Slugify string

Slugify is a developer utility that makes strings safe and optimized for URLs. It removes diacritics and accents, discards non-alphanumeric punctuation, lowercases the result, and joins words with hyphens to produce a safe ASCII web path. This process is essential for generating clean, readable URLs from blog post titles, product names, or user input. Because privacy and security are paramount, all processing happens locally in your browser—your text is never uploaded, stored, or sent to any external processing server, meaning your user data never leaves your device.

To ensure a string is perfectly safe for a web URL, the slugification process relies on a few core text-processing steps, directly addressing common questions about text manipulation:

1. Unicode NFD Decomposition: The tool uses Normalization Form Decomposition (NFD) to separate accented characters (like é or ç) into their base characters (e, c) and their diacritical marks.
2. Diacritic and Symbol Stripping: Once decomposed, the combining diacritical marks are completely removed, leaving only the base ASCII letters. Non-Latin alphabets (like Arabic or Cyrillic) and special symbols are dropped, as they cannot be mapped to standard ASCII characters safely.
3. Hyphenation and Compaction: Spaces and remaining punctuation are replaced with hyphens. The algorithm actively collapses consecutive hyphens into a single hyphen and trims any leading or trailing hyphens to ensure a neat, polished output (e.g., preventing URLs ending in -).
4. Enforcing ASCII-Only Output: Unlike standard kebab-case, which might preserve certain special characters or Unicode formatting, a true slug strictly enforces an alphanumeric ASCII-only result.

• Blog Post Routing & CMS Paths: Generate SEO-optimized, predictable URLs automatically from article titles. For example, “10 Tips for C# Developers!” smoothly transforms into /blog/10-tips-for-c-developers.
• E-Commerce Product Identifiers: Normalize complex, user-entered product names into clean canonical product slugs for an online store catalog.
• Database Primary Keys: Convert human-readable labels or names into clean, URL-safe string identifiers or constraint keys without having to rely entirely on random UUIDs.
• Git Branch Naming: Automatically transform descriptive Jira ticket titles or feature descriptions into standard, terminal-friendly git branch names (e.g., feature/add-new-checkout-flow).

• Keep slugs short: Search engines prefer concise URLs. While a title might be long, consider truncating the slug to only include the core keywords (e.g., 10-tips-csharp-developers instead of the full sentence).
• Avoid stop words: Words like “and”, “or”, “the”, and “a” can often be manually stripped prior to slugification to keep the final URL leaner.
• Immutability: Once a slug is published and indexed by search engines, avoid changing it. If a title is updated, keep the original slug to prevent broken links, or implement a 301 redirect from the old slug to the new one.

While slugification removes spaces and punctuation that often trigger simple injection attacks (like SQL injection or XSS), a slug should never be inherently trusted in a database query without standard parameterized inputs or ORM sanitization. Slugs are designed for formatting and routing, not primary input validation or security filtering.

If you want to implement slugification in your own codebase, here are a few standard approaches using modern programming languages.

JavaScript / TypeScript

function slugify(text: string): string {
  return text
    .normalize("NFD") // Decompose Unicode characters
    .replace(/[\u0300-\u036f]/g, "") // Remove combining diacritic marks
    .toLowerCase()
    .replace(/[^a-z0-9\s-]/g, "") // Remove non-alphanumeric (except spaces/hyphens)
    .trim()
    .replace(/[\s-]+/g, "-"); // Replace spaces and consecutive hyphens with a single hyphen
}

Python 3

import unicodedata
import re

def slugify(text: str) -> str:
    # Decompose into base characters and combining marks
    text = unicodedata.normalize("NFD", text)
    # Remove combining marks
    text = "".join(char for char in text if unicodedata.category(char) != "Mn")
    # Lowercase and replace non-alphanumeric with hyphen
    text = re.sub(r"[^a-z0-9]+", "-", text.lower())
    # Trim leading/trailing hyphens
    return text.strip("-")