Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and evidence-backed explanations. Localizes keywords, meta tags, numbers, dates

Blog Translate, SEO Optimized Blog Translation Translates an existing blog post into one or more target languages. Unlike generic translation, this skill produces SEO optimized, publication ready content with localized keywords, meta tags, and culturally correct formatting. Adapted from claude blog multilingual by Chris Mueller (Pro Hub Challenge, March 2026). Original: https://github.com/Chriss54/multilingual int Key References Load on demand: references/translation rules.md , format preservation, number/date/currency formats per locale, quote handling, quality criteria. references/cultural adaptation.md , cultural profiles per locale (DACH, Francophone, Hispanic, Japanese, custom). This file is shared with blog localize (do not duplicate). Workflow Phase 1: Input Parsing 1. Read the source file (markdown, MDX, or HTML) only after resolving it against the project root/current working directory. Reject symlinked paths, traversal outside the root, files over 10 MB, and binary files. 2. Auto detect source language. Order of preference: Frontmatter lang field. HTML lang attribute. Content analysis (script, common stop words). 3. Parse target languages from to as comma separated Google compatible hreflang tags ( de , fr , es MX , ja , pt BR , zh Hant ). If to is missing, ask the user once: "Which languages should I translate to? Provide hreflang tags such as de, fr, es MX, ja, pt BR." 4. Normalize every code with the shared multilingual locale rules used by blog multilingual , blog localize , and blog locale audit : ISO 639 1 language in lowercase, optional ISO 15924 script in title case, optional ISO 3166 1 Alpha 2 region in uppercase. Reject invalid codes with a suggestion ( jp becomes "Did you mean ja for Japanese?"). Require a region or explicit neutral mode for ambiguous language only targets such as es , pt , and zh . If a target equals the normalized source language, skip it with a notice. Phase 2: Content Analysis Extract the translatable surface: Frontmatter: title , description , tags , author (only when translatable, e.g. role labels, not personal names). All headings (H1, H2, H3). Body paragraphs. Image alt text and <figcaption content. Chart <text and <tspan content; preserve every SVG attribute ( x , y , font size , fill , transform ). FAQ questions and answers. Evidence backed explanation text. Key Takeaways or summary box. CTA text. Internal link zone anchor text. Preserve unchanged: Markdown and HTML structure, tags, attributes. Image URLs, link URLs, frontmatter keys. Executable code fences and inline code. Translate comments only outside executable code, or when the user explicitly asks for localized tutorial comments. Internal link zone markers ( [INTERNAL LINK: ...] ). Source organization names in citations (Gartner, McKinsey, etc.). Person names. Schema JSON LD blocks (translate user facing content strings only; never translate Person, Organization, or Brand names, URLs, IDs, @id , or sameAs ). Identify the primary and secondary keywords for Phase 3. Phase 3: Keyword Localization For each target language: 1. Decide whether the source keyword is the established term in the target market. If yes (e.g., "Content Marketing" stays in German), keep it. 2. If a local equivalent has real search behavior, swap to it. 3. Apply the same logic to secondary keywords. 4. Record the mapping. The translator agent uses it to update title, meta description, and H2 headings consistently. Phase 4: Translation Spawn the blog translator agent (via Task) for each target language with: The source content. The keyword localization map from Phase 3. The target language code. Pointer to references/translation rules.md . Instruction to limit this phase to language, register, natural topical coverage, and formatting. Reserve brand, legal, statistic, and cultural substitutions for blog localize . Run agents in parallel when translating into multiple languages. The agent returns the fully translated post in the same format as the input. Phase 5: Post Processing For each translated version: 1. Add or update locale frontmatter: 2. Verify structural integrity: Same number of H2 and H3 sections as the original. All images present with translated alt text. All SVG charts present with translated text labels (length adjusted: DE +30%, FR +15%, JA 20%, others see references/translation rules.md ). FAQ count matches. Evidence backed explanations from the source remain intact where present. 3. Save translated files: When invoked from blog multilingual , save into multilingual/{lang}/{localized slug}.{ext} instead. Before writing, slugify {localized slug} to lowercase ASCII with only a z , 0 9 , and hyphens, rejecting empty or reserved names. Create the language directory from the normalized hreflang code only. Resolve the final path and require it to stay inside the intended output root; reject symlinked output paths. Phase 6: Translation Quality Guardrails Scan the output for machine translation artifacts before reporting done: Literal idioms (English idioms transliterated, not adapted). Unnatural word order (SOV translated as SVO into a non SVO language, or the reverse). Mixed language sentences (other than established loanwords). Number, date, or currency strings still in source format. Frontmatter strings still in the source language. Flag every issue inline (file path, line number, fix suggestion). The translator agent should re pass any flagged passage before delivery. Phase 7: Delivery Error Handling Scenario Action Unsupported language code Suggest the correct Google compatible hreflang code Source equals a target Skip with "Source is already in [lang]" File not found Report error with suggested path Translator agent timeout Retry once, then report partial results Binary or non text file Report error, suggest correct file Cross References Next step (cultural depth): /blog localize <file locale <code QA sweep across all language versions: /blog locale audit <directory One command pipeline: /blog multilingual <topic languages <codes

blog-translate