name: glin-profanity description: Profanity detection and content moderation library with leetspeak, Unicode homoglyph, and ML-powered detection. Use when filtering user-generated content, moderating comments, checking text for profanity, censoring messages, or building content moderation into applications. Supports 24 languages.
Profanity detection library that catches evasion attempts like leetspeak (f4ck, sh1t), Unicode tricks (Cyrillic lookalikes), and obfuscated text.
# JavaScript/TypeScript
npm install glin-profanity
# Python
pip install glin-profanity
import { checkProfanity, Filter } from 'glin-profanity';
// Simple check
const result = checkProfanity("Your text here", {
detectLeetspeak: true,
normalizeUnicode: true,
languages: ['english']
});
result.containsProfanity // boolean
result.profaneWords // array of detected words
result.processedText // censored version
// With Filter instance
const filter = new Filter({
replaceWith: '***',
detectLeetspeak: true,
normalizeUnicode: true
});
filter.isProfane("text") // boolean
filter.checkProfanity("text") // full result object
from glin_profanity import Filter
filter = Filter({
"languages": ["english"],
"replace_with": "***",
"detect_leetspeak": True
})
filter.is_profane("text") # True/False
filter.check_profanity("text") # Full result dict
import { useProfanityChecker } from 'glin-profanity';
function ChatInput() {
const { result, checkText } = useProfanityChecker({
detectLeetspeak: true
});
return (
<input onChange={(e) => checkText(e.target.value)} />
);
}
| Feature | Description |
|---|---|
| Leetspeak detection | f4ck, sh1t, @$$ patterns |
| Unicode normalization | Cyrillic fսck → fuck |
| 24 languages | Including Arabic, Chinese, Russian, Hindi |
| Context whitelists | Medical, gaming, technical domains |
| ML integration | Optional TensorFlow.js toxicity detection |
| Result caching | LRU cache for performance |
const filter = new Filter({
languages: ['english', 'spanish'], // Languages to check
detectLeetspeak: true, // Catch f4ck, sh1t
leetspeakLevel: 'moderate', // basic | moderate | aggressive
normalizeUnicode: true, // Catch Unicode tricks
replaceWith: '*', // Replacement character
preserveFirstLetter: false, // f*** vs ****
customWords: ['badword'], // Add custom words
ignoreWords: ['hell'], // Whitelist words
cacheSize: 1000 // LRU cache entries
});
import { analyzeContext } from 'glin-profanity';
const result = analyzeContext("The patient has a breast tumor", {
domain: 'medical', // medical | gaming | technical | educational
contextWindow: 3, // Words around match to consider
confidenceThreshold: 0.7 // Minimum confidence to flag
});
import { batchCheck } from 'glin-profanity';
const results = batchCheck([
"Comment 1",
"Comment 2",
"Comment 3"
], { returnOnlyFlagged: true });
import { loadToxicityModel, checkToxicity } from 'glin-profanity/ml';
await loadToxicityModel({ threshold: 0.9 });
const result = await checkToxicity("You're the worst");
// { toxic: true, categories: { toxicity: 0.92, insult: 0.87 } }
const filter = new Filter({
detectLeetspeak: true,
normalizeUnicode: true,
languages: ['english']
});
bot.on('message', (msg) => {
if (filter.isProfane(msg.text)) {
deleteMessage(msg);
warnUser(msg.author);
}
});
const result = filter.checkProfanity(userContent);
if (result.containsProfanity) {
return {
valid: false,
issues: result.profaneWords,
suggestion: result.processedText // Censored version
};
}
这个 Skill 文档写得比较详细,展示了脏话过滤的多种检测方式,代码示例也覆盖了多种语言和框架。但不足之处在于只有文档说明,没有实际的代码文件实现,一些高级功能缺乏具体的技术细节。总体来说文档质量尚可,但仅有文档而缺少实现代码,让人对其实际效果存疑。改进方向是补充真实的代码文件和更详细的配置说明。