I18N Localization

i18n & Localization

Internationalization (i18n) and Localization (L10n) best practices.

1. Core Concepts

2. When to Use i18n

3. Implementation Patterns

React (react-i18next)

import { useTranslation } from 'react-i18next';

function Welcome() {
  const { t } = useTranslation();
  return <h1>{t('welcome.title')}</h1>;
}

Next.js (next-intl)

import { useTranslations } from 'next-intl';

export default function Page() {
  const t = useTranslations('Home');
  return <h1>{t('title')}</h1>;
}

Python (gettext)

from gettext import gettext as _

print(_("Welcome to our app"))

4. File Structure

locales/
├── en/
│   ├── common.json
│   ├── auth.json
│   └── errors.json
├── tr/
│   ├── common.json
│   ├── auth.json
│   └── errors.json
└── ar/          # RTL
    └── ...

5. Best Practices

DO ✅

• Use translation keys, not raw text
• Namespace translations by feature
• Support pluralization
• Handle date/number formats per locale
• Plan for RTL from the start
• Use ICU message format for complex strings

DON'T ❌

• Hardcode strings in components
• Concatenate translated strings
• Assume text length (German is 30% longer)
• Forget about RTL layout
• Mix languages in same file

6. Common Issues

7. RTL Support

/* CSS Logical Properties */
.container {
  margin-inline-start: 1rem;  /* Not margin-left */
  padding-inline-end: 1rem;   /* Not padding-right */
}

[dir="rtl"] .icon {
  transform: scaleX(-1);
}

8. Checklist

Before shipping:

• All user-facing strings use translation keys
• Locale files exist for all supported languages
• Date/number formatting uses Intl API
• RTL layout tested (if applicable)
• Fallback language configured
• No hardcoded strings in components

Script

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags i18n-localization <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns