Hebrew text editing requires specialized knowledge of ktiv maleh spelling rules, construct state grammar, gender-inclusive language patterns, acronym formatting with geresh/gershayim, and register-appropriate style. Without a dedicated copy editor skill, AI agents may produce Hebrew text with inconsistent spelling, grammar errors, or style mismatches that native speakers immediately notice.
Author: @skills-il
Proofread and copy-edit Hebrew text for grammar, spelling, punctuation, style consistency, and Academy of Hebrew Language compliance.
npx skills-il add skills-il/localization/hebrew-copy-editorAssess the text on: register (formal/business/casual), spelling standard (ktiv maleh vs chaser), gender language approach, target audience, and purpose.
Apply Academy of Hebrew Language ktiv maleh standard. Key corrections:
Match register throughout. Flag deviations.
Three approaches: A) neutral rewording, B) slash notation, C) traditional masculine. Never mix approaches in one document.
Check keyword optimization, morphological variations, title tags (50-60 chars), meta descriptions (120-150 chars).
Provide: summary of changes, corrected text, change log table (original | corrected | category | rule), style recommendations.
Supported Agents
Proofread this Hebrew business email for grammar, spelling (ktiv maleh), and professional register. Flag any gender language inconsistencies and suggest corrections.
Edit this Hebrew contract for grammar consistency, proper smichut (construct state) usage, and formal register throughout. Check all acronyms have gershayim formatting.
Polish this Hebrew SaaS marketing page for readability, SEO keywords, gender-inclusive language, and persuasive business-casual register. Include a change log.
Review all Hebrew UI strings in this JSON file. Check for imperative mood, ultra-short format, gender-neutral phrasing, and consistent terminology. Return corrected strings with explanations.
Trust Score
hebrew-copy-editor proofreads and edits existing Hebrew text for grammar, spelling, style, and consistency. hebrew-content-writer creates new Hebrew content from scratch (marketing copy, articles, UX text). Use the editor for existing text, the writer for new content.
Ktiv maleh (full spelling), the modern standard set by the Academy of Hebrew Language. This adds vav and yod to represent vowels (e.g., "tochnah" written as תוכנה not תכנה). Direct quotes from sources using ktiv chaser are preserved.
It supports three approaches: A) Gender-neutral rewording (recommended for tech/UX), B) Slash notation like "meshstamshim/ot" (common in business), C) Traditional masculine (for legal/government). It will never mix approaches within one document.
Yes. It checks Hebrew keyword optimization including morphological variations (root forms, inflections), title tag length (50-60 Hebrew chars), meta description length (120-150 chars), heading hierarchy, and readability for Hebrew text.
Yes. It verifies geresh for abbreviations (e.g., prof'), gershayim for acronyms (e.g., Tzahal becomes צה"ל), no period after gershayim abbreviations, and proper quotation mark usage.
Implement right-to-left (RTL) layouts for Hebrew web and mobile applications. Use when user asks about RTL layout, Hebrew text direction, bidirectional (bidi) text, Hebrew CSS, "right to left", or needs to build Hebrew UI. Covers CSS logical properties, Tailwind RTL, React/Vue RTL, Hebrew typography, and font selection. Do NOT use for Arabic RTL (similar but different typography) unless user explicitly asks for shared RTL patterns.
Write and edit professional content in Hebrew including marketing copy, UX text, articles, emails, and social media posts. Use when user asks to write in Hebrew, "ktov b'ivrit", create Hebrew marketing content, edit Hebrew text, write Hebrew UX copy, or optimize Hebrew content for SEO. Covers grammar rules, formal vs informal register, gendered language handling, and Hebrew SEO best practices. Do NOT use for Hebrew NLP/ML tasks (use hebrew-nlp-toolkit) or translation (use a translation skill).
Guide developers in using Hebrew NLP models and tools including DictaLM, DictaBERT, AlephBERT, and ivrit.ai. Use when user asks about Hebrew text processing, Hebrew NLP, "ivrit", Hebrew tokenization, Hebrew NER, Hebrew sentiment analysis, Hebrew speech-to-text, or needs to process Hebrew language text programmatically. Covers model selection, preprocessing, and Hebrew-specific NLP challenges. Do NOT use for Arabic NLP (different tools) or general English NLP tasks.
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