Hebrew Nikud Reference
Trusted78/100The Problem
AI agents adding nikud to Hebrew text often make critical errors: wrong binyan identification leads to incorrect vowel points, missing dagesh on loanwords causes mispronunciation, and over-nikuding degrades TTS quality rather than improving it.
Author: @shaharsha
Hebrew nikud (vowel points) reference for AI agents. Correct nikud rules for verb conjugations (binyanim), dagesh, gender suffixes, homographs, and common mistakes. Use before adding nikud to Hebrew text, especially for TTS.
npx skills-il add openclaw/skills/shaharsha/hebrew-nikudWhen to Apply
- When generating Hebrew text with nikud for TTS applications
- When disambiguating homographs in Hebrew content
- When transliterating foreign names and loanwords into Hebrew
- When applying gender-specific suffixes to Hebrew words
- When conjugating Hebrew verbs and unsure of correct binyan nikud
Hebrew Nikud Reference
A reference guide for adding selective nikud to Hebrew text. Designed for AI agents that need accurate pronunciation hints (e.g., for TTS).
Golden Rule
Only add nikud when you're 100% certain it's correct. Wrong nikud is worse than no nikud -- the TTS model will read your mistake literally instead of guessing correctly from context.
When to Add Nikud
- Ambiguous consonants (dagesh in begedkefet)
- Gender-specific suffixes
- Homographs (same spelling, different pronunciation)
- Foreign names and loanwords
- Stress placement that changes meaning
When in doubt -- don't nikud. Let the TTS model guess from context.
Vowel Symbols Reference
Covers all 13 Hebrew vowel symbols including Patach, Kamatz, Segol, Tzere, Hiriq, Holam, Kubutz, Shuruk, and Shva with detailed rules.
Dagesh (Hard vs Soft Consonants)
Detailed coverage of Begedkefet letters, dagesh lene vs forte, and common dagesh examples for TTS with focus on Pe/Fe, Bet/Vet, and Kaf/Khaf.
Verb Conjugations (7 Binyanim)
Complete conjugation tables for all 7 verb patterns: Pa'al, Pi'el, Hif'il, Hitpa'el, Nif'al, Pu'al, and Huf'al with common confusion warnings.
Additional Sections
- Gender suffixes with examples
- Common homographs table
- Foreign names and loanwords nikud
- Preposition nikud rules
- Quick decision tree for when to add nikud
- Common mistakes to avoid
Best Practices
- Follow the golden rule: only add nikud when 100% certain it is correct
- Focus nikud on the three audibly distinct begedkefet letters (bet/vet, kaf/khaf, pe/fe)
- Always add dagesh to loanwords and foreign names with P, B, or K sounds
- Use the decision tree: check homographs, gender suffixes, and loanwords before verbs
- When unsure of the binyan, do not nikud the verb; let TTS guess from context
Avoid
- Over-nikuding text: adding nikud to every word makes TTS worse, not better
- Guessing binyan nikud: wrong binyan identification (e.g., safar vs siper) causes critical errors
- Confusing kamatz and kamatz katan: both look the same but kamatz katan sounds like O
- Mixing gender suffixes: male (-kha) vs female (-ekh) are easy to confuse
- Adding nikud to common obvious words like ani, yesh, shalom that need no disambiguation
Try These Prompts
Add nikud to Hebrew TTS script
I have a Hebrew paragraph that will be read aloud by a TTS engine. Add selective nikud only where pronunciation is ambiguous. Focus on dagesh in begedkefet letters, homographs, and foreign names. Do not over-nikud.
Verify nikud on Hebrew verbs
Check the nikud on these Hebrew verbs. For each verb, identify the correct binyan and verify the vowel points match the conjugation pattern. Flag any errors where the wrong binyan nikud was applied.
Nikud foreign names in Hebrew text
I have a Hebrew text containing foreign names and loanwords. Add dagesh where needed to ensure correct pronunciation (P not F, B not V, K not Kh). List each word you changed and explain why.
Metadata
GitHub:hebrew-nikud
version:1.1.0
installs:0
views:0
reviews:0 (0.0)
Created:March 9, 2026
Updated:March 11, 2026
Supported Agents
Claude CodeCursorGitHub CopilotWindsurfOpenCodeCodex
Tags
hebrewnikudvowelspronunciationttslanguagereference
Security Analysis
78/100
Trust Score
Code Quality82
Permissions95
Data Handling90
Publisher Reputation55
Maintenance65
Documentation92
Passed security checksv13/9/2026
Checks Performed
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Risk Factors
Quality Score
74Architecture80
Maintainability75
Content85
Spec Compliance70
Community60
Effectiveness
Skill TypeCapability
Portability Score75
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