openclaw-i18n-skill（OpenClaw i18n 技能）

openclaw-i18n — Internationalization Layer for OpenClaw

What This Skill Does

OpenClaw is a powerful AI agent framework — but by default, models generate output that often has wrong diacritics, mixed languages, or mechanically garbled text when users communicate in non-English languages. This skill fixes that with two layers:

Layer 1 — SKILL.md: Configures the agent to think and respond correctly in the user's language. Language detection, diacritics enforcement, locale formatting, cultural communication norms.

Layer 2 — Post-Processor: A Python script that mechanically cleans the model's raw output before it reaches the user. This is the unique part — no other ClawHub skill does this. It catches the specific mechanical errors that models reliably produce on non-English text: missing diacritics, stray Chinese/Russian characters, merged words, common typos.

Language Detection

How It Works

• Auto-detect user language from conversation context
• Signals: explicit language statements, user names, place references, writing

style, diacritic usage

• Confidence scoring — if below 80%, ask the user to confirm

Supported Languages

English (en) is always the fallback when detection fails.

Configuration Commands

User:  "Set language to Romanian"  → sets active language to RO
User:  "Setează limba la română"  → same
User:  "Stelle Sprache auf Deutsch" → sets active language to DE
User:  "Reset language to auto-detect" → reverts to auto-detection

Configuration persists across sessions via OpenClaw memory tools.

Output Rules

Language Enforcement

• Always respond in the detected or configured language
• Never switch languages mid-response without explicit reason
• If a user writes in Romanian, respond in Romanian — never English
• If a user writes in German, respond in German — never English

Character Rules

• Never output Chinese, Russian, Arabic, or non-Latin characters in

non-intentional contexts. Stray characters that appear in Romanian or German output are model errors — the post-processor removes them.

• Words are always separated by correct spaces — never merge two words

into one. Example: "sivei" should never appear as "știu" split wrongly.

• Always use correct diacritics for the active language

Diacritics — Never Approximate

Romanian: ă â î ș ț — always correct, never write "s" instead of "ș" or "t" instead of "ț" in Romanian words.

German: ü ö ä ß — never substitute with "ue", "oe", "ae" unless user explicitly asks for old spelling.

What NOT To Do

• No filler phrases: "Desigur!", "Absolut!", "With pleasure!"
• No language mixing: English sentence fragments in a Romanian response
• No transliteration of diacritics: never write "sch" for "ș" in Romanian
• No "obviously", "of course", "as you know" — condescending patterns

Locale-Specific Formatting

Romanian (RO)

German (DE)

Cultural Communication Norms

German (DE) — Formal by Default

• Use Sie (capitalized) unless the user explicitly switches to du
• Formal address: "Sie" not "du", "Ihnen" not "dir"
• Titles: Herr, Frau, Dr. are relevant in professional contexts
• Email tone: structured, clear subject lines, bullet points appreciated
• When in doubt: over-formal beats casual

Romanian (RO) — Warm but Professional

• First name basis is standard once rapport is established
• Direct communication — say what you mean
• Professional but not stiff — Romanians appreciate warmth
• Avoid: excessive formality, bureaucratic language, impersonal templates

Post-Processor Integration

The i18n_processor.py script runs after the model generates output and before it reaches the user. This is critical — it fixes mechanical errors that slip through even when the model "knows" the correct form.

What the Post-Processor Fixes (deterministic, high-confidence):

1. Missing diacritics in common words (RO: "vrau"→"vreau", DE: "Mueller"→"Müller")
2. Stray non-Latin characters — single Chinese/Russian chars inserted by M2.7
3. Merged words — obvious cases where two words lost their space
4. Whitespace errors — double spaces, trailing/leading spaces
5. Common typos — model-specific patterns that are consistent and fixable

What the Post-Processor Does NOT Fix:

• Grammar errors requiring context understanding
• Wrong word choice (semantic errors)
• Stylistic improvements
• Sentence structure problems

Integration Instructions

The agent should pipe output through the processor:

from i18n_processor import process
cleaned = process(raw_output, language_code)
# send 'cleaned' to user

Invoking from SKILL.md Context

When this skill is active, every model response should be treated as raw output and passed through process(response, detected_language) before delivery. The agent never sees this step — it's transparent to the user.

Configuration Memory

Language preference is stored via OpenClaw memory tools:

• Key: i18n_language — the active language code (e.g., "ro", "de")
• Key: i18n_mode — "auto" or "manual"
• Key: i18n_detection_confidence — last confidence score (0.0–1.0)

When i18n_mode is "auto", the agent re-evaluates language on each session start but respects manual overrides until reset.

Quick Reference

Error Handling

If the post-processor encounters ambiguous text:

• Do not change if confidence < 95%
• Log the case for review
• Never risk a false positive that makes output worse

Rule: The cost of a missed diacritic is lower than the cost of a wrong one.