locale, hreflang, structured JSON-LD data, UI component translations, and multi-language sitemap generation. Use when the user asks to: add i18n support, add a new language, translate a page or component, add SEO metadata for multiple locales, implement hreflang, update the sitemap for new locales, or follow i18n best practices in a Next.js project.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 1 105 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Internationalization (i18n) guide for Next.js / Node.js web applications using the App Router. Covers translation file structure, locale routing, SEO metadata…

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorWriting and documentsMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (i18n-nextjs) differs from the folder (web-i18n-nextjs)
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 43 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 1105 tokens
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 514: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.